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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092949 molecules-27-02949 Article Investigation of Chemical Compositions and Biological Activities of Mentha suaveolens L. from Saudi Arabia https://orcid.org/0000-0003-1472-9727 Aldogman Bashayr 1 Bilel Hallouma 1 https://orcid.org/0000-0002-9672-2678 Moustafa Shaima Mohamed Nabil 2 Elmassary Khaled F. 3 https://orcid.org/0000-0003-0781-0850 Ali Hazim M. 1 Alotaibi Faddaa Qayid 1 https://orcid.org/0000-0002-7103-9426 Hamza Mohamed 2 https://orcid.org/0000-0001-9035-5638 Abdelgawad Mohamed A. 4 El-Ghorab Ahmed H. 1* Ashour Mohamed L. Academic Editor Al Musayeib Nawal M. Academic Editor Youssef Fadia S. Academic Editor 1 Department of Chemistry, College of Science, Jouf University, Sakaka 72341, Saudi Arabia; 401205881@ju.edu.sa (B.A.); hbilel@ju.edu.sa (H.B.); hmali@ju.edu.sa (H.M.A.); faddaa879@gmail.com (F.Q.A.) 2 Department of Biology, College of Science, Jouf University, Sakaka 72341, Saudi Arabia; shymaa.nabil@ju.edu.sa (S.M.N.M.); mhabdelhameed@ju.edu.sa (M.H.) 3 Flavour and Aroma Department, National Research Centre, Giza P.O. Box 12622, Egypt; kfarouk@yahoo.com 4 Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia; mhmdgwd@ju.edu.sa * Correspondence: aghorab@ju.edu.sa 05 5 2022 5 2022 27 9 294931 3 2022 01 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Mentha is an aromatic plant used since antiquity for its pharmaceutical virtues. The climate of Saudi Arabia favors the growth of aromatic plants including Mentha suaveolens L. The aim of this study is to analyze the volatile oils of different parts of fresh and dried Mentha suaveolens L. grown in Saudi Arabia (Aljouf area) using Gas Chromatography/Mass Spectrometry (GC/MS) and Gas Chromatography Flame Ionization Detector (GC/FID) techniques, to recognize the effect of drying on chemical composition, then to evaluate the antioxidant and antifungal activities of different extracts. In total, 118 compounds were identified via GC/MS and GC/FID, in which carvone is the main volatile constituent (stems, leaves, whole plant 45–64%). This investigation deduces that Mentha belonged to the carvone chemotype. Then, the analysis of non-volatile constituents of fresh and dried Mentha was performed by HPLC. The main phenolic compound of fresh and dried Mentha for different parts was rosmarinic acid (ranging from 28,002.5 to 6558 µg/g). The ethanolic extract of fresh stem showed the highest antifungal activity (53% inhibition) compared with miconazole (60% inhibition) but the ethanoic extract of dry stem showed no activity. Additionally, all ethanolic extracts, whether for fresh or dry Mentha, have antioxidant activity more than 90% while the antioxidant activity of whole plant volatile oil is equal to 53.33%. This research shows that M. suaveolens L. could be applied to manufacture natural antioxidants, antifungal, and flavoring agents. Mentha suaveolens L. carvone rosmarinic acid antioxidant antifungal activity This research received no external funding. ==== Body pmc1. Introduction M. suaveolens L. is common plant in Saudi Arabia known for its strong antifungal, anti-aflatoxigenic, and antioxidant potential [1]. Mentha species have been well recognized for their aromatic and medicinal purposes since ancient times [2]. Recently, several Mentha species gained increasing interest for their potential use as natural food preservatives because of their strong antifungal, anti-aflatoxigenic, and antioxidant properties [1,3]. These biochemical properties are mainly due to the presence of several aromatic and phenolic compounds in different parts of the Mentha species. The chemical composition of Mentha species differs according to the growing season [4,5]. Additionally, the volatile oil composition of Mentha species from various populations and geographical regions indicated that the plants relate to either carvone or menthol or linalool chemotypes [6]. Several Mentha species were investigated in the Kingdom of Saudi Arabia. Burham et al. [7] analyzed the chemical composition of the volatile oils extracted from the leaves of M. longifolia harvested from the Albaha area in Saudi Arabia using GC/MS. As well as the compound categories identified, the content of oxygenated monoterpenes was the highest in comparison with other classes. A total of 46 compounds were detected, the main oxygenated monoterpene component was piperitone (30.77%). Caryophellene was the major sesquiterpene hydrocarbon identified (5.58%), while γ-terpinene was the main monoterpene hydrocarbon (1.36%) [7]. Abdel-Hameed et al. [8] examined the effect of extraction method on its yield and chemical composition for M. longifolia harvested from Taif, Saudi Arabia. The main chemical components identified were monoterpenes and sesquiterpenes; pulegone was the most prevalent [8]. The drying of aromatic plants is an essential step of the manufacture process to ensure the high quality of the end products [9]. Two Mentha varieties cultivated in Saudi Arabia, Medina, and Hasawi, were found to be high in volatile oils and phenolic content, especially in the soluble fractions. Regarding outside KSA, previous studies have shown that the major components of Moroccan M. suaveolens essential oils were piperitenone oxide (56.28%), piperitenone (11.64%), and pulegone (6.16%) [2]. Additionally, the study of the chemical composition in various regions of Morocco showed that the chemical composition changed and the main compounds were different: piperitenone oxide (56.00%), P-cymenol-8 (20.60%), caryophyllene (5.70%); piperitone oxide (26.00%), piperitenone oxide (25.00%), caryophyllene (9.80%); pulegone (50.00%), P-cymenol-8 (10.40%), and borneol (5.60%) [10]. The study of the Italian M. suaveolens showed that the piperitenone oxide was the main component [2]. Two different populations of M. suaveolens from Eastern Iberian Peninsula were studied and the results showed that the major components are different and correspond to two different chemotypes: piperitenone oxide (35.2–74.3%) and piperitone oxide (83.9–91.3%) [11]. A study of the chemical composition of Egyptian M. suaveolens showed that the major component was carvone (50.59%) followed by limonene (31.25%) [5]. Based on the results of these studies, this difference in chemical composition between the different geographic localities can be explained by many factors such as fertilization, drying effect, climatic change, and others. M. suaveolens is an aromatic herb native to Southern and Western Europe. M. suaveolens is a perennial, herbaceous plant characterized by a sweet scent. The plant can grow up to 100 cm in height [12]. M. suaveolens cultivated in Egypt is rich in oxygenated compounds, with carvone and limonene being the predominant compounds, followed by hydrocarbons [4,5]. El-Kashoury et al. [4] identified two new triterpenes (3β-acetyl-22α-hydroxy ursa-12,20-diene and 2α, 3β-dihydroxy-olean-18-en-29-oic acid) in the ethanolic extracts of the aerial parts of M. suaveolens growing in Egypt. Moreover, seven known compounds were identified: β-sitosterol, β-sitosterol-3-O-β-D-glucoside, oleanolic acid, dihydrolimonene, 7-hydroxy-p-cymene, isoquercitrin, and rutin [4]. In Morocco, GC/MS analysis of the volatile constituents extracted from M. suaveolens L. revealed that the major components were piperitenone, pulegone, and piperitone [13]. In another study, the chemical composition of the volatile constituents extracted from 10 wild populations of M. suaveolens subsp. timija was analyzed using GC/MS analysis. Collectively, 44 compounds were identified in all samples with a percentage that reached at least 97.3% of the oil chemical composition. The major components identified were menthone, pulegone, cis-piperitone epoxide, piperitone, trans-piperitone epoxide, piperitenone, piperitenone oxide, (E)-caryophyllene, germacrene D, isomenthone, and borneol [14]. In a recent study, Benali et al. [15] investigated the chemical composition of the volatile constituents extracted from M. suaveolens in Morocco using GC/MS analysis. The main compounds identified were piperitenone oxide, germacrene D, β-trans-caryophyllene, piperitone oxide, 1-4-terpineol, and δ-terpinene, among a total of 17 compounds identified [15]. The major phenolic compounds identified by HPLC in different Mentha varieties are caffeoylquinic acid, salvianic acid, rosmarinic acid, luteolin, salvigenin, chrysoeriol, thymonin, carnosol [6]. Elansary et al. [16] investigated phenolic compounds of M. piperita and stated that rosmarinic acid, cryptochlorogenic acid, and chlorogenic acid are the major compounds. The non-volatile extract of Mentha plant extracts are rich sources of phenolic compounds and flavonoids [16]. The most important and frequently encountered phenolic acids in Mentha species are caffeic acid and its derivative compounds and other acids such as cinnamic acid, gentisic acid, protocatechuic acid, hydroxybenzoic acid, and vanillic acid [17]. Antioxidants are of major interest to scientists, however, many carcinogenic and toxic effects were reported for synthetic antioxidants, making natural antioxidants promising alternatives [18]. The volatile oils of M. suaveolens have several proven and beneficial biological activities, especially antioxidant activity [4]. Other studies also showed that the volatile oils extracted from M. suaveolens exhibited potent antifungal activity against Aspergillus niger and Candida albicans [5]. To our knowledge, there is no report on the chemical composition and biological activities of the volatile oils of M. suaveolens L. native to Saudi Arabia (Aljouf area). Therefore, the objective of this study was to extract non-volatile and volatile oils from M. suaveolens L. and to identify the chemical constituents using GC, GC-MS and high-performance liquid chromatography (HPLC). Moreover, biological activities (antioxidant and antifungal activities) are presented in this study. 2. Results 2.1. Analysis of Volatile Oils of Fresh and Dried M. suaveolens L. by GC/MS and GC/FID The volatile constituents obtained from M. suaveolens L. under study were pale yellow with a pleasant and distinct odor. The total yield percentages of volatile constituents from M. suaveolens L. of fresh and dried whole plant extracts were 0.57 ± 0.03% and 0.225 ± 0.01%, respectively. The total yield percentages of fresh and dried leaf extracts were 0.235 ± 0.012% and 0.24 ± 0.016%, respectively. The total yields of fresh and dried stem extracts were 0.24 ± 0.016% and 0.9 ± 0.05%, respectively. The analysis of volatile oils of fresh Mentha was performed by GC/MS and GC/FID; 125 volatile constituents of fresh Mentha were identified. The percentages of chemical identification of whole plant, leaf, and stem extracts were 99.14%, 97.35%, and 96.58%, respectively. The main volatile constituent of Mentha in whole plant, leaf, and stem extracts was carvone at 43.65%, 64.31%, and 58.8%, respectively. The other major volatile constituents in whole plant, leaf, and stem extracts were myrcenol (5.88%, 3.93%, and 3.85%, respectively), terpineol<1-> (4.29%, 5.61%, and 4.4%, respectively), pulegone (3.8%, 2.1%, and 2.2%, respectively), and limonen-10-ol (2.79%, 1.24%, and 0.15%, respectively). All components are listed in Table 1. The analysis of dried Mentha volatile oils was completed by GC/MS and GC/FID. In total, 125 volatile constituents of dried Mentha were identified. The percentages of these chemical compounds of whole plant, leaves, and stems were 99.98%, 100%, and 99.28%, respectively. The major compound found in whole plant, leaf, and stem extracts was carvone at 45%, 53.45%, and 49% respectively. The other major volatile constituents in whole plant, leaves, and stems were copaene (β) (11%, 3.81%, and 0.08%, respectively), aromadendrene (allo) (3.63%, 0.12%, and 0.05%, respectively), octen-2-ol (3.22%, 0.18%, and 0.08%, respectively), epi-α-muurolol (3.07%, 2.67%, and 2.44%, respectively). All constituents are reported in Table 1. 2.2. Drying Effect on Volatile Oil Constituents of Mentha A variation in chemical composition concentration for some compounds was found due to the drying effect as shown in Figure 1. As shown in the figure, the LOC is the highest concentration in all plant extracts. While the S and the HOC concentration increases after the drying process in all plant extracts, no significant changes were found in the concentration of the M after the drying process. 2.3. Chemical Composition of Non-Volatile Constituents of Fresh and Dried Mentha by Using HPLC The analysis of fresh Mentha non-volatile constituents was performed by HPLC. The major phenolic derivatives identified in the ethanolic extract from the fresh whole plant were rosmarinic acid (2223.3µg/g), rutin (676.7 µg/g), and ferulic acid (226.7 µg/g). Additionally, the main phenolic derivatives found in fresh leaves of Mentha were rosmarinic acid (28,002.5 µg/g), rutin (3383.8 µg/g), and ferulic acid (1520 µg/g). In fresh stems of Mentha, the main phenolic derivatives identified were rosmarinic acid (6558µg/g), catechin (1340.4 µg/g), and naringenin (371.8 µg/g). The other components are listed in Table 2. In addition, the analysis of dried Mentha non-volatile constituents was performed by HPLC. The major phenolic derivatives identified in the ethanolic extract from the dried whole plant of Mentha were rosmarinic acid (21,191.9 µg/g), rutin (252.16 µg/g), and quercetin (153.8 µg/g). The main phenolic derivatives found in dried leaves of Mentha were rosmarinic acid (15,165.1 µg/g), rutin (194.7 µg/g), and quercetin (156.98 µg/g). Moreover, the main phenolic derivatives found in dried stems of Mentha were rosmarinic acid (8378.4µg/g), naringenin (130.3 µg/g), and catechin (68.1 µg/g). The other components are recorded in Table 2. 2.4. Biological Activities of Mentha suaveolens 2.4.1. Antioxidant Activity of Volatile Oils and Non-Volatile Extracts of Fresh and Dried Mentha suaveolens L. Antioxidant activities of Mentha were evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) method. DPPH radical scavenging activity of the positive standard Butylated hydroxytoluene (BHT) and plant extracts were expressed by inhibition percentage (%). The ability of the tested extract to act as an electron donor in the conversion of DPPH• into DPPH-H was studied. Antioxidant Activity of Volatile Oils from Mentha suaveolens L. The percentages of inhibition were measured for all the samples presented in Figure 2. The results showed that the antioxidant activity of samples ranged from 31% to 53%. Antioxidant Activity Non-Volatile Constituents from Mentha DPPH• (purple-colored) was reduced to DPPH-H (yellow colored) by using tested samples. The obtained results showed that all the ethanolic samples have good antioxidant activity with values exceeding 90%. The inhibition% measured for all samples is presented in Figure 3. 2.4.2. Antifungal Activity of Fresh and Dried M. suaveolens L. Isolated fungi of the studied white cheese samples show that a total of 13 fungal isolates were recovered on PDA media. The isolated fungi were identified as seven species belonging to four genera. The sequence of the most dominant species was closely related to Penicillium glabrum (Genbank accession number, MW534476.1) with 100% similarity https://www.ncbi.nlm.nih.gov/nuccore/MW534476.1 (2 February 2021). Penicillium sp. was the most predominant genus encountered in 37.4% of the total fungi recovered on PDA media. Two species of Penicillia were most common, P. glabrum was the most prevalent (22.4% of the total fungi) and P. aurantiogriseum (14.9%). Three species of Aspergilla were identified, A. niger was the most prevalent (11.21% of the total fungi), followed by A. ustus (9.3%), and A. fumigatus (7.47%). Geotrichum was isolated from one sample only cultivated on PDA medium. Antifungal Activity of Volatile Oils from M. suaveolens L. Antifungal activity was estimated by determining the capacity of inhibition of mycelial growth of the fungi species under study. Leaves of fresh Mentha presented the highest biological activity compared with the other extracts from Mentha, as shown in Figure 4. In total, 20 mg\ml of fresh and dried Mentha leaf extracts prevented the growth of the fungi by 51.18 ± 0.15 and 47.06 ± 0%, respectively. Additionally, the fungi can be inhibited by using fresh and dried stems of Mentha extracts, and the percentage is 48.53 ± 0.15% and 45.44 ± 0.23%, respectively, followed by inhibition by the whole plant, fresh and dried Mentha extracts, with the percentages of 41.53 ± 0.23% and 35.29 ± 0%, respectively. Antifungal Activity of Non-Volatile Constituents from Mentha Antifungal activity was estimated by determining the capacity of inhibition of mycelial growth of the species under study. Fresh stem extracts of Mentha had the highest biological activity compared with the other extracts from Mentha. A concentration of 20 mg/mL of fresh stems, leaves, and whole plant extracts of Mentha can prevent the growth of the fungi by 52.94 ± 0.24%, 41.47 ± 0.05%, and 39.12 ± 0.096%, respectively, as shown in Figure 5. The other extracts, including dried (whole plant, leaves, and stems), of Mentha did not affect the growth and sporulation of the tested fungi. Antifungal activity of volatile oils and non-volatile constituents of Mentha, examined using analysis of variance and presented in Table 3, shows that mean squares were highly significant for treatments. 3. Discussion The volatile oils obtained from Mentha under study were pale yellow with a pleasant and distinct odor. The total yield percentages of volatile oils from different parts of fresh and dried Mentha varied from 0.225% to 0.9%. Our results were in close agreement with Petretto et al. [29] and Kasrati et al. [14], who reported that the volatile oil yields of the aerial parts of M. suaveolens ssp. insularis from Sardinia was 0.2%. The non-volatile constituents obtained from Mentha under study were yellow to deep green with a pleasant and distinct odor. The total yield percentages of non-volatile constituents from different parts of fresh and dried Mentha varied from 0.6% to 3.4%. Our results agree with Barchan et al. [30] who found that the content of non-volatile constituents of M. pulegium and M. piperita from Morocco to be 3.4% and 4%, respectively. In contrast, non-volatile constituents were found in high levels (5%) in the extracts of three Algerian mints [31]. Moreover, El-Ghorab [32] reported that non-volatile constituents of Egyptian Mentha were 4.5%. The volatile constituents of fresh and dried Mentha from Sakaka belonged to the carvone chemotype. Our results agree with El-Kashoury et al. [4], who reported that the carvone was the main compound in Mentha in different seasons during the year (56% to 31%). Other studies are in agreement with our results where Mentha was classified by the carvone pathway [6,33]. Hussain et al. [34] and Boukhebti et al. [35] analyzed the chemical composition of Mentha from Pakistan and Algeria, in which the main compound in both studies was carvone (59.5% and 59.4%, respectively). Although the carvone percentage in leaves was higher (64.31%) than that reported by Hussain et al. [34], the percentage was lower in whole plant (43.65%) compared to Boukhebti et al. [35]. These differences in carvone concentration might be due to different environmental conditions. In another study of fresh Mentha in Morocco, pulegone was the major compound, accounting for 17.61% [13], while in our study, the percentage was 3.8% in the whole plant, 2.1% in leaves, and 2.2% in stems. Additionally, Sutour et al. [36] reported a higher level of pulegone in their samples (44.4%) compared to our results. In another study of dried Mentha in Morocco (Iguer region), pulegone was absent, agreeing with our results [14]. These changes in the concentration of pulegone might be due to the different habitats from which plants were collected. In our results, α-Terpineol concentration in fresh Mentha whole plant (0.15%) was lower than that reported by Salhi et al. (0.4%) [37]. In another study of M. suaveolens ssp. insularis in Sardinia, the percentage of the linalool was 1.37%, slightly lower than what was observed in this study for fresh whole plants (1.54%) [29]. In an recent study, α-Humulene was found in a high concentration (1.09%) in dried Mentha (Morocco) whole-plant extract compared to our results (0.04% in dried whole plant) [15]. In the same study, terpinene (γ) was among the major compounds accounting for 5.33% in dried whole-plant extract, which is a much higher percentage than the one we observed (0.03% in dried whole-plant extract). The chemical compounds in fresh and dried Mentha analyzed by GC/MS can be categorized into four chemical classes: M, LOC, HOC, and S as shown in Table 2. The chemical classes were classified in the following decreasing order: high percentage of LOC due to its high content of carvone in all plant extracts, followed by S, HOC, and M in all different extracts of fresh and dried Mentha. The above results (Table 2) were similar to those reported by Petretto et al. [29] during their work on fresh M. suaveolens ssp. insularis in Sardinia, especially high percentages were reported for oxygenated compounds (87.1%) in the whole plant extract. Moreover, the percentage of sesquiterpenes (14.2%) was similar to the percentage reported in the fresh whole plant extract (15.83%) in our study [29]. El-Kashoury et al. [4] analyzed the chemical composition of fresh M. suaveolens Ehrh. in Egypt. The highest percentage of oxygenated compounds was 62.9% in their whole plant extract, which was much lower than the percentages reported in our study. Our results were in close agreement with Hussain et al. [34], who identified oxygenated compounds in dried leaves of Mentha from Pakistan occurring at 81.5%. In the same study, sesquiterpenes were found at 6.1% in dried leaf extract compared to 12.90% in our analysis. However, the monoterpenes percentage was (1.15%) lower than reported in their study (9.09%) [34]. In another study of dried Mentha in Taif from KSA, the percentage of the oxygenated compounds was 91.9%, which was slightly higher than our results [8]. In contrast, Burham et al. [7] reported a much lower percentage of oxygenated compounds in dried leaves (30.4%) of Mentha from Albaha Area southern KSA. They also reported higher percentages of monoterpenes and sesquiterpenes (54.3% and 26.08%, respectively) compared to our observations in leaf extracts (1.15% and 12.90%, respectively) [7]. Based on the above results, significant change in the chemical composition of volatile oils might be due to different plant parts, environmental factors, seasons, geographical location, and plant age of the Mentha plant. Previous investigation of the chemical composition of the volatile oils from Mentha in Senegal showed that the pulegone rate dropped in the oils of the dried plants [38]. Similarly, our results showed a sharp drop in pulegone concentration to very low or zero levels in dried M. suaveolens L. Kohari et al. [9] reported the drop in α-Terpineol percentage in the dried Japanese peppermint (0.27% in fresh to 0.18% in dried). This result was in close agreement with our results. The 1,8-cineole concentrations in Mentha from Senegal were lower in the dried plants, which was similar to our observations [38]. Moreover, in our results, the percentages of monoterpenes after drying the whole plant and leaves decreased. Similar results were previously reported [9,39]. In a study of Mentha from Senegal, monoterpene levels increased from 3.4% to 4.7% after drying [38]. Sesquiterpenes were also increased after drying M. suaveolens L. in our analysis, which also agrees with the studies of Diop et al. [38] and Ahmed et al. [39]. Different factors could contribute to the observed variation in volatile constituents. One of these factors is the chemical properties of volatile oils, mainly their structure and volatility. The type of plant is also a significant factor in this regard. There is also a chance for new compounds to form based on the chemical reactions involved, such as glycoside hydrolysis, oxidation, esterification, etc. [39]. Changes in volatile compound concentrations after drying have been reported and explained by the continuous biosynthesis after harvest [40]. Egyptian and Moroccan essential oils of M. suaveolens L. were studied and the results show that β-copaene is present in low amounts (less than 1%) [4,41]. Additionally, β-copaene had been detected in some Algerian species of Mentha with a concentration ranging from 0.82 to 0.9% [42]. Boukhebti et al. showed that the content of β-copaene in the essential oil of Mentha spicita was 0.347% [35]. Another study conducted in the USA showed that β-copaene was present in Peppermint EO (0.07%), Native Spearmint (0.25%), and Scotch Spearmint (0.18%) [43]. In this study, α and β copaene were detected in whole plant, leaves, and stems where the concentration of α copaene was 0.57%, 0.8%, and 1.37%, respectively and β copaene was 11%, 3.81%, and 0.08%, respectively. Non-volatile components of fresh and dried Mentha were in close agreement with those reported by Elansary et al. [16] and Mišan et al. [44]. They investigated the phenolic compounds of Mentha species and reported, rosmarinic acid as a main compound. In comparison, Kulig et al. [45] reported that rutin was the primary phenolic acid in Mentha from the Slovak Republic, which was the case in our study for the fresh and dried whole plant and fresh and dried leaf extracts. Additionally, Farnad et al. [17] analyzed Mentha from West Azerbaijan and found that the main compounds were chlorogenic acid and rutin. Previous studies on Mentha species revealed some phenolic derivatives in the genus, such as rosmarinic acid, caffeic acid, ferulic acid, and catechin [31,43,44]. The highest antioxidant activity (53.33%) was observed in the whole-plant extract of dried Mentha, which can be explained by its high carvone content (45%), and which is known for its antioxidant activity. Indeed, Elmastaş et al. [46] reported that the antioxidant activity of carvone is equal to 95%. Additionally, it contains a high concentration of copaene (β) (11%), which has high antioxidant activity [47]. The mild antioxidant activity of the volatile oils of Mentha may be due to its weak phenolic compounds. Approximate results were obtained by Bardaweel et al. [48], who showed that volatile oils have weak antioxidant activity. Good antioxidant activity of non-volatile constituents of Mentha was observed (94.23 ± 0.007% for the fresh whole plant, 94.50 ± 0.005% for fresh leaves, 94.27 ± 0.01% for fresh stems, 92.83 ± 0.02% for the whole dried plant, 90 ± 0.014% for dried leaves, and 93.33 ± 0.007% for dried stems). These results agree with Mata et al. [49], who reported that ethanol extracts have good antioxidant activity. The high antioxidant activity of fresh whole plant can be explained by its high content of rosmarinic acid (2223.3 µg/g) [50,51], rutin (676.7 µg/g) [52], and ferulic acid (226.7 µg/g) [53], which are known for their high antioxidant activity. The large effect of fresh leaves was attributed to the high content of rosmarinic acid (28,002.5 µg/g) [50,51], rutin (3383.8 µg/g) [52], ferulic acid (1520 µg/g) [53], and caffeic acid (1141.5 µg/g) [53]. The phenolic compounds of fresh stems contain rosmarinic acid (6558 µg/g) [50,51], naringenin (371.8 µg/g) [54], and rutin (194.6 µg/g) [52], which also exhibited high antioxidant activity. The good antioxidant activity for dried whole plant may be due to its high content of rosmarinic acid (21,191.9 µg/g) [50,51], rutin (252.16 µg/g) [52], and naringenin (53.22 µg/g) [54]. The high antioxidant activity of dried leaves was due to the high content of rosmarinic acid (15,165.1 µg/g) [50,51] and rutin (194.7 µg/g) [52]. The phenolic compounds of dried stems contain rosmarinic acid (8378.4 µg/g) [50,51], naringenin (130.3 µg/g) [54], and rutin (41.6 µg/g) [52], which have high antioxidant activity. The high antifungal activity of volatile oils of Mentha can be explained by its high content of carvone, which showed high antifungal activity [55]. The volatile oils of fresh Mentha contain pulegone, which also exhibited high antifungal activity [56]. Additionally, it contains 1,8-cineole, which showed good antifungal activity but was lower than carvone [57]. The high antifungal activity was observed for the non-volatile constituents of fresh extracts of Mentha due to the high concentration of rosmarinic acid, catechin, and luteolin, known for their high antifungal activity [58,59,60]. Moreover, the fresh stem extracts of Mentha contained a high concentration of eugenol, which showed high antifungal activity [61]. 4. Materials and Methods 4.1. Chemicals and Plant Material All standard (authentic compounds of essential oils (Decan, 1,8 Cineole, Ocimene (Ε-β), Benzyl formate, Terpinolene, Linalool, Myrceno, Terpineol<1->, Terpinen-4-ol, α-Terpineol, γ-Terpineol, Carveol (Trans Carveol(cis), Carvone, Pulegone, Elemene (δ-), Copaene (α), Aromadendrene) and phenolic compounds (Protocatechuic acid, p-hydroxybenzoic acid, Catechin, Vanilic acid, Cinnamic acid, Naringenin, Eugenol, Caffeic Acid, Coumaric acid, Ferulic acid, Rutin, Luteolin, Quercetin and Rosmarinic acid)) and chemical reagents were supplied from Sigma Aldrich company (Burlington, MA, US and used without further purifications. Fresh M. suaveolens L. plants were collected from a local farm in Sakaka, Aljouf, Saudi Arabia, in September 2020. Plant identification was performed by Hamdan A., Al-Jouf, KSA.) the herbarium (59-CPJU) voucher specimen was stored at College of Pharmacy, Jouf University (Sakaka, Saudi Arabia). Fresh M. suaveolens L. plants were dried under shade at room temperature in a fume hood. 4.2. Extraction Methods 4.2.1. Extraction of Volatile Oil of M. suaveolens L. The volatile oils of different samples were extracted, 200 g of each sample, by hydro distillation using Clevenger apparatus (Clevenger Apparatus was supplied from Shiva Scientific Glass Private Limited, New Delhi, India.) during 5 h. Then, the volatile oils were extracted by dichloromethane (DCM) (3 × 50 mL) and dried by adding magnesium sulfate anhydrous. The DCM was filtered and removed in a rotary evaporator (Model: R-3001, Evaporating Flask: 500 mL, 1000 mL, Rotation Speed: 10–280 rpm, Temperature Range: Room temp +5 °C~95 °C. Evaporating Speed: 23.5 mL/min (Water) supplied from GWSI manufacture, Zhengzhou, China) and then stored at −4 °C in opaque containers [8]. 4.2.2. Extraction of Non-Volatile Constituents of M. suaveolens L. Each sample of Mentha (100 g) was extracted with 99.8% ethanol (200 mL for each part), for 72 h, in a dark place and with stirring. Then, the obtained extract was filtered and dried by adding anhydrous magnesium sulfate. The rotary evaporator was used for solvent removal from extract and then stored in dark containers at −4 °C [32]. 4.3. Analysis of Volatile Oils of M. suaveolens L. by GC and GC/MS 4.3.1. GC Analysis Volatile oils were analyzed using GC/MS and GC, instrumental details and condition according to the reported method [19]. See Supplementary Materials. 4.3.2. GC/MS Analysis The quantitative determination and chemical composition of volatile oils were estimated using the adjusted reported method [14,15,16,17,18,19,20,21,22,23,24,62] (see Supplementary Materials). 4.4. HPLC Analysis of Non-Volatile Compounds The non-volatile extracts were analyzed using HPLC, instrumental details and condition according to the reported method [63]. All phenolic standards were prepared in methanol within the range from 0.5 to 100 µg/mL and these standards were used for quantitative and qualitative identification of phenolic compounds in M. suaveolens L. extract chemical composition depending on the standard calibration curve for each standard and retention time, respectively (see Supplementary Materials). 4.5. Antioxidant Activity The scavenging DPPH free radical ability was determined in vitro according to the procedure of Yue and Xu [64]. DPPH solution (0.1 mM, 1.8 mL) was mixed with 0.2 mL of each diluted Mentha extract stock solution (5 mg/mL) and incubated in the dark for 30 min at 25 °C. In this assay, the percentage of DPPH reduction by samples was compared to BHT. The experiment was repeated three times. The following formula was used to quantify radical scavenging activity:Inhibition (Scavenging effect) (%) = [A0 – A1)/A0] × 100(1) A0 is the absorbance of the control reaction (t = 0 min). A1 is the absorbance of the tested extract solution (t = 30 min). 4.6. Antifungal Activity Ten samples of locally fabricated cheese were gathered arbitrarily from farmer’s houses of Al-Jouf–Sakaka in KSA. Samples were collected in (sterile, clean, and dry) containers and shipped to test centre within 1–2 h of collection (at 4 + 2 °C). 4.6.1. Detection and Isolation of Fungi The dilution-plate method [22] was used to isolate and identify fungi from the collected cheese samples. Samples were inoculated on potato dextrose agar (PDA) obtained from Oxoid [24]. Rose Bengal (30 ppm) was used as an antibacterial agent. One gram of white cheese was suspended in 90 mL sterilized distilled H2O using a rotating shaker to homogenate the suspension. Then, 10 serial dilutions were prepared, and 1 mL of each dilution was inoculated into a petri dish. Then, melted medium was poured, mixed well, and left to solidify. After solidification, Petri dishes were incubated at 27 ± 2 °C for 5–7 days. Colonies were counted and isolated for purification and identification. 4.6.2. Identification of the Isolated Fungi Morphological identification of the isolated fungi was carried out based on their macro and microscopic characteristics using the taxonomic methods [28,63]. Additionally, the most dominant species were identified using molecular analysis of the ITS1-5.8S rRNA–ITS2 region (animal health research institute, Dokki, Giza, Egypt). 4.6.3. Effect of Volatile Oils and Non-Volatile Extract on Mycelium Growth of Isolated Fungi In total, 15 mL of PDA medium were placed in each Petri dish (9 cm diam.), and after solidification, a circular hole (2 cm, diam.) was formed. Then, 1 mL of each different concentration of the tested extract was added to each well. Each Petri dish was inoculated with four fungal species and incubated at 28 °C for seven days. PDA medium without additives was used as a negative control, whereas plates containing 20 mg/mL Miconazole Nitrate were used as a positive control. Each treatment was repeated three times and subjected to statistical analysis. The diameter of inhibition zones was measured for each well, and the results were statistically analyzed. In total, 1 mL of plant extract (20 mg/mL) was added to each well to determine the inhibition zone and inhibition percentage (%). The diameter of inhibition zones was measured, and the percentage of antifungal activity was calculated according to Equation (2):(2) Inhibition (%)=DC−DTDC×100 DC = the diameter of fungal mycelium growth in the control Petri dish. DT = the diameter of fungal mycelium growth in the treated Petri dish, containing Mentha extracts. 4.7. Statistical Analysis Separated completely randomized designs as one-way ANOVA were conducted for statistical analysis procedure of the obtained data. Both antioxidant and antifungal activities of volatile oils and non-volatile extracts of Mentha were carried out in triplicate experiments. Each experiment included seven treatments (whole plant of fresh Mentha, whole plant of dried Mentha, leaves of fresh Mentha, leaves of dried Mentha, stems of fresh Mentha and stems of dried Mentha as well as the control treatment with BHT). Duncan’s multiple range test [65] was used to estimate the performance of mean treatments depending on significance of mean of squares according to the ANOVA table at a significance level of p = 0.05, where a different superscript letter refers to a significant difference among those treatments. MSTAT- Cv.2.10 software program package processed all data [66]. 5. Conclusions M. suaveolens L. from the Sakaka region was investigated for its chemical components and biological activities for the first time. Regarding the main chemical components of volatile oils, carvone dominated in all samples. The volatile oil M. suaveolens L. was carvne chemo type. Additionally, the main phenolic compounds in ethanolic extract were rosmarinic acid, rutin, catechin, and naringenin which are responsible for the antioxidant and antifungal activities of Mentha. It was found that copaene had the highest concentration in M. suaveolens L. in comparison with the same species in another locality. Fungi (Penicillium glabrum) isolated from white cheese was inhibited by Mentha extracts for the first time as well as fungi (Penicillium glabrum) isolated from white cheese that was inhibited by Mentha extracts for the first time. From the previous results, the M. suaveolens L. has high antioxidant and antifungal activity, confirming that it can be subject to application in pharmaceutical or food industries. Acknowledgments The authors would like to thank the Deanship of Graduate Studies at Jouf University for funding and supporting this research through the initiative of DGS, Graduate Students Research Support (GSR) at Jouf University, Saudi Arabia. Supplementary Materials The following supporting information can be downloaded at: The following are available online at https://www.mdpi.com/article/10.3390/molecules27092949/s1, Analysis of volatile oils of Mentha by GC and GC/MS and HPLC analysis of non-volatile compounds. Click here for additional data file. Author Contributions Conceptualization, A.H.E.-G., H.B. and S.M.N.M.; methodology, B.A.; software, F.Q.A.; validation, H.M.A. and F.Q.A.; formal analysis, H.M.A., M.A.A. and M.H.; investigation, K.F.E.; writing—original draft preparation, B.A.; writing—review and editing, B.A., H.B., S.M.N.M. and A.H.E.-G.; supervision, A.H.E.-G. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Sample Availability Samples of the compounds are available from the authors. Figure 1 Drying effect on volatile oils constituents of M. suaveolens L. Sesquiterpenes (S), heavy oxygenated compounds (HOC), light oxygenated compounds (LOC), and monoterpenes (M). Figure 2 Antioxidant activity of volatile oils of M. suaveolens L. Figure 3 Antioxidant activity of non-volatile constituents of M. suaveolens L. Figure 4 Antifungal activity of volatile oils of M. suaveolens L. Figure 5 Antifungal activity of non-volatile constituents of M. suaveolens L. molecules-27-02949-t001_Table 1 Table 1 Chemical components of volatile oils from fresh and dried Mentha suaveolens L. analyzed by GC/FID and GC/MS. a Conc. % b Calculated KI c KI Data Average Name d Type Identification Methods Whole Plant Leaves Stems Fresh Dried Fresh Dried Fresh Dried 0.01 3.22 0.03 0.18 0.01 0.08 978 977 Octen-2-ol LOC KI and MS 0.03 0.12 0.02 0.1 0.02 0 989 989 Decan<1> M KI and MS and St. 0.07 0.31 0.04 0.14 0.67 0.23 998 999 Ethyl hexanoate LOC KI and MS 0.04 0.05 0.02 0 0.34 0.22 1005 1007 Hexenyl acetate(3Ƶ) LOC KI and MS 0.07 0.06 0 0.02 0.03 0.02 1007 1007 Hexenoic acid (2E) LOC KI and MS 0.09 0.03 0.03 0 0.03 0.01 1008 1008 Linalool oxide (dehydroxy-cis) LOC KI and MS 0.1 0.06 0 0.04 0.01 0.01 1011 10,011 δ-Carene (3) M KI and MS 0.03 0.03 0 0.01 0.03 0.01 1013 1013 Hexenyl acetate (2E) LOC KI and MS 0.54 0.01 0.25 0 0.35 0 1014 1013 1,8 Cineole LOC KI and MS and St. 0.39 0.03 0.16 0.02 0.24 0.86 1017 1017 Terpinene(α) M KI and MS 0.01 0 0 0 0.01 0.04 1024 1025 p-Cymene M KI and MS 0.12 0 0 0.08 0 0.08 1026 1026 Menthene (1-p) M KI and MS 0.04 0.01 0 0.01 0.03 0.09 1029 1029 β-Phellandrene M KI and MS 0.04 0.01 0.01 0.01 0.02 0 1037 1038 Ocimene(Ƶ-β) M KI and MS 0.62 0.15 0.33 0.31 0.1 0.18 1050 1048 Ocimene(Ε-β) M KI and MS and St. 0.09 0.03 0.04 0.01 0.03 0.01 1059 1060 Terpinene(-γ-) M KI and MS 0.13 0.03 0.03 0 0 0.1 1066 1065 Octen-1-ol(2Ε) LOC KI and MS 1.71 0.29 0.71 0.56 1.26 0.2 1076 1076 Benzyl formate LOC KI and MS and St. 0.81 0.25 0.49 0.48 0.34 0.25 1088 1086 Terpinolene M KI and MS and St. 1.54 0.46 1.13 0.9 1.18 0.18 1096 1097 Linalool LOC KI and MS and St. 0.03 0 0.01 0.01 0 0.05 1101 1102 Hexyl propanoate LOC KI and MS 0.05 0.03 0.04 0.01 0.03 0.01 1104 1103 Methyl butyl isovalerate(2) LOC KI and MS 0.13 0 0.08 0.05 0.02 0.05 1113 1115 Camphenol(6-) LOC KI and MS 0 0.01 0.01 0.01 0.02 0.03 1121 1122 Menth-2-en-1-ol(cis-p) LOC KI and MS 5.88 2.81 3.93 4.98 3.85 2.85 1122 1122 Myrcenol LOC KI and MS and St. 4.29 1.84 5.61 2.44 4.4 1.04 1133 1132 Terpineol<1-> LOC KI and MS and St. 0.22 0.05 0.17 0.09 0.1 0.04 1144 1143 Ocimene(neo-allo) M KI and MS 0.02 0.01 0.01 0 0.05 0.04 1153 1155 Thujanol(neo-3) LOC KI and MS 1.38 0.23 0.83 0.52 0.13 0.09 1159 1159 Isopulegol(iso) LOC KI and MS 0 0.01 0.03 0.03 0 0.08 1160 1159 Isoborneol LOC KI and MS 0.09 0.01 0.05 0.02 0.07 0.08 1164 1164 Terpineol(cis dihydro) LOC KI and MS 0.03 0.06 0.03 0.01 0.01 0.33 1165 1164 Menthol(neo) LOC KI and MS 0.3 0.07 0.17 0.11 0.07 0.15 1170 1170 Pinocampheol LOC KI and MS 0.26 0.03 0.13 0.06 0.07 0.06 1171 1171 Iso pulegol (neoiso) LOC KI and MS 0.16 0.05 0.09 0.04 0 0.09 1174 1174 Linalool oxide (cis) LOC KI and MS 0 0.03 0.01 0.03 0.02 0.05 1176 1176 Linalool oxide (Trans) LOC KI and MS 0.09 0.01 0.06 0 0.06 0.01 1177 1177 Terpinen-4-ol LOC KI and MS and St. 0.18 0.02 0.06 0.02 0.2 0.05 1186 1187 Dillether LOC KI and MS 0.15 0.07 0.08 0.04 0.05 0.01 1188 1188 α-Terpineol LOC KI and MS and St. 0.07 0.01 0.04 0.01 0.03 0 1189 1189 Verbanol (neoiso) LOC KI and MS 0.02 0.02 0.04 0.01 0.04 0.03 1192 1192 Dihydro carveol LOC KI and MS 0.05 0.01 0.01 0.01 0.05 0 1193 1194 Dihydro carveol(neo) LOC KI and MS 0.03 0.03 0.01 0 0.03 0.02 1196 1196 Decanol(3-) LOC KI and MS 0.27 0.15 0.45 0.23 0.11 0.06 1199 1199 γ-Terpineol LOC KI and MS and St. 0.91 0.09 0.44 0.15 0.08 0.08 1200 1201 Dihydro carveol (Trans) LOC KI and MS 0.15 0.01 0.11 0.03 0.72 0.03 1208 1208 Piperitol LOC KI and MS 0.01 0.03 0.02 0.05 0.9 0.69 1214 1213 Pulegol (Trans) LOC KI and MS 0.11 1.62 0 2.52 0.48 1.28 1215 1216 Dihydro myrcenol acetate LOC KI and MS 1.77 0.49 3.45 0.88 2.12 0.63 1216 1216 Carveol (Trans) LOC KI and MS and St. 0.05 0.01 0.1 0.02 0.05 0.01 1219 1220 Cyclo citral LOC KI and MS 0.05 0.07 0.27 0.13 0.01 0.11 1227 1227 Prenyl cyclo pentanone LOC KI and MS 0 1.84 2.24 2.65 2.5 0.02 1229 1229 Carveol(cis) LOC KI and MS and St. 0.54 0 0 0 0.03 1.69 1230 1232 Mentha-1,8-dien-2-ol (cis-p) LOC KI and MS 0.11 0 0 0.01 0 0.01 1234 1235 linalool acetate (tetrahydro) LOC KI and MS 43.65 45 64.31 53.45 58.8 49 1234 1234 Carvone LOC KI and MS and St. 3.8 4 2.1 0 2.2 0.01 1237 1237 Pulegone LOC KI and MS and St. 0.03 0.08 0.01 0 0.04 0.02 1242 1242 Verbenyl acetate(Trans) LOC KI and MS 0.12 0.07 0.06 0.02 0.2 0.06 1244 1244 Isomenthene(2-ethyl) S KI and MS 0 0.03 0.01 0 0.06 0.05 1247 1247 Carvotan aceton LOC KI and MS 0.01 0.02 0.15 0.01 0.05 0.01 1253 1254 Myrtanal (cis) LOC KI and MS 0.11 0.02 0 0.02 0.05 0.01 1254 1253 Piperitone epoxide (cis) LOC KI and MS 0.09 0.01 0 0.01 0.01 0.01 1255 1254 Piperitone epoxide (trans) LOC KI and MS 0.01 0.03 0.01 0.02 0.02 0.11 1256 1256 Sabinene hydrate acetate LOC KI and MS 0.34 0 0.13 0 0.32 0 1258 1257 Carvenone LOC KI and MS 0.11 0.02 0.03 0.01 0.17 1.26 1261 1262 Myrtanol(Trans) LOC KI and MS 0.05 0.01 0.02 0.02 0.07 0.04 1263 1263 Carvonoxide(cis) LOC KI and MS 0.05 0.06 0.05 0.01 0.06 0.06 1265 1265 Cauaiacol acetate<o> LOC KI and MS 0.03 0.01 0.15 0.01 0.05 0.34 1276 1267 Thujanol acetate(neo-3) LOC KI and MS 0.42 0 0 0 0.04 0 1277 1275 Isopulegyl acetate LOC KI and MS 0.33 0.46 0.19 0.85 0.22 0.04 1282 1282 Verbenyl acetate(cis) LOC KI and MS 0.22 0.08 0.07 0.11 0.08 0.01 1283 1282 Thujanol acetate(neo iso-3) LOC KI and MS 0 0.02 0.01 0 0.09 0.02 1285 1285 Terpinen-7-al(α) LOC KI and MS 0.09 0.34 0.4 0.02 0.27 0.01 1288 1288 Fenchol(2-ethyl-endo) LOC KI and MS 2.79 1.23 1.24 1.85 0.15 0.7 1289 1288 Limonen-10-ol LOC KI and MS 0.04 0.26 0.02 0 0.07 0.06 1290 1291 Thymol LOC KI and MS 2.66 2.32 1.09 2.14 0.65 1.2 1328 1329 Silphiperfol-5-ene S KI and MS 0.1 0.01 0 0.03 0.05 0.01 1333 1334 cis-Carvyl acetate LOC KI and MS 0.05 0.08 0.02 0.05 0.04 0.02 1336 1336 Presilphiperfol-7-ene S KI and MS 0.06 0.03 0.05 0.08 0.02 0.01 1338 1338 Elemene(δ-) S KI and MS and St. 1.69 1.29 0.68 1.31 0.44 0.7 1351 1351 Cubebene(α) S KI and MS 0.47 0.11 0 0.15 0.01 0.04 1352 1353 Thymol acetate LOC KI and MS 0.52 0.26 0.26 0.55 0.25 0.22 1353 1354 Lengipinene(α) S KI and MS 0.06 0 0.02 0.02 0.45 0.01 1354 1354 Ethyl nerolate LOC KI and MS 0.21 0.06 0.04 0.06 0.45 0.01 1359 1358 Dihydro carveol acetate LOC KI and MS 1.41 0.58 0.56 0.95 0.71 0.59 1371 1372 Cyclo sativene S KI and MS 0.17 0.02 0 0.06 0 0.08 1372 1373 p-Menthane-1,2,3-triol LOC KI and MS 1.37 0.6 0.53 0.8 0.13 0.57 1376 1375 Copaene(α) S KI and MS and St. 0.53 0.16 0.18 0.29 0.01 0.09 1380 1380 cis-Jasmone LOC KI and MS 0.04 0.02 0 0.01 0.02 0.04 1381 1381 Patchoulene(β-) S KI and MS 0.19 0.01 0.07 0.07 0.05 0 1382 1383 Daucene S KI and MS 0.57 0.21 0.23 0.3 0.06 0.24 1388 1388 Cubebene(β-) S KI and MS 0.25 0.1 0.07 0.1 0.08 0.13 1390 1391 Longifolene(iso) S KI and MS 0.14 0.06 0.07 0.04 0.09 0.07 1391 1392 Elemene(β-) S KI and MS 0.07 0.05 0.02 0.04 0.04 0.02 1392 1393 Sativene S KI and MS 0.32 0.17 0.16 0.08 0.27 0.1 1400 1400 Longipinene(β-) S KI and MS 0.83 0.04 0.35 0.36 0.54 0.14 1402 1403 Funebrene(α) S KI and MS 0.04 0.03 0.07 0.03 0.09 0.04 1408 1408 Caryophyllene(Ƶ) S KI and MS 0.19 0.12 0.1 0.05 0.1 0.11 1409 1409 Gurjunene(α) S KI and MS 0.07 0.03 0 0.03 0.04 0 1412 1412 β-Caryophyllene S KI and MS 0.56 0.16 0.29 0.2 0.13 14 1417 1416 Santalene S KI and MS 0 0.03 0.05 0.04 0.03 0.07 1419 1418 Caryophyllene(Ε-) S KI and MS 0.08 0.01 0.05 0.08 0.07 0.21 1423 1424 Menth-1-on-9-ol acetate LOC KI and MS 0.08 0.01 0.05 0.03 0.03 0.29 1430 1430 Ionone(Ε-α) LOC KI and MS 4 11 0.03 3.81 0.01 0.08 1431 1431 Copaene(β) S KI and MS 0.02 0.02 0.02 0.07 0.04 0.02 1436 1435 Elemene(-γ) S KI and MS 0.09 0.03 0.07 0.05 0.05 0.02 1441 1441 Aromadendrene S KI and MS and St. 0.03 0.04 0 0.1 0.03 0.08 1444 1444 α-Humulene S KI and MS 0.04 0.05 0 0.05 0.06 0.1 1446 1445 γ-Muurolene S KI and MS 0.13 0.04 0.1 0.03 0.01 0.02 1447 1447 Cabreuva(A) S KI and MS 0.14 0.07 0.07 0.05 0.18 0.13 1451 1451 Himachalene(α) S KI and MS 0.04 0.13 0.04 0.09 0.21 0.2 1454 1454 Neryl propanoate LOC KI and MS 0.02 0.05 0.03 0.1 0.07 0.01 1456 1457 Carvyl propanoate(Trans) HOC KI and MS 0.19 3.63 0.1 0.12 0.14 0.05 1460 1462 Aromadendrene(allo) S KI and MS 0.04 0.93 0.03 1.37 0.11 0.54 1466 1467 Dodecanal S KI and MS 0.1 0.43 0.07 0.21 0.07 0.05 1469 1469 Ethyl-(2Ε,4Ƶ)-decadienoate HOC KI and MS 0.02 0.72 0.01 1.08 0.07 0.01 1470 1471 Pinchotene acetate HOC KI and MS 0.02 0.8 0.05 0 0.02 0.04 1477 1478 Geranyl propanoate HOC KI and MS 0.06 0.06 0.02 2.43 0.09 2.81 1478 1478 Allyl decanoate HOC KI and MS 0.05 0.11 0.03 0 0.07 0.1 1480 1481 Cabreuva oxide D HOC KI and MS 0.01 0.03 0.02 0.08 0.06 0.06 1482 1482 Menthyl lactate HOC KI and MS 1.44 0.32 0.19 0.03 1.88 0.17 1515 1516 Gernyl isobutanoate HOC KI and MS 0.03 0.24 0.15 0.17 0.14 0.21 1517 1517 Himachalene(α-dehydro-ar) HOC KI and MS 0.13 2.68 0.1 2.23 0.13 1.91 1522 1522 Isobornyl isovalerate HOC KI and MS 0.13 2.34 0.18 2.86 0.26 7.06 1524 1524 Isobornyl-2-methyl butanoate HOC KI and MS 2.5 3.07 0.2 2.67 4.01 2.44 1640 1640 Epi-α-Muurolol HOC KI and MS a concentration of area %, b KI (Kovat’s index), c reported Kovats index, [19,20,21,22,23,24,25,26,27,28]; www.webbook.nist.gov (accessed on 30 March 2022), d Sesquiterpenes (S), heavy oxygenated compounds (HOC), light oxygenated compounds (LOC), and monoterpenes (M). Standard compound (St). Notes: The analytical replicates were performed twice. molecules-27-02949-t002_Table 2 Table 2 The chemical composition of fresh and dried M. suaveolens L. analyzed by using HPLC. Phenolic Compounds Concentration (µg/g) ± SD Whole Plant LEAVES Stems Fresh Dried Fresh Dried Fresh Dried Protocatechuic acid 5.5 ± 0.3 n.d 45.4 ± 3.21 3.5 ± 0.17 19.96 ± 1.56 3.6 ± 1.23 p-hydroxybenzoic acid n.d 3.95 ± 0.23 526.9 ± 21.87 2.23 ± 0.15 n.d 2.62 ± 0.12 Catechin 198.3 ± 1.8 85.03 ± 0.98 462.3 ± 12.45 54.19 ± 3.14 1340.4 ± 13.76 68.1 ± 2.56 Vanilic acid n.d n.d 12.13 ± 1.12 n.d n.d n.d Cinnamic acid 0.33 ± 0.01 10.51 ± 1.02 46.3 ± 3.05 15.35 ± 1.16 1.54 ± 0.13 1.84 ± 0.13 Naringenin 2.3 ± 0.15 53.22 ± 3.23 13 ± 1.23 7.2 ± 0.54 371.8 ± 3.89 130.3 ± 10.23 Eugenol n.d n.d n.d n.d 86.6 ± 7.34 n.d Caffeic Acid n.d 13.84 ± 1.08 1141.5 ± 13.35 1.79 ± 0.09 n.d 3.7 ± 023 Coumaric acid 0.05 ± 0.001 n.d 51 ± 3.67 n.d 5.8 ± 0.43 n.d Ferulic acid 226.7 ± 3.8 3.89 ± 0.22 1520 ± 17.34 0.63 ± 0.0054 1.92 ± 0.12 1.95 ± 0.12 Rutin 676.7 ± 4.45 252.16 ± 12.92 3383.8 ± 15.45 194.7 ± 12.7 194.6 ± 12.43 41.6 ± 2.34 Luteolin 122.3 ± 1.17 78.65 ± 4.34 514.9 ± 12.34 83.7 ± 6.42 41.6 ± 3.52 38.6 ± 2.12 Quercetin 22.5 ± 1.08 153.8 ± 7.8 377.3 ± 24.20 156.98 ± 11.65 n.d 58.6 ± 6.23 Rosmarinic acid 2223.3 ± 9.8 21,191.9 ± 24.8 28,002.5 ± 32.6 15,165.1 ± 17.15 6558 ± 15.25 8378.4 ± 23.75 n.d: not detected. SD standard deviation. Notes: The HPLC analysis was carried out in duplicate molecules-27-02949-t003_Table 3 Table 3 Mean squares of analysis of variance for antifungal activity of volatile and non-volatile constituents of M. suaveolens L. Source of Variation df Volatile Oils Ethanolic Non-Volatile Extracts Between 6 182.952 ** 2160.532 ** Within 14 0.025 0.013 ** indicates significance at 1% probability level. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Shahbazi Y. Application of Carboxymethyl Cellulose and Chitosan Coatings Containing Mentha spicata Essential Oil in Fresh Strawberries Int. J. Biol. Macromol. 2018 112 264 272 10.1016/j.ijbiomac.2018.01.186 29408003 2. Božovic M. Pirolli A. Ragno R. Mentha suaveolens Ehrh. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093105 sensors-22-03105 Article Computationally Efficient Implementation of Joint Detection and Parameters Estimation of Signals with Dispersive Distortions on a GPU https://orcid.org/0000-0003-2812-6724 Lipatkin Vladislav I. * https://orcid.org/0000-0002-4165-9421 Lobov Evgeniy M. https://orcid.org/0000-0002-6695-6297 Kandaurov Nikolai A. Ribas Luís Castedo Academic Editor Science and Research Department, Moscow Technical University of Communications and Informatics, Moscow 111024, Russia; lobovrts@yandex.ru (E.M.L.); kandaurov@srd.mtuci.ru (N.A.K.) * Correspondence: lipatkin.24@gmail.com 19 4 2022 5 2022 22 9 310504 3 2022 18 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The detector is an integral part of the device for receiving and processing radio signals. Signals that have passed through the ionospheric channel acquire an unknown Doppler shift and are subject to dispersion distortions. It is necessary to carry out joint detection and parameter estimation to improve reception quality and detection accuracy. Modern hardware base developing makes it possible to implement a device for joint detection and evaluation of signals based on standard processors (CPU) and graphic processors (GPU). The article discusses the implementation of a signal detector that allows for real-time operation. A comparison of implementations of algorithms for estimating the Doppler frequency shift through multiplication by a complex exponent and the fast Fourier transform (FFT) is performed. A comparison of computational costs and execution speed on the CPU and GPU is considered. DSP GPU FFT communications dispersion distortions Doppler shift ionosphere radar ==== Body pmc1. Introduction Ionospheric radio communication is a highly reliable and cost-effective solution for organizing communication with outlying regions, as well as with regions whose infrastructure has been disrupted due to natural disasters. Currently, development of decameter ionospheric radio communication systems is on the way to increasing the speed of information transmission [1,2,3,4,5,6]. When using broadband signals in the decameter range, the frequency dispersion has a significant effect on the signal [7,8,9,10,11,12,13,14]. Thus, due to the frequency dispersion, at different frequencies wideband signals have different propagation delays. Such a difference leads to a synchronization error and affects the quality of signal detection and the quality of information reception [15,16,17]. A separate problem is the detection of long signal preambles with a duration of about several seconds long, with a spectrum wider than 100 kHz [18,19,20] and with a coherent accumulation of the detected signal energy throughout its duration. In this case, the signal base reaches a value exceeding 50 dB, and the required accuracy of estimation and compensation of the Doppler frequency shift is in the tenths and in some cases hundredths of a hertz. Otherwise, the coherent accumulation of signal energy over time intervals of units or even tens of seconds becomes impossible. Simultaneously, with the evaluation of the Doppler shift [21,22,23,24,25,26], it is also required to evaluate and compensate for the dispersion distortions of the detected signals. In this paper, we show the possibility of constructing a device for the joint detection and estimation of the parameters of signals with dispersion distortions on graphic processors. Implementations proposed in this paper allow for the simultaneous detection of signals and estimation of dispersion distortions, delay, Doppler shift, and initial phase in real time. 2. Related Work Stein, Tolimieri, and Winograd are the founders of research on algorithms for calculating uncertainty functions. Stein has described a processing approach for obtaining joint delay and frequency offset (DTO/DFO) estimates for continuous signals based on the efficient calculation of complex ambiguity functions [27]. Typically, it involves a two-mode process called coarse and fine modes. Coarse mode is used to greatly reduce the time delay and frequency offset uncertainty, after which fine mode calculations are performed. Precise mode uses product/filter mixing interpretation, greatly reducing the processing load. Tolimieri and Winograd proposed an algorithm for the discrete ambiguity function calculation in [28]. They rely on the fact that, in most basic applications, it is necessary to calculate the limited parts of the DFT of a discrete ambiguity function. To do this, they first pass a long sequence through a decimated FIR filter, and then they use the FFT algorithm. Additionally, computationally efficient algorithms for the joint estimation of the Doppler shift and time delay are considered in [29,30]. These papers propose a new method based on a pre-weighted Zoom FFT with a cascaded filter algorithm to minimize the processing load of cross-ambiguity functions without compromising performance. The weighting process in the Zoom FFT method provided an opportunity for the researchers to get rid of redundant calculations. The multi-stage filtering method was used to reduce complexity and to obtain a high-performance system. A method for processing segments was also proposed, adapted to calculate the ambiguity function when imposing input data frames. By considering the calculation of the cross-ambiguity functions of overlapping data frames as the calculation of the FFT of the overlapping data, the redundancy of the calculations can be eliminated. Modern techniques for reducing the complexity of the cross-ambiguity function (CAF) are based on numerical fitting for CAF [31]. These algorithms make full use of the property that the CAF is symmetrical in the frequency domain. Simulation results show that, compared to the method that looks for the CAF peak, the proposed algorithm can significantly reduce computational complexity while meeting the accuracy requirements of the joint time-frequency estimate. In paper [32], the authors propose a method for solving the problem of determining the mutual delay time of ultra-wideband signals. A modified algorithm, which can be implemented by parallel calculation of the cross-ambiguity function, was used to compensate Doppler shift of the recorded signals. This algorithm was based on the division of an ultra-wideband signal into separate frequency channels. An increase in the computational efficiency of the proposed algorithm was achieved by parallel calculation of the convolution function and cross-ambiguity. However, all the above works do not take into account the problem of compensating for dispersion distortions and processing signals with a base over 50 dB. There are also no computationally efficient solutions implemented on the GPU that allow for the real-time detection of signals with a base of more than 50 dB (the signal spectrum width is hundreds of kHz, the duration is a few seconds) with the simultaneous estimation of dispersion distortions, delay, Doppler shift, and initial phase. Given these features, the joint detection and estimation of signal parameters requires large computational resources. The modern technology level makes it possible to consider the possibility of developing a computationally efficient implementation of various algorithms on GPUs. For example, such GPU implementation allows you to build systems for parallel simulation of MIMO radars [33] and build digital downconverter [34]. Additionally, GPUs are very often used in deep learning [35]. Thus, computing on GPUs is becoming more and more efficient. 3. Analytical Formulation of the Problem The complex envelope of the signal at the joint detection and signal parameters estimation device input can be represented as a composition of the useful signal complex envelope, distorted by the frequency dispersion of ionospheric channel, and the complex envelope of white Gaussian noise:(1) y˙i(φ,τ=l⋅Δt,fd,s)=e−jφej2πfd(i−l)Δtx˙i−l(s)+n˙i,i=0÷Np−1, where x¯˙(s)=x¯˙∗h¯˙(s) is distorted by the ionospheric channel useful signal complex envelope, h˙i(s) is the ionospheric channel impulse response (IR) complex envelope, x˙i is the complex envelope of useful undistorted signal, fd is the doppler frequency shift, τ is the delay in seconds, l is the delay in samples, Δt is the sample time, s is the slope of the dispersion characteristic (parameter that characterizes dispersion distortions), φ is the unknown phase shift, n˙(t) is the complex envelope of white Gaussian noise with zero mean and variance σɯ2, and Np is the number of samples. The ionospheric channel impulse response (IR) complex envelope connects with frequency response of the ionospheric channel H˙(j2πf) through Fourier transform H˙(j2πf):h˙(t,s)=∫−∞∞H˙(j2πf)ej2πfdf, where x(t) is a transmitted signal that is known at the receiving side. The ionospheric channel model, which takes into account frequency dispersion, is proposed in [8]. We consider version of this model with a linear dispersion characteristic. Then frequency response of the ionospheric channel in the absence of multipath signal propagation can be described as (2) H˙(j2πf)=e−jπsf2, f∈[−Δf/2; Δf/2], where Δf is the bandwidth of the ionospheric channel. The decision statistic can be found as:(3) λ˙i(φ,τ,fd,s)=∑n=0Np−1y˙n(φ,τ=l⋅Δt,fd,s)g˙i−n∗(fd,s), where the matched filter impulse response g˙ is defined as (4) g˙Np−1−i(fd,s)=∑n=0Np−1x˙nej2πfdnΔth˙i−n∗(s). Then, the parameter estimates can be found as:(5) φ^,τ^,fd^,s^=argmaxφ,τ,fd,sλ˙i(φ,τ,fd,s), where φ^,τ^,fd^ and s^ are estimates of φ,τ,fd and s, respectively. 4. Implementation of a Matched Filter This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn. From Equation (2) it can be seen that the number of matched filters to obtain a complete set of decision statistics λ˙i(φ,τ,fd,s) is determined by the number of possible Doppler frequency shifts fd and slopes of the dispersion characteristic s:(6) Nmf=NfdNs, where Nmf is the number of matched filters, Nfd is the number of possible Doppler frequency shifts fd, and Ns is the number of possible slopes of the dispersion characteristic s. A large number of matched filters imposes high requirements on the computing platform. Doppler frequency shift fd consideration (for its estimation) can be carried out after matched filtering, then Equation (2) can be represented as:(7) λ˙i(φ,τ,fd,s)=ej2πfdiΔt∑n=0Np−1y˙n(φ,τ=l⋅Δt,fd,s)g˙i−n(s), where (8) g˙Np−1−i(s)=∑n=0Np−1x˙nh˙i−n∗(s). The above transformation reduces number of required matched filters to Nmf=Ns, which can significantly reduce computational costs. However, in the conditions of an ionospheric channel, due to the presence of a Doppler frequency shift during the observation of the complex envelope at the input of the matched filter, a phase drift occurs, which leads to losses in the SNR at the output of the matched filter. To minimize these losses, we will convolve not with a reference signal of duration Np, but with signals (see Figure 1):(9) x˙m,n=x˙n+m⋅Npp, n=0÷Npp−1, m=0÷M−1, where Npp=NpM, and M is the number of splits of the original sequence. In this case, matched filtering can be performed using a series-matched filter, which is a set of filters matched with sequences x˙m,n. 4.1. Estimation Algorithm via Complex Exponents A filter matched with a series of sequences is shown at Figure 2. The signal at the output of each matched filter can be written as:(10) λ˙m,n(s)=∑l=0Npp−1y˙m,lg˙m,n−l∗(s), n=0÷Npp−1, m=0÷M−1, where g˙M−1−m,Npp−1−n(s)=∑k=0Np−1x˙k+mNpph˙n−(k+mNpp)∗(s) is the complex impulse response envelope of the filter matched to the m-th sequence. Doppler frequency shift is taken into account:(11)  λ˙m,n(fd,s)=λ˙m,n(s)⋅ej2πfd(n+mNpp)Δt. The decision statistics at the matched filter output can be obtained as:(12) λ˙n(fd,s)=∑m=0M−1λ˙m,n(fd,s). The interval of allowable values of the Doppler frequency shift is [−fs2Npp:fs2Npp], where fs is the sample rate. Within this interval, value of the estimated Doppler frequency shift can be arbitrary. A significant drawback of this implementation is the requirement for the amount of RAM to store arrays with complex exponents. Joint detection and signal parameters estimation device scheme is shown in Figure 3. 4.2. Algorithm with Doppler Estimation via FFT Multiplication by complex exponents and the subsequent summation to further estimate the Doppler frequency shift can be done using the FFT. Let fd=kfsN, then Equation (10) can be represented as:(13) λ˙n,k(fd=k⋅Δf,s)=∑m=0M−1λ˙m,n(s)⋅ej2πkm, where (14) λ˙m,n(s)=∑l=0Npp−1y˙m,lg˙m,n−l∗(s),n=0÷Npp−1,m=0÷M−1. Equation (11) can be calculated using FFT algorithms from λ˙m,n(s) for each k. This algorithm, in contrast to the algorithm with multiplications by complex exponents, makes it possible to estimate the Doppler frequency shift only for fd=k⋅Δf, where k=[−Npp2:Npp2]. The scheme of the filter matched with a series of sequences with searches for Doppler frequency shifts through the FFT is shown in Figure 4. 5. GPU Implementation A matched filter with a series of sequences on the GPU is implemented using the fast convolution algorithm “Overlap and Save” [36] and the FFT and IFFT parallel computation library on the GPU–clFFT, implemented on OpenCL [37] (see Figure 5). The clFFT library is developed by clMathLibraries, an OpenCL library implementation of discrete fast Fourier transforms. The input data are loaded into the GPU in blocks of Npp samples. Loading is performed into a circular buffer Binput, size Npp(M+1). After loading the next block of samples, the buffer Binput is fed to the calculation of the FFT with the size of 2Npp with an overlap in Npp samples. FFT results are written to a buffer BFFT, size 2NppM. Post-FFT samples are multiplied with frequency response samples Hi(s),i=0,1,…,M−1. The multiplication result is written to the buffer BMUL and fed to the calculation of the IFFT, size 2Npp. Samples after this IFFT are placed in the BIFFT buffer. The second half of each 2Npp sample is the response of the filter λ˙m,n(s) matched to the m-th sequence. Received responses are transferred to the module for taking into account Doppler frequency shifts and obtaining the total decision statistics. This module is made in two versions. The first option is to directly multiply by complex exponents and then sum the filter responses. Multiplication operations by complex exponents are performed by calculating different samples of decision statistics using different GPU work items (WI). The work items set wi,j of the graphic processor is represented as a matrix W, dimension R1×R2 (see Figure 6). Where R1 and R2 are numbers of work items in the 1st and 2nd dimension, respectively. These values determined by GPU implementation and have to be taken into account in the parallelization of the algorithm adaptation for GPU. Within the available number of work items, it is proposed to parallelize the calculation of all samples of the decision statistics for all possible values of the Doppler frequency shifts fd. The required number of work items to compute decision statistic samples λ˙n(fd,s) for a single Doppler frequency shift value is Npp. The maximum number of work items per calculation of the decision statistic samples for one value of the Doppler frequency shift can be calculated as:(15) Nmax_items_exp=⌊R1R2Nfd⌋. Then, the actual number of work items is defined as:(16) Nitems_exp=min(Nmax_items_exp,Npp). In the case when required number of work items exceeds number of available GPU items, some work items will calculate several samples of decision statistics λ˙n(fd,s). When performing calculations on the GPU, work items are combined into work groups (WG). The best performance is achieved by setting the work group size Nsize_work_group to the maximum, which is determined by the specific GPU implementation. The number of work groups for computing decision statistics samples λ˙n(fd,s) for one value of Doppler frequency shift:(17) Nwork_group=⌈Nitems_expNsize_work_group⌉. The distribution of calculations between work items and GPU work groups is shown in Figure 7. This figure shows that the decision statistics values calculation λ˙n(φ,τfd,s) is divided into Nfd groups by Nwork_group×Nsize_work_group work items. Each of these groups performs the calculation of the decision statistics samples λ˙n(φ,τfd,s) for one of the possible values of the Doppler frequency shift fd. This improves the performance of the algorithm by performing parallel computations. The second option for building a module for taking into account Doppler frequency shifts and obtaining the total decision statistics was performed using the FFT through the clFFT library. According to Equation (11) and Figure 4, the FFT must be taken from the n-th samples of all responses λ˙m,n(s). The clFFT library allows you to perform all the necessary FFTs using a buffer BIFFT without additional memory operations. Figure 8 shows that the clFFT library allows you to perform an FFT from all n-th samples for all λ˙m,n(s), n=0÷Npp−1,m=0÷M−1 that were in the buffer BIFFT without additional data copies. The number of these FFT operations is M. The FFT results are written to the buffer Bmf in such a way that the decision statistics λ˙n(fd,s) for different values of the Doppler frequency shift are sequentially stored in the memory. 6. Comparison of Algorithms Computational Complexity Computational complexity is affected by the number of possible values fd and s, which are defined as Ns and Nfd, respectively. Computational complexity is given in the number of complex multiplications per one input sample. Computational complexity of the device for joint detection and estimation of signal parameters for two implementations of the algorithm is defined as:(18) Ncm=(2M(log2(2Npp)−1)+MNfd)Ns, (19) Ncm_fft=(2M(log2(2Npp)−1)+Nfd2(log2(Nfd)−2))Ns. Thus, computational complexity of the proposed algorithm depends on the number of partitions of the original sequence M, the duration of one part of the original sequence Npp, the number of possible values of Doppler shifts in frequency Nfd, and slopes of the dispersion characteristic of the ionospheric channel Ns. 7. Test Results on CPU and GPU For the experiment, a six-core Intel Core i7-8700 CPU with a clock frequency of 3.2 GHz and a Geforce RTX 3060 GPU with 3584 CUDA cores, a base clock frequency of 1.32 GHz, and a 192-bit memory bus were used. The experiment was run on a computing platform of 32 GB of RAM with a speed of 2400 MT/s. The experiment was carried out in the operating system Linux Ubuntu 20.04 with Nvidia GPU driver version 460.73.01. The used clFFT library version was 2.12.2. For algorithm implementation, compilation was used with a gcc 9.4.0 compiler with compiler flags set to o2. To execute calculations, five cores and 10 threads of Intel Core i7-8700 CPU were used. One core and two threads were left for the needs of the operating system. Testing was performed on a signal with a bandwidth ΔF=400 kHz and a duration T=7 s. The base of this signal was 64.5 dB. These parameters were chosen based on the results of field experiments carried out on single-hop ionospheric paths up to 3000 km long. The search ranges for the Doppler frequency shift and the slope of the dispersion characteristic of the ionospheric channel were also selected based on the results of field experiments. Dependence of the computational complexity on the number of possible values of Doppler shifts in frequency Nfd for a different number of slopes of the dispersion characteristic of the ionospheric channel Ns for Npp=32768 and M=86 is shown in Figure 9. This graph shows that an increase in the number of possible values Nfd leads to a slight increase in computational complexity compared to an increase in the number of possible values Ns. The dependence of the number of complex multiplications on the number of possible values fd for a different number of splits M of the original signal at is shown in Figure 10. The number of experiments performed to obtain averaged results was 1000. Increasing the number M leads to an increase in computational complexity. Table 1 shows the dependence of the algorithm running time on the block duration for fd=−5:0.05:5Nfd=201. Table 2 shows how many times RTX 3060 GPU is faster than base Intel i7-8700 processor. It can be seen that the performance gain of the RTX 3060 GPU in the algorithm without FFT decreases with increasing block duration, while in the algorithm with FFT, it remains constant. The TDP of the RTX 3060 GPU is 170W, while the TDP of the Intel Core i7-8700 is 65W. Thus, the increase in power consumption when using the RTX 3060 GPU compared to the Intel Core i7-8700 CPU is 2.62 times, and the minimum performance increase is 4.37 times. Therefore, it is advisable to use a GPU, since the increase in performance exceeds the loss in power consumption. Dependence of the response level of the matched filter on the block duration at the Doppler shift fd=3 is shown in Figure 11. Implementation with Doppler shift estimation via FFT on the GPU is the most efficient and allows for processing one sample in less than 2 µs with a loss of no more than 0.5 dB. With a block duration of less than 80 ms, the loss does not exceed 0.5 dB. 8. Conclusions This paper proposes two implementations of the joint detection and estimation of the parameters of signals with dispersion distortions on the CPU and GPU. In the first method, the estimation of the Doppler frequency shift is performed in a direct way, by multiplying by complex exponents. In the second method, estimation of the Doppler frequency shift is performed through the FFT. All FFTs in the proposed implementations are performed through the “Overlap and Save” fast convolution algorithm. The computational complexity of the proposed implementations of joint detection and estimation of signal parameters is calculated. It is shown that the method based on the estimation of the Doppler frequency shift through the FFT is the most computationally efficient. Implementation of this method on the GPU allows for the joint detection and estimation of signal parameters in real time. It is shown how the duration of a block in a matched filter with a series of sequences affects the response level. Reducing the block duration results in a reduction in matched response level loss but results in an increase in computational complexity. Author Contributions Conceptualization, V.I.L. and E.M.L.; methodology, formal analysis, and investigation V.I.L.; software, writing—original draft preparation, and writing—review and editing, V.I.L. and N.A.K.; validations and supervision E.M.L. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Reference signal divided into M parts. Figure 2 Matched filter with a series of sequences with complex exponents. Figure 3 Scheme of the device for joint detection and signal parameter estimation. Figure 4 Matched filter with a series of sequences with searches over Doppler frequency shifts via FFT. Figure 5 Implementation diagram of a matched filter with a series of sequences. Figure 6 A set of GPU work items. Figure 7 Distribution of computations between GPU work items. Figure 8 Scheme of the module for taking into account Doppler frequency shifts and obtaining the total decisive statistics, implemented through the FFT. Figure 9 Dependence of the number of complex multiplications on the number of possible values fd for a different number of possible values of the slope of the dispersion characteristic of the ionospheric channel Ns, M=86, Npp=32768. Figure 10 Dependence of the number of complex multiplications on the number of possible values fd for a different number of splits M of the original signal at Ns=10. Figure 11 Dependence of the response level of the matched filter on the duration of the block with a Doppler frequency shift fd=3. sensors-22-03105-t001_Table 1 Table 1 Experimental running time of the algorithms per one input sample, with different block durations. Algorithm Implementation Type Block Length 10.24 ms µs Block Length 20.48 ms µs Block Length 40.96 ms µs Block Length 81.92 ms µs Block Length 163.84 ms µs Doppler without FFT on CPU 251.1 124.4 62.59 31.3 15.91 Doppler with FFT on CPU 17.83 9.17 5.88 3.98 2.51 Doppler without FFT on GPU 7.36 4.21 2.49 1.61 1.19 Doppler with FFT on GPU 3.91 2.03 1.29 0.91 0.55 sensors-22-03105-t002_Table 2 Table 2 GPU RTX 3060 Performance Boost vs. CPU Intel Core i7-8700. Algorithm Implementation Type Block Length 10.24 ms Block Length 20.48 ms Block Length 40.96 ms Block Length 81.92 ms Block Length 163.84 ms Doppler without FFT 34.12 29.55 25.14 19.44 13.37 Doppler with FFT 4.56 4.52 4.56 4.37 4.56 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Jorgenson M.B. Johnson R.W. Nelson R.W. An Extension of Wideband HF Capabilities Proceedings of the IEEE Military Communications Conference San Diego, CA, USA 18–20 November 2013 1202 1206 2. Pijoan J.L. Altadill D. Torta J.M. Alsina-Pagès R.M. Marsal S. Badia D. Remote Geophysical Observatory in Antarctica with HF Data Transmission: A Review Remote Sens. 2014 6 7233 7259 10.3390/rs6087233 3. Kandaurov N.A. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091837 polymers-14-01837 Article Characterization of Viscoelastic Poisson’s Ratio of Engineering Elastomers via DIC-Based Creep Testing Sotomayor-del-Moral Jonathan A. 1† https://orcid.org/0000-0002-8812-7190 Pascual-Francisco Juan B. 1† Susarrey-Huerta Orlando 2 Resendiz-Calderon Cesar D. 3 Gallardo-Hernández Ezequiel A. 2 https://orcid.org/0000-0002-5008-5888 Farfan-Cabrera Leonardo I. 3* Stoček Radek Academic Editor Heinrich Gert Academic Editor Kipscholl Reinhold Academic Editor 1 Departamento de Mecatrónica, Universidad Politécnica de Pachuca, Carretera Pachuca-Cd. Sahagún Km. 20, Ex-Hacienda de Santa Barbara, Zempoala 43830, HGO, Mexico; allan16@micorreo.upp.edu.mx (J.A.S.-d.-M.); jbpascualf@hotmail.com (J.B.P.-F.) 2 SEPI-Escuela Superior de Ingeniería Mecánica y Eléctrica, Instituto Politécnico Nacional, Unidad Zacatenco, Col. Lindavista, Ciudad de México 07738, CDMX, Mexico; osusarrey@ipn.mx (O.S.-H.); egallardo@ipn.mx (E.A.G.-H.) 3 Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, NL, Mexico; resendiz.cesar@tec.mx * Correspondence: farfanl@hotmail.com † These authors contributed equally to this work. 29 4 2022 5 2022 14 9 183702 4 2022 25 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). New data of creep and viscoelastic Poisson’s ratio, ν(t), of five engineering elastomers (Ethylene Propylene-Diene Monomer, Flouroelastomer (Viton®), nitrile butadiene rubber, silicone rubber and neoprene/chloroprene rubber) at different stress (200, 400 and 600 kPa) and temperature (25, 50 and 80 °C) are presented. The ν(t) was characterized through an experimental methodological approach based on creep testing (30 min) and strain (axial and transverse) measurements by digital image correlation. Initially, creep behavior in axial and transverse directions was characterized for each elastomer and condition, and then each creep curve was fitted to a four-element creep model to obtain the corresponding functions. The obtained functions were used to estimate ν(t) for prolonged times (300 h) through a convolution equation. Overall, the characterization was achieved for the five elastomers results exhibiting ν(t) increasing with temperature and time from about 0.3 (for short-term loading) to reach and stabilize at about 0.48 (for long-term loading). rubber material testing rheology Poisson’s ratio viscoelasticity Tecnologico de MonterreyThe APC was funded by Tecnologico de Monterrey. ==== Body pmc1. Introduction Elastomers are viscoelastic materials widely used in engineering applications. The performance of elastomeric mechanical components not only depends on the material’s mechanical properties but also on viscoelastic properties such as creep compliance, stress relaxation and viscoelastic Poisson’s ratio, ν(t), which are time- and temperature-dependent properties. The viscoelasticity of a material is represented by a combination of both elasticity and viscosity properties in different proportions, which is the reason that an elastomer exhibits a variable elastic modulus dependent of time, stress and temperature. The Poisson’s ratio, ν, is defined as the negative constant ratio between transverse and axial strains in a uniaxial state of stress, which is applied along the axial direction for any elastic, homogenous and isotropic material. Practically, for polymers, this property is assumed as a constant, ν, instead of a time and temperature variable, ν(t), in design and performance simulation of engineering components due to the complexity for its experimental determination [1,2,3]. However, the progress of computing and software technology for modern engineering design has promoted the inclusion of more complex or nonlinear properties, namely, the viscoelastic properties (stress relaxation, creep and ν(t)) of soft materials for obtaining more accurate predictions of performance and service life of engineering components via simulation [1,2,3,4]. For elastomers, the data of viscoelastic properties, particularly ν(t), are scarce in the literature. There only few works reporting on the characterization of the time-dependent Poisson’s ratio of some elastomers. For example, Kuggler et al. [4] determined the ν(t) of different Hypalon-based rubbers and hydroxyl terminated polybutadiene rubber by using an optoelectronic system constant strain rate and stress relaxation tests; they found an increase in Poisson’s ratio with time for all cases. Saseendran et al. [5] determined the evolution of ν(t) of the commercial LY5052 epoxy resin at different cure states under uniaxial tension subject to constant deformation stress relaxation testing. They found that Poisson’s ratio evolved from 0.32 to 0.44 over time depending on the cure state of the resin. Pandini and Pegoretti [6] investigated the phenomenology of the dependence of Poisson’s ratio with temperature, time and strain of two crosslinked epoxy resins with different glass transition temperatures using contact extensometers and the simultaneous measurement of the axial and transverse deformations under two dissimilar tensile and relaxation testing. They found that ν(t) increased with strain rate, temperature, and time. Cui et al. [7] proposed a fully numerical framework based on a theory of stress relaxation for the determination of time-dependent Poisson’s ratio for solid propellants (elastomer composites). The time-dependent Poisson’s ratio was obtained under different cohesive parameters, namely, loading conditions (loading temperature, loading rate and fixed strain) and area fraction. They found that the numerical simulation revealed that time-dependent Poisson’s ratio can be nonmonotonic or monotonic depending on different cohesive parameters. In addition, all time-dependent Poisson’s ratios increased at the beginning of the relaxation stage because of cohesive contact. Then, once transverse and axial strains stop changing, all time-dependent Poisson’s ratios achieved equilibrium values. In a more recent research work, Cui et al. [8] proposed constitutive models relating ν(t) with a classical creep constitutive model using a Laplace transform method and compared with stress relaxation models. They found that, in analytical analyses, creep and relaxation models solutions correlated well. It can be a reference that ν(t) can be obtained from creep or stress relaxation data. According to theory of viscoelasticity [9,10,11], ν(t) can be directly obtained from stress relaxation tests by measuring the transversal strain with time, εx(t), after applying a constant axial deformation, ε0, as expressed by Equation (1). (1) ν(t)=−εx(t)ε0 In this manner, ν(t) is difficult to obtain accurately due to the minimal transverse strain produced with time during a stress relaxation test even using sophisticated measurement technology with high resolution. This is one of the reasons that published data of ν(t) of elastomers are rarely reported. An alternative method to obtain ν(t) is through creep tests followed by a converse methodological approach [9,12]. Creep tests are advantageous because they allow the generation of larger strains in both axial and transverse directions with time under a constant tensile or compressive load, which can be measured more accurately and easily. This methodological approach and its rationalization have been recently published previously elsewhere [13]. It has been demonstrated to be effective for the evaluation of ν(t) of elastomers under different stress levels and temperatures by using digital image correlation (DIC) for strain measurement. Generally, the measurement of creep strain has been achieved by using gripping extensometers or strain gauges adhered or gripped to the material sample in standard tensile creep testing devices. Nevertheless, the application of these strain measurement gauges can penetrate the sample producing disturbances in structural homogeneity and producing stress concentrators in the material, as well as, restricting the free strain produced in the sample. It is known to introduce some errors in the collected strain data. Hence, in order to avoid errors in the strain measurement, some additional data correction techniques [6] and techniques based on non-contact optical measurement such as Moire interferometry, electronic speckle pattern interferometry (ESPI), shearography, and digital image correlation (DIC) have been applied effectively [12,14,15]. The utilization of a non-contact strain measurement technique such as DIC allows accurate creep strain determination in elastomers without interfering with the sample deformation during the creep strain state. Other alternatives have been proposed and used by several research groups for evaluating the viscoelastic behavior of soft materials such as elastomers, particularly creep. For instance, the standard methods include ASTM-D2990 and ISO 899–1:2003, some non-standard tensile test methods [16], dynamic-mechanical analysis (DMA) [17], some methodologies based on nanoindentation [18,19] and micro- and macro-indentation with axi-symmetric indenters [20,21,22,23]. Nonetheless, DIC-based creep testing, in particular, has been demonstrated as a very suitable and accurate tool for mechanical and viscoelasticity characterization of a wide range of materials, including elastomers [24]. Moreover, DIC has been employed for purposes in elastomers. For example, it has been used for studying fatigue crack growth behavior of elastomers, in which plane strain tensile samples (thin and rectangular strips), also named as pure shear samples, are preferred for this testing [25,26,27]. DIC is a non-invasive optical full-field measurement technique based on the comparison of digital images of an image in different stages of change/deformation. For the comparison of the images, it recognizes patterns with different light intensity of an area. Usually, the light intensity pattern is represented by small and well-defined contrasting points detected in the images taken and processed. The points identified in the undeformed image is recognized by contrasting with the light intensity pattern from the surrounding area. Depending on the light intensity of each point, the points with identical light intensity are identified in the deformed image. About 256 levels of grayscale are used for the digitization of the light intensity in black and white images. Using a single camera-based DIC system is sufficient to measure in-plane (two directions) deformations simultaneously, which is sufficient to determine creep and ν(t). Thus, the aim of this paper is to obtain and provide new data from an extensive novel characterization of the ν(t) of various common engineering elastomers under different tensile loads and temperatures through creep tests and using a single camera-based DIC for obtaining accurate axial and transverse strain measurements in accordance with the methodology reported in [13], which is described in the following section for purpose of this research. 2. Materials and Methods The ν(t) of five commercial elastomers (Ethylene-Propylene-Diene Monomer (EPDM), Flouroelastomer (Viton®) (FKM), nitrile butadiene rubber (NBR), silicone rubber (VMQ) and neoprene/chloroprene rubber (CR)), which are contemporarily used in a wide range of engineering applications (static and dynamic seals, belts, support inserts, vibration insulators, etc.), was determined by a methodological approach using tensile creep tests and strain measurement by DIC. Overall, it comprises the methodological steps shown in Figure 1: (1) measurement of creep strains (generation of the strain map by DIC) in transverse, εx(t), and axial, εy(t), directions of an elastomeric sample during a determined creep test period at constant temperature; (2) determination of the creep strain functions in both directions by fitting the obtained data to a known viscoelastic model; (3) estimation of the ν(t) using a numerical solution of a convolution equation for each material and condition. Both the axial and transverse creep strains of rectangle-shaped samples (60 mm × 5 mm and 2 mm thickness) cut from black sheets of each elastomer were obtained simultaneously by using a DIC equipment (Q-450: Dantec Dynamics, Skovlunde, Denmark) instrumented in a home-built creep test set-up, as shown in Figure 1. Commonly, carbon black is added to the elastomers during their manufacturing process to enhance their mechanical properties [28] and provide black pigmentation to elastomers, which is the case of tested elastomers. It is noteworthy that the strain measurement with DIC can be also applied successfully in the study of materials pigmented with other colors, or even colorless, as long as the required speckle pattern be achieved. The mechanical properties of the elastomers and parameters of the creep test and DIC measurement are shown in Table 1. The tensile tests were conducted according to the method specified in ASTM-D412 using dumbbell shape specimens with a gauge length of 30 mm at a strain rate of 50 mm/min by using a tensile tester (UE22XX Digital Electronic Tensile Testing Machine, Laryee, Beijing, China) with a load cell of 1 kN. The hardness measurements were performed according to the method in ASTMD2240 in square specimens with dimension of 20 mm using a Shore A type digital durometer (DD-100 Digital Shore Durometer Tester: ABQ Industrial, The Woodlands, TX, USA). The surface roughness of each material was determined in an optical profilometer (Contour GT-K: Bruker, Billerica, MA, USA) with an objective of 5X. The mean roughness of area (Sa) was found to be in the range of 0.2–0.47 µm for all elastomers. The DIC parameters selected have been useful and effective for the creep characterization of elastomers, as reported in a previous research work [24]. The tested samples were finely speckled with white paint for enabling the suitable detection for DIC. The creep tests were run under different proportional tensile stresses and temperatures. They consisted of hanging a rectangular specimen on a frame inside a thermal chamber with temperature control and then applying the predefined tensile load through a dead weight lever system. Once the sample is heated up and maintained at a predefined temperature, the tensile load is applied while DIC measurements are run simultaneously. The sample temperature is measured and monitored by three infrared sensors positioned over different regions of the sample to confirm a quasi-homogenous temperature inside the chamber. To measure εx(t) and εy(t) produced in the sample, a CCD camera (SpeedSense 9070: Phantom, Wayne, NJ, USA) possessing a Zeiss Makro-Planar 50 mm f/2 ZF.2 lens and an image resolution of 1280 × 800 pixels was employed. The camera was connected to a computer loaded with software Istra 4D (Istra 4D: Dantec Dynamics, Skovlunde, Denmark) for the camera configuration, data collection, image processing, and strain computing. The Istra 4D software integrates an algorithm for the numerical calculation of displacements and strain in the x- and y-directions. This calculation is based on the tracking of every point of the image of the speckled sample at any time. Thus, the “true” strain is directly calculated by the software. The tests were run in triplicate (using new specimens without load history), each for 30 min, for all elastomers and conditions. The time selected was enough to generate the first and second creep stages consistently in all elastomers, which is required to estimate ν(t) [9,12]. Once the creep data (εx(t) and εy(t)) were obtained by DIC, the mean creep strain functions (εx(t) and εy(t)) were determined from the three repeats. Afterwards, both mean creep functions were used to predict the ν(t) functions for each material and condition by Equation (2), which is a convolution integral equation based on a secure analytical foundation of the viscoelasticity theory [9]. (2) ∫0tν(t−u)duεy(u)du=εx(t)+νgεy(t) νg is the glassy (instantaneous) Poisson’s ratio. Due to there not being an available analytical solution for Equation (1), ν(t) should be evaluated for any time (tn) using the next recurrence formula [1,12]:(3) ν(tn)=−2εx(tn)+ν(tn−1)(εy(t0)−εy(tn−tn−1))+X(tn)εy(tn)+εy(tn−tn−1) where (4) X(tn)=∑i=1i=n−1(ν(ti)+ν(ti−1))(εy(tn−ti)−εy(tn−ti−1)) with (5) ν(t0)=νg=−εx(t0)εy(t0) and the following is the case. (6) ν(t1)=−−2εx(t1)+νg(εy(t1)−εy(t0))εy(t1)+εy(t0) Equation (3) should be evaluated for tn≥2, where tn is the time to be evaluated, tn−1 is the immediate previous time and t0  stands for t=0. In this manner, ν(t) can be estimated and predicted for the time required. 3. Results and Discussions The data obtained from the three repeats were considered for the creep characterization of all materials and conditions. As an example of the creep raw data results obtained by DIC, the dispersion of the results of transverse and axial creep from the three repeats obtained for FKM at 600 kPa and different temperatures is presented in Figure 2 and Figure 3, respectively. The maximum and minimum creep values, as well as the corresponding average behavior of creep strain of both transverse and axial creep data, are plotted. Considering the dispersion of the three repeats (gray shaded area), an average curve was generated for each case. It was observed that three tests were sufficient to obtain significant repeatability. The standard deviations (% error) obtained from the three repeats were in the range of 1.5–5.1 and 2.2–6.5 mm/m for axial and transverse creep strain, respectively. For most of the materials, it was observed that the higher standard deviations correspond to the measurements of creep in transverse direction (x). This is ascribed to the magnitude of the transverse strains generated; they are very small in contrast to those obtained in the axial direction. The DIC technique is known to be less effective for small strains. When the strain is lower, accuracy and effectiveness become lower. The range of measurement of the DIC system is from 100 micro-strains up to several 100% strain. In addition, other sources causing error in the measurement are rigid body motion when load is applied to the specimen, material structural defects, the non-uniformity in the geometry of the specimens, and the speckle pattern painted over the sample. Rigid body motion is produced when load is applied to the specimen. The electromechanical lever generates small vibrations, especially in the transverse direction, which generates slight oscillations to the specimen. Defects or inconsistencies in the homogeneity, continuity, and properties of the material promote different creep behavior, generating a wider creep strain dispersion. The specimens’ preparation, particularly the cutting of the samples, can produce some irregularities such as non-uniform geometry. Variations in the geometry, in particular the cross-section of the sample, causes higher or less stress to the sample and, therefore, higher or lower strains varying the creep results. On the other hand, in a small extent, another source of the error in the results is the variation of the light intensity captured by the CCD camera together with the non-uniformity of the speckle pattern generated in the surface of the specimens. The ideal speckle pattern for DIC should be composed of many as possible points with similar geometry and light intensity and separated by a well contrasting area in order to an accurate identification of the light patterns by DIC. However, it is very difficult to achieve this speckle pattern homogeneity. Finally, despite the data dispersion obtained by the creep tests and repeats, a clear trend of the creep curves was observed for all materials and test conditions. For all cases, including all the repeatability tests, the first and second stages of creep were clearly generated in both the axial, εx, and transverse, εy, directions. The first stage is characterized by the instantaneous (elastic) strain and an abrupt strain rate decrease while the second creep stage is recognized by a behavior approaching a nearly constant strain rate. Thus, according to the creep strain behavior obtained in both axial and transverse directions for all elastomers and conditions, it was found that all εx and εy average curves correlated well with a four-element creep model. The model involves the connection in series of the Maxwell and Kelvin–Voigt models [29], as expressed by the following:(7) ε(t)=σ0R1+σ0η1t+σ0R2(1−e−R2tη2) where σ0 is the imposed constant stress, R1 and R2 are the elastic constants in the Maxwell and Kelvin–Voigt models, respectively, and η1 and η2 are the viscous constants in each model. Hence, the data of both mean transverse and axial creep strains were fitted to the model in Equation (7), obtaining the corresponding creep strain function and elastic and viscous constants for each elastomer and condition tested. The average creep functions and the errors (standard deviations) of the fitted models obtained from the three test repetitions conducted for all materials and conditions are summarized in Table 2. Using the mean axial and transverse creep functions, ν(t) was determined for 300 h for the different elastomers and test conditions. 300 h were selected as a considerable long-term use period. However, using the same axial and transverse creep function reported in Table 2 and Equations (2)–(6), longer predictions of ν(t) can be estimated. The ν(t) results for each tested elastomer at the different stress and temperature for 300 h are shown in Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8. t0 was assumed to occur at 1–2 s during stress application since the frame rate employed was 1 fps. Hence, the first strains (used to obtain glassy value) detected by DIC were obtained by the correlation of the two first frames taken. In all cases, except EPDM and CR at the highest temperature (80 °C) and stress (600 kPa), ν(t) increased with time and temperature reaching a stable behavior. The increasing ν(t) with temperature has been demonstrated also for other elastomers, e.g., hydroxyl-terminated poly-butadiene (HTPB) propellant [30]. In the cases of EPDM and CR at the highest temperature and stress, ν(t) decreases with time. It is because these materials are not resistant to those temperatures. They exhibit very high creep rates reaching the third creep stage at high temperatures promoting very large axial strains and minimal transverse strains, which reduces ν(t), especially at high stress. The increase in ν(t)  of the elastomers with temperature is associated to the approach of a liquid-like behavior with increasing temperature, which tends to reach the Poisson’s ratio of an incompressible material (ν≈0.5) [31]. Stress had also influence on ν(t) for all the elastomers; however, it did not exhibit a clear trend. Overall, ν(t) of all the elastomers increased from about 0.3 to 0.48 with time; the last being near to the constant values frequently used for characterizing elastomers (≈ 0.45–0.5). Thus, it can be stated that ν(t) is low (about 0.3) at short-term loading, but it increases and stabilizes to about 0.48 with the long-term loading for the elastomers tested. This growth of ν(t) with time until reaching an almost stable value has been also reported by other research groups for other viscoelastic materials [4,5,6,30,31,32,33]. It is noteworthy that this behavior is also similar to the ν(t) behavior for stress relaxation for linear viscoelastic materials, as reported in [32,33]. Considering the foundations of linear viscoelasticity, Aili et al. [32] and Charpin and Sanahuja [33] postulated that the instantaneous and the stable ν(t) are similar for creep and stress relaxation. Finally, the viscoelastic behaviors of the tested elastomers somehow depend on their molecular weight, crosslinking strength and reinforcements (silica, carbon black, graphene, carbon nanotubes, etc.). However, a deeper analysis of the relationship between chemical composition/structure and ν(t) is out of the scope of the present work. It requires extensive further research. The new behavior data of ν(t) for different elastomers at different conditions obtained by this characterization work can be useful for modern design of a wide range of elements with different engineering applications in which Poisson’s ratio plays an important role on their performance with short- and long-term use. The experimental method followed in this work for characterizing ν(t)  was demonstrated to be suitable for evaluating different elastomers with relative ease. Moreover, the implementation of DIC for creep measurement allowed an accurate (with acceptable error) measurement of creep without restricting the strain produced in the material opposite to those techniques employing strain gauges gripped on the specimens. 4. Conclusions The viscoelastic properties (εx(t), εy(t) and ν(t)) of ethylene-propylene-diene monomer, flouroelastomer (Viton®), nitrile butadiene rubber, silicone rubber and neoprene/chloroprene rubber at different stress (200, 400 and 600 kPa) and temperatures (25, 50 and 80 °C) were successfully characterized through an experimental methodological method based on creep testing and strain measurements via digital image correlation (DIC). The entire field of DIC techniques was successfully employed for the simultaneous measurement of strain in axial (εy) and transverse (εx) directions with time. The tests were effective to obtain the first and second creep stages for all elastomers and conditions. The creep behaviors obtained in both the transverse and axial directions for all the materials and conditions were found to correlate well with a four-element creep model. Thus, average creep curves and models for transverse and axial directions for each material and condition were obtained and used for estimating the corresponding ν(t). The reported ν(t) of the five elastomers were estimated through the convolve equation for 300 h. However, it can be determined for more prolonged times by using the axial and transverse creep functions presented in this work and the solution of the convolve equation. Overall, the new data reported in this work suggest ν(t) to increase with temperature and time, raising from about 0.3 (for short-term loading) to reach and stabilize a value to about 0.48 (for long-term loading) for all tested elastomers. Finally, these results can be potentially applied for more accurate analytical or numerical strain and stress analyses of the components made of elastomers for both short- and long-term uses. In addition, the method implemented in this work will facilitate the characterization of complex viscoelastic properties, particularly, creep and ν(t), of conventional and new soft materials (e.g., elastomers), which could be a potential tool for screening materials produced by different manufacturing technologies. Acknowledgments The authors would like to acknowledge the financial support of Tecnologico de Monterrey in the production and publication of this work. Moreover, the authors would like to thank Consejo Nacional de Ciencia y Tecnología (CONACyT) of the Government of Mexico for funding and scholarship grants. Author Contributions J.A.S.-d.-M.: Investigation; validation; writing—original draft. J.B.P.-F.: Conceptualization; data curation; formal analysis; investigation; writing—original draft; methodology; supervision. O.S.-H.: Resources; visualization; data curation. C.D.R.-C.: Resources; visualization; data curation. E.A.G.-H.: Visualization; data curation. L.I.F.-C.: Conceptualization; writing—review and editing; formal analysis; resources; data curation; project administration; supervision. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Experimental setup and methodological steps for determining viscoelastic Poisson’s ratio of elastomers. Figure 2 Dispersion of transverse creep data (“x” direction) obtained from three test repeats for FKM at 600 kPa and different temperatures. Figure 3 Dispersion of axial creep data (“y” direction) obtained from three test repeats for FKM at 600 kPa and different temperatures. Figure 4 Viscoelastic Poisson’s ratio of EPDM at different temperatures and stress: (a) 200 kPa; (b) 400 kPa; (c) 600 kPa. Figure 5 Viscoelastic Poisson’s ratio of CR at different temperatures and stress: (a) 200 kPa; (b) 400 kPa; (c) 600 kPa. Figure 6 Viscoelastic Poisson’s ratio of NBR at different temperatures and stress: (a) 200 kPa; (b) 400 kPa; (c) 600 kPa. Figure 7 Viscoelastic Poisson’s ratio of VMQ at different temperatures and stress: (a) 200 kPa; (b) 400 kPa; (c) 600 kPa. Figure 8 Viscoelastic Poisson’s ratio of FKM at different temperatures and stress: (a) 200 kPa; (b) 400 kPa; (c) 600 kPa. polymers-14-01837-t001_Table 1 Table 1 Material properties and parameters of creep test and DIC measurement. Material/Test Property/Parameter Value Ethylene-Propylene-Diene Monomer (EPDM) (Manufacturer: Rodillos BMR®, Guadalajara, Jalisco, México) Hardness, ASTM-D2240 (Shore A) 68.5 ± 2 Tensile breaking strength, ASTM-D412 (MPa) 14 ± 0.6 Flouroelastomer, Viton® (FKM) (Manufacturer: Rodillos BMR®, Guadalajara, Jalisco, México) Hardness, ASTM-D2240 (Shore A) 77.5 ± 2 Tensile breaking strength, ASTM-D412 (MPa) 11 ± 0.7 Nitrile Butadiene Rubber (NBR) (Manufacturer: Rodillos BMR®, Guadalajara, Jalisco, México) Hardness, ASTM-D2240 (Shore A) 73 ± 2 Tensile breaking strength, ASTM-D412 (MPa) 6.9 ± 0.5 Silicone rubber/Vinyl-Methyl silicone (VMQ) (Manufacturer: Rodillos BMR®, Guadalajara, Jalisco, México) Hardness, ASTM-D2240 (Shore A) 47.5 ± 1.5 Tensile breaking strength, ASTM-D412 (MPa) 5 ± 0.8 Neoprene/Chloroprene Rubber (CR) (Manufacturer: Rodillos BMR®, Guadalajara, Jalisco, México) Hardness, ASTM-D2240 (Shore A) 69 ± 2 Tensile breaking strength, ASTM-D412 (MPa) 3.5 ± 0.5 Strain measurement/DIC parameters Subset (pixels) 17 Step (pixels) 3 Field of view (mm × mm) 55 × 36 Measurement points (points) 425 Temporal resolution (fps) 1 Camera distance (mm) 200 Image resolution (pixels × pixels) 1280 × 800 Spatial resolution (mm) 0.1 Strain resolution (mm/m) 0.25 Frame amount 1800 Measurement time (minutes) 30 Creep test Tensile load (N) 2, 4, 6 Stress (kPa) 200, 400, 600 Temperature (°C) 25 ± 1, 50 ± 2 and 80 ± 2 Test time (minutes) 30 polymers-14-01837-t002_Table 2 Table 2 Axial and transverse creep strain functions for the elastomers and conditions tested. Material Stress (kPa) Temperature (°C) Axial Creep Strain Function, εy(t) Error (%) Transverse Creep Strain Function, εx(t) Error (%) EPDM 200 25 εy(t)=20+0.0078t+35(1−e−0.0492 t) 3.7 εx(t)=5+0.0035 t+10(1−e−0.0835t) 9.5 50 εy(t)=30+0.0111 t+30(1−e−0.0912 t) 5.5 εx(t)=5+0.0051 t+13(1−e−0.0912 t) 4.4 80 εy(t)=40+0.01754t+85(1−e−0.0321 t) 1.8 εx(t)=10+0.008 t+25(1−e−0.0227 t) 3.1 400 25 εy(t)=35+0.0069 t+75(1−e−0.023 t) 1.6 εx(t)=10+0.0031 t+28(1−e−0.0298 t) 3.5 50 εy(t)=100+0.0061 t+57(1−e−0.0121 t) 2.0 εx(t)=35+0.0028 t+19(1−e−0.0244 t) 2.7 80 εy(t)=120+0.0226 t+120(1−e−0.0097 t) 3.6 εx(t)=40+0.0104t+30(1−e−0.0117 t) 3.6 600 25 εy(t)=150+0.0061 t+70(1−e−0.0051 t) 3.0 εx(t)=40+0.0026 t+30(1−e−0.0088 t) 2.0 50 εy(t)=170+0.0139 t+90(1−e−0.0062 t) 4.0 εx(t)=60+0.0061 t+23(1−e−0.0074 t) 3.3 80 εy(t)=200+0.0611 t+350(1−e−0.0047 t) 4.8 εx(t)=80+0.0104 t+30(1−e−0.0055t) 2.5 CR 200 25 εy(t)=30+0.0061 t+18(1−e−0.0317 t) 3.1 εx(t)=8+0.0026 t+4(1−e−0.0212 t) 6.3 50 εy(t)=40+0.0099 t+32(1−e−0.0215 t) 1.4 εx(t)=10+0.0044 t+7(1−e−0.0514 t) 3.4 80 εy(t)=70+0.0192 t+40(1−e−0.0204 t) 2.4 εx(t)=15+0.0085 t+14(1−e−0.0119 t) 4.1 400 25 εy(t)=80+0.0069 t+20(1−e−0.0084 t) 1.9 εx(t)=10+0.0031 t+28(1−e−0.0298 t) 4.8 50 εy(t)=100+0.0113 t+54(1−e−0.0127 t) 3.0 εx(t)=33+0.0052 t+12(1−e−0.0218 t) 3.8 80 εy(t)=140+0.0226 t+100(1−e−0.0055 t) 2.3 εx(t)=35+0.0106 t+35(1−e−0.01 t) 3.2 600 25 εy(t)=100+0.0069 t+56(1−e−0.0101 t) 2.3 εx(t)=35+0.0031 t+18(1−e−0.0147 t) 4.6 50 εy(t)=170+0.0174 t+150(1−e−0.0029 t) 2.2 εx(t)=60+0.0073 t+12(1−e−0.0066 t) 4.5 80 εy(t)=400+0.0874 t+350(1−e−0.0031 t) 3.1 εx(t)=100+0.0104 t+50(1−e−0.0113 t) 4.2 NBR 200 25 εy(t)=10+0.0036 t+18(1−e−0.0660 t) 2.0 εx(t)=2+0.0015 t+7(1−e−0.0822 t) 11 50 εy(t)=25+0.0038 t+22(1−e−0.0379 t) 3.0 εx(t)=8+0.0017 t+3(1−e−0.1207 t) 11 80 εy(t)=30+0.0052 t+22(1−e−0.0379 t) 1.6 εx(t)=10+0.0024 t+1(1−e−0.3615 t) 4.6 400 25 εy(t)=60+0.0026 t+15(1−e−0.024 t) 1.0 εx(t)=10+0.0012 t+9(1−e−0.064 t) 3.1 50 εy(t)=70+0.0066t+23(1−e−0.0301 t) 3.0 εx(t)=20+0.0031 t+3(1−e−0.0572 t) 5.5 80 εy(t)=90+0.0071 t+33(1−e−0.0172 t) 2.3 εx(t)=20+0.0034 t+10(1−e−0.036 t) 3.6 600 25 εy(t)=100+0.0043 t+33(1−e−0.0173 t) 2.0 εx(t)=30+0.0019 t+12(1−e−0.0145 t) 2.4 50 εy(t)=120+0.0061 t+55(1−e−0.0103 t) 2.7 εx(t)=40+0.0027 t+12(1−e−0.007 t) 2.6 80 εy(t)=150+0.0157 t+80(1−e−0.0056 t) 2.0 εx(t)=50+0.0073 t+17(1−e−0.0099 t) 3.9 VMQ 200 25 εy(t)=60+0.0015 t+20(1−e−0.0232 t) 3.2 εx(t)=10+0.0006 t+25(1−e−0.023 t) 4.2 50 εy(t)=80+0.0034 t+8(1−e−0.0578 t) 1.9 εx(t)=20+0.0015t+12(1−e−0.0479 t) 3.6 80 εy(t)=80+0.0036 t+8(1−e−0.1044 t) 1.3 εx(t)=20+0.0017 t+5(1−e−0.0724 t) 5.5 400 25 εy(t)=140+0.0026 t+28(1−e−0.0129t) 2.6 εx(t)=40+0.0012 t+38(1−e−0.0151 t) 3.3 50 εy(t)=180+0.0036 t+45(1−e−0.0154 t) 1.7 εx(t)=60+0.0017 t+17(1−e−0.0102 t) 2.4 80 εy(t)=200+0.0054 t+45(1−e−0.0127t) 1.5 εx(t)=70+0.0026 t+5(1−e−0.0722 t) 2.0 600 25 εy(t)=400+0.0069 t+150(1−e−0.0115 t) 2.0 εx(t)=120+0.0031t+30(1−e−0.0278 t) 3.0 50 εy(t)=400+0.0034 t+95(1−e−0.0181 t) 1.1 εx(t)=120+0.0016 t+17(1−e−0.0492 t) 2.3 80 εy(t)=400+0.0034 t+30(1−e−0.0154 t) 2.2 εx(t)=100+0.0017 t+20(1−e−0.0087 t) 2.2 FKM 200 25 εy(t)=15+0.0038 t+18(1−e−0.0256 t) 4.3 εx(t)=3+0.0015 t+5(1−e−0.1151 t) 22 50 εy(t)=40+0.0083 t+15(1−e−0.0305 t) 2.4 εx(t)=4+0.0034 t+5(1−e−0.0167 t) 5.5 80 εy(t)=50+0.0062 t+28(1−e−0.0203 t) 1.4 εx(t)=10+0.0026 t+8(1−e−0.0451 t) 5.6 400 25 εy(t)=40+0.0052 t+15(1−e−0.0114 t) 2.4 εx(t)=10+0.002 t+8(1−e−0.0217 t) 3.6 50 εy(t)=40+0.0076 t+50(1−e−0.0166 t) 3.8 εx(t)=10+0.0031 t+8(1−e−0.0105 t) 3.7 80 εy(t)=90+0.0075 t+45(1−e−0.0126t) 2.3 εx(t)=20+0.0031 t+15(1−e−0.024 t) 2.8 600 25 εy(t)=50+0.0061 t+55(1−e−0.0103 t) 3.2 εx(t)=10+0.0024 t+15(1−e−0.0115 t) 3.9 50 εy(t)=80+0.0095 t+80(1−e−0.007 t) 2.7 εx(t)=15+0.004 t+28(1−e−0.0094 t) 2.9 80 εy(t)=150+0.0069 t+60(1−e−0.0059 t) 3.3 εx(t)=40+0.0029 t+27(1−e−0.0064 t) 3.1 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Nanomaterials (Basel) Nanomaterials (Basel) nanomaterials Nanomaterials 2079-4991 MDPI 10.3390/nano12091559 nanomaterials-12-01559 Article Synthesis of Al–Al2O3–CNF Composite by Cold Spray Method: Powder Preparation and Synthesized Objects Characterization https://orcid.org/0000-0003-2475-4811 Nalivaiko Anton Yu. 12* Doroshenko Vitaliy V. 1 Kuang Nguyen 1 https://orcid.org/0000-0003-1553-4526 Ozherelkov Dmitriy Yu. 1 https://orcid.org/0000-0003-1592-3062 Pelevin Ivan A. 1 https://orcid.org/0000-0002-2641-2607 Gromov Alexander A. 1 Eckert Jürgen Academic Editor Bica Ioan Academic Editor 1 MISIS Catalysis Lab, National University of Science and Technology MISIS, 119991 Moscow, Russia; v.doroshenko@mail.ru (V.V.D.); nquang.misis@mail.ru (N.K.); d.ozherelkov@gmail.com (D.Y.O.); i.pelevin@misis.ru (I.A.P.); a.gromov@misis.ru (A.A.G.) 2 Directorate of Science, Moscow Polytechnic University, 107023 Moscow, Russia * Correspondence: nalivaiko@misis.ru; Tel.: +7-(495)-955-01-37 04 5 2022 5 2022 12 9 155920 3 2022 03 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This paper is devoted to studying the composite material of the aluminum–alumina–carbon nanofiber (CNF) system. The paper considers in detail the process of preparation of the specified composite by ball milling, as well as the process of synthesis of a solid object (coating) by the cold spray method. The synthesized objects were studied using optical and electron microscopy, and the hardness of objects of various compositions was measured. The processes of interaction of composite particles are discussed in detail. The influence of CNF on the distribution of particles in a solid object and on the hardness of objects has been considered and discussed. additive manufacturing aluminum alumina carbon nanomaterials cold spray method Russian Science Foundation21-79-10240 075-00268-20-02 (ID: 0718-2020-0040) The study of the synthesis process and the characterization of solid samples was carried out with the financial support of the Russian Science Foundation (Project # 21-79-10240). Obtaining the raw materials for the preparation of powder compositions was carried out within the framework of the State assignment # 075-00268-20-02 (ID: 0718-2020-0040). ==== Body pmc1. Introduction Aluminum and its alloys are widely used in aerospace, automotive, and other fields due to their advantages such as high specific strength and rigidity, low density, and excellent heat and electrical conductivity [1,2,3,4]. However, low hardness limits the use of aluminum in complex engineering applications, which led to the development of aluminum-based composites to improve strength and hardness [5,6]. An alternative approach to increasing the hardness of aluminum alloys is surface treatment technologies. Currently, there are many ways to improve the surface properties of materials, for example, laser weld deposition [7,8], thermal spraying [9,10], anodizing [11,12], and others. Among the methods considered, the cold spray method is a relatively new and perspective manufacturing process [13,14]. During the synthesis of coatings by thermal methods, operating temperatures reach or exceed the melting point of metal particles, which leads to their melting, compression after cooling, and, as a result, the occurrence of residual stress. When using the cold spray method, the particles do not reach the melting point and form a solid material due to the high kinetic energy of collision [15]. Moreover, unlike traditional thermal spray processes, in cold spray coating synthesis, the particles remain in the solid phase due to the relatively cold temperature of the working gas [16]. Thus, the cold spray method can be used to modify the surface of various metallic parts, increasing their mechanical, corrosion, and friction properties [17,18]. In particular, it is applied to aluminum alloys to form strong bonding between surface and material without causing undesirable effects such as surface oxidation, material melting, phase transformations, and changes in the chemical composition at the interface [19]. In previous studies [20,21,22], the process of aluminum–alumina coatings forming by the cold spray method was considered in detail. These studies were aimed at investigating the morphology of the initial particles’ dependence on the performance properties of synthesized coatings/solid objects. In particular, it was found that alumina in the composition of the source material significantly increases wear resistance, hardness, and adhesive strength [23,24]. Moreover, good adhesion and strong bonding between Al2O3 and aluminum substrate were found, especially when some Al was added to the feedstock powder for cold-spray [18]. In paper [25], the cold spray synthesis of aluminum–carbon nanotube (CNT) coatings was studied. Within that study, several compositions of composite materials containing from 0.2 to 4% CNTs were considered. The synthesized alumina with 2% CNT composite showed the best wear resistance and hardness, wherein, CNTs acted in two ways: (a) as a strengthener of composite surface and (b) made a self-lubricating surface improving friction properties. In the study [26], a combined method for the formation of aluminum–carbon nanotube coatings was considered, including the cold spray method and plasma electrolytic oxidation. A composite consisting of aluminum and 1% CNT was investigated, and an increase in the coating hardness was found. It is known [27,28,29,30] that the modification of Al-containing composites with carbon nanomaterials has a positive effect on the properties of the obtained objects and leads to an increase in mechanical properties and improves functional characteristics. The most important carbon nanoscale modifiers are CNT [31] and carbon nanofibers (CNF) [32] due to their exceptional mechanical properties, low density, low coefficient of thermal expansion, and high thermal conductivity [33]. The introduction of CNF into the composite materials improves the performance properties of the product and also improves the technological properties of the original composite powder material. Thus, CNFs are a promising modifying additive for materials used in new production technologies [34]. CNFs are already widely used to improve the mechanical characteristics of building materials and can be used in the energy and biomedical industries [35,36]. Based on the above, this study aims to synthesize coatings/solid objects from aluminum–alumina–CNF composite material using the cold spray method. In previous studies, only binary composites (aluminum–carbon nanomaterial or alumina–carbon nanomaterial) were used, while the present study considers a ternary system in the range of CNF concentrations of 0.5–1.5%. It also should be noted that both the original aluminum powder and the substrate were made from the same alloy. Therefore, the present study is of high practical importance since the considered compositions of composites and synthesis regimes can be used to increase the surface properties of a particular aluminum alloy. 2. Materials and Methods The initial Al powder ASP-30 (produced by UC RUSAL, Moscow, Russia) was used in this work. This powder was obtained by the molten metal spraying method and had a particle size D50 = 30 μm (maximum particle size for 50% of the cumulative mass). For the production of these powders, technical-grade aluminum (99.7% Al) was used. Particle size distribution was determined using the laser diffraction method on the Analysette 22 NanoTecPlus device (Fritsch GmbH, Idar-Oberstein, Germany) with a full-scale range of 0.01–2000 µm. Al2O3 was obtained from aluminum chloride [37] that consisted of no more than 1% of impurities and corresponded to the α-modification of alumina. It was processed in a ball mill and thoroughly sieved. The average size of the Al2O3 powder was D50 = 20 µm. CNF was prepared by the CVD method. Conditions for CNFs production used in the study were the following: T = 650 °C in propane-butane on Ni–Cu catalyst. The particle size of the CNFs used did not exceed 600 nm. A detailed description of the production method and characterization of the CNFs used are presented in the study [38]. Powder morphology and microstructure characterization were performed using scanning electron microscopy (SEM) with FEI Quanta 200 (Hillsboro, OR, USA). Chemical composition and maps of the chemical element distribution in the material were obtained using an X-ray energy dispersive microanalysis system (EDS), Octane Super EDS. CNF morphology was studied using a JEM-2100 transmission electron microscope (JEOL Ltd., Tokyo, Japan). The morphology and microstructure of the obtained samples were controlled by optical microscopy using a Carl Zeiss Axio Observer A1m (Oberkochen, Germany) microscope. For microstructure analysis and porosity measurement, ImageExpert Pro 3 software was used. The etching for microstructural investigations was performed in Keller’s reagent. Microhardness was measured using Tukon 1102 (ITW Test & Measurement GmbH, Dusseldorf, Germany) with an applied load of 50 g, 10 sec exposure, and ten measurements for each sample. X-ray diffraction analysis (XRD) was carried out on a Rigaku Ultima IV (Tokyo, Japan) X-ray diffractometer using CuKα radiation. Four sample compositions (see Table 1) were prepared for cold spray (CS). The weight of every prepared powder sample was 240 g. Powders were mixed in a planetary mill using an argon atmosphere and vacuum system to prevent mechanoactivation. Steel balls, 4 g each, were used as mixing bodies. The powder-to-steel balls mass ratio was 1:20. The mixing procedure consisted of three iterations of 3 min of mixing and a 1 min break between iterations. After mixing prepared powders were thoroughly sieved. CS of the prepared powder mixtures was made using DIMET equipment shown in Figure 1. The powder was sprayed at 7-bar pressure and, after heating to 400 °C, it was deposited on the AlSi12 aluminum alloy substrate. The speed of nozzle movement was 1 mm/s. The size of each sample was 12 × 12 mm2, with at least 1 mm deposited layer thickness. 3. Results and Discussion The SEM of initial powders is presented in Figure 2. The initial Al2O3 powder consisted of agglomerates with a 25 µm diameter. During mixing in a mill, these agglomerates were ground to the more dispersed particles with approximately 2–3 µm diameter. A 25–35 µm size order of both aluminum and alumina particles/agglomerations was shown [39] as optimal to form dense coating during CS. The presence of around 30 wt.% of aluminum on the coating composition is essential since pure Al2O3 powder, which consists of hard particles, cannot be used for cold spraying without ductile additions. Hard ceramic particles could only form a monolayer on the ductile aluminum surface, whereas further layers do not have enough adhesion to continue coating formation [39]. This reason also leads to a decrease in the deposition efficiency, i.e., the volume of alumina in the coating is always less than in the initial mixture [40]. The estimated deposition efficiency of the CS of Al–Al2O3 mixture with 70 wt.% of alumina is around 10–15% which is similar to the efficiency of pure Al cold spraying, wherein alumina content in the coating is about two times lower than in the feedstock powder [22]. The agglomerations of alumina observed in Figure 2b also could influence the loss phenomena since the destruction of such agglomerates due to collisions during the CS process possibly leads to random scattering of their components. All these features of Al2O3 cold spraying should be taken into account during the planning of experiments and analysis of the coatings’ microstructure. Figure 3 demonstrated the distribution of CNF on the surface of Al powder after mixing. Powders demonstrated a homogeneous covering of particles with CNF without any noticeable agglomerations. Along with the homogeneous distribution of CNFs, the particle shape and morphology of the aluminum were almost unchanged. The noticeable transformation occurred in the CNF length, which significantly decreased. Since nano-sized carbon matter possesses high strength along with high brittleness, the mixing procedure led to the destruction of the CNFs. On the one hand, short fibers concede the long ones in strengthening effect; on the other hand, it is simpler to obtain a homogeneous distribution of CNFs on the aluminum particles’ surface. The overall morphology of composite powders after mixing at low magnification is shown in Figure 4. The presented SEM results demonstrated the efficiency of the powder mixing process. For an efficient CS process, the high quality of Al2O3 and CNF distribution is essential to obtain a dense and defect-free layer with increased mechanical properties. Figure 5 and Figure 6 demonstrate the optical microscopy of the samples after CS. As can be seen from Figure 5, the general features of the images were identical for all studied samples. Each sample had good adhesion to the substrate and closely adhered to the construction plane. The microstructure of the synthesized samples is shown in Figure 6. As can be seen from the figure, each sample had a gradient structure, and with an increase in the CNF amount, the frequency of the construction layers interchange increased. The dark areas on the figures represent a powder material with a high content of alumina (Al2O3). In Figure 6a, the distance between layers was about 15 µm, but in Figure 6d, there was practically no distance between the dark areas, and the layers were more densely packed. Additionally, a decrease in the number of agglomerated particles with an increase in the amount of CNF was noticed. As can be seen from Figure 6, with an increase in CNF concentration, the number of agglomerates decreased significantly. Figure 6a,b demonstrated spheroidal-shaped agglomerates. The size of such agglomerates decreased with the increase in the CNF amount, and in the case of 1.5% of CNF (Figure 6d), almost no agglomerations were noticed. The SEM results shown in Figure 7 also indicated that an increase in the CNF amount has a positive effect on the density of the synthesized samples. According to the obtained microstructures, samples containing 1.0–1.5% of CNF have noticeably fewer microstructural defects compared to the samples with 0.5% of CNF. The porosity for the samples with 0.5%, 1%, and 1.5%, CNF content were found to be 4.6 ± 0.5%, 3.1 ± 0.9%, and 2.0 ± 0.5%, respectively. Ductile Al particles promote defect-free structure formation by deforming plastically when interacting with brittle and hard Al2O3 particles and CNF during cold spray. On the other side, ceramic particles are unable to provide plastic deformation when interacting with each other. Thus, the main source of the obtained porosity is the absence of plastic deformation during the interaction between Al2O3 particles and CNF, leading to localized agglomerations, as shown in Figure 8c. Such locations of entrapped agglomerations in the structure between Al regions are the main type of porosity obtained in the samples. The change in the phase interface also indicated the influence of the amount of CNF on the formation of the surface layer (see Figure 7). As can be seen, with the highest content of CNF, the boundary line is smooth, and with a smaller amount of CNF, the phase interface has a wave character. This is probably due to the interaction of particles in the synthesis process. Alumina performs the function of abrasive material. When applying a layer, alumina peels the oxide film from the surface of the substrate, while the oxide layer on aluminum particles is also destroyed. CNF envelops aluminum particles due to their small size and prevents the formation of agglomerates (see Figure 8). As can be seen from Figure 7 and Figure 8, with the largest amount of CNF, the synthesized sample has the least number of defects. A map of chemical elements’ distribution is shown in Figure 9. As can be seen from the figure, Al and Al2O3 particles have a good distribution in the synthesized sample. To control the presence of CNF in the sample after the cold spray process, the XRD analysis of the sample (see Figure 10) with 1.5% CNF addition was performed. The synthesized sample was detached from the substrate to ensure the analysis results. Based on the XRD results, besides characteristic peaks for Al and Al2O3, a small characteristic peak of CNF at around 26 degrees was obtained. This peak demonstrated the presence of CNF in the structure of composite after cold spray synthesis. Based on the obtained results, CNF had a positive effect on the structure of the synthesized samples. The obtained effect was associated with the following factors. CNF increases the bulk density of the initial powder material due to the smaller size compared to the initial Al powder. Due to such size differences, voids between the larger powder particles filled with smaller CNF, increasing the density of the synthesized samples. The second factor is the high antifriction properties of carbon. CNF reduces the number of agglomerates in the initial powder, resulting in a decrease in agglomerated particles in the synthesized samples. The described factors have a positive effect on the overall microstructure of the synthesized samples, reducing the number of cracks and voids. To determine the effect of CNF content on the mechanical properties of the specimens, hardness tests were carried out. As a comparison, the average microhardness value for the initial Al powder without additions of Al2O3 and CNF is 24.1 ± 1.2 HV. The obtained microhardness results, measured in the cross-section of the synthesized samples, are presented in Figure 11. For each sample, 15 measurements were carried out. Following the obtained results, it can be concluded that the hardness level of the samples increases with an increase in CNF content. The obtained hardness increase in the case of 1.5% CNF addition is about 20% and was associated with three main contributions. The first one is the dispersion strengthening mechanism due to the much higher strength and hardness of alumina compared with the aluminum matrix. Only a 20% increase in hardness could be explained by alumina loss during the CS process, as was mentioned above. The second contribution concerns the mechanical deformation of the ductile Al particles within the coating because of collision with the substrate surface, hard alumina particles, and each other. The deformation of the particles which become grains within the coating leads to an increase in dislocation concentration and hardening of the material. The third contribution is associated with the presence of CNF with also high strength and hardness. This contribution is relatively small because of the low CNF concentrations, but their nanoscale and uniform distribution provide reasonable strengthening. An increase in hardness is due to the synergistic effect of using microsized alumina and nanosized carbon fibers as functional strengthening additives. Due to the good antifriction properties of carbon, the fluidity of the material increased by reducing the friction force between the particles. The nanosized additive was evenly distributed between the particles of aluminum and alumina, filling the voids and increasing the bulk density of the original composite. Additionally, due to the high thermal conductivity of carbon, the temperature gradient between the deposited layers decreased, which in turn reduced residual stresses and minimized the negative thermal effects. During the synthesis of objects by the cold spray method, nanosized CNF formed a composite structure that prevented the movement of the dislocations under mechanical loads due to the Orowan strengthening mechanism. 4. Conclusions 1. Al–Al2O3–CNF powder composite materials for the cold spray process are investigated in this study. The method of preparation of powder composites and the process of sample synthesis are considered in detail. 2. CNF affects the microstructure of samples synthesized by the cold spray method. This is due to an increase in the bulk density of the initial powder material, as well as the high antifriction properties of carbon, which significantly affects the density of samples and reduces the number of agglomerates. 3. Increasing the concentration of CNF has a positive effect on the hardness of synthesized objects. With a CNF content of 1.5%, the microhardness of the samples is on average 20% higher, which is due to the mechanism of dispersive strengthening. Author Contributions Conceptualization, A.Y.N.; data curation, V.V.D. and N.K.; formal analysis, I.A.P., D.Y.O. and A.A.G.; investigation, V.V.D. and N.K.; methodology, I.A.P., D.Y.O. and A.A.G.; supervision, A.Y.N. and A.A.G.; writing—original draft, V.V.D. and A.Y.N.; writing—review and editing, I.A.P. and D.Y.O. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. Conflicts of Interest The authors declare no conflict of interest. Figure 1 DIMET cold spray equipment. Figure 2 Initial powders before mixing: (a) Al powder; (b) Al2O3 powder; (c) CNF. Figure 3 Distribution of CNF on the surface of Al powder after mixing. Figure 4 Morphology of composite powders after mixing: (a) 30% Al + 70% Al2O3; (b) 29.5% Al + 70% Al2O3 + 0.5% CNF; (c) 29% Al + 70% Al2O3 + 1.0% CNF; (d) 28.5% Al + 70% Al2O3 + 1.5% CNF. Figure 5 Morphology of samples after CSM: (a) 30% Al + 70% Al2O3; (b) 29.5% Al + 70% Al2O3 + 0.5% CNF; (c) 29% Al + 70% Al2O3 + 1.0% CNF; (d) 28.5% Al + 70% Al2O3 + 1.5% CNF. Figure 6 Microstructure of the synthesized samples: (a) 30% Al + 70% Al2O3; (b) 29.5% Al + 70% Al2O3 + 0.5% CNF; (c) 29% Al + 70% Al2O3 + 1.0% CNF; (d) 28.5% Al + 70% Al2O3 + 1.5% CNF. Figure 7 SEM results of the synthesized samples: (a) 30% Al + 70% Al2O3; (b) 29.5% Al + 70% Al2O3 + 0.5% CNF; (c) 29% Al + 70% Al2O3 + 1.0% CNF; (d) 28.5% Al + 70% Al2O3 + 1.5% CNF. Figure 8 Typical defects of microstructure after CSM demonstrated using the 29% Al + 70% Al2O3 + 1.0% CNF sample: (a) microstructure overview; (b) example of voids within coating microstructure; (c) example of particle agglomerations; (d) cracks. Figure 9 Distribution of chemical elements in the cross-section of the 28.5% Al + 70% Al2O3 + 1.5% CNF sample. Figure 10 XRD of the 28.5% Al + 70% Al2O3 + 1.5% CNF sample. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092973 molecules-27-02973 Article Ultra-Thin Wrinkled Carbon Sheet as an Anode Material of High-Power-Density Potassium-Ion Batteries Cheng Boshi 12 Li Xing 12* Pan Linhai 2 Xu Hongqiang 2 Duan Haojie 2 Wu Qian 2 Yin Bo 2* He Haiyong 2 Kim Dukjoon Academic Editor 1 School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China; chengboshi@nimte.ac.cn 2 Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China; panlinhai@nimte.ac.cn (L.P.); xuhongqiang@nimte.ac.cn (H.X.); duanhaojie@nimte.ac.cn (H.D.); wuqian20@nimte.ac.cn (Q.W.); hehaiyong@nimte.ac.cn (H.H.) * Correspondence: lixing@nbu.edu.cn (X.L.); yinbo@nimte.ac.cn (B.Y.) 06 5 2022 5 2022 27 9 297307 3 2022 14 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Although K+ is readily inserted into graphite, the volume expansion of graphite of up to 60% upon the formation of KC8, together with its slow diffusion kinetics, prevent graphite from being used as an anode for potassium-ion batteries (PIBs). Soft carbon with low crystallinity and an incompact carbon structure can overcome these shortcomings of graphite. Here, ultra-thin two-dimensional (2D) wrinkled soft carbon sheets (USCs) are demonstrated to have high specific capacity, excellent rate capability, and outstanding reversibility. The wrinkles themselves prevent the dense stacking of micron-sized sheets and provide sufficient space to accommodate the volume change of USCs during the insertion/extraction of K+. The ultra-thin property reduces strain during the formation of K-C compounds, and further maintains structural stability. The wrinkles and heteroatoms also introduce abundant edge defects that can provide more active sites and shorten the K+ migration distance, improving reaction kinetics. The optimized USC20−1 electrode exhibits a reversible capacity of 151 mAh g−1 even at 6400 mA g−1, and excellent cyclic stability up to 2500 cycles at 1000 mA g−1. Such comprehensive electrochemical performance will accelerate the adoption of PIBs in electrical energy applications. potassium-ion batteries anode two-dimensional carbon sheet High-quality Development Project of Ministry of Industry and Information Technology of People’s Republic of ChinaTC210H041 the National Natural Science Foundation of China51872304 Ningbo S&T Innovation 2025 Major Special Program2019B10044; 2020Z101 This work was supported by High-quality Development Project of Ministry of Industry and Information Technology of People’s Republic of China (TC210H041), the National Natural Science Foundation of China (Grant No. 51872304) and Ningbo S&T Innovation 2025 Major Special Program (2019B10044; 2020Z101). ==== Body pmc1. Introduction After research and development in recent years, potassium-ion batteries (PIBs) are considered to be a promising new energy storage system that can replace lithium-ion batteries (LIBs) in a number of application scenarios [1,2]. Carbon-based anode materials, which are rich in raw materials, have excellent conductivity, and are environmentally friendly, [3,4,5] have been successfully commercialized for LIB anodes, and also show great application prospects in the field of PIBs [6,7,8]. Ju et al. adopted P and O co-doped graphene as a PIB anode material, and delivered a specific capacity of 165 mAh g−1 at 2000 mA g−1 [9]. However, in practical applications, the heteroatom doping strategy may slash the initial coulombic efficiency (ICE) and increase the voltage hysteresis of the electrode material [10,11]. It is well known that hard carbon has poor conductivity [12,13], as well as high discharge potential and low energy density when serving as an anode for PIBs [13]. In regard to the application requirements of PIBs, soft carbon is a better choice. Soft carbon attracts much attention because of its low charge–discharge voltage and high specific capacity [6,14]. However, soft carbons are subject to huge expansion stress during the insertion of K+, which can lead to the collapse of the electrode [12,14,15]. Thus, designing structures with high stress tolerance is important for developing new electrode materials. The power density of soft carbon as an anode for PIBs also needs to be improved because of the slow diffusion kinetics of K+ in soft carbon. To accommodate expansion stress, elastic carbon aerogel is expected to be an outstanding candidate for improving the structural stability of PIB anodes [12,13]. Up to now, various elastic carbon aerogels have been built by nano-carbons, such as carbon nanotubes, graphene, graphene oxide, and biomass-derived carbon [5,13,16]. Carbon aerogel materials show potential in the field of potassium storage because of their high surface area, marvelous mechanical strength, and high conductivity [5,12,16]. On the other hand, reducing the number of stacked layers in the c-axis direction of soft carbon, i.e., preparing materials with thin sheet-like structures, likewise reduces the strain of K+ insertion/extraction. In addition, an ultra-thin structure facilitates the insertion/extraction of K+ and maintains structural integrity more easily. Therefore, an ultra-thin skeletal structure can achieve an excellent rate of performance and offer a long service life when serving as an anode for PIBs. Therefore, the template effect of melamine (MA) in the carbonization process was used to prepare ultra-thin two-dimensional (2D) wrinkled soft carbon sheets (USCs). The wrinkled morphology is beneficial to absorb expansion stress and shorten K+ migration distance during the electrochemical process, boosting the rate capability. Even at 6400 mA g−1, the reversible specific capacity of USC20−1 still exceeds 151 mAh g−1. USC20−1 also owns an ultra-long cycling life span, with a specific capacity of 137 mAh g−1 after 2500 cycles at 1000 mA g−1. Meanwhile, USC10−1 exhibits a high reversible capacity of up to 444 mAh g−1 at 25 mA g−1. 2. Results and Discussion As shown in Figure 1a–d and Figure S1, USCs are made of wrinkled sheets of microscale diameter. Careful observation has revealed that the higher the proportion of MA and NH4Cl in the precursors, the thinner the resulting microscale sheets. The hemi-transparency property of the TEM and the enlarged SEM images suggest the ultra-thin nature of the sheets (Figure 1a–h). The wrinkled sheet structure not only prevents the dense stacking of electrode materials, but also alleviates the volume effect during charge and discharge. Most importantly, the ultra-thin property can reduce the strain during the insertion/extraction of K+, while the wrinkled property shortens the migration distance of K+ in the K-C compounds by offering abundant edge defects [13,15]. The HRTEM images (Figure 1i–l) clearly show the lattice fringes of carbon. The short-range ordered and long-range disordered structure of USCs not only accelerate the diffusion of alkali metal ions between the carbon interlayers, but also enhance the electron conductivity [15,17]. Therefore, USCs are an ideal choice for a PIB anode material. The specific surface area (SA) and pore structure of USCs were analyzed in detail by N2 adsorption and desorption tests (Figure 1m,n). Type IV isotherms with an H3 hysteresis loop demonstrate that all USCs have a mesoporous/macroporous structure [18,19]. Based on the Brunauer–Emmett–Teller (BET) method, the SAs of all the USCs were calculated and found to obey the following relationship: USC10−1 (117 m2 g−1) < USC20−1 (141 m2 g−1) < USC30−1 (205 m2 g−1) < USC40−1 (219 m2 g−1). Interestingly, the SA of USC50−1 decreased to 190 m2 g−1. This indicates that the ratio of MA in a reaction system can well regulate the SA of USCs, and the appropriate SA is conducive to increase the contact area between the electrode material and the electrolyte, as well as decrease the amount of generated solid electrolyte interphase (SEI) [9]. The pores around 20 nm can fully adsorb electrolyte due to capillary force, and serve as a K+ reservoir. This effectively shortens the ion migration distance and thus improves the rate performance of the electrode material. As shown in Figure 2, XRD and Raman were used to analyze the structure of carbon. The XRD patterns of all samples in Figure 2a showed a slight shift and broadening of the (002) diffraction peak with increasing MA and NH4Cl content in the precursor, which means that the interlayer distance was enlarged and the stacked layers along the c-axis were reduced [20,21]. This is mainly caused by the following two reasons. First, MA and NH4Cl in the precursor act as a N source, and the introduction of heteroatoms promotes the expansion of interlayer spacing. Secondly, the template effect of MA is helpful to reduce the thickness of the prepared material. The enlarged interlayer spacing allows K+ to shuttle more easily within the material [22,23]. To summarize, the content of MA and NH4Cl in the precursor not only modulate the thickness of carbon sheet, which has also been confirmed by SEM and TEM, but also promote the expansion of interlayer spacing to some extent. There are two main peaks, namely the D band (1354.58 cm−1) and the G band (1587.6 cm−1) in the Raman spectra of the USCs in Figure 2b [17,24]. The G peak can be attributed to the vibrational peak of sp2 hybrid carbon, which reflects the crystallinity of the carbon material. The D peak (sp3 graphite configuration) reflects the disorder of the carbon material, mainly due to the introduction of heteroatoms and defects [25,26]. With the increase in MA and NH4Cl content in the precursor, the ID/IG value of the carbon sheet gradually increased and finally reached a constant value (USC10−1 (0.94) < USC20−1 (0.95) < USC30−1 (0.97) = USC40−1 (0.97) = USC50−1 (0.97)). As shown in Figure 3a,c,e and Figure S2a,c, a cyclic voltammetry (CV) test was used to study the electrochemical properties of all materials in the voltage range of 0.01 to 3.0 V at a scanning rate of 0.1 mV s−1. A strong irreversible reduction peak appeared near 0.6 V in the first cycle, which was related to the generation of solid electrolyte interphase (SEI) during the first discharge process [10,20]. The formation of SEI affects the initial coulombic efficiency (ICE) of the material, which is directly related to the SA of the carbon sheet. It is believed that the reduction peak of the USCs at ~0.65 V corresponded to K+ adsorption on the carbon sheet surface, while the reduction peak at ~0.16 V corresponded to K+ intercalation into the carbon sheet, and the oxidation peak at ~0.35 V was related to K+ de-intercalation from the carbon sheet [14,27,28,29]. The electrochemical process was also confirmed by the charge–discharge curves at the current density of 25 mA g−1 (Figure 3b,d,f and Figure S2b,d). With careful observation of the charge–discharge curves of the USCs, it can be seen that USC40−1 sample had the highest discharge specific capacity and a perfect coincidence of curves, which were inseparable from the material structure. USC40−1 had the largest SA, which exposed more active sites and increased the adsorption capacity in the high voltage region. The potassium storage performance of the material in the voltage window of 0.01–3.0 V was tested by constant current charge and discharge. As shown in Figure 4a, the USC40−1 electrode displayed the highest specific capacity above 1 V, which can be attributed to the abundant heteroatom doping and the largest SA of USC40−1. Moreover, the specific capacity of USC40−1 remained at 308.7 mAh g−1 after 50 cycles at 25 mA g−1, superior to the other samples (Figure 4b). The charge and discharge curves of USC20−1 at different current densities are shown in Figure 4c. The reversible specific capacity was maintained at 151 mAh g−1, even at a high current density of 6.4 A g−1. The rate performances in Figure 4d show that USC20−1 had the highest discharge specific capacity at high current densities (>1.6 A g−1), while USC40-1 stood out at 0.05, 0.1 and 0.2 A g−1, and USC30−1 performed best at 0.4 and 0.8 A g−1. This means that this strategy can modulate the thickness of the electrode material for optimal performance depending on the application scenario. Such amazing rate performance is inseparable from the carbon structure. The enlarged interlayer spacing facilitates the transport of K+ and, at the same time, the wrinkled sheet structure prevents the dense stacking of 2D sheets and provides a buffer space for volume changes. It is well known that service life is another important technical parameter for evaluating the prospects of electrode materials. No significant capacity degradation was observed for USCs after 1000 cycles at 1000 mA g−1. Furthermore, USC20−1 could still deliver a discharge specific capacity of 136.7 mA g−1 after 2500 cycles with an average decay rate of 0.017%. It is believed that USCs, as advanced anode materials for PIBs, will have broad application prospects. To further clarify the potassium storage kinetics of USCs, CV curves were recorded for all materials over the scan rate range of 0.1 to 1.0 mV s−1 in a voltage window of 0.01 to 3.0 V (Figure 5a and Figure S3a,e,i,m). With the stepwise improvement of scan rates, the response currents of the redox peaks of the USC20−1 electrode material increased rapidly, but the peak positions were only slightly shifted, indicating the fast redox reactions of USC20−1. According to the literature, there is a following relationship between peak current (i) and scanning rate (ν): i=a∗νb, where a and b are constants [30,31]. When the b value is close to 0.5 or 1, the electrochemical behavior is dominated by diffusion or surface pseudocapacitance reactions, respectively [32,33]. As shown in Figure 5b and Figure S3, all the b values of USCs were close to 1.0, indicating that the potassium storage processes of the prepared electrode materials were controlled by capacitance behavior. The large SA of the wrinkled carbon sheets and the introduction of N doping sites were conducive to the adsorption of K+. The pseudo-capacitance contribution ratio can be calculated according to the following empirical formula:i=k1∗v+k2∗v0.5 [34,35]. Where k1 and k2 are constants. As shown in Figure 5c and Figure S3, the fitted response current contributed by the capacitance process is represented by the pink area, and the total measured current at the scanning rate of 1.0 mV s−1 is represented by the green area. In general, the capacitance contribution ratios of all USCs electrodes increased with the improvements in scanning speed (Figure 5d and Figure S3d,h,l,p). However, the capacitance contribution ratios of USC20−1 at various scan rates were always higher than those of the other materials, which is in line with the best rate performance of USC20−1 at high current densities (>1.6 A g−1). 3. Experimental Section Synthesis of USCs: First, certain amounts of MA and NH4Cl were weighed at a mass ratio of 4:1, and mixed thoroughly in an agate mortar (about 1 h). Then, different dosages (10 g, 20 g, 30 g, 40 g and 50 g) of the above prepared mixture were uniformly dispersed in a certain amount of N, N-dimethylformamide (DMF) solution by means of ultrasonication and stirring. Next, 1 g of pitch was added to the above-mentioned mixed solution and stirred at least for 6 h to fully dissolve the pitch. Subsequently, the solvent was completely volatilized by heating and then vacuum drying. The dried product was put into a tube furnace filled with argon for annealing after fully grinding. The annealing procedure was as follows: the sample was first heated to 200 °C at a heating rate of 1 °C min−1 and maintained for 2 h to soften the asphalt. Then, it was heated to 400 °C at 2 °C min−1 (kept for 2 h), and further heated to 600 °C at 4 °C min−1 (kept for 2 h). The final step was to heat the sample to 1100 °C at 5 °C min−1 (kept for 3 h). After natural cooling to room temperature, the final products were obtained and named as USC10−1, USC20−1, USC30−1, USC40−1 and USC50−1 according to the usage of the mixture of MA and NH4Cl. Structural characterization: S4800 cold field emission scanning electron microscopy (SEM) and transmission electron microscopy (TEM Tecnai F20) were used to collect the microscopic morphology characteristics of the samples. A D8 ADVANCE DAVINCI X-ray powder diffractometer was used to collect the X-ray diffraction (XRD) spectra of the samples. A Confocal Raman Reflectance Microscope (Ram Enishaw Invia REFLEX) was used to accurately analyze the crystallinity and defects of the sample. The adsorption data of the multipoint Brunauer–Emmett–Teller (BET) method was used to calculate the specific surface area and pore size of the sample. Preparation of anode electrode: At first, the active materials, sodium carboxymethyl cellulose (CMC-Na) and Super-P were mixed in a mass ratio of 8:1:1, and then stirred in deionized water to form a uniform viscous slurry. Subsequently, the paste was scraped on the clean copper foil. Then, the copper foil loaded with slurry was first dried in a drying oven at 60 °C for 4 h; it was then transferred to a vacuum drying oven at 90 °C for 12 h. Finally, the dried electrode was cut into discs with a diameter of 12 mm by a punching machine, and then collected and labeled for later use. Electrochemical measurements: The resulting product was used as a working electrode, with glass fiber (Whatman GF/D) as a separator and potassium foil as the reference electrode. The electrolyte composition was 0.8 M KPF6 in ethylene carbonate (EC)/diethyl carbonate (DEC) (v/v = 1:1). The CR2016 half-cells were assembled in a glove box filled with argon gas (H2O < 0.1 ppm, O2 < 0.1 ppm). A constant current charge–discharge cycle test was performed on the LAND battery test system (Wuhan Lande Electronics Co., Ltd., Wuhan, China) at room temperature (30 °C), and the potential window was 0.01–3.0 V. A CHI660d electrochemical workstation was used for cyclic voltammetry (CV) testing with a potential window of 0.01–3.0 V. 4. Conclusions In summary, the ultra-thin 2D wrinkled soft carbon sheet prepared by using the template effect of MA was investigated as an anode material of PIBs. Due to their flexible and adjustable micro-structure, USCs manifest high specific capacity, excellent rate capability, and a long cycle life, showing fascinating application prospects in high-power scenarios. Both the thickness and crystal structure of USCs can be easily adjusted to regulate K+ storage behavior and optimize K+ transport kinetics. Especially, USCs electrode materials fulfill both high power and long service life requirements. The wrinkles on the micron-sized sheets allow ample space for volume expansion during K+ insertion, boosting the cycling stability of USCs. It is believed that USCs have taken a critical step in the development of anode materials for high-power and long-life PIBs. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules27092973/s1, Figure S1: SEM images of USC10−1; Figure S2: The CV curves at 0.1mV s−1 scanning speed and charge-discharge curves at 25 mA g−1: (a,b) USC10−1, (c,d) USC50−1; Figure S3: (a,e,i,m) CV curves at different scan rates, (b,f,j,n) linear fitting relationship between log i and log v at different redox peaks, (c,g,k,o) CV curves of electrode capacitance contributions at a scan rate of 1.0 mV s−1, (d,h,l,p) contribution ratio of pseudocapacitive response at different scan rates. Click here for additional data file. Author Contributions Data curation, B.C., X.L. and B.Y.; Formal analysis, B.C., X.L., L.P., H.D., Q.W. and B.Y.; Funding acquisition, H.H.; Investigation, B.C., X.L., H.X., Q.W. and H.H.; Methodology, B.C., X.L. and H.X.; Software, B.C. and B.Y.; Supervision, B.C., L.P. and B.Y.; Validation, L.P. and H.D. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available in supplementary material. Conflicts of Interest The authors declare no conflict of interest. Figure 1 SEM and TEM images of (a,e,i) USC20−1, (b,f,j) USC30−1, (c,g,k) USC40−1 and (d,h,l) USC50−1. (m) N2 adsorption and desorption curves, and a (n) pore volume distribution diagram. Figure 2 (a)XRD patterns and (b) Raman spectra of all samples. Figure 3 The CV curves at 0.1 mV s−1 scanning speed and charge–discharge curves at 25 mA g−1: (a,b) USC20−1, (c,d) USC30−1, (e,f) USC40−1. Figure 4 Electrochemical test. (a) Comparison of the capacities above and below 1 V at 25 mA g−1 of USCs; (b) cycle stability at 25 mA g−1; (c) charge and discharge curves of USC20−1 at different current densities; (d) rate performance; (e) cycle stability at 1.0 A g−1. Figure 5 (a) CV curves at different scan rates; (b) linear fitting relationship between log i and log v at different redox peaks; (c) CV curves of electrode capacitance contributions at a scan rate of 1.0 mV s−1; (d) contribution ratio of pseudocapacitive response at different scan rates. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Goodenough J.-B. Park K.-S. The Li-Ion Rechargeable Battery: A Perspective J. Am. Chem. Soc. 2013 135 1167 1176 10.1021/ja3091438 23294028 2. Jian Z. 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==== Front J Clin Med J Clin Med jcm Journal of Clinical Medicine 2077-0383 MDPI 10.3390/jcm11092396 jcm-11-02396 Article Meibomian Gland Density: An Effective Evaluation Index of Meibomian Gland Dysfunction Based on Deep Learning and Transfer Learning Zhang Zuhui 1 Lin Xiaolei 2 Yu Xinxin 1 Fu Yana 1 https://orcid.org/0000-0002-9980-5524 Chen Xiaoyu 1 https://orcid.org/0000-0002-7629-0193 Yang Weihua 3* https://orcid.org/0000-0002-9950-6161 Dai Qi 14* Romano Vito Academic Editor Zheng Yalin Academic Editor Ferrara Mariantonia Academic Editor Andrès Emmanuel Academic Editor 1 School of Ophthalmology and Optometry, The Eye Hospital of Wenzhou Medical University, 270 Xueyuanxi Road, Wenzhou 325027, China; zhzhang@eye.ac.cn (Z.Z.); xinxinyu@eye.ac.cn (X.Y.); fuyana@eye.ac.cn (Y.F.); xiaoyuchenny@163.com (X.C.) 2 Department of Ophthalmology and Visual Science, Eye, Ear, Nose, and Throat Hospital, Shanghai Medical College, Fudan University, Shanghai 200126, China; 19111260013@fudan.edu.cn 3 Affiliated Eye Hospital, Nanjing Medical University, No.138 Hanzhong Road, Nanjing 210029, China 4 College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China * Correspondence: benben0606@139.com (W.Y.); dq@mail.eye.ac.cn (Q.D.); Tel.: +86-13867252557 (W.Y.); +86-18667127070 (Q.D.); Fax: +86-025-8667-7779 (W.Y.); +86-0571-8819-3999 (Q.D.) 25 4 2022 5 2022 11 9 239605 1 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). We aimed to establish an artificial intelligence (AI) system based on deep learning and transfer learning for meibomian gland (MG) segmentation and evaluate the efficacy of MG density in the diagnosis of MG dysfunction (MGD). First, 85 eyes of 85 subjects were enrolled for AI system-based evaluation effectiveness testing. Then, from 2420 randomly selected subjects, 4006 meibography images (1620 upper eyelids and 2386 lower eyelids) graded by three experts according to the meiboscore were analyzed for MG density using the AI system. The updated AI system achieved 92% accuracy (intersection over union, IoU) and 100% repeatability in MG segmentation after 4 h of training. The processing time for each meibography was 100 ms. We discovered a significant and linear correlation between MG density and ocular surface disease index questionnaire (OSDI), tear break-up time (TBUT), lid margin score, meiboscore, and meibum expressibility score (all p < 0.05). The area under the curve (AUC) was 0.900 for MG density in the total eyelids. The sensitivity and specificity were 88% and 81%, respectively, at a cutoff value of 0.275. MG density is an effective index for MGD, particularly supported by the AI system, which could replace the meiboscore, significantly improve the accuracy of meibography analysis, reduce the analysis time and doctors’ workload, and improve the diagnostic efficiency. meibomian gland dysfunction meibomian gland density deep learning transfer learning artificial intelligence ==== Body pmc1. Introduction Meibomian gland dysfunction (MGD) is a chronic, diffuse abnormality of the meibomian glands (MGs), commonly characterized by terminal duct obstruction and/or qualitative/quantitative changes in glandular secretion and also a major cause of dry eye [1,2]. It can cause tear film instability and ocular surface inflammation, resulting in ocular irritation symptoms, and may even damage the cornea and affect visual function in severe cases. In the absence of a gold-standard diagnostic test, finding effective diagnostic parameters for MGD is imperative. Currently, an intuitive index for assessing MGD is the degree of MG atrophy, which is both common and subjective. In addition, morphological changes in the MGs can also predict the severity of MGD [3,4]. Studies have also confirmed that morphological indices of the MGs, such as their length, width, and tortuosity, are related to their function [5,6]. Ban et al. found that MG morphology in the upper eyelid was significantly correlated with the condition of the tear film or ocular surface epithelium [4]. MG atrophy grading has been proven to be an effective diagnostic index for MGD [7,8,9,10]. Based on the findings of these studies, further studies used ImageJ and other software to manually label the MG for a quantitative analysis. However, manual labeling of MGs is subject to insurmountable subjective errors and is time-consuming, resulting in low efficiency. In subsequent studies, image-processing algorithms have become popular research tools for MG image analysis. Some analytical methods showed superiority in MG morphological analysis. Arita et al. reported an image processing system that could analyze the MG morphology and obtain relatively accurate results [11,12]. Llorens-Quintana et al. reported a new methodology for analyzing, in an automated and objective fashion, infrared images of the MG [13]. Ciężar et al. reported that global 2D Fourier transform analysis of infra-red MG images provides values of two new parameters: mean gland frequency and anisotropy in gland periodicity. Their values correlate with MGD [14]. Yeh et al. reported a nonparametric instance discrimination approach that automatically analyses MG atrophy severity from meibography without prior image annotations and categorizes the MG characteristics through hierarchical clustering [15]. However, traditional image algorithms still have some limitations, such as unstable region detection and weak characterization of the extracted features. In addition, the overall evaluation index is based on the dropout grade classification of MGs, and it is impossible to extract and analyze each gland separately [16]. Previous studies on artificial intelligence (AI), such as convolutional neural networks, have proven effective in the automatic evaluation of meiboscore [17,18,19]. However, these studies focused on MG dropout grade classification and did not segment each MG. Consequently, they could not be further analyzed. The purpose of this study was to develop an AI-based evaluation system for MG morphology based on deep learning and transfer learning for segmenting each MG and evaluating MG morphological indices accurately. Furthermore, the study also aimed to make it possible to diagnose MGD using MG density, an index that requires many annotations and calculations. 2. Materials and Methods 2.1. Patients and Materials The subjects used in the AI model training were the same as those in our previous report [20], and a total of 60 randomly selected subjects were recruited. Sixty original annotated meibography images of the upper eyelids were used in this study. Of these, 40 were used as the original training images. A total of 245,760 images were generated from these 40 images as a training set using image enhancement software. Another 20 annotated meibography images were used as validation sets. Subsequently, we adjusted the parameters and trained the AI to apply it to the lower eyelid. Sixty original annotated meibography images of the lower eyelids were used for the validation. First, 85 eyes from 85 subjects (age, 8–83 years) were enrolled for the AI system analysis and evaluation of the efficacy of MG density for MGD diagnosis. Only one eye of each subject was randomly selected and included for the comprehensive dry eye and MG examination. The exclusion criteria were as follows: (1) history of ocular trauma or surgery; (2) systemic drugs or eye drops affecting MG function or tear film used in the last 2 weeks; (3) contact lenses worn in the last 2 weeks; and (4) ocular or systemic diseases known to affect tear film or MG function. A total of 53 subjects with obstructive MGD (20 males and 33 females; median age, 35.00 (30.00–50.00) years) were included in the MGD group, and 32 healthy subjects (13 males and 19 females; median age, 25.00 (16.25–32.75) years) in the control group. All 53 subjects with obstructive MGD were diagnosed by two experienced ophthalmologists when any two of the three scores were abnormal: (I) ocular symptom score ≥ 3; (II) lid margin abnormality score ≥ 2; and/or (III) meiboscore ≥ 3 [21]. Subjects diagnosed with obstructive MGD by both ophthalmologists were included in this study. If the ophthalmologists provided different diagnoses, the subjects were excluded from the study. A total of 4006 meibography images (including 1620 upper eyelids and 2386 lower eyelids) from 2420 randomly selected subjects (age ≥18 years) were used for MG density analysis using the AI system. All 4006 meibography images were graded according to the meiboscore (range, 0–3) by three experienced ophthalmologists, and their majority opinion was obtained. A qualified meibography image needed to meet two requirements: (1) the tarsal plates must be entirely exposed, and (2) the meibography image must be focused correctly and clearly. Unqualified meibography images would interfere with the meiboscore and MG density results. The correlation between MG density obtained by the AI system and meiboscore from ophthalmologists was analyzed. All subjects were from the Eye Hospital, Wenzhou Medical University. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics Committee of the Eye Hospital, Wenzhou Medical University (approval number: 2020-209-K-191). This study is registered on http://www.chictr.org.cn (ChiCTR2100052575, 31 October 2021). Informed consent to publish was obtained from all participants before their inclusion in the study. 2.2. Methods 2.2.1. Data Collection and Processing of Samples Samples were collected, optimized, and processed using the previously reported method [20]. First, images of both the upper and lower MGs were captured using the Oculus Keratograph 5M (K5M; Oculus, Wetzlar, Germany). Second, these images were optimized, converted to grayscale, and then standardized and normalized. 2.2.2. Network Structure and AI Training The tarsus segmentation model was based on Mask R-CNN [22]. Based on the pre-trained Mask R-CNN model (https://github.com/matterport/Mask_RCNN, 20 March 2018), we used 100 annotated images of upper and lower tarsus for fine-tuning and obtained fine-tuned model parameters after iterating 200 epochs. Another 20 sample images were used to test the fine-tuned model. Finally, we used the fine-tuned Mask R-CNN model to segment the tarsus. Transfer learning was used to apply the pretrained model and parameters on ImageNet [23] to our previously reported deep learning model (Figure 1A). The residual neural network (ResNet) exhibits excellent performance in image classification and target detection [24]. The 50-layer ResNet (ResNet50) was replaced with the max-pooling layers of the previous U-net model; however, the upsampling layer remained the same (Figure 1B). We call this the ResNet50_U-net. Forty annotated meibography images of the upper eyelids were included as the basis for the training set. In each iteration of training, four images of these 40 original meibography images were randomly selected. The data enhancement model (https://github.com/aleju/imgaug#citation, 6 February 2020, Figure 2) was used to enhance the input of four images with random use of algorithms and parameters, with four new images generated. The final version of the model was iterated a total of 61,440 times in all training and generated 245,760 new images as the training set. The amount of data can preliminarily meet the needs of training a deep convolutional neural network. The original meibography (Figure 3A) was preprocessed to show the glands more clearly (Figure 3B). Compared to the manually annotated result (Figure 3C), the AI system exhibited superior recognition ability (Figure 3D). Figure 4 shows a sample of the original meibography, manual annotation, and AI segmentation of the MGs. We used another 20 annotated original upper eyelid meibography images apart from the training set as the validation set. We used the intersection of unions (IoU) to evaluate the accuracy of the MG recognition model (Figure 5). It can be simply understood as the ratio of the intersection of the ground truth (manual annotation) and AI result (AI segmentation) to their union. 2.2.3. Clinical Parameters The clinical assessments were performed sequentially as follows [20]. All subjects completed the Ocular Surface Disease Index (OSDI) questionnaire and were asked whether they had any of the 14 MGD-related ocular symptoms (symptom score) [25]. Images of both the upper and lower MGs were captured using the Keratograph 5M. The central tear meniscus height (TMH) of the lower eyelid was measured 5 s after blinking using the Keratograph 5M. Tear break-up time (TBUT) was measured and corneal fluorescein staining (CFS) was performed after the instillation of fluorescein. TBUT was measured three times, and the mean value was recorded. CFS was graded according to the Baylor grading scheme from 0 to 4 [26]. Four lid margin abnormalities (irregular lid margin, vascular engorgement, plugged meibomian gland orifices, and anterior or posterior replacement of the mucocutaneous junction) were scored from 0 to 4, according to the number of these abnormalities present in each eye [21]. The MG expressibility scores ranged from 0 to 45 by assessing the meibum quality and quantity of the 15 glands on each lower eyelid [27]. 2.2.4. MG Indices To assess the degree of MG dropout, we used the method described by Arita et al. to calculate the meiboscore: 0, no loss of MGs; 1, the lost area was less than one-third of the total area of the MGs; 2, the lost area was between one-third and two-thirds of the total area of the MGs; and 3, the lost area was over two-thirds of the total area of the MGs [9]. The total meiboscore of the upper and lower eyelids ranged from 0 to 6. MG density was automatically calculated by the AI system using the following formula [28]: the sum of the area of MGs divided by the total area of the tarsus in pixels. ∑i=1nSMGi = the sum of pixels of all MGs, St = the total pixels of the tarsus. MG density=∑i=1nSMGiSt 2.2.5. Statistical Analysis The normality of data distributions was analyzed using the Kolmogorov–Smirnov test, and the abnormal data distributions were analyzed using the non-parametric statistical analyses. Values are expressed as the mean ± standard deviation (SD) or (range) or median (interquartile range [IQR]). Either the independent samples t-test or the Mann–Whitney U-test was used to compare differences between MGD subjects and normal control subjects. The generalized estimating equation was used to adjust the age difference. Kruskal–Wallis H-test was used to compare the MG density and the severity score of the meiboscore scale. The correlations between various MG morphological parameters and MG function parameters (i.e., OSDI, TBUT, CFS, lid margin score, meiboscore, and meibum expressibility score) were determined using Pearson’s or Spearman’s correlation analysis. The χ2 test was used to compare the sex ratios between the two groups. Receiver operating characteristic (ROC) curve analysis was used to determine the predictive value of MG density for the diagnosis of MGD. A two-sided p < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics 23.0 (IBM, Armonk, NY, USA). 3. Results 3.1. AI Training and Testing The AI system of Mask R-CNN achieved 93% accuracy (IoU) and 100% repeatability for tarsus segmentation. The AI system of ResNet50_U-net training lasted for 4 h, a significant reduction from the duration of our previous U-Net model training (15 h). Additionally, the ResNet50_U-net model achieved 92% accuracy (IoU) and 100% repeatability for MG segmentation. Subsequently, we adjusted the parameters and trained the AI to automatically segment the lower MGs and achieved the same level of IoU. The processing time of each meibography was 100 ms with a GTX 1070 8G GPU. 3.2. Characteristics A total of 85 eyes from 85 randomly selected subjects were enrolled for AI system effectiveness testing. These included 53 subjects with obstructive MGD (20 males and 33 females, median age, 35.00 (30.00–50.00) years) and 32 normal volunteers (13 males and 19 females, median age, 25.00 (16.25–32.75) years). Because the age difference between patients with MGD and the normal control group was significant, the generalized estimating equation was used to adjust for age. No significant difference in sex was observed between patients with MGD and normal controls. The baseline characteristics of the 85 subjects are summarized in Table 1. 3.3. MG Density and Functions The MG density in the upper eyelid was significantly correlated with OSDI (r = −0.320, p = 0.003), TBUT (r = 0.484, p < 0.001), lid margin score (r = −0.350, p = 0.001), meiboscore (r = −0.749, p < 0.001), and meibum expressibility score (r = 0.425, p < 0.001). The MG density in the lower eyelid was significantly correlated with OSDI (r = −0.420, p < 0.001), TBUT (r =0.598, p < 0.001), lid margin score (r = −0.396, p < 0.001), meiboscore (r = −0.720, p < 0.001), and meibum expressibility score (r = 0.438, p < 0.001). The MG density in the total eyelid was significantly correlated with OSDI (r = −0.404, p < 0.001), TBUT (r = 0.601, p < 0.001), lid margin score (r = −0.416, p < 0.001), meiboscore (r = −0.805, p < 0.001), and meibum expressibility score (r = 0.480, p < 0.001). However, there were no significant correlations between MG density and CFS or TMH in upper eyelid, lower eyelid and total eyelid (all p > 0.05). These results are shown in Table 2. 3.4. MG Density with Meiboscore After analyzing 4006 random meibography images using the AI system, it was observed that the MG density in the upper eyelid was significantly negatively correlated with the meiboscore (r = −0.707, p < 0.001), as was that in the lower eyelid (r = −0.472, p < 0.001). The corresponding relationship between the MG density and meiboscore is shown in Figure 6. 3.5. MG Density to Meiboscore We compared the correspondence between the MG density and meiboscore, as shown in Table 3. The MG density distribution in the upper eyelid on each meiboscore scale was not the same, and the difference was significant (H = 882.932, p < 0.001). The MG density distribution in the lower eyelid on each meiboscore scale was not the same, and the difference was significant (H = 596.815, p < 0.001). Figure 7 depicts meibography images with varying MG densities and corresponding meiboscores to help readers gain insight into the relationship between MG density and meiboscore. 3.6. Sensitivity and Specificity of MG Density Figure 8 shows the results of the ROC curve analyses, which indicated the sensitivity and specificity of MG density for the diagnosis of MGD. The area under the curve (AUC) was 0.836 for MG density in the upper eyelid. The sensitivity and specificity were 73% and 81%, respectively, at a cut-off value of 0.265. The AUC was 0.888 for MG density in the lower eyelid. The sensitivity and specificity were 82% and 88%, respectively, at a cut-off value of 0.255. The AUC was 0.900 for MG density in the total eyelids. The sensitivity and specificity were 88% and 81%, respectively, at a cut-off value of 0.275. 4. Discussion Diagnosis of MGD is difficult because most of the diagnostic criteria are subjective and are usually based on a combination of a high meiboscore, dry eye symptoms, and lid margin abnormalities [29]. A comprehensive analysis of MG morphology is the key for determining the severity of MGD. Currently, the most widely used MG morphology criterion is a qualitative MG dropout grading index similar to the meiboscore, and its effectiveness has been proven by a large number of studies. However, the meiboscore and other qualitative grading indices also have the limitations of strong subjectivity and poor repeatability, especially regarding the results adjacent to the grading transition zone. For example, when the MG dropout ratio is 1/3 or 2/3, the meiboscore becomes unstable. This study proposes a novel MG dropout index, the MG density. It is a linear quantitative index that extracts the image of each MG gland and calculates the ratio of the precise gland area relative to the tarsus area. This novel index greatly improves the accuracy compared with the traditional MG atrophy grade method, but it also shows instability and inaccuracy owing to anthropogenic annotation errors, limiting its effectiveness when using manual calculations. This MG density index requires many calculations, which limits its clinical application. AI has a quick mathematical calculation ability and high reliability, which is suitable for calculating MG density. There was no need to control between-group variance and repeatability, such as within-subject SD (SW), within-subject coefficient of variation (CVw), and intraclass correlation coefficient (ICC), as in our previous study [20,28]. In this study, the AI system achieved a 92% IoU and 100% repeatability. The AI model used in this study is the latest iteration of the CNN model used in our previous study [20]. To further improve the recognition accuracy of AI systems, a large training dataset is required. To overcome the dilemma of fewer MG images, we selected a data enhancement model to manipulate MG images and a combination of deep and transfer learning for AI model building. Transfer learning techniques attempt to transfer knowledge from previous tasks to a target task when the latter has less high-quality training data. This can be accomplished using a network that has already been pretrained on millions of general-purpose images (ImageNet [30]) without any additional retraining needed for the deep convolutional neural networks on our specific dataset. Using transfer learning, we were able to use a pretrained neural network in our image recognition network, which greatly reduced the dependence on training data and improved the training speed (from 15 h for the U-Net model to 4 h for the ResNet50_U-net model) and accuracy. Transfer learning has been used to study ophthalmic diseases, such as age-related macular degeneration [31] and glaucoma [32]. Although a small number of subjects were used to train AI in this study, the detection accuracy was very high owing to the combination of deep learning and transfer learning. After comparing the relationship between the MG morphological indices extracted by the AI system and clinical parameters, as previously reported [3,33,34], the AI system in this study revealed that MG dropout was significantly correlated with MGD symptoms, tear film stability, lid margin abnormality, and meibum expressibility. One step further than previous research [7,9,18,19,35,36], our study used MG density instead of meiboscore to evaluate the degree of MG dropout successfully. ROC curve analysis revealed that MG density showed high diagnostic efficiency for MGD. MG density in the total eyelids showed good efficiency, sensitivity, and specificity for the diagnosis of MGD, with a sensitivity and specificity of 88% and 81%, respectively, at a cut-off value of 0.275. Furthermore, regarding MG atrophy evaluation, a quantitative index based on the continuous numerical result of MG density is a better criterion than a qualitative index based on the MG dropout grade of the meiboscore. It is difficult to provide precise meiboscores when MG atrophy is near the grading transition limits (0%, 33%, and 66%), whereas MG density can be used in such situations. MG density can be used to effectively assess the atrophy condition of the MG in each grading transition area. Simultaneously, we also proposed the corresponding and conversion relation between the MG density and meiboscore by analyzing 4006 meibography images. There was a significant linear correlation between the MG density and meiboscore, especially in the upper eyelid. The MG density of the lower eyelid was slightly less correlated with the meiboscore, which may be related to the fact that the lower palpebral conjunctiva was mistakenly identified as the tarsus by the AI system because of the excessive turnover of the lower eyelid. In the future, based on AI assistance, the quantitative index of MG density can be used to replace the qualitative index of MG dropout, such as the meiboscore. This study has some limitations. The sample size for AI training was small. Even though we used imgaug, a data enhancement library, which could partially obtain a large amount of information from the original meibography used for AI training and greatly reduce the workload of annotation, it still could not change some basic information of the meibography, such as the number of glands. Therefore, it could not completely replace the newly annotated images. In addition, the sample size for evaluating the diagnostic efficacy of MG density was small. In future studies, the author’s team will recruit more subjects for AI system training and testing. 5. Conclusions MG density is an accurate and effective evaluation index that can completely replace the meiboscore for the quantitative diagnosis of MG dropout. We propose MG density as a novel quantitative index for AI-based diagnosis of MGD. Simultaneously, the AI system can reduce the subjective bias of the observer and doctors’ workload, improve efficiency, and assist nonprofessional doctors with MGD diagnosis. Acknowledgments The authors are grateful to Jing Ye for technological guidance. They are grateful to Xuewen Chen, Lu Li, Chaoqun Zhang, and Yingyu Mao, who works in the department of dry eye, for their help in collecting data. Author Contributions Conceptualization and writing original draft, Z.Z.; formal analysis, X.L.; investigation, X.Y.; writing—review and editing, Y.F.; data curation, X.C.; supervision, W.Y.; project administration, Q.D. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Zhejiang Provincial Medical and Health Science Technology Program of Health and Family Planning Commission (grant number: 2022PY074; grant number: 2022KY217), a Project Supported by Scientific Research Fund of Zhejiang Provincial Education Department (Y202147994) and the Nanjing Enterprise Expert Team Project. Institutional Review Board Statement The study was approved by the review board of the Eye Hospital of Wenzhou Medical University (approval number: 2020-209-K-191) and adhered to the tenets of the Declaration of Helsinki. This study is registered on http://www.chictr.org.cn (ChiCTR2100052575, 31 October 2021). Informed consent to participate in the study was obtained from a parent or guardian for participants under 18 years old. Informed Consent Statement Informed consent to participate in the study was obtained from a parent or guardian for participants under 18 years old. Data Availability Statement The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Network Structure. (A) The network structure of the modified U-net model as we reported previously; (B) the network structure of the ResNet50_U-net model in this study. Figure 2 Theimage enhancement modelexample. (A) Original training image. (B–L) Output images with 11 enhancement methods. Figure 3 The samples of image processing, annotation and segmentation. (A) The original meibography. (B) The preprocessed image. (C) The manually annotated MGs in yellow outline. (D) The segmented MGs by AI system are shown in yellow. Figure 4 The comparisons of manual annotation and AI automatic segmentation. (A1,B1,E1,F1) The original meibography of the upper eyelid. (A2,B2,E2,F2) The manual annotation of the upper eyelid (yellow outline). (A3,B3,E3,F3) The AI segmentation MGs of the upper eyelid (yellow part). (C1,D1,G1,H1) the Original meibography of the lower eyelid. (C2,D2,G2,H2) The manual annotation of the lower eyelid (yellow outline). (C3,D3,G3,H3) The AI segmentation MGs of the lower eyelid (yellow part). Figure 5 The intersection (green part) of the ground truth (manual annotation, blue part) and the AI result (AI segmentation, yellow part) divided by their union (red part) is IoU. Figure 6 Corresponding relationship between MG density and meiboscore. (A) The corresponding relationship between the upper eyelid MG density and meiboscore. (B) The corresponding relationship between lower eyelid MG density and meiboscore. The “hot” red areas represent data-intensive areas. The maximum number was 80 and 60 meibography images on the upper eyelid and lower eyelid, respectively. The “cold” green areas are the opposite. The minimum value is 1 meibography image. Figure 7 Meibography images with MG densities and meiboscores. Rows 1 to 4 refer to meibography images with meiboscore 0 to 3, respectively. MG density was calculated from the meibography images by our AI system. Figure 8 ROC curve analysis of MG density for the diagnosis of MGD. jcm-11-02396-t001_Table 1 Table 1 Clinical parameters of the 85 subjects. Parameter Normal (n = 32) MGD (n = 53) p p * Age (years), Median (IQR) 25.00 (16.25–32.75) 35.00 (30.00–50.00) <0.001 - Sex (n, male/female) 13/19 20/33 0.794 - OSDI (0–100), Median (IQR) 4.47 (0.30–12.35) 25.00 (13.24–37.80) <0.001 <0.001 Symptom score (0–14), Median (IQR) 2.00 (0–4.00) 7.00 (5.00–8.00) <0.001 <0.001 TBUT (s), Median (IQR) 5.00 (5.00–7.75) 2.50 (1.33–3.67) <0.001 <0.001 CFS (0–20), Median (IQR) 0 (0–0) 0 (0–0) 0.058 0.021 TMH (mm), Median (IQR) 0.19 (0.16–0.23) 0.20 (0.17–0.24) 0.461 0.871 Lid margin score (0–4), Median (IQR) 0 (0–1.00) 2.00 (1.00–2.00) <0.001 <0.001 Meiboscore (0–6), Median (IQR) 2.00 (1.00–2.00) 3.00 (2.00–4.50) <0.001 <0.001 Meibum expressibility score (0–45), Median (IQR) 38.50 (30.00–45.00) 18.00 (5.50–34.50) <0.001 <0.001 MGD = meibomian gland dysfunction; IQR = interquartile range; OSDI = Ocular Surface Disease Index; TBUT = tear break-up time; CFS = corneal fluorescein staining; TMH = tear meniscus height; Values are expressed as the median (IQR). Mann–Whitney U-test was used to compare differences between MGD subjects and normal control subjects. * p values adjusted for age by generalized estimating equation. jcm-11-02396-t002_Table 2 Table 2 Correlations of MG density with tear film functions and MG status in 85 subjects. OSDI TBUT CFS TMH Lid Margin Score Meiboscore Meibum Expressibility Score MG density Upper eyelid −0.320 † 0.484 ‡ −0.162 −0.059 −0.350 † −0.749 ‡ 0.425 ‡ Lower eyelid −0.420 ‡ 0.598 ‡ −0.177 −0.058 −0.396 ‡ −0.720 ‡ 0.438 ‡ Total eyelid −0.404 ‡ 0.601 ‡ −0.166 −0.070 −0.416 ‡ −0.805 ‡ 0.480 ‡ MG = meibomian gland; OSDI = Ocular Surface Disease Index; TBUT = tear break-up time; CFS = corneal fluorescein staining; TMH = tear meniscus height; Spearman’s rank correlation coefficient test. † p < 0.005. ‡ p < 0.001. jcm-11-02396-t003_Table 3 Table 3 Comparison table of MG density and meiboscore. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093397 sensors-22-03397 Article Automated Data Generation for Raman Spectroscopy Calibrations in Multi-Parallel Mini Bioreactors https://orcid.org/0000-0002-0401-7470 Graf Alexander 1 Woodhams Angus 2 Nelson Michael 3 https://orcid.org/0000-0001-8488-6758 Richardson Douglas D. 3 Short Steven M. 3 Brower Mark 3 Hoehse Marek 1* De Luca Anna Chiara Academic Editor 1 Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, 37079 Goettingen, Germany; alexander.graf@sartorius.com 2 Sartorius Stedim TAP, York Way, Royston SG8 5WY, UK; angus.woodhams@sartorius.com 3 Merck & Co., Inc., 2000 Galloping Hill Rd., Kenilworth, NJ 07033, USA; michael.nelson@merck.com (M.N.); douglas.richardson14@merck.com (D.D.R.); steven.short@merck.com (S.M.S.); mark_brower@merck.com (M.B.) * Correspondence: marek.hoehse@sartorius.com 28 4 2022 5 2022 22 9 339706 3 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Raman spectroscopy is an analytical technology for the simultaneous measurement of important process parameters, such as concentrations of nutrients, metabolites, and product titer in mammalian cell culture. The majority of published Raman studies have concentrated on using the technique for the monitoring and control of bioreactors at pilot and manufacturing scales. This research presents a novel approach to generating Raman models using a high-throughput 250 mL mini bioreactor system with the following two integrated analysis modules: a prototype flow cell enabling on-line Raman measurements and a bioanalyzer to generate reference measurements without a significant time-shift, compared to the corresponding Raman measurement. Therefore, spectral variations could directly be correlated with the actual analyte concentrations to build reliable models. Using a design of experiments (DoE) approach and additional spiked samples, the optimized workflow resulted in robust Raman models for glucose, lactate, glutamine, glutamate and titer in Chinese hamster ovary (CHO) cell cultures producing monoclonal antibodies (mAb). The setup presented in this paper enables the generation of reliable Raman models that can be deployed to predict analyte concentrations, thereby facilitating real-time monitoring and control of biologics manufacturing. Raman spectroscopy mini bioreactor process development mammalian CHO cell culture process analytical technology (PAT) This research received no external funding. ==== Body pmc1. Introduction Protein-based biologics are becoming increasingly complex in terms of molecular attributes and the cell culture process required to manufacture them. A number of process development and technical transfer stages are required to optimize the product titer and critical quality attributes (CQAs) of biotherapeutics, before they can be delivered to patients. Automated mini bioreactor systems have proven their potential for process optimization and development in several studies, as they are designed to have similar geometry to those found in pilot and commercial scale bioreactors [1]. These platforms have also been shown in scale-up studies to generate comparable cell growth and protein titer profiles up to the 3000 L scale [2,3]. Furthermore, the technology has been proven via several studies to have comparable performance to benchtop bioreactors [3,4,5] and has also been shown to be a qualified scale-down model (SDM) for commercial scale mAb production [6]. Thus, the process development is made much faster and, in turn, significantly shortens the time to clinic and consequently time to market. Critical process parameters (CPPs), such as nutrient and metabolite concentration and protein titer in the cell culture, are mostly detected off-line, requiring regular sampling of the bioreactor. The need to routinely sample the bioreactor, which is typically restricted to trained laboratory staff, is both time- and labor-intensive. Additionally, the risk of bioreactor contamination during sampling is a common concern [7]. Finally, off-line sampling typically occurs at 24-h testing intervals, rendering direct process control of the measured analytes within relatively tight action limits not possible. Raman spectroscopy is a well-suited PAT tool to nondestructively measure cell culture analytes in-situ, using immersion probes or flow cells. This vibrational technique uses a laser to generate monochromatic light that will scatter when interacting with complex biological samples. The inelastic scattered light yields structural information regarding the covalent bonds of the interrogated molecules with high molecular specificity and robustness. Raman spectroscopy has been widely adopted in biomanufacturing as a multipurpose analytical technique [8] for real-time monitoring of cell culture performance parameters, such as glucose, glutamine, glutamate, lactate, viable cell density (VCD) and product titer [9,10,11,12]. Additionally, Craven et al. [13], Berry et al. [14] and Rowland-Jones and Jaques [15] have monitored glucose via on-line Raman measurement, and Matthews et al. [16] have used Raman lactate measurements with feedback control to maintain glucose and lactate, respectively, at constant concentrations in CHO cell cultures. A study by Berry et al. [17] exemplified that Raman spectroscopy can be used to maintain glucose at low concentration levels in CHO cell cultures, which in turn reduced glycation in the mAb product from ~9% to 4%. In-line Raman spectroscopy is mainly used with pilot and manufacturing bioreactors, due to scale limitations of the spectroscopic devices. More recently, preliminary studies suggest that this technique has the potential to be integrated in micro bioreactors (15 mL working volume) via automated routines to simultaneously measure multiple metabolites [15,18]. Presently, the largest barrier to the proof of this concept is the lack of an integrated reference system (e.g., glucose measurement used to construct the Raman calibration model), which in turn requires the use of off-line referencing systems that introduce model error as a result of the mismatch between the time of spectral acquisition and the reference measurement. Therefore, mini bioreactors featuring a robust, scalable, and fully integrated sampling and spectroscopic platform with automated solutions for liquid management, data acquisition, data alignment, and data export offer significant benefits. This minimizes manual intervention, de-risks bioreactor contamination, and mitigates the extensive work to compile calibration datasets that can otherwise be read by multivariate data analytics software. With this approach, a cost-efficient spectroscopy implementation is combined with the benefits of automation and data management, resulting in an efficient system for data generation and model building. The creation of a robust Raman spectroscopy model that is applicable to production scales relies on the deployment of calibration models that correlate spectral signals with analytical measurements. As reviewed by Tulsyan et al. [19], constructing a quantitative model involves several critical steps. Initially, well-characterized spectral and analytical datasets are collected for cell culture parameters of interest, either from experiments in small-scale (benchtop) bioreactors or larger scale production bioreactors. Data pre-processing is subsequently performed to improve the signal-to-noise ratio and/or to reduce disturbances and to minimize equipment variations from the probe heads or spectrometers. In the penultimate step, these data are modeled using multivariate statistical methods, such as orthogonal partial least squares (OPLS), to correlate the spectral data to the parameter(s) of interest. Finally, the model is utilized for monitoring and control applications. Good scientific practice for robust Raman model building relies on testing multiple cell culture parameters, ideally utilizing a design of experiments (DoE) approach, to obtain statistically relevant data. This includes challenging the model using independent datasets (i.e., those not used to construct the model) to determine if unknown correlations or trends are compromising performance. This is of particular importance for upstream bioprocesses, as nearly all analyte trends correlate with other analytes or with batch maturity. Ideally, this is accomplished by varying the process analyte conditions, such that the analyte targets fluctuate independently from one another. However, this may not always be practical and can be augmented with spiking studies, where pure analytes of interest are added to the bioreactor to build in the required spectroscopic dynamic range. The latter results in step changes of single analyte trends only being valid for the spiked analyte alone. Thus, producing a well-fitted, predictive Raman spectroscopy model can be time-consuming and costly in terms of the required media, reagents and staff resources, which ultimately can have a deleterious impact on commercialization timelines for biologics, such as monoclonal antibodies (mAbs). In a typical example of a standard workflow for Raman model building, one study ran 37 separate production runs of a fed-batch bioprocess, ranging from a 2 L shake flask up to a 5000 L bioreactor [11]. They acquired measurements at 12 different (time) points across each run for a total of 444 data points to construct their Raman model. This kind of study is not only time-consuming but is also very expensive, particularly at larger scales (media costs alone are around €100,000 for a 2000 L run). Furthermore, executing model building runs in production environments is almost impossible, as induced process variations would require the batch to be discarded due to suboptimal quality attributes for the end biological product. Therefore, a robust Raman model is currently achieved by monitoring dozens of production runs and arbitrary (smaller) variations of in-specification batches, with considerable value being introduced when unfortunate out-of-specification events occur during routine manufacturing. Utilizing a small-scale bioreactor setup enables the use of sound experimental design methodologies to induce variations at acceptable costs, while one run consisting of twenty-four bioreactors yields a larger and more robust design space compared to many more large-scale production batches, consisting of only arbitrary variations. If this model can be transferred to larger scales (e.g., by adding a few points from larger scales), the robustness of the low effort small-scale bioreactor model is made available for applications across all scales. This study describes the implementation of a prototype Raman flow cell in a 250 mL mini bioreactor system, ultimately paving the way to monitor and control key process and product attributes during small-scale cell culture manufacture. The system described herein enables automated bioreactor sampling and reference measurements paired with on-line Raman spectroscopy, which facilitates robust model building through the analysis of induced process variations, according to DoE principles and automated spiking experiments. The prototype Raman spectroscopy probe head connected to the flow cell utilizes an optical interface that can also be found in single-use bioreactors at 50 to 2000 L scale, which facilitates the scalability of the Raman model. The application of this PAT tool across the scales has the potential to rapidly deliver high quality data during process development, which could later be applied to commercial manufacturing for on-line monitoring and control of large-scale bioreactors. 2. Materials and Methods 2.1. Experimental Setup, Raman Spectroscopy Integration Prototype and Reference Measurements An automated 250 mL bioreactor system with 24 vessels (Ambr® 250 High Throughput, Sartorius, Royston, UK) was equipped with an integrated analysis module, including a prototype Raman flow cell, to enable automated spectroscopic Raman measurements and with an integrated cell culture analyzer to enable reference measurements. The liquid handling capability of the analysis module was extended by adding a prototype optical flow cell (1 mm path length, sapphire windows, ~40 µL volume) (Figure 1). The entrance to the flow cell was connected to the sample cup, while the exit port was diverted to a waste bottle. Culture samples from the mini bioreactors were analyzed using an integrated Bioprofile® FLEX2 (Nova Biomedical Corporation, Waltham, MA, USA) automated cell culture analyzer with an External Sample Module (ESM), which managed and transferred samples (0.5 mL) for pH, pCO2, pO2, VCD, glucose, glutamine, glutamate, lactate, and ammonium analysis. Titer reference data were generated upon the completion of the experiment, by analyzing refrigerated cell-free bioreactor samples from select process days via Protein A affinity high performance liquid chromatography (ProA HPLC). A prototype optical probe head was connected to the flow cell and HyperFlux PRO Raman Spectroscopy system (Tornado Spectral Systems, Toronto, ON, Canada). The latter was also connected to an Ambr® 250 system for the automated generation of Raman spectra. The Ambr® 250 software was modified to allow instrument control of the Raman spectrometer (start/stop measurement) and the transfer of spectral data from the spectrometer to the bioreactor control software. These data were then merged with relevant bioreactor data (e.g., vessel number, sample ID, batch ID, sampling time, batch age, and reference data) and jointly exported as a CSV file for statistical analysis and model building. 2.2. Cell Lines and Media A Chinese hamster ovary (CHO) cell line expressing an IgG4 monoclonal antibody (mAb) was used for the cell culture experiments. The cells were expanded and subsequently cultured in proprietary, chemically defined basal media. Starting from a cryo-vial thaw, the cells were serial passaged into consecutively larger shake flask cultures, until a desired cell count was achieved to inoculate the 250 mL bioreactors at the specified seeding densities. 2.3. Cell Culture and Data Acquisition Process To produce statistically relevant data for robust Raman Spectroscopy model building, a run was performed in the Ambr® 250 high throughput mini bioreactor system (Sartorius, Royston, UK), utilizing 24 mini bioreactors at a working volume of 180–250 mL. As for the standard settings, the bioreactors were operated at a 36.5 °C, 400 rpm stirring speed, and a dissolved oxygen (DO) of 30% air saturation for 14 days. The pH was controlled between 6.8 and 7.1, with CO2 sparging to decrease the pH and the addition of an NaOH base to increase the pH as needed. Proprietary, chemically defined feed media were added throughout the run, based on the daily metabolite readings in the reactors. The feed regimen was based on a combination of continuous and bolus feeds, as described in the work by Manahan et al. [6]. For the process run, a design of experiments (DoE), analogous to the one that was used during standard commercial process development, was replicated for this case study (Figure 2A). The process parameters of initial cell density, daily glucose feed target, pH setpoint, and DO setpoint were controlled and deliberately varied in sixteen vessels, while the remaining eight vessels were kept at the standard process settings mentioned before for control replicates. Automated reference measurements were carried out using an integrated Bioprofile® FLEX2 as previously described. This experimental setup was chosen in order to demonstrate that Raman data generation can be accomplished in tandem with a standard commercial process development experiment, to take advantage of the wide range of metabolite and product titer profiles generated from the experimental design. In general, the presented approach is also feasible in other experimental set-ups, such as OFAT trials. However, this may result in lower variations within some of the analytes and lead to inferior model performance or the need of additional runs. Sequentially, during this high-throughput study, Raman spectra were periodically acquired from bioreactor samples both before and after being spiked (spiking from day six onwards), with known concentrations of key analytes (Figure 2B,C; Table S1). A total of 48 samples (24 unspiked and 24 spiked) were collected on a daily cadence and analyzed via Raman spectroscopy. All the spectroscopy related liquid handling was automated and performed overnight to not interfere with the standard daily sampling and control, as well as feeding. For spiking, one sample per vessel (140 µL) was taken and mixed with one volume of one of the following analytes: glucose, lactate, glutamine, or glutamate (nominal spiking volume 20–60 µL of analyte stock solution), as well as purified “mAb1” protein product (generated from previous experiments), according to the scheme shown in Table S1. The spiked samples were mixed in a microwell plate directly before analysis via Raman spectroscopy. As the spiking solution is composed of a different matrix to the bioprocess sample, the addition of high amounts of the stock solution may lead to a significant change in the spectrum compared to the unspiked sample and in turn, lead to more outliers or offsets, but neither were observed (Figure S1A) in Supplementary Materials. Two stock solutions, one lower and one higher in concentration, were prepared for each analyte to generate an equally distributed range. The precise concentrations of stock solutions varied for each analyte, depending on the normal process range between 4 and 32 g/L. Spiking studies were used to break the correlations between the different analytes of interest, as well as with batch maturity. Since the spiking of one analyte at a time leads to the decrease in all the other analytes, this could possibly introduce new correlations. To minimize this effect, high stock solutions as well as different combinations of stock concentration and spiking volumes were utilized. As previously reported by Rowland-Jones, Graf et al., the positive effects of breaking correlations are far stronger than any newly introduced ones [18]. The sampling scheme consisted of one reference measurement per vessel, followed by the acquisition of spectra from an unspiked sample, and finally from the spiked sample, as shown in Figure 2B. The FLEX2 reference measurements were used to align with spectra from the unspiked samples. The concentrations of the spiked samples were automatically calculated by the Ambr® software, using both the reference value of the unspiked sample combined with the concentration and volume of the added stock solution. To minimize errors that stem from the liquid handling system, the systematic offset of the pipetting at the relevant volumes was determined in advance and used for these calculations (Table S1). If necessary, different amounts of air in the sample, e.g., due to foaming, could be programmed into the liquid handling system. Within this study, foaming was not a source of disturbance, just as changing viscosities within the samples, e.g., due to increasing cell counts. The reference measurements for the mAb1 protein titers were more limited, since the integrated FLEX2 had no protein titer assay. Therefore, an additional unspiked set of samples from each bioreactor was taken on every second day over the duration of the cultivation and one further set of spiking samples just on day four. For this analyte, the spectra were captured directly after sampling automatically, similar to before, while all titer reference data were generated upon the completion of the experiment, by analyzing the refrigerated cell-free bioreactor samples via ProA HPLC. To align the spectra and reference values, the latter were first manually normalized to the highest value and then matched by vessel number and timepoint to the corresponding spectrum within SIMCA 16 (Sartorius Data Analytics AB, Umea, Sweden). Overall, the single run produced a theoretical total of 528 Raman spectra from 24 vessels measured daily without spiking and including the additional spectra of the spiking samples from day six onwards. 2.4. Raman Spectroscopy Measurement All measurements were acquired using a HyperFlux PRO Raman Spectroscopy system (Tornado Spectral Systems, Toronto, ON, Canada), equipped with a 785 nm laser for excitation set at 495 mW. As previously described, the system was controlled by the Ambr software. Here, the overall measurement time per sample was set to 5 min, which was comprised of an average of five separate 1 min spectra taken from one sample (calculation within the Ambr software (BioPAT Spectroscopy Data Manager, Sartorius, Royston, UK)). Each 1 min spectrum was, in turn, averaged from x spectra measured at a set integration/exposure time It within the spectrometer control software. The increase in fluorescence throughout the run required a reduction in It to not risk sensor saturation. Thus, the averaging (avg) was adapted by the operator accordingly, to guarantee an acquisition time of 1 min in all cases (e.g., It = 0.2 s, avg = 300; It = 1 s, avg = 60). Maintaining the overall acquisition time similar ensures that the signal to noise ratio within each spectrum stays identical; therefore, the spectrum can be recalculated to the initially longer exposure time without a loss of sensitivity. The design of the measurement chamber was optimized to block any direct incoming ambient light; however, a dark scan was performed prior to each sample measurement to mitigate the impact of any variations in ambient light that might have strayed into the sample chamber. 2.5. Multivariate Raman Model Construction Orthogonal partial least squares (OPLS) regression models were developed from acquired Raman spectra which were correlated to standard reference measurements performed using the integrated FLEX2 bioanalyzer or the ProA HPLC for the mAb titer. Spectral and reference data were first aligned within the Ambr® software in an automated fashion, followed by averaging five spectra from each sample (each spectrum having a total acquisition time of one minute) to improve the signal-to-noise ratio. Initially, before data pre-treatment, the raw spectra were scanned to exclude aberrant measurements (e.g., due to an empty measurement chamber), by checking the overall intensity and that of the water bands in particular. Due to the different levels of fluorescence background, all spectra were then baseline-corrected with an asymmetric least-squares algorithm [20] (Figure S2B), as this correction method proved to be superior to the standard pre-treatment with standard normal variate (SNV) and first derivative analysis. The water band at 1650 cm−1 was utilized to normalize all spectra, and therefore correct the potential variations that were not caused by the process itself, but to other confounding factors (Figure S2C). Examples for these disturbances are small air bubbles still present in the measurement chamber, or differences in the optical sampling volumes, due to varying cell densities. As described in several studies, the Raman water signal can be exploited for this task very reliably [10,18,21]. As the sample mainly consists of water, small peaks from other analytes that may be present in this region can be neglected. All pre-treatments were done in Python 3 with the help of the numpy, pandas, and rampy module and similar modules for these pre-treatments can also be found in R. After pre-treatment, the spectra and reference data were transferred to SIMCA 16 Multivariate Data Analysis software (Sartorius Data Analytics AB, Umea, Sweden). A PCA model was built to identify the possible outliers outside the Hoteling’s T2-boundaries in the score plot, which might have been missed in the previously mentioned investigation. Any remaining outliers were identified at a later stage of model building, using the observed vs. predicted plot after verification with their distance to the model (DmodX) value. While a completely empty measurement chamber is easy to detect, these additional outliers can stem from several minor reasons. These causes include sub-optimal sample transfer into the measurement chamber and, therefore, some amount of air or tiny air bubbles is still present, which obscures the measurement. Depending on the ratio and position of the air in relation to the laser focus, in some cases, this can be corrected by the water band normalization. Other reasons for outliers can be miss-measurements of reference analytics, or falsely calculated analyte concentrations after spiking, due to bad mixing of the sample. In summary, the overall number of outliers stayed well below 5%. Quantitative models for the different analytes of interest were generated with the help of an OPLS algorithm. The X-block consisted of the spectral variables that were mean-centered, while the Y-block was composed of the scaled (unit variance) reference measurements. OPLS, instead of classic PLS, was chosen, as it increases the model interpretability while maintaining the same predictive power. This increase is achieved by removing variance in the X-Block (i.e., the spectra), which has no correlation to the variation in the Y-Block (i.e., the reference data) or in mathematical terms, removing systematic variation in X that is orthogonal to Y. This results in a model that consists of one predictive component (as one model is built for each Y-variable separately) and a number of orthogonal components that can differ in each model [22,23]. Each model is, thus, denoted by 1 + x principal components. To further reduce the impact of the correlations between the analyte trends and batch maturity, the spectral regions for each analyte were matched to those that were found to be unique to the analyte of interest in previous trials [18] (Table S2). In this case, DoE studies with mixtures of the main analytes were performed to determine the unique spectral regions of interest for each analyte. Additionally, these lab trials using a simplified system (accurate concentrations in buffer/water) are good indicators of the maximum achievable performance of the technique. Lower prediction errors in cell culture than in the lab DoE are an indicator for in-process correlations that help to decrease the prediction error; however, models with this prediction error profile bear the risk of model failure, as future changes to the process may invalidate these correlations that the model is highly dependent on. Even though model building relies on the user’s experience, especially when it comes to overfitting, certain (statistical) tools can be used as a basis for selecting an optimal model. The goodness of fit (R2) and goodness of prediction (Q2) should be as high as possible, while simultaneously minimizing the difference between them. Differences larger than 0.3 can indicate either the presence of outliers in the dataset, which may have sufficient justification to be omitted from the dataset, or that the model is overfit, requiring one or more model components to be removed in order to enhance accuracy and long-term robustness [23]. Several performance metrics, such as the root mean square error evaluation (RMSEE) and the root mean square error of cross-validation (RMSEcv), are essential markers for the estimation of model error. While the RMSEE shows how well the model performs, the RMSEcv indicates how well the model can predict future datapoints, given that this data does not deviate significantly from the original dataset. Both of these error values should be as low as possible, while simultaneously minimizing the difference between them, as larger divergences between the two metrics indicates the overfitting of the model to the available data. The two values are calculated as follows, with n being the number of samples in the model, yi denoting the observed reference value, and ycal, and yCVpred, representing the predicted values from the model and cross-validation, respectively:(1) RMSEE=∑yi−ycal2n (2) RMSEcv=∑yi−yCVpred2n The cross-validation (CV) groups must be consciously selected for the RMSEcv to be a reliable measure of model performance. For example, the selection of a high number of CV groups results in a lower number of samples that are left out of testing during the cross-validation routine, ultimately serving as a weaker challenge to the model under consideration. The reader is directed to a study by Eriksson et al. [23] for additional information on how cross-validation works and its associated advantages and limitations. Consequently, the trial dataset was split into four CV groups according to their vessel number. Therefore, complete batches were left out of the sub-models of the cross-validation routine. The selection of four CV groups was determined to be a good compromise between omitting too many samples (leading to highly inaccurate CV models) and leaving out too few samples (therefore not stressing the model enough) to obtain dependable RMSEcv values. Additionally, cross-validation with full batches can also be understood as an average of four external datasets yielding a better overview of the model performance when challenged with fully independent datasets, as opposed to using a single external test set with 75% of batches and predicting the remaining 25% of vessels. 3. Results and Discussion 3.1. Data Generation with the Experimental Prototype For a fully automated sample analysis, an Ambr® 250 was modified to integrate both a FLEX2 metabolite analyzer and a HyperFlux PRO Raman Spectroscopy system, equipped with a second-generation prototype probe head and a novel micro-volume flow cell (Figure 1). The analyte concentrations, measured via the integrated FLEX2, were used as the reference points for the development of the predictive Raman model. Multiple actions were required to complete the analysis of the cell culture samples, including (i) the withdrawal of a sample from the mini bioreactor via the automated liquid handler, (ii) the distribution of the sample to the FLEX2 analysis module via the External Sample Module (ESM), (iii) the release of the sample into the analysis module (AM) sample cup, (iv) the discard of the residual sample into the ESM waste, and (v) a cleaning cycle with standard washing liquids to prevent sample carry over. Antibody titer reference measurements were generated via an off-line ProA HPLC analysis of the clarified daily sample retains. The wavelength axis of the spectroscopy instrument was first calibrated with a mercury-argon lamp (HG-1, Ocean Optics, Orlando, FL, USA). The white light spectrometer calibration (also referred to as y-calibration or intensity calibration) included the complete optical path by back illumination via a separate fiber connection of the probe head, connected to a tungsten halogen light source (HL-2000, Ocean Optics, Orlando, FL, USA). Reference samples were automatically taken and measured by the integrated FLEX2 system immediately prior to the Raman measurement. The analysis of a cell culture sample from each mini bioreactor for Raman measurements involved the liquid handler withdrawing and releasing the sample into the AM sample cup. With the help of an integrated syringe pump, the sample was transferred to a flow cell for Raman measurements. To prevent sample carryover, the sample was discarded following spectral analysis and a cleaning cycle was initiated with standard AM washing liquids. The spiked samples required manual manipulation by the robotic liquid handler. First, a cell culture sample (~250 µL) was dispensed into a microwell plate. Then, a certain volume of stock solution was aspirated in a tip, followed by the aspiration of 140 µm of the cell culture sample in the same tip. Next, the cell culture sample and stock solution were mixed in a different well by pipette aspiration and release, and finally transferred to the sample cup for delivery to the flow cell for spectral analysis. The acquired spectra from the unspiked samples were time-aligned with FLEX2 measurements to merge the two datasets. The concentration of spiked samples was automatically calculated in the Ambr® system software, using the reference value of the unspiked sample and accounting for the spike itself (i.e., volume addition and stock solution concentration). The careful integration of the software and hardware components yields negligible time differences between the reference and spectral measurements, which ultimately increases the accuracy and selectivity of the models compared an application capable of using only off-line reference measurements (e.g., those acquired via a reference analyzer that may or may not reside in the same physical location as the spectrometer). One single run of the experimental setup described in this paper produced a total of 528 Raman spectra from 24 vessels (16 DoE and 8 Golden Batch vessels) measured twice a day (same sample unspiked and spiked) for 14 days. Those spectra, together with the simultaneously acquired reference data, were then used to develop the predictive OPLS models. 3.2. Predictive OPLS Models Individual OPLS models were developed to correlate the pre-processed Raman spectral data with the reference data to produce predictive models of glucose, lactate, glutamine, glutamate and mAb titer. Figure 3 shows the parity plots for these analytes using the measured reference values and the predictions during cross-validation from the Raman-based models. Generally, high cell densities did not have an impact on either the liquid handling system nor the quality of spectra, i.e., resulting in more outliers. Peak viable cell densities of up to 14.5 × 106 cells/mL were reached towards the middle of the cultivation. Whenever the viable cell density decreases by several million cells per mL towards the end of the run, the data points drift away from the main group (Figure S1B), indicating a fundamental change in the spectra. However, these data points are not necessarily outliers and are mostly still part of the analyte models shown below. In summary, this shows that (a) the liquid handling system works reliably at different viscosities, (b) high cell densities and, therefore, increased turbidity do not interfere with the spectroscopic measurement and (c) when fundamental changes within the process occur, the data pre-treatments are able to compensate these. Table 1 summarizes the key parameters and figures of merit for the models constructed using unspiked bioreactor samples. The Raman models generated in this study showed lower prediction errors for glucose and lactate measurement than those published, e.g., by Rowland-Jones, van den Berg et al. [24], where the measured glucose concentration showed a RMSEcv of 0.92 g/L and lactate concentration showed a RMSEcv of 1.11 g/L in a similar process. The normalized product titer model also showed good accuracy, with a Q2 value of 0.911 and RMSEcv of 0.08 g/L. The Raman models for the unspiked samples of glutamate and glutamine were found to have lower coefficients of determination, with a Q2 value of <0.8, indicating lower predictive ability. The main issue with the unspiked models is that glutamine and glutamate are present over a very narrow concentration range (1 g/L for glutamine and 2 g/L for glutamate), which makes it more difficult for the chemometrics algorithm to distinguish between the real concentration changes and process noise. Additionally, Raman scattering for lactate and glucose produces more dominant bands than for glutamine and glutamate. Bearing in mind the limitations, spiking might be useful to increase the analyte range to improve model quality. These observations are consistent with other cell culture studies where weaker Raman activity, particularly with glutamate [14,25,26], has produced less accurate models compared to those for glucose or lactate. Altogether, the process at hand delivered comparably high concentrations of all the analytes without concentration levels below 0.5 g/L, except for the mAb titer. If the models are transferred to other scales or the processing is changed, possibly leading to lower concentrations than those covered in the model, this can result in higher prediction errors, or in the worst case, to a wrong prediction of a much higher analyte concentration than actually present. This, in turn, could leave the model useless for process monitoring and control. To counteract this, various measures can be taken to further extend the range of the model to cover lower concentrations. These include, instead of spiking, the dilution of the sample with an analyte of interest-free medium (i.e., glucose or glutamine free) and an added experiment where some vessels are either run in batch mode, or have a significantly different feeding regimen, thus leading to a deprivation of the metabolites. A third choice would be to combine data from this process with data from a different process that ideally uses similar media, but is run differently and is known to have lower analyte concentrations present. Previous studies have shown that the latter is possible, even if the cell lines and target products are different [18]. Overall, past studies have shown that the limits of detection for the different analytes between 0.1 g/L and 0.3 g/L with Raman spectroscopy in bioprocessing can be achieved [18,27]. 3.3. Predictive OPLS Models of Combined Non-Spiked and Spiked Samples OPLS models, including spiked data, were employed to extend the dynamic range and potentially improve the correlation with reference data. These models were developed using the same pre-processing and outlier identification methods as previously discussed for unspiked samples. Figure 4 shows the observed (reference) versus the predicted (Raman) data from the models developed for glucose, lactate, glutamine, glutamate and titer in spiked cell culture samples. Additionally, not all the cell culture processes yield high concentrations of glutamine and glutamate (highest concentration e.g., below 1 g/L). For these processes, spiking can make the difference between a functional and a non-functional model. Spiking, therefore, has the potential to generate more valid OPLS models for glutamine and glutamate. Table 1 shows the summary statistics of the spiked and unspiked Raman OPLS models. The models including both spiked and unspiked data were developed. Generally, the models for a particular analyte demonstrated good performance, regardless of whether spiked or native samples were used. However, the inclusion of spiked samples for glutamine and glutamate (extending the concentration range of the unspiked samples) resulted in a better fit, with a Q2 values above 0.9 and RMSECV values of 0.24 g/L and 0.23 g/L, respectively. This indicates that Raman models can predict glutamine and glutamate concentrations within a similar accuracy and specificity as previously demonstrated for other analytes (e.g., glucose, lactate), further instilling confidence that the predictive performance of the models for these less explored analytes is limited by spurious correlations. While spiking did extend the calibration range for glucose and lactate models, it did not have a considerable positive impact on the Q2 values for either analyte (Table 1). This is a consequence of the variations in the process conditions via the DoE, the wavenumbers selected for inclusion in the models, and the large concentration range of lactate and glucose in the native samples, which together resulted in minimal residual variance that was unexplained by the optimized models. Additionally, the fed-batch process requires the addition of feed media, such as nutrients, that are naturally depleted during the course of cell culture, which enhances the glucose range as this was a component of the media used in this case study. It should be noted that the models based on spiked samples are expected to have a slightly higher model error than the optimal models based on non-spiked data. This anticipated decrease in model accuracy is a result of the variations caused by the slight inaccuracies associated with the stock solution concentration and additional pipetting and mixing steps, all of which impact the ability to successfully correlate the changes in Raman scattering, with the corresponding changes in reference values. All the Raman generated models showed a slight difference in the slope/offset between the spiked (Figure 4) and unspiked (Figure 3) samples, which was most notable for glucose. The differences in slope and offset were hypothesized to be a consequence of sample dilution or insufficient mixing. To evaluate this hypothesis, further tests were performed, such as comparing several measurements taken one from one mixture, as well as comparing different mixing procedures. These tests proved non-optimal mixing of the sample and spiking solution as the sources of these variations. The pipette mixing technique of the early prototype led to the moving of layers, but to the insufficient mixing of these layers. This resulted in a concentration gradient between the first and the last part of the spiked sample. Given that the first portion of the sample was also used to rinse the flow cell, a Raman analysis was performed on the samples with concentrations that exceeded what nominally should be present when assuming sample homogeneity. This led to further refinement of the spiking methodology, which minimized the differences between the spiked and non-spiked samples, suggesting that sample inhomogeneity was effectively mitigated. The confirmation of the lab results is planned as a part of a separate study and will require separate cultivations, which can serve as independent prediction datasets to further evaluate the accuracy and robustness of the models discussed herein. 3.4. Application of Raman Models Robust and predictive OPLS models for glucose, lactose, glutamine, glutamate and mAb titer in cell culture were developed using Raman spectra, generated with the integrated 250 mL mini bioreactor system. To determine the potential of using spectral data for on-line monitoring, one specific 250 mL vessel was selected to illustrate the analyte levels over a 14-day process run. The data of this vessel were not used in model building, in order to validate the model performance with an outside dataset. The results (Figure 5) demonstrate that the Raman model for the selected analytes, as well as the mAb titer, align well with the reference data, indicating that the Raman models alone are likely to be suitable to control the cell culture. It is of particular note that the models even provide reasonable data for the glutamate concentration on day 6 and mAb titer on day 10, when the suspected sampling errors are believed to have caused reference measurement deviations. Overall, these results are promising and support the next phase of work, which is to transfer models based on small-scale data to larger scale single-use bioreactors to evaluate model scalability. 4. Conclusions Mini bioreactor systems are increasing in popularity within the biopharmaceutical industry, fueled by the growing library of applications underscoring their ability to reduce timelines and the overall cost associated with process development and cell culture scale-up for manufacturing. A prototype system, integrating a Raman spectrometer into a multiparallel mini bioreactor system with an integrated bioanalyzer, was designed to facilitate the continuity of PAT across scales. A CHO cell line expressing a mAb was selected as a case study to demonstrate that accurate and robust Raman models could be developed using the prototype system, which would be difficult and costly to generate using conventional model building approaches that are currently predicated on larger bioreactors. Notably, the integration of both a Raman flow cell and a bioanalyzer allowed for a reliable correlation between the Raman spectra and reference measurements because this experimental setup eliminates the otherwise necessary time-consuming manual collection of reference data. Due to the minimal time gap between the reference measurement and Raman spectroscopy, the models generated in this study show even lower prediction errors than the models generated with similar systems, but with manual collection of reference data [24]. In conclusion, using a multi-parallel mini bioreactor system integrating Raman spectroscopy and reference measurements, this study generated automated Raman spectroscopy models for a range of nutrients, metabolites and product titer, offering the potential to significantly reduce time and resource costs of commercial process development. Future case studies will investigate under which conditions the models generated in this study can be transferred to larger scales and whether they can be utilized for process monitoring and control. Acknowledgments The authors acknowledge the Ambr® development team in Royston for their dedicated work on the prototype development and support. We thank Barney Zoro for the review of the publication. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s22093397/s1, Figure S1: PCA Plot of all spectra used for outlier detection, i.e., points outside Hoteling’s T² ellipse are possible outliers. Colored according to (A) spiking vs. non-spiking, (B) cultivation day, Figure S2: Example Raman spectra taken at three different timepoints during a cultivation. (A) Raw spectra without corrections; (B) Spectra after baseline correction with ALS algorithm—fluorescence underground is removed successfully; (C) Spectra after normalization to the area under the waterband between 1550 and 1750 cm−1—removal of intensity differences due to perturbations not caused by the analytes of interest, e.g., by high turbidity in the sample due to high cell counts, Table S1: Overview over spiking solutions and spiking regimen over the duration of the cultivation. Each day from day six onwards samples from 24 Vessels were spiked with shown volumes from one of the two spiking solutions, Table S2: Wavenumber pre-selection for single analytes derived from pre-trials. Several mixtures of the analytes at different concentrations following a DoE approach were prepared. After acquiring a spectrum for each sample, spectra were pre-treated with the ALS-algorithm and separate OPLS for each analyte were built. Very Important Parameters (VIPs), i.e., those Wavenumbers with a high influence on the model were selected and used for future modeling. Click here for additional data file. Author Contributions All authors contributed to the preparation of the manuscript. Hard- and software development was performed by A.G., A.W. and M.H. Data analysis and modeling was performed by A.G. The Ambr experiments were designed and performed by M.N., D.D.R., S.M.S. and M.B. All authors have read and agreed to the published version of the manuscript. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Experimental setup: schematic diagram of the automated 250 mL mini bioreactor system (Ambr® 250 high throughput, Sartorius, Royston, UK) with a prototype Raman flow cell (path length 1 mm; internal volume ~40 µL) integrated into the Ambr® analysis module, enabling automated Raman spectroscopy and an integrated reference measurement system (BioProfile FLEX2, Nova Biomedical Corporation, Waltham, MA, USA). Figure 2 Experimental design and data acquisition of Ambr® 250 runs to generate Raman spectra and reference analyte data. (A) In 16 DoE vessels, inoculation density, as well as glucose, pH and dissolved oxygen setpoint were varied, while process settings were kept constant in the 8 Golden Batch vessels, which served as control replicates. (B) Schematic diagram illustrating automated data acquisition and duration. Notably, the time gap between the FLEX2 reference measurement and Raman spectroscopy of the non-spiked sample is only 5 min. (C) Schematic diagram illustrating that automated spiking leads to increased model robustness and validity. Figure 3 Parity plots of the observed values vs. predicted values from cross-validation for OPLS models of unspiked cell culture samples for (A) glucose, (B) lactate, (C) glutamine, (D) glutamate and (E) normalized titer with unspiked cell culture samples from 250 mL mini bioreactors. Figure 4 Parity plots of the observed values vs. predicted values from cross-validation for OPLS models, including spiked cell culture samples for (A) glucose, (B) lactate, (C) glutamine, (D) glutamate and (E) normalized titer with spiked cell culture samples from the 250 mL mini bioreactors. Figure 5 Observed vs. predicted development of analyte concentration during batch maturation. Comparison of observed (by reference method; black diamonds) and predicted (from the Raman spectroscopy models; yellow squares) analyte concentration vs. batch maturity from cell culture in a single 250 mL mini bioreactor; (A) glucose, (B) lactate, (C) glutamine, (D) glutamate, (E) mAb titer. sensors-22-03397-t001_Table 1 Table 1 Summary of key parameters for Raman OPLS models from spiked and unspiked samples from 250 mL mini bioreactors. Range indicates the concentration range of the analyte within the model; n states the number of datapoints used in the model; # of principal components states, in addition to one predictive component, how many orthogonal components the model utilizes; Q2 denotes the goodness of prediction; RMSEcv equals to the root mean square error of cross-validation. Analyte Range n # of Principal Components Q2 RMSEcv Glucose 2–8 g/L 224 1 + 1 0.97 0.20 g/L With Spiking 2–13 g/L 254 1 + 3 0.97 0.36 g/L Lactate 1.5–11 g/L 221 1 + 3 0.98 0.23 g/L With Spiking 1.5–11 g/L 255 1 + 5 0.94 0.43 g/L Glutamine 1–2.5 g/L 146 1 + 4 0.58 0.21 g/L With Spiking 1–6 g/L 173 1 + 3 0.93 0.24 g/L Glutamate 0.5–2.5 g/L 215 1 + 4 0.76 0.21 g/L With Spiking 0.5–5.5 g/L 248 1 + 3 0.94 0.23 g/L Titer (normalized) 0–1 83 1 + 3 0.91 0.08 With Spiking 0–3 108 1 + 3 0.97 0.10 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Bareither R. Bargh N. Oakeshott R. Watts K. Pollard D. Automated disposable small scale reactor for high throughput bioprocess development: A proof of concept study Biotechnol. Bioeng. 2013 110 3126 3138 10.1002/bit.24978 23775295 2. Xu P. Clark C. Ryder T. Sparks C. Zhou J. Wang M. Russell R. Scott C. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094662 ijms-23-04662 Article Regulatory T Cells with Additional COX-2 Expression Are Independent Negative Prognosticators for Vulvar Cancer Patients Ansorge Nadine 12 https://orcid.org/0000-0002-5141-4831 Dannecker Christian 2 https://orcid.org/0000-0003-2623-3235 Jeschke Udo 12* Schmoeckel Elisa 3 Heidegger Helene Hildegard 1 Vattai Aurelia 1 Burgmann Maximiliane 1 https://orcid.org/0000-0001-6589-4736 Czogalla Bastian 1 Mahner Sven 1 Fuerst Sophie 1 Ulisse Salvatore Academic Editor Laganà Antonio Simone Academic Editor 1 Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Marchioninistrasse 15, 81337 Munich, Germany; nadine.ansorge@uk-augsburg.de (N.A.); helene.heidegger@med.uni-muenchen.de (H.H.H.); aurelia.vattai@med.uni-muenchen.de (A.V.); maximiliane.burgmann@med.uni-muenchen.de (M.B.); bastian.czogalla@med.uni-muenchen.de (B.C.); sven.mahner@med.uni-muenchen.de (S.M.); sophie.fuerst@med.uni-muenchen.de (S.F.) 2 Department of Obstetrics and Gynecology, University Hospital Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany; christian.dannecker@med.uni-augsburg.de 3 Department of Pathology, LMU Munich, Thalkirchner Str. 36, 80337 Munich, Germany; elisa.schmoeckel@med.uni-muenchen.de * Correspondence: udo.jeschke@med.uni-muenchen.de; Tel.: +49-821-400-165505 22 4 2022 5 2022 23 9 466201 2 2022 21 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Vulvar cancer incidence numbers have been steadily rising over the past decades. In particular, the number of young patients with vulvar cancer has recently increased. Therefore, the need to identify new prognostic factors and, in addition, therapeutic options for vulvar carcinoma is more apparent. The aim of this study was to analyze the influx of COX-2 positive tumor-infiltrating lymphocytes and monocytes and their influence on prognosis. Using subtyping by immunofluorescence, the majority of COX-2 expressing immune cells were identified as FOXP3-positive regulatory T cells. In addition, peri- and intra-tumoral macrophages in the same tumor tissue were detected simultaneously as M2-polarized macrophages. COX-2 positive immune cells were independent negative prognostic markers in long-term overall survival of patients with vulvar cancer. These results show an influence of immune cell infiltration for vulvar carcinoma patients. Immune cell infiltration and immune checkpoint expression may, therefore, become interesting targets for further research on new vulvar cancer treatment strategies. vulvar cancer regulatory T cells COX-2 tumor-infiltrating lymphocytes M2-polarized macrophages ==== Body pmc1. Introduction In 2020, more than 17,000 women worldwide died from vulvar cancer. The number of new cases has been steadily increasing over recent decades, with a worldwide incidence of 45,240 new cases [1]. An additional threat is the fact that a continuous increase in new cases in young women has been observed [2,3]. A total of 90% of vulvar carcinomas are squamous cell carcinomas (VSCC). Non-keratinized squamous cell carcinomas are often human papilloma virus (HPV)-associated and mainly affect younger women [4,5]. Essential for malignant transformation in HPV-dependent carcinogenesis is the inactivation of p53 and the retinoblastoma tumor-suppressor gene product by the viral gene products E6 and E7 [6]. In contrast, keratinized squamous cell carcinomas are usually HPV-independent and, due to chronic genital inflammatory disease, such as lichen sclerosus, affect older women [7,8]. Beside HPV, the development of vulvar carcinomas is associated with other risk factors: immunosuppression, smoking [9], and sexually transmitted diseases, such as herpes simplex virus 2 infections [5], are associated with an increased risk of this disease. Radical surgical interventions are predominantly used for therapy, often ending in vulvectomy in the case of extensive involvement. To date, the mental consequences of such a serious and extensive intervention have not been studied extensively. Sexual behavior restrictions, micturition problems, or even mental effects that impair quality of life are long-term consequences of this radical form of therapy [10,11]. As for prevention, HPV vaccination, for example, has been seen as a new hope in fighting against HPV-related tumors such as cervical, anal, and vulvar cancers [12,13]. In 2016, the EURO Vaccination Meeting listed Belgium as the top country with a vaccination rate of 84%, while in Germany the vaccination rate reached a critical 31% in 2015 [14]. Based on the low vaccination rate and an aging population in Germany it can be assumed that the need for newly found prognostic factors for vulvar carcinoma is even more apparent. Our group showed negative prognosticators in vulvar cancer patients, such as LDOC1 [15] or combined expression of COX-2 and PPARγ in cytoplasm of vulvar cancer tissue [16]. An increasing number of new cases, younger patients, radical therapy, and a lack of comprehensive prevention due to the low vaccination rates, at least in this country, are concerning observations. Cyclooxygenase-2 (COX-2) features as a long-standing object of scientific interest in the context of carcinogenesis in several tumor entities [17,18,19,20,21]. COX-2, in contrast to the constitutive housekeeping enzyme COX-1, is inductively expressed as a known inflammatory enzyme [22,23]. Tissues of the brain, kidney, testis, and tracheal epithelium are exceptions with respect to constitutive COX-2 expression [24,25]. The inducing of the COX-2 enzyme is triggered by cell damage or inflammation by the release of various factors, such as growth factors like epidermal growth factor (EGF) [26], prostaglandins, or chemokines like TNF-γ [27]. Affecting COX-2 products, the prostanoids appear to be associated with the development and progression of tumor disease. Factors such as angiogenesis, invasion, apoptosis inhibition, growth, and aggressiveness of the tumor seem to be highly dependent on COX-2 and its products [28,29]. It is thought that products of COX-2, such as prostaglandin E2 (PGE2), critically influence the development of tumors e.g., in angiogenesis [30,31]. COX-2 is also known to be active in cancer-associated immune cells [32,33]. We used the abbreviation sTILs (stromal tumor-infiltrating lymphocytes) in our study for these cells. They have become important players in immuno-oncology with regard to predicting prognosis in cancer patients [34,35,36,37]. Already in the treatment of breast carcinomas, the presence of sTILs is taken into account in the interpretation of tumor biology and ultimately in the decision-making process of the optimal therapy according to guidelines [38]. Due to the diverse cell populations within sTILs, the subgroups are not fully understood in their prognostic role and are potentially conceivable as biomarkers in the future [39]. Types of sTILs and iTILs form a specific group of immune cells called tumor-infiltrating lymphocytes (TILs) and were assessed based on the recommendations of the International TIL Working Group (ITILWG). According to this working group, lymphocytes organized in tumor nests are defined as intratumoral TILs (iTILs). They making cell-to-cell contact without intervening stroma or interacting directly with carcinoma cells. Hence, stromal TILs (sTILs) are located in the stroma among carcinoma cells and have no direct contact with carcinoma cells [40]. In this study, we specifically investigated sTILs and no iTILs. Our study analyzed and characterized sTILs expressing COX-2 as mainly Treg cells in tissues of VSCC, their relevance as a prognostic factor, and in addition, the subtyping and polarization of infiltrating macrophages in the tumor microenvironment. 2. Results 2.1. High COX-2 Intensity of sTILs as an Independent Negative Prognostic Factor in Long-Term Overall Survival Figure 1A,B show the sTILs with low and high intensity of COX-2. The Kaplan–Meier curve shows a significant survival disadvantage for patients whose sTILs have a COX-2 intensity > 2 in overall survival, especially in long-term survival from 60 months (Figure 1C). A total of 47% of the tumor samples have a COX-2 intensity > 2 for COX-2 in sTILs; the remaining 41% were below this intensity value. As the Kaplan–Meier test illustrates, the 10-year overall survival rate of patients with an IRS value > 2 was 52%, but patients with a lower COX-2 intensity lived longer at 79% (Figure 1C). These data show a median survival advantage for patients with lower intensity values (≤2) compared with patients with higher intensity values (>2) at 87 months (Table 1). Multivariate analysis revealed that COX-2 expression in the sTILs of vulvar cancer patients who were alive at 60 months acted as an independent prognostic factor for overall survival (* p = 0.007, Table 2). However, tumor stage, nodal status, grading, p16 status and FIGO classification did not act as independent prognostic factors (Table 2). In addition, the calculations demonstrated for this patient collective that the intensity of COX-2 expression of sTILs has positive correlations with the general percentage of COX-2 expression in tumor tissue (* p = 0.001), the IRS score of COX-2 expression in tumor tissue (* p = 0.014), and the combined cytoplasmic expression of COX-2 and PPARγ (* p = 0.011). An analysis of total COX-2 high stroma cell expression was also performed, although without significant differences. The results are presented as Supplementary Figure S1. In addition, we also analyzed the influence of tumor COX-2 and sTILs infiltration and found that COX-2 is a negative prognosticator in cases with high stromal COX-2 intensity (Supplementary Figure S2). 2.2. Significant Majority of COX-2 Positive sTILs Are FOXP3 Positive Treg Cells The immune cell subpopulations were quantified by counting CD56-, CD68-, and FOXP3-positive cells per field of view (20× magnification). Subtyping COX-2 expressing sTILs by immunofluorescence staining revealed parallel expression of FOXP3 in a clear majority (Figure 2 and Figure 3). Because the transcription factor FOXP3 is considered a specific marker of natural CD4 + CD25 + Treg cells, the FOXP3 + COX-2 + sTILs are scored as stromal regulatory T- cells. 70.2% of all counted COX-2 positive sTILs showed concomitant expression of FOXP3 and thus could be detected as Treg cells. There were also in 20.3% CD56 positive sTILs, specific marker for NK cells, and in 9.4% CD68 positive sTILs, macrophages, detected in the subtyping. However, the proportion of these two subtypes was shown to be much lower compared with the proportion of FOXP3 positive sTILs. (** p < 0.001, Figure 3). 2.3. M2-Polarized Macrophages Are Mainly Located on and in Tumor Tissue In a further step, the CD68-positive macrophages were differentiated into M2-polarized and non-M2-polarized macrophages using PPARγ as specific marker. In fact, it was already reported that PPARγ plays an essential role as a nuclear receptor for the maturation of alternatively activated M2 macrophages [41] and also primes monocytes into M2 macrophages [42]. M2-polarized macrophages were found to be located peri- to intratumorally and those without M2-polarization remained in the stroma. (Figure 4A,C). 3. Discussion Our study observed COX-2 positive Treg cells to be an independent negative prognosticator in long-term overall survival for vulvar cancer patients (Table 2); also detected in this context are M2-polarized macrophages, which have been previously described as negative influencing factors in several tumor disease [43,44,45]. Regulatory T cells, also known as Treg cells, are also increasingly becoming the focus of research in the field of tumor immunology. As a subset of immunosuppressive T cells, they account for approximately 4–8% of CD4+ T cells in peripheral blood and are characterized by the transcription factor FOXP3 as a specific intracellular marker. Sakaguchi et al. discovered the function of Treg cells as a key role in human immune self-tolerance: a depletion of this cell population in mice showed an induction of autoimmune disease due to their inhibitory effect on CD8+ cytotoxic T cells. [46,47] As already demonstrated in several tumor entities, the expression of Treg cells is found to be a determinant of survival and prognosis of affected patients—but still without definitive knowledge of the pathways in which Treg cells are involved. Immunological processes in the context of tumorgenesis and maintenance of tumor growth rely on enhancing effect as tumor promotion and on inhibitory effects as tumor suppression. For M2-polarized macrophages and Treg cells in the tumor environment, a tumor-promoting effect was reported [48]. Other studies observed that population of Treg cells in sTILs is significantly higher in carcinoma tissue than in normal healthy tissue [49,50]. Our study shows a clear majority of FOXP3-positive Treg cells with 70.2% (Figure 2 and Figure 3) within the sTILs, so a negative impact on long-term survival in patients with VSCC is assumed. A significant impact of immunologic processes on long-term survival is already known and has been described before [51], but for ovarian cancer Yuan et al. [52] showed that an increased number of Treg cells occur in the tumor microenvironment in patients with gastric carcinoma. There is also an elevated expression of FOXP3 in tumor-infiltrating Treg cells which correlates with up-regulation of COX-2 and its product PGE2 [52]. The COX-2 positivity of the Treg cells can be explained in terms of the COX-2/PGE2 pathway (Figure 5). As Baratelli et al. have established, there is an upregulation of Treg cells by PGE2, an important product of COX-2 [53]. COX-2 as a significant negative prognostic factor on overall survival of the studied patient population has already been demonstrated in our previous study [16]. Studies by Rothwell et al. using COX-inhibitors showed impressive results in terms of improving the prognosis and reducing the incidence of colorectal cancer [54]. Besides Treg cells, we also identified tumor infiltrating macrophages with COX-2 expression. Macrophages are an important component of the innate immune system and are involved in many immunological processes [55]. In cancer research, these immune cells are gaining increasing attention. Tumor-associated macrophages, so-called TAMs, resemble macrophages in regenerating and growing tissue [56]. The different polarization of macrophages, and thereby functional distinction into M1-polarized, tumor-inhibiting macrophages, and M2-polarized, tumor-promoting macrophages, shows the controversial role of this cell group in immunology. Subtyping is indicated by different stimuli. M2-polarization is indicated by Th2 cytokines such as IL-4 and IL-13 and leads to macrophage functionalization in the context of anti-inflammatory processes, tissue repair, and immunoregulatory and tumor-promoting processes [57]. Martinez et al. showed that M2-polarized macrophages exert a major influence on lipid metabolism; this includes induction of COX-2 activity and thus increased production of its enzyme products, such as prostaglandins [58]. For cervical cancer, our group could identify TAMs as a major source of CCL22, a chemokine responsible for Treg cell infiltration [59] (Figure 5). One of the limitations of our study is the retrospective design. Only a highly limited number of patients included in this collective was alive during the examination of the tissue sections so that there would have been the possibility of applying a scan for cells within serum or primary cells. The evaluation of the tissue sections was not performed by an objectifiable measurement tool; but tissue sections were objectified by two independent investigators who evaluated the expression pattern blinded. There was no possibility to overview the process of the embedment of patient tissue, which leads to the fact that in the analyzed collective only tumorous tissue is accessible. Double staining of FOXP3 and COX-2 in the immunofluorescence revealed evidence of nuclear expression of FOXP3, but also evidence of partially cytoplasmic expression of the transcription factor FOXP3. In the Human Protein Atlas, the reference images also show partial cytoplasmic expression, which is described as expression in the nucleoplasm [60]. The data of the collective do not contain information about the medication status of the patients at the time of tissue sampling; therefore, no conclusion can be drawn about a possible intake of a COX- inhibitor. However, it is unlikely that medication alters the expression pattern because drugs only affect activity and not expression. In addition, the serological test results of the patients are not available and subsequent blood diagnostics are not possible. Comparable studies with such a high number of examined tissue sections from vulvar cancer patients do not currently exist. Only Sznurkowski et al. reported the results of their study of the influence of Treg cells in vulvar carcinoma; using a patient collective of 110 patients, they observed that Treg cells had no effect on overall survival [61]. In addition, a strong prognostic factor for survival of vulvar carcinoma patients is the nodal status [62]. In our study, Cox regression did not identify the nodal status as an independent prognostic factor. This can be explained by a high number of nodal-negative patients (42.6%), as well as an equally high number of unknown lymph node status (29.8%). The missing data mainly concerned patients whose date of diagnosis was decades ago. Because of this time gap between diagnosis and the analysis for this study, there were missing values in some cases. Due to the rarity of this cancer, especially compared with other cancers such as breast cancer, the sample size of our collective in the current literature is a very large one. The increasing rate of new cases again highlights the greater relevance of the need for new knowledge regarding prognostic factors and linkage to tumor immunology in a world where the role of immunotherapy is rapidly gaining importance. 4. Materials and Methods A total of 177 patients with VSCC primarily diagnosed in the period from 1990 to 2008 were included in this study. The entire patient group was treated at the department of Obstetrics and Gynecology of the Ludwig-Maximilians-University in Munich, Germany. Surgically obtained tissue samples were histopathologically processed and specified. All follow-up and survival data were provided by the Munich Cancer Registry (MCR) from the Munich Tumour Centre (TZM—Munich Tumour Centre, Munich, Germany). For immunohistochemical staining, 157 of the 177 samples were available. During the evaluation, a further 16 tissue samples were excluded, as the incisions did not contain a tumor, but only precancerous stages of the carcinoma. Therefore, in the end a collective of 141 slides of VSCC was assessed, one slide per staining and case. The median age of the investigated collective was 70 years, ranging from 20 to 96 years, with 72 of the 141 patients younger than 70 years (=51.8% of the collective). All relevant clinic–pathologic parameters are listed in Table 3 below. The collective is the same as described by Ansorge et al. in previous studies by our research group [16]. 4.1. Ethical Approval All patients data were completely anonymized and the study was performed according to the standards set in the Declaration of Helsinki 1975. The examined tissues were residual material that had been collected in the first instance for histopathological diagnostic procedures. The actual study was approved in writing by the Ethics Committee of the Ludwig-Maximilians-University, Munich, Germany (approval number 367-16). The authors were blinded for clinical information during the experimental analysis. 4.2. Immunohistochemistry The already formalin-fixed and paraffin-embedded samples were then cut by microtome to 4µm from the paraffin block and mounted on SuperFrost Plus microscope slides (Menzel Glaeser, Braunschweig, Germany). To deparaffinize the tissue, samples were processed with xylol for 20 min and washed by 100% ethanol. All slides were prepared with 3% hydrogen peroxide diluted in methanol for 20 min to stop activity of endogenous peroxidase. Afterwards, rehydration took place in a descending alcohol series (100%, 70%, 50%) and the samples were washed with distilled water. The samples were then heated with citric acid buffer in a pressure cooker to uncover antigen epitopes. Furthermore, slides were washed two times with phosphate buffered saline (PBS). A Zytochem-Plus HRP Polymer-kit (Zytomed, Berlin, Germany) was utilized for blocking and antibody staining. After saturating the electrostatic charges in the tissue with blocking solution for 5 min, either the polyclonal rabbit IgG anti- COX-2 antibody (Sigma, St. Louis, MI, USA, SAB4502491) or the polyclonal rabbit IgG anti-PPARγ antibody (abcam, Cambridge, United Kingdom, ab59256) was applied on tissue specimens. Anti-COX-2- antibody was diluted at a ratio of 1:400 and anti-PPARγ antibody at a ratio of 1:100. The incubation time of both antibodies amounted to 16 h at 4 °C in a humidity chamber. Slides were incubated by post-block reagent for 20 min and thereafter by HRP-Polymer for 30 min at room temperature in the humidity chamber. After each application with the antibody, post-block, and HRP-Polymer, the samples were washed two times with PBS. 3,3′-Diaminobenzidine (Dako, Hamburg, Germany) catalyzed the peroxidase substrate staining so that the color precipitation was detectable with a light microscope. Finally, the slides were counterstained with hemalum, washed again using 100% ethanol, and covered with glass. Both antibodies were stained in placenta tissue as a positive control to validate the staining method. The staining was considered positive in the case of cytoplasmic positivity of COX-2, and in the case of cytoplasmic and nuclear positivity of PPARγ. The semi quantitative immunoreactive score (IRS) by Remmele and Stegner [63] was used to evaluate immunostaining, together with a light microscope (Leitz, Wetzlar, Germany). For this purpose, a product of two factors, the intensity and the proportion of staining in the tumor tissue, was formed. The intensity was classified into 0 = no, 1 = weak, 2 = moderate, 3 = strong; the proportion of tumor tissue was also categorized: 0 = no staining, 1 ≤ 10%, 2 = 11% to 50%, 3 = 51% to 80%, 4 ≥ 81%. The antibodies showed expressions in cytoplasm and in nucleus, so both expression templates were examined independently by IRS. Patient data were correlated by IRS and by its two IRS-forming factors of staining intensity and percentage of positively stained cells. Neither HPV testing nor an immunohistochemical survey of p16 status was performed as part of this study; information on this was obtained from archives. 4.3. Immunofluorescence The same formalin-fixied and paraffin-embedded samples were placed in xylol for 20 min for deparaffinization. Subsequently, the sections were panned in ethanol in order of descending concentrations (100%, 70%, 50%) and washed in distilled aqua. Unmasking of antigens was performed simultaneously with the immunohistochemistry protocol by a 5 min heat pretreatment in a pressure cooker in the citrate buffer-use solution described previously. After washing in distilled water and PBS for 4 min, incubation with UltraV block solution (Labvision, Fremont, CA, USA) was performed. The solution was tipped off after 15 min and the primary antibodies were applied. COX-2 was stained at a ratio of 1:400 together with CD56 (Bio-Rad, Oxford, United Kingdom, MCA591) at a ratio of 1:100, CD68 (Sigma, St. Louis, MO, USA, AMAb90874) at a ratio of 1:8000, or FOXP3 (Abcam, Cambridge, United Kingdom, ab20034) at a ratio of 1:300 in dilution medium (Dako, Glostrup, Denmark, S3022) to differentiate cells in a double staining procedure. CD56 is a known structural protein of natural killer (NK) cells [64], CD68 is known as a structural protein of macrophages, and FOXP3 is expressed specifically by regulatory T (Treg) cells [65,66,67]. Double staining with PPARγ and CD68 was performed to subtype macrophages [68]. Here, a concentration of 1:100 was chosen for the PPARγ antibody (Abcam, Cambridge, United Kingdom, ab27649) and CD68 was added, as in the double staining with COX-2. Incubation was performed for 16 h at 4 °C. After washing in PBS, the experimental room was darkened and the mixed secondary antibodies were applied: Cy-2- conjugated antibody at a ratio of 1:100, which later fluoresced green (Dianova, Hamburg, Germany, 115-546-062) or Cy-3- conjugated antibody at a ratio of 1:500, which fluoresced red (Dianova, Hamburg, Germany, 111-225-144). After 30 min of incubation, the excess secondary antibodies were washed off in PBS. In the dark, the preparations dried at room temperature and were cover slipped with Mounting medium for fluorescence with DAPI, which stains the cell nuclei as a blue light impression. During the performance of immunofluorescence double staining, the primary antibodies CD56, CD68 and FOXP3 appear green in the fluorescence microscope and COX-2 is perceived as red fluorescence. The double staining was evaluated and assessed using a fluorescence microscope (Zeiss, Oberkochen, Germany). A total of 18 tumor microenvironments were examined in a sampling from the previously described patient population using this described method, and based on this, subtyping of stromal tumor-infiltrating lymphocytes was performed. 4.4. Statistical Analysis For statistical analysis, SPSS Statistic version 25 (IBM Corp., Armonk, NY, USA) was used. The non-parametric Kruskal–Wallis test was used to compare between and among groups. Correlation analyses were performed using the Spearman rank correlation coefficient. Kaplan–Meier curves were generated from the collected survival data of patients with vulvar carcinoma. The differences between these curves of sTILs with high and with low COX-2 expression were tested with the log-rank test. The identification of these sTILs was analyzed by specific markers of Treg cells, macrophages and NK cells. Cox regression models were applied for multivariate analysis. Patient-specific pairwise analysis of significance differences in immune cell subtypes of Treg cells, NK cells, and macrophages was performed using the Wilcoxon test. The level of statistical significance was accepted at p ≤ 0.05 and all tests were two-sided. 5. Conclusions In this study, COX-2 positive sTILs were observed to be an independent prognostic factor in long-term overall survival in the patient population studied. Furthermore, subtyping by immunofluorescence revealed that most sTILs are Treg cells. This is the first time that COX-2 positive Treg cells have been observed to have a significant impact in the context of long-term survival in patients with vulvar cancer. This finding might help on the way towards individualized immunotherapy and could eventually lead to further investigations of new immune checkpoints. Further research goals are in vitro experiments to reliably demonstrate a direct causal relationship between COX-2 and Treg cells and the planning of a prospective study model using COX-2 inhibition to add an important further approach to the limited therapy options and prognosis of vulvar cancer. Acknowledgments The authors thank Christina Kuhn and Martina Rahmeh for their excellent technical assistance. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23094662/s1. Click here for additional data file. Author Contributions Conceptualization, N.A., U.J. and S.F.; methodology and experiments, H.H.H. and A.V.; software and visualization, C.D. and U.J.; data analyses, S.F., U.J. and N.A.; writing—original draft preparation, N.A. and S.F.; data interpretation, H.H.H., A.V., E.S., M.B., B.C., S.M. and U.J.; writing—Review and Editing, H.H.H., A.V., M.B., B.C., S.M. and U.J.; supervision, S.M., U.J. and S.F.; project administration, U.J.; funding acquisition, S.M., U.J. and S.F. All authors have read and agreed to the published version of the manuscript. Funding Internal departmental resources. This research received no external funding by any funding agency in the public, commercial or not-for-profit sector. Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Ludwig-Maximilians-Universität, Munich, Germany (Approval number 367-16, 29 December 2016). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical issues. Conflicts of Interest S.M. received research support, advisory board, honoraria, and travel expenses from AstraZeneca, Clovis, Medac, MSD, PharmaMar, Roche, Sensor Kinesis, Tesaro and Teva. All other authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Abbreviations COX-2 Cyclooxygenase-2 EGF Epidermal growth factor FOXP3 forkhead-box-protein P3 HPV Human papilloma virus IRS Immunoreactive score ITILWG International TIL Working Group MDPI Multidisciplinary Digital Publishing Institute NK cells natural killer cells PPARγ Peroxisome proliferator-activating receptor Gamma iTILs intratumoral tumor infiltrating lymphocytes sTILs stromal tumor infiltrating lymphocytes TAMs Tumor-associated macrophages TLA Three letter acronyme Treg cells Regulatory T cells TNF-γ Tumor necrosis factor Gamma VSCC Vulvar squamous cell carcinoma Figure 1 Immunohistochemistry staining of COX-2 (10× and 25× magnification) in vulvar cancer tissue showing expression of stromal sTILs with intensity of COX-2 expression < 2 (A) and ≥2 (B). The arrows in both figures mark exemplarily sTILs (A,B). The Kaplan–Meier curve shows a significantly worse overall survival rate for the patients with a strong COX-2 intensity of sTILs > 2 in long- term survival of 10 years (* p = 0.013, (C)). (C) is labeled with the percentage of patients with vulvar carcinoma still alive after 10 years: In the group of patients with COX-2 expression of sTILs ≤ 2 of vulvar carcinoma, 79% are still alive, whereas among the diseased women with higher COX-2 expression of sTILs, only 52% are still alive in comparison. Figure 2 Image series (A) demonstrates the double staining of COX-2 (red) and FOXP3 (green) with inserts of magnification 40× to show nuclear staining of FOXP3. The majority of COX-2 positive immune cells peritumorally are FOXP3 positive and thus detected as Treg cells. Image series (B) presents the double staining of COX-2 (red) and CD68 (green). The subtyping shows that some macrophages are COX-2 positive. Figure series (C) shows the double staining of COX-2 (red) and CD56 (green). The CD56 positive NK cells are weakly pronounced and only singly distributed in the stroma. Figure 3 The boxplots reveal the absolute number of COX-2 positive Treg cells (FOXP3+), NK cells (CD56+) and macrophages (CD68+). There is a clear distribution in the direction of the Treg cells. The box plots indicate mild outliers, which are marked with circles. These outliers (case numbers 11 and 19) show an interquartile distance to the third quartile of values that is less than three times higher than the third quartile of values. The difference of the occurrence of the subtypes Treg cells to NK cells, as well as Treg cells to macrophages, is highly significant (** p < 0.001). Figure 4 The picture series (A–C) demonstrate the double staining with PPARγ (red) and CD68 (green). This allows a subtyping of CD68-positive macrophages into M2-polarized and M2-unpolarized macrophages. PPARγ acts as a marker for the M2-polarization of the macrophages. In the selected tissue sections, it was found that more doubly stained, M2-polarized macrophages are resident in the immediate environment and within the tumor association than in the extratumoral stroma. Figure 5 COX-2 produces PGE2, a prostaglandin, from the substrate arachidonic acid as part of lipid metabolism. Upregulation of Treg cells is affected by this prostaglandin. M2-polarized macrophages attract regulatory T cells by chemotaxis, e.g., by CCL22. In addition, M2-polarzied macrophages induce expression of COX-2, which in turn leads to higher enzyme activity and thus higher accumulation of products, such as PGE2 [27,28,48,54,55]. ijms-23-04662-t001_Table 1 Table 1 There is a clear difference in long-term overall survival after 10 years for patients with intensity values for COX-2 expression in sTILs > 2. Patients with intensity values > 2 live with a median of 129 months, whereas patients with lower IRS values survive 216 months. As the table shows, the survival of patients of both groups differs by 87 months, i.e., more than 7 years. Even in the total group, a survival difference is recorded: patients live a total of 163 months, but still lose 34 months of life with higher intensity values. Median for Long-Term Overall Survival Time (Months) after 10 Years COX-2 Intensity in sTILs Estimate Lower 95% CI Upper 95% CI IRS ≤ 2 216.000 37.953 290.388 IRS > 2 129.000 14.736 157.882 Overall 163.000 21.286 204.720 ijms-23-04662-t002_Table 2 Table 2 Cox regression of clinical–pathological variables regarding long-term overall survival after 10 years in VSCC patients. Variable Significance Hazard Ratio of Exp (B) Lower 95% CI of Exp (B) Upper 95% CI of Exp (B) COX-2 intensity in sTILs 0.007 4.731 1.525 14.676 pT 0.063 6.576 0.192 2.463 pN 0.633 0.996 0.980 1.012 Grading 0.078 2.204 0.914 5.312 FIGO 0.565 0.687 0.192 2.463 p16 status 0.024 0.243 0.071 0.831 ijms-23-04662-t003_Table 3 Table 3 Clinicopathological parameters of VSCC patients’ collective. Clinicopathologic Parameters n Percentage (%) Histology keratinizing 134 95 warty/basaloid 7 5 Tumor size T1 51 36.2 T2 74 52.5 T3 9 6.4 unknown 7 5 Nodal status N0 60 42.6 N1 31 22 N2 8 5.7 unknown 42 29.8 FIGO I 45 31.9 II 45 31.9 III 36 25.5 IV 9 6.4 unknown 6 4.3 Grading G1 24 17 G2 87 61.7 G3 29 20.6 NOS/unknown 1 0.7 p16 status positive 34 24.1 negative 57 40.4 unknown 50 35.5 COX-2 expression of sTILs Positive 136 96.5 negative 4 2.8 unknown 1 0.7 Progression status positive 61 43.3 negative 79 56 unknown 1 0.7 Local recurrence status positive 35 24.8 negative 105 74.5 unknown 1 0.7 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. International Agency for Research on Cancer Globocon 2018, Cancer/Vulva (C51) WHO Available online: https://gco.iarc.fr/today/data/factsheets/cancers/21-Vulva-fact-sheet.pdf (accessed on 2 February 2022) 2. Woodruff J.D. Julian C. Puray T. Mermut S. 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==== Front Nanomaterials (Basel) Nanomaterials (Basel) nanomaterials Nanomaterials 2079-4991 MDPI 10.3390/nano12091418 nanomaterials-12-01418 Article Silica Nanoparticles Enhance the Disease Resistance of Ginger to Rhizome Rot during Postharvest Storage https://orcid.org/0000-0002-9891-8869 Zhou Jie 1† Liu Xuli 1† Sun Chong 12 Li Gang 1 Yang Peihua 1 Jia Qie 1 Cai Xiaodong 1 Zhu Yongxing 1* https://orcid.org/0000-0002-3375-8622 Yin Junliang 1* Liu Yiqing 1* Díez-Pascual Ana María Academic Editor Pereiro Rosario Academic Editor 1 Spice Crops Research Institute, College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China; zj188719@163.com (J.Z.); liuxuli33@163.com (X.L.); zbgqsc1987@163.com (C.S.); lg13733590933@163.com (G.L.); yph919701@163.com (P.Y.); jiaqie020@163.com (Q.J.); caixiao.dong@163.com (X.C.) 2 Special Plants Institute, College of Landscape Architecture and Life Science, Chongqing University of Arts and Sciences, Chongqing 402160, China * Correspondence: xbnlzyx@163.com (Y.Z.); w.yinzi@163.com (J.Y.); liung906@163.com (Y.L.) † These authors contributed equally to this work. 21 4 2022 5 2022 12 9 141806 3 2022 17 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Silica nanoparticles (SiNPs) offer an ecofriendly and environmentally safe alternative for plant disease management. However, the mechanisms of SiNPs-induced disease resistance are largely unknown. This research evaluated the application of SiNPs in controlling the postharvest decay of ginger rhizomes inoculated with Fusarium solani. In vitro study showed that SiNP had little inhibitory effect on mycelial growth and spore germination of F. solani and did not significantly change mycelium’s MDA content and SDH activity. In vivo analysis indicated that SiNPs decreased the degree of decay around the wounds and decreased the accumulation of H2O2 after long-term pathogenic infection through potentiating the activities of antioxidant enzymes such as SOD, APX, PPO, and CAT. SiNP150 increased the CHI, PAL, and GLU activity at the onset of the experiment. Moreover, SiNP150 treatment increased total phenolics contents by 1.3, 1.5, and 1.2-times after 3, 5, and 7 days of treatment, and increased total flavonoids content throughout the experiment by 9.3%, 62.4%, 26.9%, 12.8%, and 60.8%, respectively. Furthermore, the expression of selected phenylpropanoid pathway-related genes was generally enhanced by SiNPs when subjected to F. solani inoculation. Together, SiNPs can effectively reduce the fungal disease of ginger rhizome through both physical and biochemical defense mechanisms. silica nanoparticles postharvest decay fungal disease Zingiber officinale ==== Body pmc1. Introduction Ginger (Zingiber officinale Roscoe), one of the most economically important vegetables in the Zingiberaceae family, has been widely planted worldwide [1]. It is used as food, spice, flavoring agent and herbal remedy owing to its beneficial characteristics relating to its aroma, pungency, nutrients, and medicinal properties [2]. Ginger suffers from dehydration, sprouting, and decay caused by pathogens during storage after 3 to 4 weeks of harvesting [3]. Significant losses in harvested ginger can be directly attributed to decay fungi. Soft rot (rhizome rot), generally caused by Fusarium solani, is one of the major constraints in the production of ginger both in the field and during storage [4]. It not only causes rhizome rot in ginger at different growth stages but also threatens the postharvest storability of ginger. Thus, there is an urgent need to reduce the fungal pathogen-induced decay and extend the postharvest life of ginger. The application of chemical fungicides is considered an effective method to control the soft rot of ginger; nevertheless, the extensive use of fungicides poses a serious risk to environmental and human health. Disease-management tools, which can serve as alternatives to conventional synthetic chemical fungicides for postharvest disease control, are being actively investigated in many horticultural plants [5]. Several nanoparticles, such as silver nanoparticles, TiO2 nanoparticles, and ZnO nanoparticles, have been investigated in postharvest management and developed to control disease in citrus, grape, banana, apple, mango, peach, and nectarine [6]. Silicon (Si) is generally regarded as a safe substance (GRAS) and an effective elicitor to activate plant defenses [7]. Recently, Silica nanoparticles (SiNPs; food additive E551) were reported as a novel Si source [8] that can be used to improve plant resistance to salt, drought, and disease [9] and are considered more efficient than their bulk particles owing to their small size and high surface area and reactivity. Moreover, SiNPs exhibit potent antibacterial properties against a variety of plant diseases through the formation of physical barriers (deposition and polymerization of Si below the cuticle) or the induction of biochemical reactions (production of secondary metabolites). They can also modulate plant antioxidant activity. For example, as mentioned in a recent report, SiNPs have the potential to induce local and systemic disease resistance in Arabidopsis thaliana against the bacterial pathogen Pseudomonas syringae [9]. Thus, exogenous SiNPs have significant prospects as an inexpensive, highly efficient, and sustainable alternative in environmentally friendly disease management. Initial studies found that SiNPs may induce stress tolerance similar to conventional Si products, but a clear mechanistic understanding of the underlying processes is still lacking. Moreover, as a food additive (SiNPs), there is little literature available on the role of SiNPs in the postharvest handling and storage of vegetables. Therefore, this study explores the mechanism by which SiNPs induce the postharvest resistance of ginger infected with F. solani. We first determined the effect of SiNPs on the postharvest disease resistance of ginger, following which the initiation mechanism of defense responses was elucidated through a comprehensive analysis of antioxidant enzymes, disease resistance-related enzyme activities, secondary metabolites, and reactive oxygen species (ROS), as well as key genes in the lignin and flavonoid synthesis processes. This research will provide a foundation for using inexpensive and highly efficient SiNPs in postharvest disease management. 2. Materials and Methods 2.1. Plant Materials Healthy ginger rhizomes (Z. officinale Ros. cv. fengtou) were harvested at the Yangtze University ginger planting base (E:112.026207, N:30.361273) in November (180 days after planting) and immediately taken to the laboratory within 2 h. Ginger with uniform size, identical color, without signs of pests or diseases, and being free of any mechanical damages or fungal decays were selected to perform the following experiments. 2.2. Fungal Pathogen The pathogenic fungi of Fusarium solani were isolated from the rhizomes of rotten ginger and identified by morphology and sequence of the internal transcribed spacer 1 (ITS1 primer: 5′-GCTCAGCGGCTTCCTATTG-3′) and internal transcribed spacer 4 (ITS4 primer: 5′-CGGGGTATTCATCATTCACTTCA-3′) rDNA region. Then F. solani was cultured onto a potato dextrose agar (PDA) plate at 28 °C. After 7 days of culture, the surface of the mycelium was gently washed with 0.05% Tween-80 and filtered with a double-layer gauze to obtain the spore suspension. A spore suspension was adjusted to 1 × 108 spores mL−1 with a hemocytometer before use. 2.3. Effect of SiNPs on F. solani In Vitro Silica nanoparticles (SiNPs) are an E551 food additive purchased from Sigma-Aldrich (Lot 637238, the purity is 99.5%, and particle size is 10–20 nm). The SiNP (50 mg L−1, 100 mg L−1, and 150 mg L−1) was suspended in water by sonicating the silica bundles via an ultrasonicator at 10 MHz for ∼40 min resulting in a partially homogeneous solution. 2.3.1. Determination of Mycelial Growth The inhibitory effect of SiNPs on the mycelial growth of F. solani was determined by the agar dilution method [10]. Briefly, sterile water, 0.05% tween-80, SiNP50 (50 mg L−1), SiNP100 (100 mg L−1), and SiNP150 (150 mg L−1) were diluted into a melting PDA medium respectively and poured into 60 mm-diameter petri dishes. The 6 mm-diameter mycelial disks taken from a 5-day-old culture of F. solani were placed in the center of each petri dish and incubated at 28 °C. Mycelial growth was then surveyed by measuring the diameter of the colonies using the cross-bonded method after 5 days. Then, sterile water-washed mycelia were collected at 5 days and stored at −80 °C until use. 2.3.2. Determination of Spore Germination A spore suspension was prepared as described before. Spore suspension (200 µL) was added into the PDA medium (containing sterile water, 0.05% tween-80, and SiNP150, respectively). The germination of the spore was evaluated with the aid of an optical microscope (DM500, Leica, Wetzlar, Germany). Each treatment randomly selected three visual fields to calculate the number of germinating spores. The experiment was conducted twice. 2.3.3. Determination of Malondialdehyde (MDA) Content and Succinate Dehydrogenase (SDH) of F. solani About 0.5 g of mycelium was ground using 1 mL phosphate buffer (0.1 M, pH 7.5). Then the mixture was treated with an ultrasonic cell crusher (JY92IIDN, Ningbo Xinzhi Biotechnology Co., Ltd., Ningbo, China) for 5 min (200 W, interval 2 s) to break the cell. After centrifuging at 4000× g for 10 min at 4 °C, the supernatant was collected and used as a crude enzyme solution for further analysis. MDA content was determined according to Dhindsa et al. [11]. About 2 g mycelium sample was homogenized with 2 mL of 10% cold thiobarbituric acid (TCA) and then incubated in an ice bath for 10 min. After centrifugation, the 2 mL supernatant was collected and mixed with 2 mL thiobarbituric acid, and this mixture was boiled in a water bath for 20 min. The absorbance was measured at 450, 532, and 600 nm. The MDA content was calculated using the following formula and expressed as nmol g−1 of mycelium fresh weight basis: MDA content (nmol g−1) = 6.45 × (OD532 − OD600) − 0.56 × OD450. Succinate dehydrogenase activity was assayed using an assay kit (Nanjing Jiancheng Bioengineering Institute, China) by measuring the absorbance at 600 nm. 2.4. SiNPs Dioxide Treatment of Postharvest Ginger Rhizomes All ginger rhizomes were uniformly wounded with a sterilized borer (6 mm deep × 6 mm wide) and randomly divided into four groups. Ginger rhizomes were treated as follows: (1) Control (CK), ginger rhizomes immersed in sterile-distilled water for 10 min at room temperature. After being air-dried, ginger rhizomes were inoculated with 100 µL of sterile water into the wound. (2) SiNP150, ginger rhizomes immersed into 150 mg L−1 SiNPs for 10 min at room temperature. After air-dried, ginger rhizomes were inoculated with 100 µL of sterile water into the wound. (3) F. solani, ginger rhizomes immersed in sterile-distilled water for 10 min at room temperature. After being air-dried, ginger rhizomes were inoculated with 100 µL of the F. solani spore suspension into the wound. (4) SiNP150 + F. solani, ginger rhizomes immersed in 150 mg L−1 SiNPs for 10 min at room temperature. After being air-dried, ginger rhizomes were inoculated with 100 µL of the F. solani spore suspension into the wound. All treated ginger were separately incubated in a plastic box covered with preservative film at 28 °C and 90 ± 5% relative humidity for 7 d. A 15–20 mm sample annulus around the wound was collected at 0.5, 1, 3, 5, and 7 days after treatment. Each sample was frozen in liquid nitrogen immediately and stored at −80 °C until analysis. 2.5. Scanning Electron Microscope (SEM) Analysis For SEM analysis, tissue sections of 10 mm × 10 mm × 2 mm were fixed in 2.5% glutaraldehyde solution and dehydrated in different concentrations of anhydrous ethanol (30%, 50%, 70%, 80%, 90%, 95% and 100%) [12]. Then the sample was dried thoroughly for 2 h in a Critical Point Dryer (Rockville, Maryland, USA), sputter-coated with gold at 5 mA and 1.5 kV using a coater (Ion Sputter JFC-1100, Tokyo, Japan), and then observed using a JSM-7100F Scanning Electron Microscope (Tokyo, Japan). 2.6. Determination of H2O2, O2− and MDA 2.6.1. DAB and NBT Histochemical Staining and Determination of H2O2 and O2− Content The DAB (diaminobenzidine) and NBT (Nitroblue tetrazolium) were used for histochemical staining of hydrogen peroxide (H2O2) and superoxide anion (O2−), respectively [13]. Briefly, for histochemical staining of H2O2, ginger from different treatments for 7 days was cut into thin slices from the wound and placed in a solution of DAB (1 mg mL−1, pH 5.0, dissolved in 10 mM Tris-acetate) and vacuum pumping for 5 min, then the samples were placed in the dark for 3 h at room temperature until brown spots appeared. In order to detect O2−, the ginger slices were soaked in a 0.1% solution of NBT (dissolved in 10 mM K-phosphate buffer, pH 6.4). Then samples were vacuum-infiltrated for 10 min and illuminated until the appearance of dark blue spots and then photographed. All samples collected after 7 days were used to determine the content of H2O2 and O2− according to Kumar et al. [14]. 2.6.2. Determination of Malondialdehyde (MDA) Content MDA content was determined as described in Section 2.3.3. 2.7. Enzyme Assays About 1.0 g of fresh tissue was ground with 9.0 mL sodium phosphate buffer (50 mM, pH 7.0), and collected the homogenate centrifuged at 9661 g for 20 min at 4 °C. The supernatant was used as an enzyme source to measure peroxidase (POD, EC 1.11.1.7), superoxide dismutase (SOD, EC 1.15.1.1), catalase (CAT, EC 1.11.1.6), ascorbate peroxidase (APX, EC 1.11.1.11), phenylalanine ammonia-lyase (PAL, EC 4.3.1.5), polyphenol oxidase (PPO, EC.1.10.3.1), chitinase (CHI, EC 3.2.1.14) and β-1,3-glucosidase (GLU, EC 3.2.1.73) activities. The protein content in the crude enzyme extract was measured according to Bradford [15]. The specific activity of all the enzymes was expressed as units per milligram protein (U mg−1 protein). POD activity was measured according to the method of Wang et al. [16]. We mixed 3.0 mL guaiacol solution (25 mM) with 0.5 mL of enzyme extract, and then 200 µL of a 0.5 mol L−1 H2O2 solution was added and mixed quickly to start the reaction. POD activity was defined as the amount of enzyme that caused an increase in absorbance of 0.01 per min at 470 nm. The reaction system (3.0 mL) for SOD activity contained a 65 mmol L−1 sodium phosphate buffer (pH 7.8), 13 mmol L−1 methionine, 75 μmol L−1 nitro blue tetrazole (NBT), 10 μmol L−1 EDTA, 2 μmol L−1 riboflavin and 0.1 mL of an enzyme extract. After mixing, we placed the control tube in the dark and the determination tube in the light for 15 min; then, the absorbance was measured at 560 nm [17]. CAT activity measuring refers to the method of Zhu et al. [18]. Specifically, about 2.9 mL of 20 mmol L−1 H2O2 solutions were mixed with 0.1 mL of enzyme extract. Then the absorbance was measured at 240 nm every 30 s. One unit was defined as a change of 0.01 per min. Reaction mixtures for APX activity determination contained 2.4 mL sodium phosphate buffer (50 mM, pH 7.5), 0.2 mL of 2 mmol L−1 H2O2 and 0.4 mL of supernatant. The APX activity was estimated by the decrease in absorbance at 290 nm [19]. For PAL, the reaction system contained 1.0 mL of 0.02 mol L−1 phenylalanine, 2.0 mL of boric acid buffer (pH 7.8), 1.0 mL of an enzyme solution; we shook this well, and then placed it in a 30 °C water bath for 60 min. After that, 0.2 mL of 6 mol L−1 HCl was added to terminate the reaction. The absorbance of the reaction solution was detected at 290 nm [20]. CHI and GLU activity was determined using Enzyme Activity Kit (Solarbio, China, BC0820 and BC0360) following the instructions, and the OD value was measured at 585 nm and 540 nm, respectively. 2.8. Determination of Total Phenolics, Total Flavonoid and Lignin Contents Total phenolics and flavonoid contents were measured according to the Toor and Savege method [21]. Briefly, 0.25 g freeze-dried ginger powder was dissolved into 8 mL distilled water and sonicated for 60 min to dissolve the residue completely. Then, the mixture was centrifuged at 9661 g for 20 min. The 0.1 mL supernatant and 0.4 mL distilled water were added to 0.5 mL of 1 mol L−1 Folin-Ciocalteau reagent. After reaction for 5 min in the dark, 2.0 mL of 7.5% Na2CO3 solution was added to the mixture, and 1.0 mL distilled water was finally added and mixed well. The mixture was incubated at 40 °C for 30 min in the dark. The absorbance was measured at 765 nm. Total phenolic content was expressed as g of gallic acid per kg of sample dry weight basis. Total flavonoid was determined according to Zhi et al. [22]. About 0.05 g of dried ginger powder was added to 1.0 mL of 60% ethanol and ultrasonically extracted at 60 °C for 30 min, and then centrifuged at 9661 g for 10 min to obtain supernatant. The supernatant and 15 μL 5% sodium nitrite solution were mixed and placed at a normal temperature reaction for 5 min. Then we added 15 μL Al(NO3)3 solution with 10% mass fraction, standing at normal temperature for 5 min, 120 μL of NaOH solution with 4% content, and 90 μL of ethanol solution with 60% volume fraction were finally added and mixed and bathed in water at 37 °C for 45 min. The absorbance was measured at 510 nm, and the result was expressed as rutin per kg of fruit dry weight basis. Lignin was detected quantitatively using a lignin thioglycolic acid method [23]. About 3.0 g of ginger tissue was ground into homogenate with 5 mL of 95% ice ethanol. The homogenate was centrifuged at 9661 g for 10 min at 20 °C; the precipitate was rinsed three times with 95% alcohol, then the mixture was rinsed with ethanol and n-Hexane (Vethanol:VHexane, 1:2) three times and the samples were dried at 80 °C in the oven. Then the precipitate was added to 25% acetyl bromide (dissolved with glacial acetic acid) and incubated at 70 °C for 30 min; 1.0 mL of 2 mol L−1 NaOH was used to terminate the reaction. Then 0.1 mL of 7.5 mol L−1 hydroxylamine hydrochloride and 2.0 mL glacial acetic acid were added to the mixture and centrifuged at 30,000× g for 10 min at 20 °C. Finally, acetic acid was added to 20 μL supernatant to a final volume of 5 mL. Absorbance was measured at 280 nm. 2.9. Quantitative Real-Time PCR (qRT-PCR) Assays F. solani and SiNP150 + F. solani treated ginger rhizomes were used to perform qRT-PCR analysis. Total RNA was prepared using the Trizol reagent (Invitrogen) in accordance with the manufacturer’s protocol. Total RNA (1 μg) was used for cDNA synthesis using the HiScript Reverse Transcriptase with gDNA Eraser (Vazyme, Nanjing, China). The qRT-PCR analysis was performed with CFX 96 Real-Time PCR system (Bio-Rad) using a Cham SYBR qPCR Master Mix (Vazyme, Nanjing, China). The primer sequences used for qPCR analysis are listed in Supplementary Table S1. The relative expression levels of target genes were calculated with the 2−ΔΔCt formula. Each sample consisted of three biological replicates. 2.10. Statistical Analysis All experiments were performed using a completely randomized design, with three biological replicates for each treatment level. The results are presented as the mean ± SD. All statistical analyses were performed by SPSS 19.0. The data were subjected one-way analysis of variance (ANOVA), and mean separation was performed by Duncan’s multiple range test. The differences at p < 0.05 were considered significant. Data are presented as the means ± standard deviations (SD). 3. Results 3.1. Effect of SiNPs Treatment on F. solani In Vitro To be more aware of the physical and chemical properties of the nanoparticles being used in this study, we performed a scanning electron microscope (SEM), transmission electron microscope (TEM), and Fourier Transform infrared spectroscopy (FTIR) spectra analysis of silica nanoparticles (Figure S1). The morphology of SiNPs characterized using SEM (Figure S1A) and TEM (Figure S1B) analyses revealed almost spherical nanoparticles. The FTIR spectra displayed the broad peaks detected at 1105.33 (corresponding to the Si-O-Si) and 470.95 cm−1 (corresponding to the Si-O band) ranges (Figure S1C). Therefore, this study used SiNPs due to their high stability and purity. As can be seen from Figure 1A,B, different concentrations of SiNP treatment had little inhibitory effect on the colony diameter and spore germination compared with CK. However, the colony diameter and spore germination increased with the extension of inoculation time (Figure 1C,D). To further determine the effects of SiNPs on F. solani, the malondialdehyde (MDA) content and SDH activity of the mycelia were measured. As shown in Figure 1E, compared with the control, SiNP50, SiNP100, and SiNP150 slightly increased the MDA contents, with the SiNP150-treated mycelia having the highest MDA content. Similarly, compared with the control, the SiNP50, SiNP100, and SiNP150 treatments did not affect SDH activity (Figure 1F). 3.2. Effect of SiNPs Treatment on Rhizome of Ginger Inoculated with F. solani To further explore the potential mechanisms by which Si induces resistance to fungal pathogens in plants, the inhibitory effect of SiNPs against F. solani in vivo was measured. In preliminary experiments, we studied the effect of 50, 100, and 150 mg L−1 SiNPs on the postharvest disease resistance of ginger. The results showed that 150 mg L−1 (SiNP150) was the optimal concentration for controlling postharvest decay in ginger. Therefore, SiNP150 was selected and used for the following experiments. As shown in Figure 2A, no difference was observed between the control and SiNP150 treatment after 7 days of inoculation (Figure 2A1,A2). In the F. solani treatment, obvious white hyphae grew superficially along the sample surface (Figure 2A3), whereas the hyphae were sparsely distributed around the wounds in the SiNP150-treated sample (Figure 2A4). SiNP150 treatment decreased the lesion diameter around the wounds compared to F. solani treatment alone (Figure 2A3,A4). Scanning electron microscopy (SEM) observation of F. solani colonization was performed three days after inoculation. As shown in Figure 2B, obvious white Si deposition was observed in the SiNP150 and SiNP150 + F. solani treatment, and the epidermal cells of the control group and SiNP150 treatment were arranged neatly (Figure 2B1,B2). After F. solani treatment for 3 days, the hyphae of F. solani grow superficially along the sample surface. The hyphae of the inoculation group penetrated into the cell, whereas SiNP150 prevented hyphal penetration into the cells through the formation of white Si layers (Figure 2B3,B4). 3.3. Effect of SiNP150 on In Vivo Visualization and Content of Reactive Oxygen Species (ROS) We further examined how SiNPs activate the biochemical defense response of ginger. For the visualization of H2O2 and O2−, histochemical analyses were performed at 7 days of treatment. Compared with CK and SiNP150, F. solani inoculation markedly increased the dye staining levels of H2O2 and O2−, while SiNP150 decreased H2O2 and O2− staining compared with F. solani inoculation alone (Figure 3A,B). Within 1–7 days of treatment, the contents of H2O2 and O2− in each group showed an upward trend (Figure 3C,D). Compared with the control, F. solani inoculation increased the H2O2 and O2− contents, whereas SiNP150 + F. solani inhibited these increases, especially after 3 days of treatment. Compared with F. solani treatment alone, SiNP150 + F. solani decreased the H2O2 content by 6.6%, 18.26%, and 14.29% after 3, 5, and 7 days of treatment. SiNP150 + F. solani decreased the O2− content by 3.5%, 33.3%, and 24.8% after 3, 5, and 7 days of treatment, respectively. The MDA content in each treatment group exhibited an upward trend during the entire storage process before 5 days. The SiNP150 treatment showed similar fluctuations as the control. The F. solani treatment increased the MDA content throughout the experiment, while SiNP150 + F. solani reduced and delayed MDA accumulation. It should be noted that the MDA content peaked at 5 days of treatment and then decreased in all samples, with F. solani being higher than SiNP150 + F. solani, SiNP150, and CK (Figure 3E). 3.4. Effects of the SiNPs Treatment on Antioxidant Enzyme Activities in Ginger Rhizome As can be seen in Figure 4, the peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) activities of the ginger rhizomes treated with SiNP150 generally showed similar fluctuating trends as the control. Compared with the control, SiNP150 exerted little effect on the APX activity after 0.5, 1, and 7 days but decreased the APX activity after 3 and 5 days of treatment. Inoculation with F. solani induced the APX activity of the ginger rhizomes during the entire storage process as compared with the control and F. solani treatment. Compared with F. solani treatment alone, the SiNP150 + F. solani treatment increased the APX activity from 1 to 7 days, reaching a maximum of 3.25 on the 5th day. The SOD activity of the ginger rhizomes generally showed a downward trend (Figure 4B). Compared with the control and SiNP150, F. solani and SiNP150 + F. solani treatment increased the SOD activity at 0.5 days of treatment but decreased it after 3 days of treatment. Compared with F. solani treatment alone, SiNP150 + F. solani treatment increased the SOD activity by 9.6%, 1.56%, and 13.8% after 3, 5, and 7 days of treatment (Figure 4B). Before 3 days of treatment, the CAT activity of the SiNP150 + F. solani treatment showed similar trends to F. solani treatment alone but was lower than that of the F. solani treatment. After 5 and 7 days of treatment, the activities of CAT in the F. solani treatment surpassed that of the other three treatments. The CAT activity of the SiNP150 + F. solani treatment was significantly higher than the inoculation group and reached the maximum value of 0.58 (U mg−1 protein) on the 7th day, which was 1.6 times that of the F. solani treatment alone (Figure 4C). Compared with the control and SiNP150, F. solani treatment alone decreased the POD activity before 1 day but increased it after 3 days of treatment. SiNP150 + F. solani increased the POD activity throughout the experiment compared with the other treatments, exhibiting the highest activity on the 7th day after inoculation followed by the 3rd day. 3.5. Effects of the SiNPs Treatment on Disease Resistance-Related Enzyme Activities in Ginger Rhizome In the control group, the phenylalanine ammonia-lyase (PAL) activity increased gradually throughout the treatment (Figure 5A). In the SiNP150 treatment, the PAL activity increased before 3 days and then decreased in the following days, reaching the minimum (7.2 U mg−1 protein) on the 5th day (Figure 5A). In the F. solani treatment sample, the PAL activity firstly increased and then decreased. Compared with F. solani treatment alone, the SiNP150 + F. solani treatment increased the PAL activities at the early (0.5 days and 1 day) and later stages (7 days) but decreased it at 3 and 5 days (Figure 5A). As shown in Figure 5B, in the CK and SiNP150 treatments, the PPO activity increased markedly within 1 day and then decreased during the later period of storage. Compared with F. solani treatment alone, the SiNP150 + F. solani treatments decreased the PPO activity throughout the experiment except for the 5th day. As shown in Figure 5C, compared with CK, the other three treatments generally increased the chitinase (CHI) activity, except for the 5th day, where the SiNP150 and SiNP150 + F. solani treatments slightly decreased the CHI activity. Compared with F. solani treatment alone, the SiNP150 + F. solani treatment increased the CHI activity on the 1st and 7th days but decreased it on the 5th day. As indicated in Figure 5D, the β-1,3-glucanase (GLU) in CK and SiNP150 and GLU in F. solani and SiNP150 + F. solani largely showed similar fluctuating trends throughout the experiment, except for at day 0.5, in which the SiNP150 treatment inoculated with or without F. solani increased the GLU activity compared with CK and F. solani treatment alone. Specifically, SiNP150 + F. solani, F. solani treatment alone, and CK increased the activity of GLU compared with SiNP150, among which the activity of the SiNP150 + F. solani treatment was the highest, followed by F. solani treatment alone and CK. In this study, F. solani treatment alone increased the chitinase activity throughout the experiment and increased GLU activity at 1 and 3 days of treatment, which might result from pathogen induction (Figure 5C,D). 3.6. Effects of the SiNPs Treatment on Lignin, Total Flavonoid and Phenolics Contents As shown in Figure 6A, in the CK and SiNP150 treatments, the lignin content increased throughout the experiment. The lignin contents of the SiNP150- and F. solani-treated samples were higher than in CK before 5 days of treatment. In the SiNP150 + F. solani treatment sample, the lignin content increased dramatically at the initial storage stage (1 day), decreased sharply during the later storage period (3–5 days), and increased again on the 7th day. Compared with F. solani treatment alone, the SiNP150 + F. solani treatment increased the lignin content throughout the experiment, especially after 1 and 7 days of treatment, at which time points the lignin content increased by 75% and 27.3%, respectively. In the CK, SiNP150, and SiNP150 + F. solani treatments, the total phenolic content presented an increasing trend after 1 day of treatment (Figure 6B). In the F. solani treatment alone, the total phenolic content generally decreased before 5 days of treatment, followed by an increase on the 7th day of treatment. Compared with F. solani treatment alone, the SiNP150 + F. solani treatments increased the total phenolic content by 1.3, 1.5, and 1.2 times after 3, 5, and 7 days of treatment. As shown in Figure 6C, the total flavonoid content first decreased (before 3 days of treatment) and then increased (3–7 days of treatment). The total flavonoid content in the SiNP150 treatment showed a similar variation trend as the control but was higher than that of the control at most time points (except for the 7th day of treatment). Compared with F. solani treatment alone, the SiNP150 + F. solani treatment increased the total flavonoid content throughout the experiment by 9.3%, 62.4%, 26.9%, 12.8%, and 60.8% at each of the respective time points. 3.7. Effects of the SiNP150 Treatment on the Expression of Key Phenylpropanoid Pathway Genes Related to Lignin and Flavonoid To explore the effect of SiNP150 on the expression levels of phenylpropanoid pathway-related genes, 5 PAL (PAL-1, PAL-2, PAL-3, PAL-4, and PAL-5), 2 C4H (cinnamate4-hydroxylase, C4H-1, and C4H-2), four 4CL (4-coumarate: CoA ligase, 4CL-1, 4CL-2, 4CL-3 and 4CL-4), 3 CHS (chalcone synthase, CHS-1, CHS-2, and CHS-3), 3 CCR (cinnamoyl CoA reductase, CCR-1, CCR-2, and CCR-3), and 3 CHI (chalcone isomerase, CHI-1, CHI, and CHI-3), 1 COMT (Caffeic acid-O-methyltransferase), 3 AQP (aquaporins, AQP-1, AQP-2, and AQP-3), and 4 SWEET genes (Sugars Will Eventually be Exported Transporter, SWEET-1, SWEET-2, SWEET-3 and SWEET-4) with relatively higher transcript levels within each gene family were selected based on our previous released RNA-seq data sets [2] to perform qRT-PCR analysis. Compared with control, F. solani treatment alone decreased the expression levels of 4 PAL genes, while SiNP150 + F. solani alleviated the decrease (Figure 7). To be specific, SiNP150 + F. solani increased the expression levels of PAL-1, PAL-2, PAL-3, PAL-4, and PAL-5 by 73.8%, 88.8%, 91.2%, 93.2%, 87.4%. F. solani treatment increased the expression levels of C4H-2, whereas it decreased C4H-1. In contrast, SiNP150 + F. solani decreased the expression levels of C4H-2 but increased C4H-1 compared with F. solani and CK. F. solani treatment alone decreased the expression levels of four 4CL genes, while SiNP150 + F. solani increased their expression levels by 96.8%, 42.5%, 9.9%, and 52.4%. Compared with the control, F. solani treatment alone decreased the expression levels of CHS-1 and CHS-2 and increased the expression levels of CHS-3. Compared with F. solani treatment alone, SiNP150 + F. solani increased the expression levels of CHS-1 and CHS-2 by 94.2% and 98.7%, respectively. SiNP150 + F. solani decreased the expression levels of CHS-3 as compared with F. solani treatment alone, but still higher than that of CK. The expression level of CCR, CHI, and CMOT genes was decreased by F. solani infection, while SiNP150 mitigated this decrease. SiNP150 + F. solani treatment increased the expression of CCR-2, CCR-3, and three CHI genes by 8.3%, 71.3%, 97.8%, 16.6%, 96.7% as compared with CK, and 75.2%, 78.8%, 99.2%, 62.5%, and 99.6% as compared with F. solani. AQPs and SWEET proteins are important regulators of plant-pathogen interactions in higher plants. F. solani infection decreased the expression levels of 4 AQPs, while SiNP150 addition increased the expression levels of AQP-1, AQP-2, AQP-3, and AQP-4 by 2.9, 6.6, 3.4, and 5-times as compared with F. solani (Figure 7). F. solani infection with or without SiNP150 treatment increased the expression levels of SWEET-1 and SWEET-3. SiNP150 treatment further increased the expression levels of SWEET-1 and SWEET-2 as compared with F. solani. 4. Discussion The postharvest decay of fruits and vegetables caused by pathogenic microorganisms needs to be controlled to reduce economic loss. Recently, nanomaterial treatments such as SiNPs have been investigated to reduce the application of synthetic fungicides for the control of postharvest rot in fruit and vegetables [6]. Our results showed that SiNP treatment had little significant inhibitory effect on the mycelial growth and spore germination of F. solani in vitro (Figure 1C,D), and different concentrations of SiNPs did not significantly alter the MDA content and SDH activity of the mycelium (Figure 1E,F). However, in some cases, it has been reported that sodium silicate (conventional bulk silicon) could directly inhibit spore germination and germ tube elongation [23,24]. For instance, research has found that sodium silicate could directly inhibit the spore germination and germ tube elongation of Trichothecium roseum, whereas silicon dioxide was ineffective [24]. Therefore, SiNPs may exhibit different physical and chemical properties than conventional bulk silicon, and the effect of SiNPs might be microorganism-dependent. Moreover, it may not directly influence the growth of pathogens such as F. solani but rather through other modes of action. One of the mechanisms that enable SiNPs to improve plant disease resistance includes their deposition in the epidermal cell wall, which can form a mechanical barrier that hinders the penetration of fungal attachments [25,26]. In this study, the SiNP150 treatment decreased the mycelial growth of F. solani around the wounds (Figure 2A). SEM analysis showed that a white silica layer formed on the surface of the ginger epidermis, which may contribute to the inhibition of hyphae invading the cell (Figure 2B). Additionally, the particle sizes of the material used in this study were in the nanometer range, which could have made it possible for the chemical to penetrate into the exocarp and form deposits. These results suggest a mechanism similar to the hypothesis that Si-based compounds could conceivably form mechanical reinforcement to the invasion of fungal pathogens. In addition to forming a mechanical barrier, Si may enhance plant defense against pathogens by activating the biochemical defense response. Studies have reported that sodium silicate treatment potentiated induced mitochondrial ROS accumulation, such as H2O2 and O2−, in pathogen-infected muskmelon fruits and wheat [7,25]. However, in this study, SiNP150 significantly reduced the O2− and H2O2 levels after 7 days of F. solani treatment (Figure 3C,D). The inconsistent interaction of H2O2 and O2− content may be caused by the differences in sampling times. In the present study, H2O2 and O2− were detected at a later treatment period. H2O2 production is a defense response during the early biotrophic phase of the interaction, whereas higher concentrations of H2O2 at a later stage favor disease development [27]. Moreover, SiNPs have been reported to stimulate the activity of antioxidant enzymes, which might scavenge excessive ROS and protect the tissues from pathogen injury [28]. Similarly, enhanced defense enzyme activity, such as POD, and increased SOD, CAT, and APX activities after 1 and/or 3 days of SiNP150 treatment may have contributed to decreased O2− and H2O2 levels in the later period of treatment (Figure 4). Studies attribute the other ways by which Si deters pathogen invasion to the establishment of chemical impediments, such as (i) the activation of plant defense-related enzymes; and (ii) increased expression of genes related to plant defense mechanisms and encoding of chief enzymes in the production of phenylpropanoids [29]. Chitin and pectin are two main components of the fungal cell wall, which can be hydrolyzed by ‘pathogenesis-related proteins (PR)’ CHI and GLU produced by plant cells [30,31]. Conventional bulk silicon sodium has been reported to activate plant defenses in the early defense stage. Similarly, calcium silicate enhanced the resistance of banana roots to F. oxysporum by improving CHI, GLU, and PAL activities [32]. In this study, SiNP150 and SiNP150 + F. solani treatments increased the CHI, PAL, and β-1,3-glucanase activity at the onset of the experiment (12 h and/or 1 d), suggesting that silica nanoparticles may also be regarded as effective elicitor to activate plant defenses like conventional bulk silicon. In the present study, lower activity of CHI and GLU in the middle of the treatment (3–5 days) might be due to the improved resistance induced by SiNP150, which inhibited mycelial growth and penetration into cells (Figure 5C). Therefore, there is no need for plants to maintain a higher activity of CHI in a certain stage of treatment. Recent studies reported that silica nanoparticles and soluble Si(OH)4 can induce systemic acquired resistance in Arabidopsis plants against the bacterial pathogen Pseudomonas syringae [9]. These enzymes in the present study may partly contribute to the systemic acquired resistance induced by SiNP150 in ginger rhizomes. Some studies in plants have reported the association of Si with lignin [33]. PAL is a key enzyme in the phenylpropanoid pathway and is closely related to the synthesis of secondary metabolites (e.g., lignin and flavonoids) that play important roles in the pathogen invasion resistance process [34,35]. Therefore, the increase in the activity of PAL at the early (0.5–1 days) and later stages of inoculation (7 days) (Figure 5A) may also contribute to the increased lignin content in the ginger rhizomes due to SiNP supplementation, which can effectively inhibit the expansion of pathogens. Similarly, SiNPs enhanced PAL expression and lignification in the leaves and roots of oat seedlings [36]. Secondary metabolites derived through the phenylpropanoid pathway, such as phenols and flavonoids, have been implicated in Si-induced resistance against fungal pathogens [37]. For instance, Si-induced enhanced accumulation of phenols and flavonoids contributed to enhanced resistance to powdery mildew in bitter gourd [38]. Similarly, in this study, SiNP150 treatment increased the phenol and flavonoid content in pathogen-inoculated ginger rhizomes throughout the experiment. At the same time, in un-inoculated plants, SiNP150 enhanced phenol and flavonoid contents at the early stage of treatment (Figure 6). The changes in phenol and flavonoid content seem to be inconsistent with the fluctuations in PAL in the middle stage of the treatment. There could be several reasons for this difference. Exquisite regulatory mechanisms at multiple levels control the enzymatic activity and transcription of PALs. Moreover, PAL and phenylpropanoid biosynthetic activity appear to be metabolically regulated by particular biosynthetic intermediates or chemical signals (metabolite feedback regulation) [39]. Furthermore, phenylpropanoid pathway-related genes, including 5 PAL, 2 C4H, 4 4CL, 3 CHS, 3 CCR, 3 CHI, and 1 COMT with relatively higher transcript levels within each gene family were selected to explore the possible regulating effect of SiNPs in lignin and flavonoids metabolisms in the inoculated ginger rhizome. The increased expression levels of 5 PAL, 1 C4H, 4 4CL, 2 CHS, 3 CCR, 3 CHI, and 1 COMT transcripts with SiNP150 addition compared with F. solani treatment alone (Figure 7) are consistent with SiNPs-induced accumulation of phenolics, flavonoids, and lignin. Our results indicated that Si triggered stronger defense responses in pathogen inoculated samples through stimulating the activities of POD, defense enzymes such as PAL, GLU, and phenylpropanoid pathway-related genes expression. To further explore the possible transcriptional regulation associated with the increase in plant resistance to disease by Si, the expression of several AQPs and SWEETs was studied based on our previous transcriptome studies. AQPs are membrane channel proteins primarily associated with water and small solute transport across cell membranes [40]. The SWEET proteins are a novel family of sugar transporters that mediate sugar translocation, signal transduction, plant-pathogen interactions, and stress tolerance [41]. Increasing evidence suggests that AQPs and SWEET play important roles in plant-pathogen interactions [42]. In this study, SiNP150 + F. solani treatment alleviated the decrease of four AQP genes (PIPs) under stress conditions, which may contribute to immune responses in ginger rhizomes. Considering that small molecules (e.g., nitric oxide, silica, and H2O2) are involved in a variety of metabolic processes and functions associated with plant immunity are transported across the membrane via AQP channels, a more comprehensive understanding of AQPs in ginger immunity and pathogen pathogenicity during their interaction is needed. Most studies support that pathogen attack upregulates SWEET genes. At the same time, a few reports suggest that pathogen attack not only upregulates SWEET genes but also downregulates or negatively regulates SWEET genes [42]. In this study, F. solani infection upregulated SWEET-1 and SWEET-3 but decreased SWEET-2 expression. This downregulation of SWEET-2 might be due to the interruption of sugar signaling pathways [43]. It is difficult to identify a concerted pattern of SWEET expression in response to SiNP150 treatment. The SiNP150 + F. solani treatment appeared to further increase the expression levels of SWEET-1 and SWEET-2 compared with CK and F. solani treatment, a finding that needs to be further confirmed in more SWEET gene family members. In addition, a further in-depth study is required to explore the differential expression of SWEET genes during pathogen attack to better understand the physiology of plant–pathogen interactions. 5. Conclusions The silica dioxide nanoparticles reduced Fusarium solani caused postharvest decay through physical and biochemical defense mechanisms. The application of SiNPs maintained the antioxidant defense system of ginger at a high level in response to long-term pathogenic infection and hence reduced disease indices. Moreover, the nanoparticles induced defense-related enzyme activity and phenylpropanoid pathway-related gene expression in ginger rhizomes. Therefore, SiNPs can be used as an alternative tool to chemical fungicides to promote the defense response in postharvest ginger, thus efficiently and environmentally friendly managing rhizome rot disease (Figure 8). Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nano12091418/s1, Figure S1. The SEM (A), TEM (B) micrographs and FTIR spectra (C) of silica nanoparticles used in this study, Table S1. The primers used for qRT-PCR, Table S2. Abbreviations list in this study. Click here for additional data file. Author Contributions Conceptualization, Y.Z. and Y.L.; methodology, Y.Z.; software, J.Y.; investigation, C.S.; resources, Y.L.; data curation, J.Z. and G.L.; writing—original draft preparation, J.Z. and X.L.; writing—review and editing, C.S. and P.Y.; supervision, Q.J. and X.C.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the National Natural Science Foundation of Hubei Province, No. 2021CBF512; Key Research and Development program of Hubei province, China, 2021BBA096; Jingzhou Science and Technology Plan Project, No. 2021CC28-23; Condiment industry system major special projects of Chongqing, No. 2021-2022-07. Institutional Review Board Statement The studies not involving humans or animals. Informed Consent Statement Not applicable. Data Availability Statement Data are real and effective. Conflicts of Interest The authors declare that they have no known competing financial interest or personal relationships that could appear to influence the work reported in this paper. Figure 1 Effects of different concentrations of SiNPs on F. solani in vitro. (A1–A5) Effect of SiNPs treatment on mycelial growth after 7 days of treatment. (B1–B5) Effect of SiNPs treatment on spore germination after 6 h of treatment. Effect of SiNPs treatment on (C) spore germination, (D) colony diameter, (E) MDA content, and (F) SDH activity on F. solani after 7 days of treatment. Results represent the mean ± standard error deviation (SD). Different small letters in the figure show significant difference, the same letter indicates no difference (p < 0.05). Figure 2 Effect of SiNP150 treatment on decay development and mycelia growth in ginger rhizome wounds caused by F. solani during postharvest storage. (A1–A4) The development of decay after inoculation for 7 days. (B1–B4) Scanning electron microscope observation of mycelia of F. solani treated with SiNP150 for 7 days. Figure 3 Effect of SiNPs and F. solani inoculation on the in vivo visualization of (A1–A4) O2− and (B1–B4) H2O2, and the contents of (C) H2O2, (D) O2− and (E) MDA of ginger rhizome. (A1) and (B1), CK; (A2) and (B2), SiNP150; (A3) and (B3), F. solani; (A4) and (B4), SiNP150 + F. solani. Results represent the mean ± standard deviation (SD) Different small letters in the figure show significant difference, the same letter indicates no difference (p < 0.05). Figure 4 Effects of SiNPs and F. solani inoculation on the activities of (A) APX, (B) SOD, (C) CAT, and (D) POD activity in ginger rhizomes during postharvest storage. Results represent the mean ± standard deviation (SD). Different small letters in the figure show significant difference, the same letter indicates no difference (p < 0.05). Figure 5 Effects of SiNPs and F. solani inoculation on the activities of (A) PAL, (B) PPO, (C) CHI, and (D) GLU in ginger rhizomes during postharvest storage. Results represent the mean ± standard deviation (SD). Different small letters in the figure show significant difference, the same letter indicates no difference (p < 0.05). Figure 6 Effects of SiNPs and F. solani inoculation on the contents of (A) lignin, (B) total phenolic, and (C) total flavonoid contents in ginger rhizomes during postharvest storage. Results represent the mean ± standard error deviation (SD). Different small letters in the figure show significant difference, the same letter indicates no difference (p < 0.05). Figure 7 Effects of SiNPs and F. solani inoculation on the expression of genes related to the phenylpropanoid pathway, SWEET genes, and AQPs. Results represent the mean ± standard deviation (SD). Different small letters in the figure show significant difference, the same letter indicates no difference (p < 0.05). Figure 8 Mechanism diagram of SiNPs-induced enhancement of postharvest disease resistance of ginger rhizomes. (A) In vitro antifungal activity of SiNPs. (B) Hypothetical ginger rhizome phenotypes showing suppression of decay development by Si application in vivo. (C) Physical, biochemical, and molecular defense mechanisms of SiNPs -induced resistance to postharvest diseases caused by pathogenic fungi. Generally, SiNPs deposition on the surface of ginger rhizomes prevents pathogenic fungi from penetrating epidermal cells. Meanwhile, SiNPs induced defense response involves the accumulation of secondary metabolites such as phenols, flavonoids, and lignins, as well as an increase in the activity of antioxidant enzymes such as SOD, POD, CAT, APX, and defense enzymes such as PAL, PPO, CHI, β-1,3-glucanase, which may be fine-tuned by the regulation of related genes. (PAL, Phenylalanine ammonialyase; C4H, cinnamate4-hydroxylase; 4CL, 4-coumarate: CoA ligase; CHS, chalcone synthase; CCR, cinnamoyl CoA reductase; CHI, chalcone isomerase; COMT, Caffeic acid-O-methyltransferase; AQP, aquaporins; SWEET). Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Rai M. Ingle A.P. Paralikar P. Anasane N. Gade R. Ingle P. Effective management of soft rot of ginger caused by Pythium spp. and Fusarium spp.: Emerging role of nanotechnology Appl. Microbiol. Biotechnol. 2018 102 6827 6839 10.1007/s00253-018-9145-8 29948111 2. Li H. Wu L. Tang N. Liu R. Jin Z. Liu Y. Li Z. Analysis of transcriptome and phytohormone profiles reveal novel insight into ginger (Zingiber officinale Rose) in response to postharvest dehydration stress Postharvest Biol. Technol. 2020 161 111087 10.1016/j.postharvbio.2019.111087 3. Kaushal M. Gupta A. Vaidya D. Gupta M. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095067 ijms-23-05067 Article The Energy Transfer Yield between Carotenoids and Chlorophylls in Peridinin Chlorophyll a Protein Is Robust against Mutations Tumbarello Francesco 1 Marcolin Giampaolo 1 Fresch Elisa 1 Hofmann Eckhard 2 Carbonera Donatella 1 https://orcid.org/0000-0002-1019-9100 Collini Elisabetta 1* Sabater Bartolome Academic Editor 1 Department of Chemical Sciences, University of Padova, Via F. Marzolo 1, 35131 Padova, Italy; francesco.tumbarello.1@studenti.unipd.it (F.T.); giampaolo.marcolin@studenti.unipd.it (G.M.); elisa.fresch@studenti.unipd.it (E.F.); donatella.carbonera@unipd.it (D.C.) 2 Faculty of Biology and Biotechnology, Ruhr-University Bochum, D-44780 Bochum, Germany; eckhard.hofmann@ruhr-uni-bochum.de * Correspondence: elisabetta.collini@unipd.it 03 5 2022 5 2022 23 9 506706 4 2022 29 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The energy transfer (ET) from carotenoids (Cars) to chlorophylls (Chls) in photosynthetic complexes occurs with almost unitary efficiency thanks to the synergistic action of multiple finely tuned channels whose photophysics and dynamics are not fully elucidated yet. We investigated the energy flow from the Car peridinin (Per) to Chl a in the peridinin chlorophyll a protein (PCP) from marine algae Amphidinium carterae by using two-dimensional electronic spectroscopy (2DES) with a 10 fs temporal resolution. Recently debated hypotheses regarding the S2-to-S1 relaxation of the Car via a conical intersection and the involvement of possible intermediate states in the ET were examined. The comparison with an N89L mutant carrying the Per donor in a lower-polarity environment helped us unveil relevant details on the mechanisms through which excitation was transferred: the ET yield was conserved even when a mutation perturbed the optimization of the system thanks to the coexistence of multiple channels exploited during the process. photosynthesis light harvesting energy transfer carotenoid peridinin chlorophyll PCP N89L two-dimensional electronic spectroscopy 2DES MIUR PRINThis research was funded by MIUR PRIN 2017, grant number 2017A4XRCA. ==== Body pmc1. Introduction Cars are pigments that are ubiquitously present in the light-harvesting (LH) apparatus of photosynthetic organisms, where they usually play the role of “accessory” pigments that ensure photoprotection and enhance the absorption of LH complexes in the green region of the solar spectrum [1]. In order for the energy absorbed by Cars to reach the reaction center, a prior transfer to the nearby Chls must occur; Car-to-Chl ET thus represents a fundamental step in the early events of photosynthesis, and yet many aspects of its mechanism remain unclear, mainly because of the dark character of the lowest excited electronic state, or states, of the Cars. Due to the C2h symmetry of the conjugated chain, the photophysics of all-trans-Cars is classically interpreted in terms of three states, namely, S0, S1, and S2, with symmetries 11Ag−, 21Ag−, and 11Bu+, respectively [2,3,4]. In this picture, the excitation of the S2 state is strongly allowed, while the S1 state is symmetry-forbidden and can only be populated via rapid (~100 fs [5]) internal conversion. However, these simple symmetry considerations do not account for the whole spectral complexity of Cars. First, the strong coupling of electronic and nuclear motions and the presence of possible asymmetric substitutions break the symmetry selection rules [6]. Moreover, some photosynthetic complexes bind peculiar Cars, such as Per, shown in Figure 1a, in which an electron-withdrawing carbonyl group conjugated with the π-electron system stabilizes an intramolecular charge transfer (ICT) state [7,8,9,10]. The relationship between ICT and the other electronic states was extensively discussed in the literature [11,12,13,14,15,16,17], and it was often argued that an ICT character is associated with the dark S1 state. Such an “S1/ICT” state is believed to have an enhanced capability to act as an energy donor in photosynthetic ETs [18,19,20]. The picture was made even more intricate by the detection of further intermediate states between S2 and S1 [21,22,23,24,25], the most debated of which being the so-called “Sx” state. Some works interpret Sx as an additional dark electronic state [21,26,27], while others describe Sx as a distorted conformer on the S2 potential energy surface, located in a local minimum [28] or near a transition state barrier between a planar and a twisted geometry [29,30,31,32]. Whether Sx also exists in free Cars in solution or only in Cars embedded in LH complexes, these works seem to converge on the interpretation of Sx as a potential ET channel to the Chls. Intending to provide new pieces of evidence for the comprehension of Car-to-Chl ET, we focused our attention on the PCP, a soluble antenna of dinoflagellate algae, which developed a unique strategy to maximize the efficiency of underwater photosynthesis [20,33]. The wild-type (WT) form of PCP from Amphidinium carterae (Figure 1b) is a heterodimer that binds a cluster of Per and Chl a molecules in an unusual 4:1 ratio [34] and aggregates into a trimeric quaternary structure. By exploiting the strong S2 absorption of Per, PCP captures the green light that survives the overlying water column and then transfers energy to the Chl a with an ~95% efficiency [35]. Per is thus the principal absorber in PCP, and the energy flow from Per to Chl a is the crucial step in the function of this complex. Early transient absorption studies on PCP with an ~100 fs time resolution showed that most of the energy is transferred to the Qy state of the Chl a in 2–3 ps [18,19,20,35] via a Forster-like ET from the S1/ICT state after internal conversion from S2 has occurred. The same studies also demonstrated that a significant portion (~25%) of the energy is delivered directly by the S2 state of Per [36] before the internal conversion, possibly to the Qx state of Chl a. More recently, 2DES [37,38,39] measurements have unveiled new details on the direct ET pathway from S2, generating a debate on a possible coherent contribution to the ET [28,40,41,42,43]. The relative weights of the different ET channels, the factors affecting their efficiencies, and the involvement of further intermediate states of the Car in the ET are all open questions on the workflow of this one-of-a-kind antenna. A promising strategy to survey the photophysics of the PCP complex is to compare its WT form with refolded mutant complexes and assess whether and how the modification of specific structural and energetic features perturbs the energy flow between the pigments. In the present work, we carried out a comparative analysis of WT PCP and the refolded N89L mutant, in which asparagine-89, a residue close to the conjugated chain of Per-614, is replaced with leucine. The N89L mutant is a homodimer reconstituted from the N-terminal half of the PCP polypeptide. The comparison between the crystallographic structures of the N89L protein and the refolded PCP homodimer (RF PCP), which is the basis for the N89L variant, showed that the mutation does not affect the structure of the complex [44,45]. Given the structural and spectroscopic equivalence between WT and RF PCPs [41,46], the WT PCP itself can be used for a meaningful comparison with the N89L mutant. Per-614 is believed to be the Per of the cluster with the reddest energy site and the one showing the strongest interaction with Chl a [44]. The mutation shifts the red tail of the S2 absorption band toward higher frequencies. This can be clearly seen by comparing the absorption spectra in Figure 1c. A significant part of the oscillator strength of the band above 17,000 cm−1, which is associated with the S2 transition, is blue-shifted in the mutant protein. Magnetic spectroscopies already demonstrated that the photoprotective function of Per-614 is preserved in the mutant despite the different energy sites of this pigment [46]. Here, we investigated the excitation energy flow in the WT and N89L proteins in the ultrafast (<1 ps) time regime using 2DES. Thanks to a time resolution of about 10 fs and the inherent multidimensionality, 2DES overcomes most of the difficulties connected to the short time scales of photosynthetic ETs and the spectral congestion that is typical of multichromophoric systems. In a 2DES experiment, a sequence of three ultrashort laser pulses is focused on the sample and triggers the emission of a signal electric field. The data are typically visualized as a sequence of two-dimensional spectra acquired for different values of the time delay between the second and the third pulse (the “population time”, often indicated as t2). In each 2D map, the “excitation frequency” identifies the state excited by the first pulse, while the “detection frequency” provides information about the state probed by the third pulse. Therefore, off-diagonal peaks offer a powerful tool to detect phenomena coupling different states, such as internal conversion and energy transfer processes, and can help to characterize dark electronic states through their couplings with bright states [25,26,47,48]. More details about the experimental setup and sample preparation can be found in Section 3. This work presents the first comparison of the WT PCP with its N89L mutant at the level of detail and with the temporal resolution offered by 2DES. Our approach demonstrated that the study of specific mutations can be effectively used to extract crucial information on the photophysics of protein-bound Cars. Moreover, this comparison sheds new light on the Car-to-Chl ET mechanism; as will be extensively discussed in the next sections, the cooperation of multiple ET channels may be a key factor in ensuring the well-known, but hardly understood, efficiency of this process. 2. Results and Discussion The laser spectrum used for 2DES measurements, shown in Figure 1c, was tuned to cover (i) the Qy transition of Chl a (670 nm, 15,000 cm−1); (ii) the band at 620 nm (16,100 cm−1), which includes the contributions from the Qx transition of the Chl a and a vibronic Qy’ transition; and (iii) the red tail of the S2 transition of the carotenoid (<580 nm, >17,000 cm−1). The chosen bandwidth allowed us to capture possible signatures of coupling between Per and Chl a directly as cross-peaks in the 2DES maps. Moreover, this bandwidth acts as a spectral filter to specifically select the photophysics of the reddest Per within the cluster. We were thus able to selectively study the energy flow from the reddest Per to the Chl a and exclude from the analysis the processes through which the excitation descended from the higher energy Pers to the lowest energy one. In this way, it was possible to focus our attention on the spectral region that is most affected by the mutation. In Schulte et al. [44], the major contribution to the shift of the S2 absorption band toward higher energies in N89L was associated with a destabilization of the two lowest-lying excited states of Per-614 as a result of the lower-polarity environment around this pigment in the mutant. It was also suggested that this blue shift could be such that Per-614 is no longer the reddest in the cluster [44]. However, the attribution of the reddest S2 transition to Per-614 provides only a simplified interpretation of the spectroscopic data: a more accurate description of the electronic structure of the cluster requires considering the formation of delocalized excitonic states [49], with the lowest energy one being localized mostly on Per-614 [43,50]. In any case, it is worth highlighting that the interpretation of 2DES data is not affected by this attribution since the final aim was to provide information on the relaxation dynamics following photoexcitation of the reddest states of the Per cluster manifold. Absorptive 2DES maps at selected values of the population time t2 recorded at room temperature (295 K) are shown in Figure 2. The diagonal signal centered at 15,100 cm−1 and the cross-peak at (16,100, 15,100) cm−1 are the typical spectral signatures of the Chl a, as already discussed in previous work [42]. The former is assigned to the ground state bleaching (GSB) and stimulated emission (SE) of the Qy transition, while the latter is due to an internal conversion from the Qx to the Qy state, as well as a coupling of the Qy electronic transition with vibrational modes at ~1000 cm−1 [42,51,52]. In the following paragraphs, attention will be focused on the signals appearing at the excitation frequency of Per (17,066 cm−1), as these contain the information about the processes through which the excitation migrates from the S2 state initially prepared down to the lower-lying states of Car, and eventually, to the Q bands of Chl a (Figure 3a). Two signals appearing at this excitation frequency were of particular interest. First, the broad negative bands at the detection frequency > 15,500 cm−1 (pinpointed with the square and the circle markers in Figure 2), were attributed to excited-state absorption (ESA) signals of Per. Second, the positive cross-peak at detection frequency of the Qy state of Chl a (15,100 cm−1, triangle), which was direct evidence of the ET from Per to the Qy state of Chl a. When qualitatively comparing the spectra of the WT protein to those of the N89L mutant, no appreciable discrepancies could be observed in the positions of the signals. We then looked for possible differences in the dynamic behavior of the signals. As mentioned above, the 2DES experiment provides a stack of 2D spectra for different values of the time interval t2 between the second and the third pulse. By analyzing the evolution of the signal at different coordinates as a function of t2, it is possible to gain valuable information on the various pathways that regulate the relaxation dynamics of the system. This evolution typically includes both non-oscillating exponential decay contributions (“populations” dynamics) and beating components (“coherence” dynamics) [53,54]. The temporal traces shown in Figure 3b–d were obtained by sampling the 2DES map at different combinations of excitation and detection frequencies and plotting the signal as a function of t2. These traces show an intense beating behavior superimposed on the exponential trends: the analysis results presented below first show the population dynamics, while the oscillating components are addressed afterward. First, we studied the time evolution of the signal at the excitation frequency of Per and detection frequency of the Qy state of Chl a ((17,066, 15,100) cm−1, triangle in Figure 2). This signal can be associated with the ET from Per to the Qy state of Chl a, as graphically represented by the Feynman diagrams reported in Section S2. The intensity of the signal at these coordinates was increasing with increasing values of t2, and the time constant regulating the signal rise thus provided an estimate of the effective rate of the ET. In the first place, we used a parallel (multi)exponential model to fit the temporal traces of the signals without introducing any a priori choice of a kinetic scheme. The results of the parallel fitting are summarized in Table 1 (further details on the fitting procedures are reported in Section S3.1). As shown in Figure 3b, single exponential rises correctly reproduced the non-oscillatory dynamics, with time constants of about 1.9 ps and 2 ps for the WT and the N89L protein, respectively. These values are in the lower limit of the range reported in the literature for the S1/ICT→Qy ET [14,18,19,20,28,35,40,41,42]. Different from a previous study [42], the ultrafast, possibly coherent, ET pathway from S2 could not be captured because of the different exciting conditions used here. Interestingly, the buildup of the ET signal in the mutant essentially retraced to the one in the WT, implying that the mutation did not induce significant changes in the rate of the ET process, at least in the first picosecond. Moving the attention to the negative ESA signals, they most likely enclose contributions from different excited states of the Car, which cannot be easily identified based only on the coordinates. Indeed, the excitation frequency of these signals is that of the S2 state initially prepared, but the ESA can take place from dark states reached after the ultrafast internal conversion from S2; moreover, the detection frequency depends on the energy of the arrival state, which is generally unknown. Therefore, the identification of the electronic states contributing to the ESA must be based on the dynamics of the signals. Figure 3a clearly shows that the intensity of the ESA band was distributed in two “lobes”: a lower detection frequency signal (indicated with a circle, “lower” ESA) peaking at (17,066, 15,850) cm−1 and a higher detection frequency signal (square, “higher” ESA) peaking at (17,066, 16,700) cm−1. As is extensively discussed below, the two ESA lobes originated from different electronic states in the manifold of Per, as they showed different temporal dynamics. The higher ESA signal for both the WT and the mutant proteins appeared within a time window comparable with the temporal resolution of the experiment (tens of fs). Moreover, as highlighted in Figure 3c, this signal had a lower initial intensity for the mutant than for the WT. In agreement with previous evidence [28,42], we attributed this signal to the S1/ICT→ Sn ESA. Indeed, the immediate onset of this signal in both samples could be justified by invoking the presence of a conical intersection (CI) between the S2 and the S1 states [28]. The sub-10 fs population of the S1 state through the CI from the initially excited S2 explained why this negative signal was recorded immediately after excitation. The difference in the signal intensity at early times (Figure 3c) could instead be associated with a lower quantum yield across this CI for the mutant than for the WT. To investigate the factors affecting the yield of the crossing between the two electronic surfaces, we focused our attention on the vibrational degrees of freedom that were closely involved in the dynamics of the CI. Theoretical models describing CIs distinguish between coupling modes, which induce electronic coupling, effectively forming the CI, and tuning modes, which tune the energy gap between the involved electronic states and thus regulate the access to the CI [55,56,57,58]. Paramount information on the vibrational modes active in the CI can be found in the so-called “power spectra”, shown in Figure 4, which reveal the main oscillating components contributing to the 2DES signal. They are obtained by Fourier-transforming the oscillating residues integrated over the two frequency dimensions [53]. The three most prominent beating components are all easily attributed to vibrational modes of Per; the contribution of the Chl to the power spectra is expected to be marginal due to the lower Huang–Rhys factor of its vibrational modes. The two vibrations at 1160 and 1225 cm−1 are attributed to the C-C stretching mode of Per mixed with the C-H in-plane bending mode, while the vibration at 1564 cm−1 is associated with the C=C stretching mode of the carotenoid [59,60,61]. It is noteworthy that the C=C stretching mode, already indicated as the tuning mode of the CI both in Cars [28,57] and retinal [56], showed a reduced amplitude in the N89L mutant with respect to the WT protein. It was argued that the C=C/C-C bond order reversal of the conjugated polyene backbone that accompanies the displacement along the C-C and C=C vibrational coordinates provides an ICT character to Per [11]. Therefore, after optical excitation of the S2 state, the C=C stretching mode should push Per towards the CI in half a period of the vibration (~10 fs) while developing an ICT character. The lower quantum yield of the CI could, thus, be ascribed to a poorer stabilization of the ICT character assumed by Per at the intersection between S2 and S1 in the lower polarity environment of the mutant. Indeed, the polarity-dependent stability of the ICT state is a well-documented feature [8] and was recently observed by Marcolin et al. [12] in the 2DES spectra of fucoxanthin, a carbonyl Car similar to Per. An alternative interpretation of the higher ESA signal was provided by Beck and co-workers [29,30,31,32,40,41,43], who assigned it to the absorption of a displaced S2 structure with a relatively long intrinsic lifetime labeled Sx [40,41,43]. It is worth noting that this assignation is not in contrast with the key arguments discussed above. Indeed, they describe Sx as an S2 conformer with an ICT character, which is formed after the motion along the bond-length alternation coordinate C=C/C-C and initial twisting. In their picture, however, Sx is formed immediately directly through the evolution in the S2 potential energy surface and the CI between the S2 and S1 surfaces is only reached later. It should however be noted that in those works, a bluer excitation profile was used, likely leading to the excitation of Per transitions that were different from the reddest transition addressed in our experimental conditions and, thus, to a slightly different dynamical evolution. The second relevant difference between the WT and the mutant in the dynamics at the higher ESA coordinates was the time constants estimated for its decay, corresponding to the S1/ICT relaxation (Table 1). The values of these constants were affected by a high uncertainty because they were much longer than the time window investigated (700 fs). Nonetheless, it can be clearly seen that the N89L mutant showed a significantly slower decay. Since it is known that the de-excitation of the S1/ICT state is mainly promoted by transfer to the Chl in the ps temporal regime [18,19,20,35], these data indicated a slower transfer to the Qy state of Chl a for the mutant. This could be reasonably ascribed to the poorer overlap between the S1/ICT and the Qy band caused by the destabilization of the S1/ICT state in the lower-polarity environment of the mutant. It is significant that because the rise of the ET signal is faster than the decay of the S1/ICT ESA, then the S1/ICT → Qy transfer could not be the only channel exploited in PCP for the overall Per-to-Chl a ET. When compared to the higher ESA, the temporal evolution of the lower ESA signal exhibited a different trend. It can be more properly fitted by using an exponential rise (115 fs) followed by a decay (630 fs). This indicated that the state from which the lower ESA originates was a different excited state in the manifold of Per. As mentioned in the introduction, intermediate Car states with different features (and labels) were reviewed in the literature [21,22,23,24,25,26,27,28,32]. We labeled the state generating the lower ESA as S’ to remain general and postpone the definitive identification of this state to a future, more targeted study. The notable intensity of the lower ESA at early times in Figure 3d suggested that S’ could have been directly excited by the laser pulse. A possible assignation of S’ would be a distorted S2-like state excited via transitions from twisted ground-state conformations, presumably favored by the protein scaffold and expected to lie in the red-edge of the Per absorption [29,62,63]. Moreover, the dynamic behavior in Figure 3d indicated that an additional population could be brought into S’ within a few hundreds of fs, possibly via a torsional motion from the “proper” (planar, all-trans) S2 state. Indeed, the population of twisted carotenoid structures through torsional distortion of Per in the S2 state is a hypothesis that is commonly reviewed in the literature [21,22,29,30,31,32]. In support of this hypothesis, we noticed that the peak at ~50 cm−1, which is recognizable in the power spectra of Figure 4, could be attributed to a low-frequency torsional mode. A vibrational coherence peaking at a similar frequency was detected in retinal, which has an analogous polyene backbone [56,64]. Such a torsional mode could guide Per to the S’ state within half a period of oscillation (~350 fs), passing through a series of intermediate distorted structures. Our experimental observations on S’ agreed with several already recognized properties of the Sx state, above all, its capability to directly transfer energy to Chl [29,62,63]. This evidence points toward the assignation of the decay of the lower ESA to the S’→Qy ET process. In addition, as summarized in Table 1, the fitting of the lower ESAs highlighted a higher amplitude of the rising component for the N89L mutant. This was compatible with a larger S2 population that survived the CI and underwent torsional distortion to the S’ state. The higher intensity of the low-frequency torsional mode in the power spectrum of the mutant (Figure 4) also seemed to correlate with the greater population that reached the distorted S’ state. We want to highlight that an alternative interpretation of S’ as a vibrationally “hot” S1/ICT state cannot be excluded. A hot S1/ICT state could be directly excited by borrowing oscillator strength from the almost-resonant S2 transition and further populated by internal conversion. Moreover, the involvement of a vibrationally excited S1/ICT state as an additional channel for the ET process was already proposed [18]. Altogether, these findings suggested the presence of at least two synergic pathways contributing to the overall ET from Per to Chl a in the investigated time and frequency range. Indeed, we could exclude the idea that in these conditions, the only channel was the S1/ICT→Qy transfer because the rise of the ET signal (triangle, Figure 3b) was faster than the decay of the S1/ICT ESA (square, Figure 3c). Moreover, the overall ET rate, estimated as 1.9 and 2 ps for the WT and the mutant, respectively (Table 1), remained practically unchanged in the two samples, despite the slower decay rate of S1/ICT in the N89L mutant. We thus proposed a kinetic model like the one represented in Figure 5a in which the S1/ICT and S’ states cooperate to transfer energy to the Qy state of Chl a. Based on this kinetic scheme, it was possible to devise a new fitting model (Section S3.2, Equations (S5)–(S7)) that allowed for retrieving the kinetic constants associated to each of the identified transfer pathways and their relative weights for the WT and the mutant, as illustrated in Figure 5b. The results obtained after applying this kinetic model clearly outlined that the mutation did not alter the kinetic constants relevant for the ET; instead, the ET rate was preserved thanks to a redistribution of the relative weights of the two ET channels. Indeed, according to the model in Figure 5a, once the reddest Per was excited in the S2 state, part of the S2 population was transferred to the S1 state through a CI in a timescale comparable with the temporal resolution of the experiment. The CI exhibited a lower yield in the mutant, possibly because of polarity or steric effects affecting the vibrational mode that regulated access to it. The excitation energy was then transferred from S1 to the Qy state of Chl a in a few ps (kS1Qy−1~3 ps in the WT protein, ~8 ps in the mutant). In parallel, the population in S2 was pushed via torsional motions through a series of distorted structures. Eventually, this led to the rise of a state labeled S’ on a time scale of kS′,R−1 = 409 fs (395 fs in the N89L mutant). From S’, the excitation energy could be efficiently transferred to the Chl a, with a calculated time constant of kS′Qy−1 = 501 (503) fs for the WT (N89L) sample. Altogether, during the first 700 fs, in the WT, ~85% of the excitation was transferred to the Chl a starting from the S’ state, while the remaining 15% came from the S1 state, as shown in Figure 5b; the contribution from S’ was more significant in the mutant (~94%) due to the larger population that survived in S2 and moved to S’. Overall, the ET process in the N89L mutant suffered only a slight slowdown thanks to the compensating effect of the S’ channel. 3. Materials and Methods In a 2DES experiment, the sample is excited by a sequence of three ultrashort laser pulses (with wavevectors k1, k2, and k3), separated by time delays t1 (between k1 and k2) and t2 (between k2 and k3). The signal emitted by the sample is then detected as a function of a third time interval t3 (between k3 and the signal). In our experimental setup, fully described in [65], the pulse sequence is generated starting from the output of an amplified pulsed laser source (Libra Coherent, Santa Clara, CA, US), which produced a train of 100 fs pulses centered at 800 nm with a repetition rate of 3 kHz. The central wavelength of the pulse was then tuned in the Vis range by using a non-collinear optical parametric amplifier (TOPAS White Light Conversion, Vilnius, Lithuania). The pulse was then compressed and shaped with a prism compressor and an acousto-optic programmable dispersive filter (Dazzler Fastlite, Antibes, France). The resulting pulse was 100 nm broad and centered at 620 nm; the pulse duration at the sample position was ~10 fs, as measured through the frequency-resolved optical gating (FROG) technique (Figure S1). The pulse energy was attenuated to 12 nJ using a broadband half-waveplate/polarizer system. A diffractive optic element split the pulse into four identical replicas: three replicas served as the exciting pulses k1, k2, and k3, while the fourth was attenuated with a graduated neutral filter and used as a local oscillator (LO) for detection purposes (vide infra). A donut-shaped spherical mirror (DSM) arranged the exciting pulses along the directions kA, kB, and kC, representing three vertices of an ideal square, and the LO along the fourth vertex (BOXCARS geometry). The time delays between pulses were controlled by means of couples of antiparallel CaF2 wedges. One wedge of each pair was mounted onto a translation stage (Ant95 Aerotech, Pittsburgh, PA, US) that regulated the thickness of the medium crossed by the exciting beam and provided a temporal resolution of 0.07 fs. After a second DSM focused the four beams on the sample, the latter emitted a third-order signal in the phase-matched direction kS = −kA + kB + kC. This direction coincided with the fourth vertex of the BOXCARS square and, thus, with the direction of the LO. The signal was detected as an interference with the LO (heterodyne detection) via a scientific complementary metal–oxide semiconductor (sCMOS) camera (Zyla Andor, Oxford Instruments, Belfast, Northern Ireland) after a spectrograph (Shamrock 303i Andor) had separated its frequency components. The setup also included two optical choppers to remove spurious contributions from the signal via the double lock-in modulation method [66]. The experiment is conducted by scanning the time delays t1 (from −80 to 100 fs in 1 fs steps) and t2 (from 0 to 1 ps in 5 fs time steps). The acquired data matrix was thus a function of t1, t2, and ω3, where ω3 was the frequency dimension read directly by the sCMOS camera, corresponding to the Fourier transform of the third time interval t3. By controlling the relative arrival time of the pulses, two types of experiments were performed: in the rephasing (R) experiments, the pulse propagating along the direction kA arrived first (kA = k1, kC = k2, and kB = k3, thus kS = −k1 + k2 + k3); in the non-rephasing (NR) experiments, the pulse propagating along the direction kC arrived first (kC = k1, kA = k2, and kB = k3, thus kS = +k1 − k2 + k3). The experiment on each sample was repeated five times to ensure reproducibility and averaged to reduce noise. After a processing procedure that included a Fourier transform along the t1 axis, the data were arranged as a stack of two-dimensional (ω1, ω3) spectra at different t2 times. The analysis presented in this work was conducted on the total (T) purely absorptive data, which was obtained as the sum of the R and NR data matrices. Further details on the experimental setup and the calibration, acquisition, and data processing procedures can be found in Bolzonello et al. [65]. The sample of WT PCP was prepared as detailed in Meneghin et al. [42]. The N89L sample was prepared as described in Schulte et al. [44] and diluted in a buffer containing tricine 5 mM, KCl 2 mM, and NaCl 30 mM at pH = 6.5 (all reagents were supplied by Merck, Darmstadt, Germany) until an absorbance of about 0.4 in a 1 mm cuvette was achieved in the region of the Qy band of Chl a. Such an optical density ensured a good compromise between the need for a third-order response of sufficient intensity and the avoidance of a drastic attenuation of the LO beam. In order to prevent the formation of harmful oxidizing species, oxygen was removed from the solution by degassing the sample under nitrogen flux and the cell was sealed. For each sample, steady-state absorption spectra were acquired before and after the 2DES experiments to verify that no degradation had occurred during the measurements. 4. Conclusions The comparison of the ultrafast dynamic behavior of the WT PCP and a refolded N89L mutant allowed for unveiling important information about the workflow of this light-harvesting complex. While previous studies had already shown that the N89L mutation does not affect the ET efficiency [44], here we shed light on the mechanisms underlying such robustness. By exploiting the multidimensionality of the 2DES technique, two parallel channels for the ET could be identified: along with the well-documented pathway from the S1/ICT state, transferring excitation to the Qy band of the Chl a in a few ps [18,19,20,35], we recognized the crucial role of a further intermediate state donating energy in the first hundreds of fs after photoexcitation. This second channel becomes more relevant in the mutant, where the S1/ICT channel was partially undermined because of the mutated energy landscape. These findings suggest that the cooperation of multiple pathways might be decisive in ensuring high ET performance even if a pathway is compromised to a certain degree. Although this conclusion was drawn from data collected in specific experimental conditions, recent evidence in the literature seems to indicate that in effect it might have more general validity. First, other works have demonstrated the robustness of the ET in PCP against the replacement of Chl a with different Chls [43,67]. Second, the overview of the most recent 2DES works on PCP revealed that different exciting conditions might favor specific de-excitation pathways above others by preparing different initial states of the Per donor. The exciting conditions used in this work placed a magnifying glass on the photophysics of Per structures in the red edge of the spectrum, which can be reasonably associated with distorted carotenoid geometries, possibly designed by the protein environment. Their contributions to the spectroscopic signal would have been elusive with a bluer laser band. Indeed, with a more significant excitation of the S2 state, the direct S2→Qy ET channel and the population of the S1/ICT state from S2 via CI would have become the dominant de-excitation pathways. The strong sensitivity of the spectroscopic response to the exciting conditions also justifies, for example, the multifarious interpretations proposed for the “Sx” state and its role in the ET, and the number of different possible ET pathways so far identified [28,40,41,42,43]. Upcoming analyses with a greater focus on the amplitude and phase distribution of the Car vibrational modes over the 2DES map could provide more definitive evidence on the identity of the states detected in 2DES measurements. Beyond implying that particular care must be paid in the comparison of literature data, this vast wealth of investigations is progressively unveiling the complexity and multiplicity of the mechanisms regulating the efficiency of ET in the PCP antennas. The photophysical properties of PCP have carefully evolved to be robust against mutations potentially threatening its light-harvesting function. From a broader perspective, it is likely that the diversity and complementarity of channels available for the ET may be a common strategy affecting the robustness of photosynthesis on a biologically relevant scale. Future investigations on other natural antennas would help understand whether Car-to-Chl ET mechanisms similar to the one described in this work for PCP are also relevant in LH complexes different from PCP. Acknowledgments F.T. would like to thank Alessandro Agostini for his fundamental support during the preparation of the N89L sample and the useful suggestions for the design of the experiment. Supplementary Materials Supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23095067/s1. Click here for additional data file. Author Contributions Conceptualization, F.T., D.C. and E.C.; methodology, F.T. and E.C.; software, F.T.; validation, F.T., E.H., D.C. and E.C.; formal analysis, F.T.; investigation, F.T., G.M. and E.F.; resources, E.H., D.C. and E.C.; writing—original draft preparation, F.T. and E.C.; writing—review and editing, F.T., E.H., D.C. and E.C.; visualization, F.T., D.C. and E.C.; supervision, E.H., D.C. and E.C.; project administration, E.C.; funding acquisition, E.C. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement All data supporting the findings of this study are available from the corresponding author upon request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 (a) Structure of the carotenoid Per. (b) Crystallographic structure of WT PCP, with the Per molecules colored in orange and the Chl a molecules colored in green. (c) Absorption spectra of WT PCP (blue) and its N89L mutant (green). The spectrum of the exciting pulses used in 2DES measurements is also shown (orange area). Figure 2 Absorptive 2DES maps of (a) WT PCP and (b) N89L PCP at different population times (t2); to make the evolution of the populations more evident, the oscillating contributions to the signal were attenuated using a Savitzky–Golay filter. The markers pinpoint relevant coordinates discussed in the main text. Figure 3 (a) Vertical cuts of the 2DES maps of WT PCP at excitation frequency 17,066 cm−1 (indicated with the black line) at different population times; to ease the visualization of the signal trends, the oscillating contributions to the signal were attenuated using a Savitzky–Golay filter. Temporal traces of (b) the ET signals (extracted at (17,066, 15,100) cm−1, triangle), (c) the higher frequency ESA signals (extracted at (17,066, 16,700) cm−1, square) and (d) the lower frequency ESA signals (extracted at (17,066, 15,850) cm−1, circle) for the WT and the mutant samples. Thick solid lines represent the fittings performed according to the multiexponential model described in Section S3.1 (only the non-oscillating components are shown for clarity). Figure 4 Fourier transform of the oscillating components of the 2DES signal, mediated along excitation and detection frequency axes. They are plotted as square moduli of the Fourier transform amplitudes and normalized on the intensities of the 1225 cm−1 component. Figure 5 (a) Energy level diagram illustrating the different dynamic processes included in the kinetic model used to interpret the temporal dynamics of the experimental traces. Dashed lines indicate events occurring on a time scale faster or comparable with the time resolution of the experiment, namely, the initial optical excitation and the decay of the S2 state via (i) the CI that transferred the population from S2 to S1 and (ii) the initial torsional movements that promoted the formation of distorted structures. kS′,R, kS1Qy, and kS′Qy represent the kinetic constants for the rise of S’, the S1/ICT→Qy ET, and the S’→Qy ET, respectively. (b) Contributions to the rise of the ET signal, as determined by the fitting of the experimental data using the kinetic model in panel (a). ijms-23-05067-t001_Table 1 Table 1 Results of the parallel (multi)exponential models used to fit the traces at (17,066, 15,100) cm−1 (triangle, ET signal), (17,066, 16,700) cm−1 (higher ESA, square) and (17,066, 15,850) cm−1 (lower ESA, circle); positive (negative) amplitudes indicate rising (decaying) components. A1 T1/fs A2 T2/fs WT N89L WT N89L WT N89L WT N89L ▲ ET signal 0.80 0.76 ~1900 ~2000 ∎ Higher ESA −0.70 −0.62 ~3000 ~8000 ● Lower ESA 0.20 0.28 115 112 −0.67 −0.65 630 630 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Collini E. Carotenoids in Photosynthesis: The Revenge of the “Accessory” Pigments Chem 2019 5 494 495 10.1016/j.chempr.2019.02.013 2. Hashimoto H. Uragami C. Yukihira N. Gardiner A.T. Cogdell R.J. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093281 sensors-22-03281 Article Design of a Highly Sensitive Reduced Graphene Oxide/Graphene Oxide@Cellulose Acetate/Thermoplastic Polyurethane Flexible Sensor Yang Yujie 1 Yi Tan 1 https://orcid.org/0000-0001-8104-6462 Liu Yang 123* https://orcid.org/0000-0001-8554-9411 Zhao Hui 12 https://orcid.org/0000-0003-1284-7607 Liang Chen 12 Di Bartolomeo Antonio Academic Editor 1 College of Light Industry and Food Engineering, Guangxi University, Nanning 530004, China; yangyujie0985@163.com (Y.Y.); yitansysu@163.com (T.Y.); zhh@gxu.edu.cn (H.Z.); liangchen@gxu.edu.cn (C.L.) 2 Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, Guangxi University, Nanning 530004, China 3 Guangxi Bossco Environmental Protection Technology Co., Ltd., Nanning 530000, China * Correspondence: xiaobai@gxu.edu.cn; Tel.: +86-155-7832-3385; Fax: +86-0771-3237309 25 4 2022 5 2022 22 9 328102 3 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). As a substitute for rigid sensors, flexible sensing materials have been greatly developed in recent years, but maintaining the stability of conductive fillers and the stability of micro-strain sensing is still a major challenge. In this experiment, we innovatively prepared a polyurethane-based cellulose acetate composite membrane (CA/TPU) with abundant mesopores through electrospinning. Then, we reduced graphene oxide (rGO)—as a conductive filler—and graphene oxide (GO)—as an insulating layer—which were successively and firmly anchored on the CA/TPU nanofiber membrane with the ultrasonic impregnation method, to obtain an rGO/GO@CA/TPU sensor with a GF of 3.006 under a very small strain of 0.5%. The flexibility of the film and its high sensitivity under extremely low strains enables the detection of subtle human motions (such as finger bending, joint motion, etc.), making it suitable for potential application in wearable electronic devices. electrospinning porous fiber flexible strain sensor high sensitivity ==== Body pmc1. Introduction In recent years, with the rapid development of fields within electronic technology, such as intelligent robots, flexible wearable devices, mobile intelligence, and electronic skin, the research and development of functional flexible sensors has attracted increasing levels of attention [1,2,3,4]. Due to the narrow strain range and easy plastic deformation of rigid sensors, such as traditional metal foil and semiconductor strain sensors, which have poor stretchability (ε < 5%) [5,6], it is increasingly difficult to meet the requirements of new conductive materials for a high strain-sensing ranges and deformable sensing. More importantly, the flexible sensor manufacturing process is simple, and such sensors have a low cost and light weight. Flexible sensors can respond to external signals in real time and in any form, and they can provide output in the form of electrical signals, which also makes them better for applications as wearable devices, in device motion detection, as health testing equipment, amongst other fields [1,2,4]. The response of a flexible sensor is realized by the conductive unit, which forms a path, and regular changes in the conductive unit due to external stimuli [7]. Except for a few structural self-conductive polymers, most flexible sensors are realized primarily with composite conductive polymers that are composed of non-conductive polymer materials mixed with conductive substances [8]. The electron transport mechanism of this type of composite conductive polymer is mainly explained by three theories: the “conducting path”, “tunneling effect” and “field electron emission” [9,10,11]. At present, there are many kinds of materials used for flexible sensors, such as polydimethylsiloxane (PDMS) [12], thermoplastic polyurethane (TPU) [13,14], polyvinylidene fluoride (PVDF) [15], and so on. Among them, fiber materials have attracted extensive attention due to their advantages of having a high specific surface area, being lightweight, having a simple preparation process, and low cost. As is well known, with the rapid development of the theory of the electrospinning process and nanocomposite materials, electrospinning has become the most versatile and feasible technical means of producing continuous one-dimensional ultrafine fibers [16,17]. In addition, the morphology of the fibers can be adjusted during the electrospinning process to obtain different structures to meet different needs. The use of electrospinning to fabricate sensors has also received much attention. For conductive fillers, metal/metal oxide nanoparticles [18], carbon nanotubes (CNTs) [13,15], metal nanowires [19], reduced graphene oxide (rGO) [14], etc., are commonly used. For the composite method of using conductive fillers and flexible substrates, the most common methods are blending before film formation and dipping after film formation. For example, Li and their co-workers fabricated a TPU-based flexible piezoresistive pressure sensor in an electrospun fiber network (TPUN), which was successfully decorated with c-MWCNTs and impregnated in a TPUN with excellent electrical conductivity [20]. Tang et al. prepared a CNT/TPU composite nanofiber yarn with elongation at break as high as 476% by uniformly dispersing CNT into TPU and blending to obtain a spinning solution and by using a multi-needle liquid bath electrospinning method. The nanofibers were then coated with CNTs by dip coating, resulting in a strain sensor that exhibited a high relative resistance change (440%) at 140% strain [13]. In order to maintain the stability of the sensor’s sensing, that is, to make it more less easy for the conductive filler to fall off of the flexible substrate, we innovatively constructed a microstructure on the fiber that can firmly anchor the conductive filler to improve the sensing effect of the sensor. For strain sensors, a suitable strain range and a certain sensitivity are two important factors to be considered in the design [21,22]. Cao et al. designed a silver nanowire/polyurethane (AgNW/PU) composite fiber with a shell-core structure, and by adjusting the adhesion layer between the silver nanowire and the PU substrate, they prepared fiber-based strain sensors with different GFs and working ranges. Among them, the AgNW/PU-9.2 wt%/5 min fibers had a wide working range of 0–50% and a large GF of 940; however, the GF was still in the low strain range (at <10%, the GF was almost 0) [23]. In addition, Wang et al. fabricated a sensor that exhibited a high sensitivity under extremely large tensile strains (a gauge factor of 8962.7 at 155% strain). They fabricated the sensor in a conductive polymer embedded with carbon black (CB) particles in an electrospun TPU fiber membrane matrix with a tunable scaffold network. This work demonstrates the effects of three-dimensional scaffold network structures, constructed at different rotational speeds of the collection device on the electrical response of the a TPU/CB strain sensor during electrospinning [22]. Although sensors with ultra-high sensitivity under extremely large strains have been extensively studied, sensors that can record extremely small strains also have great implications for medicine and human health. Therefore, this paper innovatively designs a sensor with high sensitivity under an extremely small strain. Thermoplastic polyurethane (TPU), whose toughness can reach 390.2 MJ·m−3 [14,24] and cellulose acetate (CA) which has good air permeability and natural polymer material [25,26], were chosen to comprise the substrate of the sensor. For the conductive filler, reduced graphene oxide (rGO) was chosen. This is because of the two-dimensional structure of graphene, which can form electron transport paths through the contact between graphene sheets. A small change in the overlapping area or the relative position between graphene layers can cause a huge change in the conductive structure of the film, which is manifested as a change in the material resistance or capacitance. The easily altered conductive structure also makes graphene a promising candidate for conductive sensing [27,28]. Then, through the difference in the boiling points of the mixed solvents and the difference in the solubilities of each solvent with respect to the raw material during the electrospinning process, the mesoporous nanofiber film was prepared in one step during the electrospinning process, which made the circuit structure of the sensor more stable, and it improved the sensitivity of the sensor. Finally, the nearly insulating graphene oxide (GO) was wrapped with the conductive filler on the surface of the nanofibers through hydrogen bonding under the action of ultrasonic waves, and a GO/rGO@CA/TPU sensor with a stable circuit structure was obtained. The experimental process is shown in Figure 1. This research has contributed new ideas for the design of the circuit structure of flexible sensing materials and for the improvement of sensitivity. 2. Materials and Methods 2.1. Materials CA powders were purchased from Macklin (Mw = 60,000, AR, Shanghai, China) and TPU granules were purchased from Bayer (90A, Germany); these were used to prepare the solutions for electrospinning. The rGO (>99%, Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China) and GO (99.7%, Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China) were purchased from Nanjing Xianfeng Nano Co., Ltd. (Nanjing, China). N,N-dimethylacetamide (DMAc, AR, Tianjin Zhiyuan Chemical Reagent Co., Ltd., Tianjin, China), acetone (PA, AR), and absolute ethanol were of analytical grade, and were used without further purification. 2.2. Preparation of the Flexible Sensor 2.2.1. Preparation of the CA/TPU Composite Film We prepared the CA/TPU composite film through electrospinning. First, we configured the spinning solution. In this experiment, CA and TPU were used as solutes, and DMAc and PA were used as solvents. Spinning solutions with different mass fractions of 12–20% were prepared by stirring under a magnetic stirrer for 2 h. Second, electrospun fibers were prepared using a syringe loaded with a 23 G metal needle. The feeding speed of the electrospinning machine was fixed at 0.18 mm/min, the distance between the needle and the receiver was fixed at 15 cm, and the voltage used for spinning was 12.5 kV. All experiments were performed at room temperature (T = (27 ± 2) °C, RH = (50 ± 2)%). 2.2.2. Preparation of the GO/rGO@CA/TPU Film The anchoring of graphene was the key to the conductivity of the composite film. First, 1.5 mg/mL rGO ethanol dispersion and a 0.5 mg/mL GO ethanol dispersion were prepared. Then, the CA/TPU nanofiber spinning films were immersed in the prepared rGO and GO dispersions and sonicated for 20 min. After completion, the sample strips were taken out, rinsed 3 times with water and ethanol, and then vacuum-dried for 6 h to finally obtain the GO/rGO@CA/TPU film that we needed. 2.3. Characterization The surface morphology of the nanofibers was observed with a desktop scanning electron microscope (SEM, Phenom ProX type Phenom, Eindhoven, The Netherlands, acceleration voltage: 10 kV) and a field–emission electron microscope (FEM, Sigma 300 type, Zeiss, Oberkochen, Germany, acceleration voltage: 5 kV). An automatic specific surface area tester (BET, TriStarII;3020, Micromeritics Instrument Ltd., Norcross, GA, USA) was used to measure the specific surface area and pore size of the spinning film. The degassing temperature of the fiber film was 110 ℃, and the degassing time was 6 h. The surface element and chemical functional group changes of the sample were characterized with an X-ray photoelectron spectrometer (XPS, ESCALAB 250XI+ X, Thermo Fisher Scientific, Waltham, MA, USA) with a spot diameter of 500 μm. The film strain response test was performed with an electrochemical workstation (AUTOLAB PGSTAT302N, Metrohm, Herisau, Switzerland), and the sampling time was 0.1 s. The viscosity of the spinning solution is measured with a micro mixing rheometer (Haake Minilab, Thermo Fisher Scientific, Karlsruhe, Germany), the measurement mode was the frequency sweep mode (25 ℃, shear rate 0~300 S−1), and the measurement was the zero–time shear viscosity. 2.4. Sensor Sensitivity Measurement The sensitivity of the sensor is expressed by the gauge factor (GF), which represents the ratio of the relative value of the change in the sensor’s resistance to the applied strain, and the formula for its calculation is given in Equation (1) [29]:(1) GF=ΔR/R0ε where GF is the strain sensitivity coefficient, R0 represents the resistance value when no strain is applied, ΔR is the difference between R0 and the resistance value at any time during the stretching process, and ε is the strain. 3. Results 3.1. Electrospinning of the Mixed Solution Voltage is an important influencing factor in the electrospinning process. As the internal driving force of electrospinning, only when the applied electric field voltage is higher than the threshold voltage will the polymer solution be drawn to form Taylor cones and cracked and drawn to form jets [30,31]. In addition to the voltage, the viscosity of the polymer is also a key factor in the spinning process. The viscosity of the polymer solution or melt, the concentration, and the molecular weight are related to each other. Increasing the concentration or molecular weight can increase the viscosity of the solution. In the electrospinning process, only when the solution concentration reaches a certain critical value can the solution be stably split in the electric field and form a jet; with a change in the solution concentration from low to high, the fiber also shows the change in the bead, from spindle, to fine fiber, to thick fiber. Finally, when the concentration increases to a certain level, the electric field can no longer overcome the viscous resistance of the solution, and the solution will also form larger droplets at the tip of the needle, blocking the needle [32,33]. We experimented with spinning solution concentrations ranging from 12% to 20%, and Figure 2 shows the effect of the spinning solution concentration on the fiber morphology and the distribution of the fiber diameter. The experimental results show that when the concentration was 12%, the electrospray could not effectively stretch and dry to form fibers. Instead, a large number of irregular beads appeared on the surface and inside of the deposited fibrous film in the form of liquid beads. When the solution concentration reached 14%, the solution was able to form a stable jet in the electric field. It can be seen from the SEM image that uniform and stable fibers could be formed when the solution concentration was 14% and 16%; however, when the concentration reaches more than 18%, the diameter of the fiber becomes larger and uneven. This is because when the concentration is too high, the molecular chains in the polymer solution are too tightly entangled, so the electrospray cannot be effectively stretched during the spinning process, and cleavage occurs during the formation of uniform and stable fibers; therefore, for this experiment, we chose a concentration range of 14–16%. 3.2. Analysis of the Pore Size and Morphology of the Electrospun Fibers The main principle used in this experiment to prepare porous materials was nonsolvent-induced phase separation (NIPS) [34,35], by using the difference between the boiling points of the spinning solution solvents and the differences in the solubility of the solvents with respect to those of different raw materials. The boiling point of acetone under standard conditions is 56.53 °C, and that of DMAc is 164–166 °C. The huge difference in boiling points makes acetone volatilize first in the spinning process, forming air pockets in the jet, which has not yet been volatilized and solidified, and diffusing to form pores in the uncured jet. At the same time, in this process, the solubility of acetone for CA is much higher than that of TPU and DMAc has good solubility for CA and TPU; therefore, during the rapid volatilization of acetone, the pores constructed by the acetone can be quickly partially cured by the precipitated TPU, and will not be filled by the flowing solution. This further ensures the uniformity and stability of the pores on the fiber surface, and holes with different pore sizes can be constructed on the fiber surface according to the difference in the fluidity of the spinning solution [36,37]. Figure 3 shows a BET analysis diagram of the fiber surface with different spinning solution concentrations. Combined with the specific surface area and average pore size corresponding to different concentrations of spinning solutions in Table 1, it can be seen that, with the increase in concentration, the pore size showed a trend of first increasing and then decreasing, and the specific surface area also showed a trend of first increasing and then decreasing, which was consistent with the change trend in pore size. The spinning solution had a high fluidity at a low concentration, and the air pockets formed by the volatilization of acetone were easily refilled by the spinning solution with a high fluidity, which caused the pore size to decrease. This was because as the concentration of the solution increases, its fluidity decreased and the pore size gradually increased; however, when the concentration increased to a certain level, the molecules in the spinning solution were tightly entangled, and the air pockets formed during the volatilization of acetone could not grow in the fiber, causing the pore size to decrease. The change in the specific surface area was caused by the change in the fiber pore size [38]. The higher specific surface area of the nanofiber film greatly improves the physical adsorption of rGO and GO, and the appropriate pore size on the nanofiber surface also causes rGO and GO to have more stable anchoring sites; therefore, our subsequent experiments will be carried out with spinning films with concentrations of 14% and 16%. The concentration of the spinning solution did not appear to exceed 50 nm in the range of 12% to 20%, and the peak in 5 to 35 nm indicated that the surface of the fiber was rich in mesopores. It was found that when the fiber concentration was 12%, 18%, or 20%, spikes appeared at multiple locations and extended to the range of the micropores, the pore size distribution on the fiber surface was uneven, and there were micropores on the fiber surface. There were very few micropores and mesopores, and most of the pores on the fibers were larger than 50 nm [39]. For example, Li et al. used the same principle to electrospin polylactic acid/chitosan and found that pores of about 60 nm were generated on the fibers, and they applied this to air purification [40]. When the spinning solution concentration was 14% or 16%, the viscosity of the spinning solution was moderate, and the air pockets formed by the rapid volatilization of acetone could expand and grow. This is because the solubility of acetone for CA is much higher than that of TPU, and DMAc has good solubility for CA and TPU; therefore, the pores formed after acetone volatilization could be quickly precipitated. The solidified TPU was partially stable, ensuring the uniformity and stability of the pores [41]. Figure 4 presents an SEM image of the morphology and pore distribution of the mesopores on the fiber surface when the spinning concentration was 14% and 16%. The mesopores on the fiber surface were round or short and rod-shaped, and they were densely distributed on the fiber surface. The appropriate pore size on the fiber surface also provided a more stable anchor point for rGO and GO, which improved the stability of the two on the fiber surface and prevented the performance of the nanofiber film from being affected by the shedding of rGO. 3.3. Characterization of Composite Film We chose rGO as the nano-conductive filler, used the CA/TPU electrospun composite film as the substrate, and selected the insulating GO to prepare the flexible sensor. In order to observe the surface and cross-sectional micromorphologies of the CA/TPU nanofiber films after anchoring graphene, SEM was used to observe the surface and cross-sectional micromorphologies of the films. As shown in Figure 5a,b, the flaky rGO sheets formed a scaly structure on the surface of the nanofibers [42], and the overlapping between fibers also caused the film to form a stable three-dimensional conductive network. In addition, it can be seen in Figure 5c,d that, after further ultrasonic anchoring of GO, the surface of the nanofibers became smooth and flat, and there were not many lifted rGO sheets on the surface of the fiber. This phenomenon was mainly due to the fact that the oxygen-containing groups on the surface of rGO were reduced, and the lamellae could not form hydrogen bonds with the nanofibers after anchoring in the micropores on the fiber surface. In addition, because the surface groups of rGO were removed, the dispersibility of rGO in ethanol was poor, and the rGO sheet could not be fully spread in the dispersion, which also caused the lifting of the rGO layer after anchoring [43]. After the secondary anchoring of GO, and since the surface edge of GO had a large number of oxygen-containing groups (–OH, –COOH, –CH(O)CH–, etc.), it was very easy for these groups to interact with the hydroxyl and carboxyl groups on the fiber surface, so GO was firmly adsorbed on the fiber surface. At the same time, due to the electrostatic repulsion of the surface groups of GO, GO had better dispersibility during ultrasonication and can be repeatedly spread in the ethanol solution. The fully stretched GO was also wrapped on the outer surface of the fiber under the action of ultrasound, and the warped rGO was wrapped inside, which also made the fiber surface smoother after anchoring GO [44,45]. In order to explore the elemental content of the film and the change in the oxygen content of the film before and after anchoring GO, an X-ray photon spectrometer was used. As shown in Figure 6a, the binding energy at 283.4 eV was the characteristic peak of C–C and C–H, and the binding energy at 284.3 eV was the characteristic peak of the cellulose acetate pyran ring and the C–O and C=O bond in the structural unit; the binding energy of 288.05 eV was the characteristic peak of O=C–N in TPU [46,47]. Similarly to Figure 5a, the photon energy spectra in Figure 6b,c also show three main characteristic peaks. It can be seen in Table 2 that, after anchoring rGO, the carbon content in the film increased from the initial 78.40% to 89.86%. After anchoring GO, due to the large number of oxygen-containing groups carried on the GO surface, the carbon content of the film decreased from 89.86% to 78.34%, and the oxygen content increased from 8.39% to 18.32%. The decrease and then increase of oxygen content, and the increase and then decrease of carbon content, also confirm the successful anchoring of rGO and GO; however, it can also be seen in Table 2 that the nitrogen content had an upward trend after anchoring GO, which may have been caused by the fact that GO and rGO did not completely wrap the fibers. In addition, the problem that the package is not tight enough needs to be solved in follow-up experiments. 3.4. Strain Response Analysis of the Sensor Figure 7 presents a graph of the tensile strain response before and after graphene anchoring. Figure 7a,b are the ΔR/R0-t curves of the CA/TPU fiber film anchored with rGO and GO under different tensile strains, respectively. It can be seen that the curves all showed obvious regularities, and with the continuous increase in strain, the range of ΔR/R0 increased. Comparing (a) and (b) in Figure 7, although the rGO@CA/TPU sensing film could output a response signal with a mode variation as low as 0.5%, it can be clearly observed that the response signal of the rGO@CA/TPU film was unstable. Although peaks and troughs in the response signal could appear during the stretching process, the surface of the curve was rough and could not reflect the strain state of the film well. This was mainly because the thickness of the film continuously decreased during the small strain stretching process, and the distance between the nanofibers in the film kept decreasing, which caused the rGO layers raised on the surface of each fiber to contact each other, and the circuit structure between adjacent fibers was turned on. As a result, the structural stability of the circuit of the film was affected, and the curve oscillated within a single strain cycle. After the insulating GO was added, the rGO on the fiber surface was wrapped, and the reduction of the distance between the fibers after stretching did not cause the circuit structure between adjacent fibers to conduct, so the circuit structure of the film was more stable. In addition, the strain sensitivity factor (GF) of the sensor was improved after anchoring GO. As shown in Table 2, after anchoring GO, the GF of the flexible conductive films increased by 33.1%, 26.9%, 13.8%, and 27.6% at 0.5%, 1.0%, 5.0%, and 10% strain amounts, respectively. In particular, the sensitivity under extremely small tensile strains has been greatly improved. Figure 7c,d show the response signal graphs of the flexible sensor at different strain rates for the 10% and 5% strains, respectively. It can be seen that, under different strain rates, the films could monitor the strain process and transmit it in real time; however, the sensing effect of the sensor was not good at a large stretching rate, which was mainly due to the insufficient elasticity of the nanofiber film due to the blending of CA, and the rebound rate was slow. Therefore, at a large stretching rate (such as 10%, 60 mm/min), the film could not return to its original state after the end of the previous stretching cycle and enter the next stretching cycle, which caused the relative positions of graphene on the fiber surface to be separated again before returning to the initial state [48]; therefore, the sheet resistance increased continuously during the cyclic stretching process, and the resistance response curve also showed a decreasing trend. At the same time, this was also the reason for why the GF of the GO/rGO@CA/TPU sensor decreased continuously with the increase in the strain variable during the testing process (Table 3); therefore, improving the rapid shrinkage ability of the film after stretching and reducing the continuous decrease of the response signal intensity with time have become urgent problems to be solved. 3.5. Human Motion Detection with the Flwxible Sensor The flexible sensor is different from a traditional rigid sensor because of its flexibility and the advantages of adapting to large tensile strains, making it suitable for monitoring the motion of human joints; therefore, we applied it to some simple human motion monitoring to prove that it has possibilities for actual applications in the future [49,50]. As shown in Figure 8, the cellulose-based film was applied to the fingers, wrists, and knees of the human body, and the sensor was connected to the electrochemical workstation through the conductive glue on the copper foil to record the resistance response of the film during the movement of the corresponding position of the human body during a bending movement. Figure 8a presents a graph of the response when applying the film to the outside of the finger. Although the fingers flexed and stretched irregularly during this process, it was found that the film could still monitor the process of the charge in the finger state well without presenting a cluttered signal [21,23]. In Figure 8b, the film was attached to the outside of the wrist. With regular high-speed bending of the wrist, the film resistance response also showed regular vibrations; however, when the wrist returned to the initial position (the sensor was not deformed), due to small vibrations in the wrist, the sensor’s resistance response also had a small vibration at the peak position. Notably, as shown in Figure 8c, the film was attached to the inside of the knee. When we simulated the rapid flexion, flexion, and extension of the knee socket during running, the film exhibited an extremely high strain response with a ∆R/R0 of 13, which was also the strength of this sensor [14]. This indicates that the sensing sensitivity of the film under folded contact work was much higher than that under tensile strain. This also provides a new option for the application of cellulose-based sensing materials with poor resilience [51]. 4. Conclusions This article reports a method of using differences in the solvent boiling point and solubility to achieve phase separation, preparing a nanofiber material with rich mesopores on the surface by electrospinning in one step, and designing a sensor with a high sensitivity under an extremely small strain. The CA/TPU composite nanofiber films with uniform and stable nanofiber diameters and rich mesopores on the surface (pore size of about 10 nm) were obtained through electrospinning. Then, the ultrasonic dip coating–drying process was used to firmly anchor the rGO/GO on the fiber surface to obtain a stable and uniform flexible rGO/GO@CA/TPU sensor. The experimental results showed that rGO was uniformly wrapped on the surface of the CA/TPU fibers, and interconnected to form a good conductive network. The GO on the surface of rGO@CA/TPU prevented the overlapping of rGO during straining and provided a basis for obtaining stable electrical signals. The optimization of the circuit structure with GO also significantly improved the sensitivity of the sensor by 33.1% compared with that of rGO@CA/TPU. Finally, the sensor that we obtained was able to realize real-time monitoring of the strain process under an extremely low strain (0.5%), and its sensitivity could reach 3.006. In addition, the sensor is wearable and can detect subtle movements of the human body, including the bending of the fingers, bending of the elbows, and so on. It can be applied for medical detection and in sports health monitoring. This provides new ideas for designing flexible and high-sensitivity wearable electronic devices; however, during the experiment, we found that the anchoring and adsorption of the graphene nanosheets on the fiber surface were not tight. In addition, the tensile resilience of the sensor was poor, and the film only showed good test results under low strain tests. It is necessary to further improve the resilience performance of the cellulose-based electrospinning membrane after stretching in subsequent experiments to improve the versatility of strain sensing. Author Contributions Y.L. proposed a design scheme for the sensor. Y.Y. and T.Y. tested the sensor performance and completed the paper. H.Z. and C.L. analyzed the sensor mechanism. All authors have read and agreed to the published version of the manuscript. Funding This study was supported by the National Natural Science Foundation of China (NSFC, 22068004), the Natural Science Foundation of Guangxi, China (2020GXNSFAA159027, 2019GXNSFBA185006, 2020GXNSFBA159023), the Foundation (No.2019ZR03) of Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, College of Light Industry and Food Engineering, Guangxi University, and the Open Funding Project of the State Key Laboratory of Biocatalysis and Enzyme Engineering (SKLBEE2020009). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare that they have no conflict of interest. Figure 1 Schematic diagram of the whole preparation process of the GO/rGO@CA/TPU nanofiber film. Figure 2 Effect of the viscosity of the spinning fluid on fiber morphology and the distribution of the fiber diameter. Figure 3 BET analysis of the electrospun fiber surface at different concentrations. Figure 4 SEM micrographs of the electrospun fiber surface: (a) 14% concentration and (b) 16% concentration. Figure 5 The surface micromorphology of anchored graphene fibers: (a) plan view of nanofibers after anchoring rGO; (b) cross-sectional view of nanofibers after anchoring rGO; (c) plan view of nanofibers after anchoring GO; (d) anchor cross-section of nanofibers after GO. Figure 6 C1S high-precision photon energy spectrum of the film: (a) CA/TPU film, (b) rGO@CA/TPU film, and (c) GO/rGO@CA/TPU film. Figure 7 Tensile strain response of the CA/TPU film before and after graphene anchoring. (a) Resistance response curves of rGO@ CA/TPU under different strains; (b) resistance response curves of rGO/GO@CA/TPU under different strains; (c) resistance response curves of rGO/GO@CA/TPU at different stretching rates under 10% strain; (d) resistance response curves of rGO/GO@CA/TPU at different stretching rates under 5% strain. Figure 8 Monitoring of the motion of the human body with the functional flexible sensor. (a) Monitoring of the lateral flexion and extension of fingers, (b) monitoring of the lateral flexion of the wrist, (c) monitoring of the dorsal flexion and extension of the knee. sensors-22-03281-t001_Table 1 Table 1 Specific surface area and average pore diameter of the composite nanofiber film. Concentration 12% 14% 16% 18% 20% Specific surface area (m2/g) 1.7754 4.5503 4.6869 4.2085 2.8931 Aperture (nm) 11.2880 8.1812 7.7864 6.8551 4.4871 sensors-22-03281-t002_Table 2 Table 2 Changes in the elemental content of the nanofiber films during successive anchoring. Electrospun Film C1S (%) O1S (%) N1S (%) CA/TPU 78.40 15.56 6.04 rGO@CA/TPU 89.86 8.39 1.75 GO/rGO@CA/TPU 78.34 18.32 3.34 sensors-22-03281-t003_Table 3 Table 3 The maximum strain sensitivity coefficient of each cellulose-based conductive film with different strain variables. Strain/% GF rGO@CA/TPU GO/rGO@CA/TPU 0.5 2.258 3.006 1.0 1.954 2.479 5.0 1.047 1.191 10.0 1.087 1.387 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Li S. Xiao X. Hu J. Dong M. Zhang Y. Xu R. Wang X. Islam J. Recent Advances of Carbon-Based Flexible Strain Sensors in Physiological Signal Monitoring ACS Appl. Electron. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092304 cancers-14-02304 Article MEOX2 Regulates the Growth and Survival of Glioblastoma Stem Cells by Modulating Genes of the Glycolytic Pathway and Response to Hypoxia https://orcid.org/0000-0002-2240-1759 Proserpio Carla 1 Galardi Silvia 1 Desimio Maria Giovanna 2 https://orcid.org/0000-0002-9133-8482 Michienzi Alessandro 1 https://orcid.org/0000-0001-5533-0304 Doria Margherita 2 https://orcid.org/0000-0003-4784-4297 Minutolo Antonella 3 https://orcid.org/0000-0002-6058-1092 Matteucci Claudia 3 https://orcid.org/0000-0001-6467-8396 Ciafrè Silvia Anna 1* Piperi Christina Academic Editor El-Habr Elias A. Academic Editor 1 Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy; carla.proserpio8@gmail.com (C.P.); silvia.galardi@uniroma2.it (S.G.); alessandro.michienzi@uniroma2.it (A.M.) 2 Research Unit of Primary Immunodeficiencies, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; mariagiovanna.dsm@gmail.com (M.G.D.); doria@uniroma2.it (M.D.) 3 Department of Experimental Medicine, University of Rome Tor Vergata, 00133 Rome, Italy; antonella.minutolo@uniroma2.it (A.M.); matteucci@med.uniroma2.it (C.M.) * Correspondence: ciafre@uniroma2.it 06 5 2022 5 2022 14 9 230428 1 2022 04 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary Glioblastoma is the most common incurable primary brain tumor in adults, typically leading to death within 15 months of diagnosis. Although there is an ongoing debate in the scientific community about the precise cellular origin of this tumor, glioblastoma stem cells (GSCs), which are able to self-renew, yield a full tumor mass, and determine chemo- and radio-resistance, are recognized to have a pivotal role. Our research aims to understand the role of the mesenchyme homeobox 2 (MEOX2) transcription factor in GSCs where it is strongly and specifically expressed. We have found that MEOX2 is indeed important for the survival of these cells. In fact, when we reduce its expression in two different GSC lines, they undergo a massive death accompanied by the inhibition of key genes of the glycolytic metabolism, the main source of energy for these cells. Our results reveal a novel function for MEOX2 in glioblastoma and suggest a mechanism through which GSCs may survive even in unfavorable conditions. Abstract The most widely accepted hypothesis for the development of glioblastoma suggests that glioblastoma stem-like cells (GSCs) are crucially involved in tumor initiation and recurrence as well as in the occurrence of chemo- and radio-resistance. Mesenchyme homeobox 2 (MEOX2) is a transcription factor overexpressed in glioblastoma, whose expression is negatively correlated with patient survival. Starting from our observation that MEOX2 expression is strongly enhanced in six GSC lines, we performed shRNA-mediated knock-down experiments in two different GSC lines and found that MEOX2 depletion resulted in the inhibition of cell growth and sphere-forming ability and an increase in apoptotic cell death. By a deep transcriptome analysis, we identified a core group of genes modulated in response to MEOX2 knock-down. Among these genes, the repressed ones are largely enriched in genes involved in the hypoxic response and glycolytic pathway, two strictly related pathways that contribute to the resistance of high-grade gliomas to therapies. An in silico study of the regulatory regions of genes differentially expressed by MEOX2 knock-down revealed that they mainly consisted of GC-rich regions enriched for Sp1 and Klf4 binding motifs, two main regulators of metabolism in glioblastoma. Our results show, for the first time, the involvement of MEOX2 in the regulation of genes of GSC metabolism, which is essential for the survival and growth of these cells. MEOX2 glioblastoma stem cells sphere formation glycolytic enzymes Fondazione Giovanni CeleghinUniversity of Tor VergataThis research was funded by a grant from “Fondazione Giovanni Celeghin”, and partially by a University of Tor Vergata “Beyond borders” grant to S.A.C. ==== Body pmc1. Introduction Glioblastoma (GBM) is a grade 4 glioma, the highest among these brain tumors, inevitably fatal, and characterized by a very short survival after diagnosis (i.e., 20.9 months, notwithstanding the latest therapeutic options) [1,2,3]. A widely accepted classification of glioblastoma based on gene expression patterns was proposed in 2010 by Verhaak et al., distinguishing four subtypes, namely classical (CL), mesenchymal (MES), proneural (PN), and neural (NL) tumors [4]; more recently the same group revised this classification, removing the NL subtype, and highlighting the plasticity of all subtypes, able to switch from one to another [5]. A recent wide-range study has proposed that most glioblastomas may be subtyped in two main groups, type I and type II, reflecting two different cell-of-origin lineages characterized by either high EGFR and SOX9 or high ERBB3 and SOX10 expression, respectively [6], that has important therapeutic implications. Extensive research on the initiation of this tumor has led to the general agreement that glioblastoma likely arises from stem-like cells though their origin is still highly debated: a hypothesis of normal stem or progenitor cells undergoing specific genetic aberrations is opposed by an alternative model of committed cells de-differentiation into stem-like cells [7]. Whatever is their origin, these glioblastoma stem like-cells (GSCs) are deemed responsible for the high chemo and radio-resistance of this tumor and for its recurrence, in most cases, the ultimate cause of patient death [8,9]. GSCs reflect the huge diversity typical of glioblastoma and represent an ideal model to study the tumor’s molecular basis. In the last decade, many efforts have been made to uncover gene expression signatures that are pivotal for GSC functions, and interesting results are now available about the transcriptome and the proteome of glioblastoma and its initiating cells [10,11,12,13,14,15,16,17]. Transcription factors containing homeodomains work as developmental regulators and are strongly implicated in tumors, including glioblastoma [18,19,20,21]. While many of these factors are encoded by clustered HOX genes, MEOX2 is encoded by an isolated gene located on human chromosome 7p21.2 [22]. A few papers have been published to date on the role of MEOX2 in glioblastoma, overall showing its overexpression in all types of glioma vs. healthy brain, a negative correlation of MEOX2 expression with survival, and also an enrichment of its expression in GBM patients who do not respond to radiotherapy [23,24,25]. MEOX2 expression was described as part of the molecular signature of the CL subtype [4] and was included in a 17-gene high-risk signature correlating with overall survival in mesenchymal glioblastomas [26]. One recent paper showed that GSCs could be classified into two groups based on distinct enhancer profiles and on the differential activity of specific developmental transcription factors, among which MEOX2 characterizes group 1 with PN and CL features [27]. These findings are important because, not only do they proposing a chromatin-based landscape definition of glioblastomas, they identify core transcription factors required for the growth of glioma cells of the two different subgroups and possibly represent druggable targets. Even more recently, a paper described the nuclear localization of MEOX2 in both GSCs and in glioblastoma tissues, suggesting its potential involvement in GSC phenotype and adhesion properties [28]. However, the investigation of the role of MEOX2 in glioblastoma stem cells is in its infancy, and we still need to understand the molecular mechanisms linking MEOX2 with the onset and aggressiveness of glioblastoma. Herein, we show that MEOX2 is strongly overexpressed in GSCs compared to stable cell lines, and we demonstrate that MEOX2 function is important for specific features of glioblastoma stem cells, in particular their survival and their ability to form spheres. 2. Materials and Methods 2.1. Cell Culture Two human glioblastoma multiforme cell lines, U87 and T98G, and patient-derived glioblastoma stem cell (GSC) lines (a generous gift from I.R.C.C.S. Foundation, Neurological Institutes Carlo Besta, Milan, Italy), were used as experimental models. All the GSCs were derived from surgical samples of consecutive primary GBMs, which were obtained at the Fondazione IRCCS Istituto Neurologico C. Besta, according to a protocol approved by the institutional Ethical Committee, and were previously described [29,30]. For all experiments, GSCs were grown in vitro for less than 10 passages. As described in De Bacco et al., 2021 [29], and with reference to Wang Q. et al. 2017 classification [5], BT373, BT462, and BT273 were classified as Proneural, BT517, BT379, and BT417 as Classical; based on Wang Z. et al. 2020 subtyping [6], BT373 were group II, BT462 and BT417 group I, BT517, BT273, and BT379 group non I-II. Normal human astrocytes isolated from human cerebral cortex (ScienCell #1800) were cultured in Astrocyte Medium (ScienCell #1801). Total human brain RNA from 3 healthy donors was purchased from Clontech (# 636530). U87 and T98G were cultured as adherent cells in DMEM (Corning, Corning, NY, USA) supplemented with 10% FBS (Aurogene, Roma, Italy), 1% penicillin/streptomycin (Corning), and 1% L-glutamine (Aurogene). GSCs were cultured as floating spheres in DMEM/F-12 (1:1) (1X) + GlutaMAX (Gibco, Waltham, MA, USA) containing 1% penicillin/streptomycin (Corning), 1% L-glutamine (Aurogene), 2% B27 (Gibco), 0.1% heparin (Sigma-Aldrich, Waltham, MA, USA), 0.002% bFGF (PeproTech, Suzhou, China), and 0.002% EGF (PeproTech) at 37 °C in a humidified 5% CO2 incubator. 2.2. Lentiviral Vectors and Infections To deplete MEOX2 endogenous expression, BT273 and BT379 cells were transduced with lentiviral particles containing the pLKO.1 vector (Merck, Kenilworth, NJ, USA) expressing shRNAs directed against MEOX2, or the SHC001 negative control vector (Merck). The MEOX2-targeting shRNA sequences were TRCN0000018253 (Merck), which we renamed shRNA53 for simplicity, and TRCN0000427218 (Merck), renamed shRNA18 for simplicity. While shRNA53 is directed against MEOX2 3′UTR, shRNA 18 targets the coding sequence of MEOX2. The sequences of the shRNA inserts were the following:shRNA53: 5′-CCGGGCATTCATATTAGCTGATGAACTCGAGTTCATCAGCTAATATGAATGCTTTTT-3′ shRNA18: 5′-CCGGCATCAGAGCTGTCGGGAATTGCTCGAGCAATTCCCGACAGCTCTGATGTTTTTTG-3′ For the production of lentiviral particles, lentiviral vectors were co-transfected with the packaging vectors pLP1, pLP2, and VSV-g (Invitrogen, Waltham, MA, USA) into HEK293T cells using LipofectamineTM 3000 (Life Technologies) according to the manufacturer’s instructions. For transduction, 1 mL of concentrated viral supernatant and 1 µL of polybrene® (8 µg/µL, Sigma) were added to the cell pellet. The transduced cells were then centrifuged at 2000× g for 1 h at room temperature. Finally, the viral supernatant was removed, and washed with DMEM/F-12 (1:1) (1X) + GlutaMAX (Dulbecco’s Modified Eagle’s Medium F-12 Nutrient Mixture, Gibco) was performed. The transduced cells were then grown in 6 mL of DMEM/F-12 culture medium, and after 48 h, 0.75 µg/mL of puromycin (Sigma) was added for the selection of stably transduced cells. 2.3. RNA Extraction and qRT-PCR The total RNA was prepared from the transfected cells using TRIzol®Reagent (Invitrogen) according to the manufacturer’s instructions or from the GSCs using the Direct-zol™ RNA MiniPrep kit (Zymo Research, Irvine, CA, USA). RNA was quantified using a NanoDrop ND 1000 Spectrophotometer (Thermo Scientific), and 1 µg of RNA was treated with DNase I RNase-free (Biolabs, San Francisco, CA, USA). Then it was reverse transcribed using M-MLV RT (Invitrogen) following the manufacturer’s instructions. The resulting cDNA (25 ng) was used for the Real-time qPCR analysis using the Luna® Universal qPCR Master Mix (New England Biolabs, NEB, Ipswich, MA, USA) on a StepOnePlus instrument (Applied Biosystem, Waltham, MA, USA) according to the protocol provided by the manufacturer. The primers used were the following:ACTIN Forward: 5′-GCACTCTTCCAGCCTTCC-3′ ACTIN Reverse: 5′-TGTCCACGTCACACTTCATG-3′ MEOX2 Forward: 5′-GCAAGAGGAAAAGCGACAG-3′ MEOX2 Reverse: 5′-CTTTCCTGGGTTTGCTGTTG-3′ PPP2CA Forward: 5′-AGGAGCTGGTTACACCTTTG-3′ PPP2CA Reverse: 5′-GCACCAGTTATATCCCTCCATC-3′ 2.4. Protein Extraction and Western Blot Analysis Cells were centrifuged at 1200 rpm for 10 min at 4 °C. The cellular pellet was lysed in NP40 Buffer (150 mM NaCl, 50 mM Tris-HCl pH 8.0, 0.5% NP40, 10% glycerol) plus protease inhibitor cocktail 50X (Promega, Milan, Italy), incubated on ice for 30 min and centrifuged at 13,000 rpm for 30 min at 4 °C. The supernatant was then collected into a new tube, and protein concentration was determined by the Bradford method. Equivalent amounts of protein extract were separated by electrophoresis on 10% or 12% SDS-PAGE gels and blotted onto nitrocellulose. The membranes were blocked with 5% non-fat dry milk and 0.1% Tween-20 in Phosphate-buffered saline and then incubated with antibodies followed by the appropriate horseradish peroxidase-conjugated secondary antibodies (1: 8000, Promega, Milan, Italy). After three washes in PBS Tween-20 0.1%, the signal was developed with the ECL system (Santa Cruz Biotechnology, INC., Dallas, TX, USA) according to the manufacturer’s protocol. The primary antibodies employed for protein detection were: Anti-alpha-Tubulin (Sigma-Aldrich, T8203, 1:5000), Anti-MEOX2 (Sigma, HPA053793, 1:2250), Anti-cleaved Caspase-3 (Cell Signaling Technology, Danvers, MA, USA, BK9664, 1:1000), anti-Caspase-3 (GeneTex, Irvine, CA, USA, GTX110543, 1:1000), anti-HK2 (abcam, ab209847, 1:1000), anti-AldoC (GeneTex, GTX102284, 1:1000), anti-PFKFB4 (GeneTex, GTX107755, 1:1000). Original Western Blot figures shown in File S1. 2.5. Sphere Formation Analysis BT273 and BT379 were plated in triplicate in a 12-well plate (3000 cells/well) in DMEM/F-12 (1:1) (1X) + GlutaMAX (Gibco). After seven days, the number and size of the neurospheres were evaluated by acquiring photos of the wells (Nikon ECLIPSE TS100). We scored as actively growing spheres those with a diameter ≥ 50 µm. Three experiments were performed in triplicate. 2.6. Growth Assay on Geltrex® Coated Plates Then, 0.3 mL Geltrex® Ready-To-Use matrix (Gibco) was used for coating the wells of a 24-well plate, which was subsequently incubated for one hour at 37 °C to allow gelling of the matrix. At the time of use, the liquid layer above the Geltrex® coating was aspirated off, and the GSCs were transduced with the viral supernatant pLKO.1-puro-ctrl, or pLKO.1-puro-shRNA18 or pLKO.1-puro-shRNA53, in pre-equilibrated DMEM/F-12 (1:1) (1X) + GlutaMAX (Gibco) and were immediately plated (20,000 cells/well) in triplicate. Cells were detached with 50 µL of Trypsin-EDTA 1X in PBS (EuroClone, Pero, Italy) and 50 µL of DPBS 1 × (Dulbecco’s Phosphate-Buffered Saline, Corning) were added to the detached cells. Then, 50 µL of this cell suspension were mixed with 50 μL of trypan blue stain 0.4% (Gibco) to be counted at the different time points using a Neubauer chamber (Marienfeld). Live cells were counted using a Nikon ECLIPSE TS100 microscope. 2.7. Cytofluorimetric Analysis of Apoptosis Apoptosis was assessed by flow cytometry analysis using a CytoFLEX (Beckman Coulter, Boulevard Brea, CA, USA) on isolated nuclei stained with Propidium Iodide (PI) (Merck KGaA, Darmstadt, Germany) using a method that distinguishes nuclei from apoptotic, necrotic, or viable cells, as previously described [31]. Early apoptotic events were detected through double-staining of the cells with fluorescent annexin-V and with a 7-amino actinomycin D (7-AAD) solution. For this purpose, the “Annexin V-FITC Kit 7-AAD (IM3614, Beckman Coulter) was used according to the manufacturer’s instructions. Briefly, 5 × 105 cells were incubated for 15 min with annexin-V-fluorescein isothiocyanate and then washed in annexin buffer. Cells were then stained with 7-AAD and analyzed immediately after staining by flow cytometry analysis. Data acquisition and analyses were performed using the CytExpert 2.0 (Beckman Coulter, Carlsbad, CA, USA) using a minimum of 150,000 events for each sample. 2.8. RNA-Seq Analysis of BT273 and BT379 GSCs The total RNA was extracted from cells using the Direct-zol RNA MiniPrep Kit (Zymo Research). The RNA library preparation, sequencing reaction, and bioinformatics analysis were conducted at GENEWIZ Germany GmbH (Leipzig, Germany). The extracted RNA samples were quantified using a Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA), and the RNA integrity was checked using an Agilent Fragment Analyzer (Agilent Technologies, Palo Alto, CA, USA). The RNA sequencing libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina using the manufacturer’s instructions (New England Biolabs, Ipswich, MA, USA). Briefly, mRNAs were initially enriched with Oligo d(T) beads. The enriched mRNAs were fragmented for 15 min at 94 °C. First-strand and second-strand cDNA were subsequently synthesized. cDNA fragments were end-repaired and adenylated at 3’ends, and universal adapters were ligated to cDNA fragments, followed by index addition and library enrichment by PCR with limited cycles. The sequencing libraries were validated on the Agilent Fragment Analyzer (Agilent Technologies, Palo Alto, CA, USA) and quantified by using the Qubit 2.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA). The sequencing libraries were multiplexed and clustered on the flowcell. After clustering, the flowcell was loaded onto the Illumina NovaSeq 6000 instrument according to the manufacturer’s instructions. The samples were sequenced using a 2 × 150 Pair-End (PE) configuration. The raw sequence data (.bcl files) generated from Illumina NovaSeq were converted into fastq files and de-multiplexed using the Illumina bcl2fastq program version 2.20. One mismatch was allowed for index sequence identification. After investigating the quality of the raw data, sequence reads were trimmed to remove possible adapter sequences and nucleotides with poor quality using Trimmomatic v.0.36. The trimmed reads were mapped to the Homo sapiens GRCh38 reference genome available on ENSEMBL using the STAR aligner v.2.5.2b. BAM files were generated as a result of this step. Unique gene hit counts were calculated by using feature Counts from the Subread package v.1.5.2. Only unique reads that fell within exon regions were counted. After the extraction of gene hit counts, the gene hit counts table was used for downstream differential expression analysis. Using DESeq2, a comparison of the gene expression between the groups of samples was performed. The Wald test was used to generate p values and Log2 fold changes. Genes with adjusted p values < 0.05 and absolute log2 fold changes >0.7 were identified as the differentially expressed genes for each comparison. The RNAseq data (fastq files) were deposited in the GEO database under the accession number GSE196141. Gene ontology analysis was performed on the statistically significant set of genes by implementing the GeneSCF software. The GO list was used to cluster the set of genes based on their biological process and to determine their statistical significance. 2.9. Transcription Factors (TF) Binding Sites Enrichment Analysis TF binding sites over-representation analysis was performed using oPOSSUM (v.3.0, Single Site Analysis tool) [32]. Differentially regulated genes were used as targets, and all genes measured by our RNA-seq were used as a background (conservation cutoff: 0.6; matrix score threshold: 85%; upstream/downstream region: 5kb/5kb; JASPAR CORE Profiles: All vertebrate profiles). 3. Results 3.1. MEOX2 Depletion Inhibits the Sphere-Forming Ability and Induces Apoptosis in Glioblastoma Stem Cells We analyzed the MEOX2 expression in six patient-derived GSCs populations and found that it was strongly expressed in all cell lines, whereas it was absent or barely expressed in all other non-stem samples used as controls, either healthy brain tissue and astrocytes or stable glioblastoma cell lines (Figure S1). With the aim of unravelling if such a strong overexpression of MEOX2 plays a functional role in GSCs, we depleted it by shRNAs delivered via lentiviral vectors. We initially assayed two shRNAs, namely shRNA18 and shRNA53, in two different GSC lines, BT379 and BT273, chosen as they belong to two different subtypes, i.e., the CL and the PN ones, respectively [30], and express comparable high levels of MEOX2 mRNA (Figure S1). In both BT379 and BT273 lines, shRNA18 reduced MEOX2 more strongly than shRNA53 at the mRNA and protein levels (Figure 1a–d). Given that self-renewal is a key aspect of cancer stem cells, we tested if MEOX2 depletion affected BT273 and BT379’s ability to reassemble into new spheres after dissociation to single cells. As shown in Figure 1e,f, in both cell lines, the strong reduction in MEOX2 expression obtained by shRNA18 was associated with a drastic decrease in the sphere-forming ability. This GSC activity, conversely, was only slightly affected by shRNA53, which was unable to effectively knock down MEOX2. Of note, not only was the number of spheres reduced upon MEOX2 knock-down, but also their size, in particular in BT379 cells. This may indicate that MEOX2 depletion affects not only the bare ability of GSCs to form new spheres but also their growth and/or viability. Then, we plated both BT379 and BT273 cells transduced with either shRNA18 or shRNA53 onto Geltrex coated plates, with the aim of counting the number of living cells while avoiding the formation of spheres. The results of such assays, depicted in Figure 1g,h, clearly show that MEOX2 knock-down had an impact on the growth ability of both cell lines, even if with some differences. In BT273 cells, MEOX2 depletion resulted in a clear reduction in viability, which was significant at 72 and 96 h from plating (Figure 1g). For BT379 cells, a reduction was evident in knocked down cells at 24 h after plating and slowly recovered at later time points (Figure 1h), suggesting that those cells that succeeded in attaching and then regained a growth rate similar to control cells. Thus, the depletion of MEOX2 in BT379 cells deeply affects their ability to survive and, consequently, to attach to plates. The reduced viability/increased death of MEOX2-depleted cells might account for the reduced size of the spheres observed in the sphere-formation experiments (Figure 1e,f). To check if the reduction in viability observed upon MEOX2 depletion may be due to the induction of the apoptotic pathway, we assayed Caspase-3 cleavage in BT273 and BT379 cells transduced with either shRNA18 or shRNA53. In both cell lines, MEOX2 knock-down mediated by both shRNAs induced an increase in Caspase 3 processing, even if this was to different extents (Figure 2a). In addition, Caspase 3 activation was much stronger in BT379 than in BT273 cells, confirming our data on the stronger reduction in the viability of MEOX2-depleted BT379 cells. In agreement with these data, when we assessed the extent of apoptotic cell death by a propidium iodide-based flow cytometric assay, we found this was induced in both BT273 and BT379 upon MEOX2 depletion by shRNA18 or shRNA53 (Figure 2c), as also shown by flow cytometry analysis of 7-AAD (7-Aminoactinomycin D)-Annexin V staining (Figure 2b and Table 1). 3.2. MEOX2 Knock down Variably Modulates Gene Expression in Different GSC Lines, but Consistently Affects the Glycolytic Pathway and the Response to Hypoxia Starting from our original finding of a great enrichment of MEOX2 expression in GSCs, we aimed to understand its role in this specific stem cell environment. Thus, we analyzed the transcriptome of BT273 and BT379 cells depleted of MEOX2. Our screening of differentially expressed genes (DEGs) upon MEOX2 knock-down was performed by setting a threshold of absolute log2 fold change ≥ 0.7 (adjusted p-value ≤ 0.05). This yielded very different numbers of DEGs in BT273 compared to BT379 cells, upon transduction with shRNA18: 171 genes were differentially expressed (among which 88 were induced and 83 were repressed) in BT273 cells (Table S1), whereas in BT379 cells, 1459 genes were affected (673 induced and 787 downregulated) (Table S1). In addition, we noticed that BT273 cells transduced with shRNA53 (less efficient than shRNA18 in knocking down MEOX2) showed differential expression of only 142 genes, among which 38 were upregulated and 104 downregulated (Table S1). This result indicated that in glioblastoma stem cells, MEOX2 expression perturbations affect gene expression in a very variable way, possibly depending on the context of the specific cell. When we compared BT273 and BT379 cells transduced with shRNA18, we found 92 DEGs consistently modulated (48 downregulated and 45 upregulated), even if on such different backgrounds (Figure 3a,b and Table S1). Of note, 19 of the shared downregulated DEGs and seven of the upregulated ones were also (log2FC ≥ ±0.7 and adj p-value ≤ 0.05) modulated in BT273 cells transduced with shRNA53 (Figure 3a and Table S1), and an additional set of 24 (12 downregulated and 12 upregulated) were consistently modulated in BT273 cells-shRNA53, but either to a lesser extent or with a less significant p-value (Table S1). As a whole, this set of genes modulated in response to MEOX2 knock-down may represent a core collection of MEOX2 regulated genes in glioblastoma stem cells. With the aim of inferring the main biological functions affected by the depletion of MEOX2 in GSCs, we performed a Gene Ontology analysis of DEGs (both down- and upregulated; absolute log2 fold change ≥ 0.7; adjusted p-value ≤ 0.05) (Table S2). The top Biological Process clearly enriched in BT273 cells transduced with either shRNA18 or shRNA53 was “Response to hypoxia”, while the much greater number of DEGs in BT379-shRNA18 resulted in a strong enrichment of GO-BPs related to mitosis and mitotic spindle organization. The term “negative regulation of mitotic metaphase/anaphase transition (GO:0045841)” was the most enriched one. This may well reflect our observations of the very reduced viability of MEOX2 knocked-down GSCs, in particular in BT379. Interestingly, terms such as “neuron development”, “neurogenesis”, and other related terms were also enriched in the BT379 cell line. We also noticed that, out of the 13 genes, all downregulated, included in the GO-BP term “response to hypoxia” enriched in BT273 knocked down for MEOX2, eight were also significantly repressed in BT379 shRNA18 (Table S2). This suggests that MEOX2 knock-down results in an impaired ability of both BT273 and BT379 cell lines to react to hypoxic, unfavorable environments. We then submitted the 26 genes that were consistently modulated in all three comparisons (i.e., BT273 shRNA 18 vs. ctrl, BT273 shRNA53 vs. ctrl, BT379 shRNA18 vs. ctrl; see above) to Gene Ontology analysis and found a strong and significant enrichment of the Reactome Pathways “Glycolysis (R-HSA-70171)” and “Glucose metabolism (R-HSA-70326)” (Table S2). The four related DEGs (PFKFB4; ENO2; ALDOC; HK2) were downregulated in all three comparisons, and we also confirmed the repression of their protein products (Figure S2). This indicates that the DEGs shared in all three conditions mainly contribute to one shared biological function, which is the negative modulation of the glycolytic pathway. 3.3. Up- and Down-Regulated Genes in MEOX2-Depleted GSCs Differ for the GC Content in Regulatory Regions MEOX2 is a transcription factor that plays a wide range of roles in cell development and in cancer, functioning as either a direct or indirect activator of its target genes [33,34,35,36]. In many cases, the mechanistic basis of its function in the different contexts has not been clarified yet. In addition, the consensus binding site for MEOX2, C/TAATTA, is an A/T rich sequence common to several other homeobox transcription factors. Thus, we used our differential gene expression data to study if the genes modulated by MEOX2 knock-down in GSCs share regulatory regions, which might be those recognized by MEOX2 or its interactors. By employing oPOSSUM-3, a system for determining the over-representation of transcription factor binding sites (TFBS) and TFBS families within a set of genes [32], we analyzed all DEGs (both down- and upregulated, in both BT273 and BT379 cells, by MEOX2 KD. The binding sites of the six transcription factors (TFs) SP1, Klf4, MZF1_1–4, EBF1, MZF1_5–13, and INSM1 were significantly enriched within regulatory regions (Z score > mean + 2 SD; Figure 4a) of genes affected by MEOX2 KD. We observed that most of the highest-ranking enriched TFs (Z score ≥ 10.00; Table S3) recognize DNA sequences with a high (>0.66) GC content. Interestingly, when we separately submitted downregulated or upregulated DEGs to oPOSSUM-3 analysis (defined as the “core down” or “core up” in Table S2), we observed that the top-ranking TF binding sites shared by downregulated genes were mainly represented by GC-rich regions, while the opposite was true (GC content < 0.33) when the analysis was performed on DEGs upregulated in response to MEOX2 KD (Figure 4b,c and Table S3). Sp1 and Klf4, both main regulators of metabolism in glioblastoma [37,38], were the highest-ranking transcription factors whose motifs were enriched in all DEGs, but in particular in the shared downregulated genes. We also obtained analogous results when we submitted DEGs from each of our cell types (BT273 or BT379), transduced with either shRNA (shRNA18 or shRNA53), compared to the controls (Figures S3 and S4 and Table S4). These results suggest that MEOX2 knock-down affects GSC biology by repressing a set of glycolysis-related genes, possibly interfering with their regulation by the transcription factors Sp1 and Klf4. 4. Discussion Reflecting on the considerable heterogeneity of glioblastomas, the nature of GSCs is highly heterogeneous, which favors the capacity of these cells to elude therapies. Several attempts to classify glioblastomas and their initiating cells into functional and molecular groups have not achieved the goal of understanding the basis of such heterogeneity. In this complex frame, transcription factors surely play major roles in the maintenance of the varied phenotypes of cancer stem cells. MEOX2 is a homeodomain-containing transcription factor whose expression is extremely enriched in gliomas compared to all other types of tumors and was very recently found to be highly expressed in GSCs as well [28]. We were intrigued by the extreme overexpression of MEOX2 in all six GSC samples we analyzed, possibly indicating a role for this factor in glioblastoma initiating cells. MEOX2 RNA levels were all very high in our small cohort of GSCs, with no apparent distinction between subtypes, as defined by either Wang Q. et al., 2017 [5] or Wang Z. et al., 2020 [6]. As previously shown by us and others, MEOX2 is known to be overexpressed in glioblastomas compared to healthy brain cells [23,24,25,39], and our present data further highlighted that GSC lines express MEOX2 while established non-stem glioblastoma cell lines essentially lack its expression. The reported inhibition of MEOX2 expression by serum-containing media in vascular smooth muscle cells [40] is not sufficient to explain its absence in established cell lines, as we did not observe any induction of MEOX2 upon the growth of those cells in serum-free conditions (data not shown). This favors a stem-specific expression/role of MEOX2, at least in the context of glioblastoma. As a readout of the stemness ability of GSCs, we measured their capacity to form new spheres and demonstrated that this function was inhibited after the stable knock-down of MEOX2. In addition, a clear reduction in viability characterized MEOX2-depleted cells that were prone to apoptotic death. We searched the transcriptome of the MEOX2 depleted cells to investigate the basis of the observed phenotypes, and, in line with the known great variability of glioblastoma stem cells, we found a large number of genes modulated by MEOX2 knock-down that were different in the two GSC lines we assayed. However, a “core set” of genes showed an overlapping differential expression as a consequence of MEOX2 repression. This group was impressively enriched in genes involved in the hypoxic response and in the glycolytic pathway, two strictly related pathways that contribute to the resistance of high-grade gliomas to therapies [41]. In fact, most solid tumors, and, in particular, glioblastomas, exploit the anaerobic glycolytic activity independent of oxygen supply in the so-called Warburg effect [42,43]. This phenomenon tightly applies to GSCs, whose metabolic shift is thought to be responsible for the high resistance to therapies of these cells upon induction of their self-renewal and invasive ability [44]. Notably, all the hypoxia- and glycolysis-related genes modulated in our GSCs after MEOX2 knock-down were negatively regulated. This suggests that MEOX2 may regulate the ability of GSCs to respond to the environment and to act as a metabolic shift. Indeed, when MEOX2 is repressed, these cells show high rates of apoptotic cell death. The four metabolic genes whose repression is shared in both the BT273 and BT379 cell lines when MEOX2 is knocked down are all known to be induced by hypoxia and play a key role in the glycolytic pathway and have all been proposed as possible therapeutic targets for glioblastoma. PFKFB4 is one of the four genes that encode 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase, and its mRNA levels significantly differentiate IDH1 wild-type primary glioblastomas from the secondary glioblastomas and from IDH mutant gliomas. Moreover, PFKFB4 expression is an unfavorable prognostic marker in glioblastoma, as it inversely correlates with survival [45]. HK2 encodes hexokinase 2, which mediates the first glycolytic step generating glucose-6-phosphate. HK2 is a key driver of metabolic regulation, growth, and resistance to therapy in GBM, and its chemical inhibition resulted in the effective reduction in tumor growth in xenograft models of glioblastoma [46]. ENO2 codes for Enolase2, which, together with ENO1, catalyzes the glycolytic production of phosphoenolpyruvate from 2-phosphoglycerate. Its repression strongly impairs tumor growth in glioblastoma xenografts [47]. Finally, ALDOC encodes for the C isozyme in the family of aldolases, which catalyzes the conversion of fructose 1,6-bisphosphatase to glyceraldehyde 3-phosphate and dihydroxyacetone phosphate (DHAP) during glycolysis, and is highly enriched in the brain as compared to any other healthy tissue, and in glioblastoma tissues [39], despite its mRNA levels being inversely correlated with glioma tumor grades (being higher in grade 2 and 3 gliomas, compared to grade 4 tumors) in one study [48]. Our prediction on the enrichment of binding sites for Klf4 and Sp1, and more generally for factors binding G-C rich motifs, in the regulatory regions of genes repressed by MEOX2 knock-down may indicate a mechanism through which MEOX2 works in GSCs, that is, acting as a modulator of the epigenetic state of G-C rich regulatory regions, also via the interference with Klf4 and Sp1 binding. More investigation is needed to unravel how MEOX2 could play this role, but a suggestive observation comes from ZNF395, one of the most commonly downregulated genes upon MEOX2 knock-down. ZNF395 is a transcription factor induced by hypoxia [49] and a mediator of the maximal hypoxic induction of proinflammatory cytokines in glioblastoma [50]. Its regulatory region largely overlaps with a CpG island, and ZNF365 itself is known to bind a CG-rich consensus sequence [51]. In the context of clear cell renal cell carcinoma, it was identified as a master regulator whose depletion results in tumor elimination, and it was shown that the epigenetic regulation of its transcription involves a “super-enhancer” [52]. These regions (defined as large enhancers located near genes encoding for master transcription factors of cell identity and disease) are relevant in glioblastoma and have been extensively studied in this tumor, together with their associated genes and core transcription factors that define the super-enhancers [53], particularly in GSCs, where they are pivotal to maintain GSC identity. Notably, MEOX2 was shown to be aberrantly activated in one of the two subgroups into which GSCs were classified based on super-enhancer chromatin states and was considered one of the master transcription factors of this subgroup [27]. It is thus intriguing to hypothesize that the results we obtained in GSCs depleted of MEOX2 are due to the role of MEOX2 as a master transcription factor involved in the modulation of the epigenetic state of super-enhancers in GSCs. Among these super-enhancers, MEOX2 might modulate those that drive the expression of ZNF395, and hypoxia-related genes, such as HK2 and VEGFA, that we found repressed by MEOX2 depletion. A limitation of our current study is that our functional results were obtained in two GSC lines only. However, our study stemmed from our original observation of an extremely high enrichment of MEOX2 expression in all six of the GSC lines we assayed. In support of our experimental data, extensive MEOX2 expression is also reported in a separate, larger set of 48 GSC cultures, whose gene expression data can be downloaded from the human glioblastoma cell culture (HGCC) portal at www.hgcc.com, accessed on 27 January 2022 [54]. In the context of the well-known heterogeneity of glioblastoma and of its initiating cells, this points to the significance of MEOX2 enrichment in GSCs. While our manuscript was in preparation, a paper was published about MEOX2 in GSCs [28]. The results described therein are different from those we obtained in our GSC lines, both from the functional and molecular points of view. In fact, the authors showed a slight increase in cell viability (actually induced by only one siRNA targeting MEOX2) in three GSC lines, and the increased phosphorylation of ERK1/2 and AKT, upon MEOX2 depletion. This led the authors to claim that MEOX2 depletion positively affects the growth of GSCs through ERK/MAPK and PI3K/AKT pathways. Moreover, in their RNA-seq experiments, they highlighted the induction of CDH10, encoding for cadherin 10, upon MEOX2 depletion. However, in our RNA-seq results, we did not find evidence of modulation of CDH10, which remained stable in our GSCs transduced with anti-MEOX2 shRNAs. While we cannot exclude those technical differences (e.g., siRNA transient transfection vs. stable lentivirally-mediated shRNA expression, with the consequentially different levels of reduction in MEOX2 expression, possibly different in different transiently transduced cells, the timing of the experiments) can account for such divergent results, the different nature of the GSC lines used might be important to explain them too. Indeed, in the original subtyping classification of glioblastomas [4], MEOX2 was initially reported as a marker of the CL subtype, and more recently, MEOX2 was shown to be aberrantly activated and one of the master transcription factors in Group 1 GSCs [27]. Thus, except for technical issues, the difference between our results and those achieved by Tachon and collaborators might once again indicate a cell type-specific role of MEOX2. Nevertheless, our results and those recently published in ref. n. 28 agree with the definition of MEOX2 as a key factor for different aspects of GSC biology. Further investigation and validation on a greater number of GSC lines are required to comprehend the molecular basis of MEOX2 action in depth and to overcome the preliminary nature of these functional studies, in turn, due to the limited number of GSC lines analyzed in both. 5. Conclusions In conclusion, our findings support the role of MEOX2 as an important transcription factor in glioblastoma stem cells, where its depletion profoundly represses key genes of the glycolytic pathway involved in the Warburg effect along with several other genes engaged in the high ability of GSCs to respond to hypoxic and other types of stress, making them resistant to therapies and to the microenvironment where they reside. Acknowledgments The authors wish to thank Serena Pellegatta, from Neurological Institutes Carlo Besta, Milan, Italy, for providing the established and characterized GSC lines used for this work. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14092304/s1, Figure S1: MEOX2 expression characterizes glioblastoma stem cells; Figure S2: MEOX2 knock-down induces the repression of key factors of the glycolytic pathway; Figure S3: Enrichment analysis of transcription factor binding motifs in the regulatory regions of genes upregulated upon MEOX2 knock-down; Figure S4: Enrichment analysis of transcription factor binding motifs in the regulatory regions of genes downregulated upon MEOX2 knock-down; Table S1: Genes Differentially Expressed (DEGs) upon MEOX2 knock down; Table S2: Gene Ontology analysis of DEGs upon MEOX2 knock down; Table S3: oPOSSUM-3 analysis of regulatory regions of DEGs upon MEOX2 knock down; Table S4: oPOSSUM-3 analysis of regulatory regions of DEGs upon MEOX2 knock down, in each of the analyzed cell types (BT273 or BT379), transduced with either shRNA (shRNA18 or shRNA53), compared to the controls. File S1: Original Western Blot figures. Click here for additional data file. Author Contributions Conceptualization, S.A.C. and S.G.; methodology, M.G.D., M.D.; validation; formal analysis, C.P., C.M., S.A.C.; investigation, C.P., S.G., A.M. (Antonella Minutolo); project administration, S.A.C.; data curation, C.P., A.M. (Antonella Minutolo), S.G., S.A.C.; writing—original draft preparation, S.A.C.; writing—review and editing, S.G. and A.M. (Alessandro Michienzi); funding acquisition, S.A.C. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The panel of six patient-derived glioblastoma stem cell (GSC) lines was a generous gift from Dr. Serena Pellegatta, I.R.C.C.S. Foundation, Neurological Institutes Carlo Besta, Milan, Italy), All the GSCs were derived starting from surgical samples of primary GBMs, which were obtained at the Fondazione IRCCS Istituto Neurologico C. Besta, according to a protocol approved by the institutional Ethical Committee [29,30]. Informed Consent Statement Not applicable. Data Availability Statement All the data and material are available on reasonable request from the corresponding author. Conflicts of Interest The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 1 MEOX2 depletion inhibits the sphere-forming and the growth ability of glioblastoma stem cells BT273 and BT379. (a,b) MEOX2 qRT-PCR analysis of BT273 (a) or BT379 (b) cells transduced with shRNA18 or shRNA53 or ctrl lentiviral constructs. The values were reported in relation to cells transduced with ctrl vector set as = 1 and normalized to PPP2CA mRNA expression (n = 3; mean ± SD). (c,d) MEOX2 Western blot analysis of BT273 (c) or BT379 (d) cells transduced with shRNA18 or shRNA53 or ctrl lentiviral constructs. For BT273 and BT379, α-tubulin and β-actin were used as the internal loading controls, respectively. Representative images are shown. The bottom histograms show the quantification of MEOX2 in relation to α-tubulin and β-actin. (n = 3; mean ± SD). (e,f) Sphere-forming assay of BT273 (e) and BT379 (f) cells transduced with shRNA18 or shRNA53 or ctrl lentiviral constructs. Histograms show the percentage of cells capable of re-forming a neurosphere seven days after dissociation (n = 3; mean ± SD). Representative micrographs are shown. (g,h) Growth curves of BT273 (g) and BT379 (h) cells transduced with shRNA18 or shRNA53 or ctrl lentiviral constructs. (n = 3; mean ± SD). Differences between two groups were assessed using unpaired Student’s t-test (two-tailed). Significance was defined as * p < 0.05; ** p < 0.01; *** p < 0.001, statistical difference compared to the control. Figure 2 MEOX2 depletion induces the apoptosis of glioblastoma stem cells BT273 and BT379. (a). Cleaved Caspase-3 Western blot analysis of BT273 and BT379 cells transduced with shRNA18 or shRNA53 or ctrl lentiviral constructs. Total Caspase-3 is shown. β-actin was used as the internal loading control. (n = 3; mean ± SD). One representative image is shown. The histograms on the right show the quantification of Cleaved Caspase-3 in relation to β-actin. (Differences between two groups were assessed using unpaired Student’s t-test (two-tailed). Significance was defined as * p < 0.05; ** p < 0.01, statistical difference compared to the control). (b). One representative dot plot of the percentage of Annexin V/7AAD positive and negative cells analysed by Flow cytometry analysis; the mean values ± s.d. of three independent experiments performed were reported in Table 1. (c). FACS analysis based on Propidium Iodide staining of hypodiploid nuclei fraction (%) in either BT273 or BT379 GSCs transduced with shRNA18 or shRNA53. Data are presented as mean values ± s.d. Differences between two groups were assessed using unpaired Student’s t-test (two-tailed). Significance was defined as ** p ≤ 0.01. Figure 3 MEOX2 knock down in glioblastoma stem cells differentially modulates gene expression in different cells, but consistently affects some subsets. (a,b) Venn diagrams showing the numbers and percentages of DEGs (absolute log2 fold change ≥ 0.7, adjusted p-value ≤ 0.05) in BT273 or BT379 cells transduced with either shRNA18 or shRNA53, compared to the same cell types transduced with a negative control. Panel a shows the downregulated genes, and panel b the upregulated ones. Each diagram is flanked, on the left, by the list of genes consistently modulated in all three conditions, i.e., BT273 shRNA 18, BT273 shRNA53, BT379 shRNA18. Figure 4 Enrichment analysis of transcription factor binding motifs in the regulatory regions of genes modulated by MEOX2 knock-down. (a) All DEGs (both down- and upregulated, in both BT273 and BT379 cells, by MEOX2 KD) were subjected to enrichment analysis of TF binding motifs using oPOSSUM-3 software. (b) Only downregulated DEGs analyzed as in (a). (c) Only upregulated DEGs analyzed as in (a). The names of the significantly enriched transcription factor binding motifs (Z score > mean + 2 SD) are shown. cancers-14-02304-t001_Table 1 Table 1 Percentage of Annexin V (ANX), 7-amino actinomycin D (7-AAD), ANX/7AAD)-positive, and percentage of Hypodiploid nuclei in BT273 or BT379 GSCs transduced with shRNA18 or shRNA53 and analysed by Flow Cytometry. Cells Percentage (%) ANX+7AAD- ANX+7AAD+ ANX-7AAD+ Hypodiploid Nuclei BT273 CTR 41.92 ± 12,71 7.62 ± 10.58 2.67 ± 2.07 10.19 ± 2.76 shRNA18 * 62.19 ± 15.65 4.29 ± 4.14 3.40 ± 4.52 13.18 ± 0.05 shRNA53 +** 71.79 ± 16.40 3.26 ± 1.01 7.79 ± 9.46 * 16.55 ± 1.04 BT379 CTR 29.37 ± 2.25 5.35 ± 3.34 7.05 ± 4.26 10.52 ± 6.61 shRNA18 * 67.54 ± 5.25 6.46 ± 5.18 0.90 ± 0.37 ** 43.52 ± 15.28 shRNA53 ++** 79.82 ± 3.38 12.99 ± 6.55 4.51 ± 4.58 ** 47.46 ± 10.19 * p ≤ 0.01, ** p≤ 0.001 vs. CTR. + p ≤ 0.01, ++ p ≤ 0.001 shRNA 18 vs. shRNA53. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Louis D.N. Perry A. Wesseling P. Brat D.J. Cree I.A. Figarella-Branger D. Hawkins C. Ng H.K. Pfister S.M. Reifenberger G. The 2021 WHO Classification of Tumors of the Central Nervous System: A summary Neuro Oncol. 2021 23 1231 1251 10.1093/neuonc/noab106 34185076 2. Fernandes C. Costa A. Osório L. Lago R.C. Linhares P. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093399 sensors-22-03399 Article Autonomous Vehicle State Estimation and Mapping Using Takagi–Sugeno Modeling Approach https://orcid.org/0000-0001-9693-1982 Chaubey Shivam https://orcid.org/0000-0002-6364-6429 Puig Vicenç * Nowicki Michał R. Academic Editor Grisetti Giorgio Academic Editor Camurri Marco Academic Editor Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens i Artigas, 4-6, 08028 Barcelona, Spain; shivam.chaubey1006@gmail.com * Correspondence: vicenc.puig@upc.edu 28 4 2022 5 2022 22 9 339930 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This paper proposes an optimal approach for state estimation based on the Takagi–Sugeno (TS) Kalman filter using measurement sensors and rough pose obtained from LIDAR scan end-points matching. To obtain stable and optimal TS Kalman gain for estimator design, a linear matrix inequality (LMI) is optimized which is constructed from Lyapunov stability criteria and dual linear quadratic regulator (LQR). The technique utilizes a Takagi–Sugeno (TS) representation of the system, which allows modeling the complex nonlinear dynamics in such a way that linearization is not required for the estimator or controller design. In addition, the TS fuzzy representation is exploited to obtain a real-time Kalman gain, avoiding the expensive optimization of LMIs at every step. The estimation schema is integrated with a nonlinear model-predictive control (NMPC) that is in charge of controlling the vehicle. For the demonstration, the approach is tested in the simulation, and for practical validity, a small-scale autonomous car is used. simultaneous localization and mapping linear matrix inequality Takagi–Sugeno linear quadratic regulator nonlinear model-predictive control Kalman filter European Regional Development Fund (ERFD)PID2020-114244RB-I00 This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020-114244RB-I00 ), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014-2020 (ref. 001-P-001643 Looming Factory) and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482). The author is also supported by a Industrial PhD AGAUR grant (Ref. 2019 DI 040). ==== Body pmc1. Introduction To solve the issue of traffic accidents and congestion, autonomous vehicles provide a promising solution [1]. Research work on this technology has immensely progressed from perception algorithms to vehicle control algorithms. The perception stack exploits the sensor measurements to provide the vehicle state as well as environmental information. The correction of these measurements is crucial to the safety of the vehicle when navigating within the environment. On the other hand, the planner and the controller use this information to plan the motion and achieve the desired trajectory while maintaining some objectives such as speed, time to reach the destination, and comfort. For such reasons, the measurement filtering and the obtaining of unmeasured states are paramount (e.g., lateral velocity in many cases can not be measured directly, which is important for lateral control). Advanced controllers, such as, e.g., NMPC, require a full state observation, which can be achieved using a state estimator in the loop for the full-body control. The state estimation of an autonomous vehicle involves an algorithm that fuses the raw information provided by the sensors, which is affected by noise, and the system model, which is affected by some uncertainty represented as a disturbance. The estimation algorithm considers the measurement model, vehicle model, sensor uncertainty, and model perturbation to estimate the correct states. The standard approach is based on some form of Kalman filter, originally developed by R.E Kalman [2]. A Kalman filter provides an optimal estimate in the least-square sense, assuming that the model perfectly matches with the real system such that the noises/disturbances are Gaussian and their covariances are known. The first assumption is not always true, especially in nonlinear systems with complex dynamics. A variant named extended Kalman filter (EKF) for the nonlinear systems has been developed, which provides the solution by linearizing the system and the measurement model around the current states. The linearization is based on a Taylor series expansion, rejecting higher-order terms. The linearization process does not preserve the random variable distributions of the states and the measurements as Gaussian, which means that the optimality condition is not guaranteed. For the systems with a complex nonlinear model, such as a vehicle, the assumption of a small range of operation for a linear model acquired by linearizing the nonlinear model is not valid. For all these reasons, new methods for extending Kalman filters to nonlinear systems are required. The TS framework offers a systematic approach to obtain a multi-model system from the original mathematical model of the system, see, e.g., for more detail, [3]. The main feature of this approach is that it allows representing the model of the nonlinear system as a set of linear system models and the overall TS model of the system is achieved by the polytopic combination of these linear system models. This allows extending the LMI design procedures for control and estimation to the nonlinear systems represented in polytopic TS form. Moreover, there exists a systematic procedure for generating the TS model for the nonlinear model and approximating it in a polytopic way [4]. In this paper, the state estimation of an autonomous vehicle is developed using an approach alternative to the use of EKF. Moreover, this approach will be combined with a LIDAR scan matching algorithm that provides a rough pose estimation. For LIDAR scan matching, the real-time correlative scan matching algorithm is being exploited instead of iterative closest point (ICP) [5], which has a drawback of expensive search for point correspondences in every iteration. As LIDAR scan matching is not within the scope of this paper, more details can be found in [6,7]. The methods such as Gmapping [8], which generally require a large number of particles to obtain good results, lead to higher computational complexity compared with our approach. In comparison with the methods such as LOAM [9], our method fuses the LIDAR and other sensor information with the accurate vehicle model prediction for precise localization. The proposed method can be extended to any degree of freedom (DoF) system if the model is known and additional sensors can be integrated. In our approach, dynamic states, such as longitudinal velocity and angular velocity, will be directly measured from the sensors. Then, the rough pose obtained from the LIDAR scan matching and direct measurements from sensors will be corrected using the TS Kalman filter design. A similar work [10] was carried out to improve the approximation error by utilizing the Takagi–Sugeno model; however, this approach considers a limited and known number of landmarks and neither ensures optimality. Another work [11] utilized a linear parameter varying (LPV) system, which is analogous to TS and has shown good performance in the simulation with different applications. To summarize, the contributions of our work are as follows:A TS Kalman filter is developed which does not need linearization at each step, as in the case of EKF. In addition, the estimator provides a stable and optimal solution. The design of the TS Kalman filter is solved offline using LMIs while online (in real-time), only the interpolation of the TS subsystems gains is required. A case study of a small-scale autonomous vehicle is presented to obtain the full state by correcting the raw measurement obtained from sensors and LIDAR scan end-point matching. The whole framework, including NMPC, is validated on a small computation board Odroid XU4. The low computational requirements of the framework, especially SLAM, make it easy to deploy on small robots. The structure of the paper is the following: Section 2 describes the proposed approach and presents the autonomous vehicle considered as a case study. Section 4 presents the proposed TS Kalman filter for the state estimation. Section 5 presents the simulation and experimental results using the considered case study. Finally, Section 6 draws the main conclusions of the paper and proposes future research paths. 2. Proposed Approach Figure 1 presents the overall architecture and integration of different modules for the autonomy. The proposed pipeline also includes an NMPC controller. Descriptions of the states x=[vx,vy,ω,X,Y,θ]T and control u=[D,δ]T symbol are listed in Table 1. The NMPC controller uses the error model of the system so the estimated states [X^,Y^,θ^]T are converted to the errors states [θ^e,s^,y^e]T using the offline planner; details can be found in [12]. For the state measurements, an inertial measurement unit (IMU), a motor encoder, and scan end-point matching modules are utilized. The mapping module is dedicated to map the environment using the scanned points and estimated pose of the vehicle. The previous map (occupancy grid) is queried by the scan-matching module to roughly estimate the relative position of the current scan. Finally, the estimator module is responsible for correcting the uncertainty in the measurements and scan matching, as well as obtaining the hidden state vy. To generalize the SLAM algorithm, i.e., free from the number of landmarks and environment, the rough pose estimation is based on LIDAR scan end-point matching by exploiting the Gauss–Newton optimization process. This matching approach allows estimating the pose without any prior information of the environment or landmarks. The scan end-points matching is similar to the work carried out in the paper [6], which is presented in Section 3. In this development, LIDAR’s scan end-points are exploited for observing the environment. Instead, a camera can also be used for the rough localization using either feature matching technique [13] or deep learning technique [14]. Then, to correct the rough pose from the scan matching and acquired sensor measurements, a TS modeling approach is considered to design an optimal and stable Kalman estimator. The whole design procedure consists of the following steps:1. Derive a TS model from the nonlinear model which embeds the nonlinearity term inside the system matrices. 2. Obtain the polytopic systems and derive the fuzzy representation of the TS model [4]. 3. Formulate LMIs for Lyapunov stability and optimal dual LQR for all the obtained polytopes or system vertices of the fuzzy model to obtain the offline Kalman gain. 4. Exploit fuzzy interpolation technique to find the online gain for the TS Kalman filter. 5. Apply online TS Kalman gain for the state estimation. Steps 3 to 5 are presented thoroughly in Section 4 of this paper, while the remaining steps are detailed in this section. Formalizing the TS model for LMIs and fuzzy gain interpolation technique is based on the concept of fuzzy theory [4,15], which offers a systematic approach to obtain a multi-model system from the original mathematical model of the system. The main feature of this model is to express the local dynamics of each fuzzy implication by a set of linear system models, and the overall fuzzy model of the system is achieved by the fuzzy blending of these linear system models. It is proved that TS fuzzy models are universal approximators with a high degree of precision of any smooth nonlinear system [16]. 2.1. Considered Autonomous Vehicle To validate the proposed approach, a case based on a small-scale autonomous car is used. The states of the car have to be estimated using available sensors, and, further, this information will be exploited by the full-body controller. The dynamic states, such as longitudinal velocity, can be roughly estimated using the radius and RPM of the rear wheel measured by the motor encoder. Angular velocity can be measured by an IMU sensor. The rough position of the vehicle can be obtained using the scan end-point matching algorithm and orientation from IMU. Figure 1 shows the high-level scheme of the proposed pipeline which includes a model-predictive controller (MPC) in the loop. We do not provide further details about the MPC algorithm, as this is not the focus of this work. We assume that the control of the car and the track plan can be obtained. As the estimator is based on a system model, the dynamics of the system are derived using the bicycle model [17] representation shown in Figure 2. The vehicle model includes kinematic as well as dynamic equations, represented by:(1) vx˙=1m(Frx−Fflatsin(δ)+mvyω)vy˙=1m(Fflatcos(δ)+Fry−mvxω)ω˙=1Iz(lfFflatcos(δ)−lrFry)X˙=vxcos(θ)−vysin(θ)Y˙=vxsin(θ)+vycos(θ)θ˙=ω where vx,vy,ω are the longitudinal, lateral, and angular velocity of the vehicle, respectively. X,Y,θ are the global pose of the vehicle in a fixed inertial frame. The available sensors are able to measure, directly or indirectly, [vx,ω,X,Y,θ] states, with some errors. The lateral velocity vy cannot be measured and will be estimated. Frx is the longitudinal force in the rear wheel which consists of force from the motor, drive-line resistance, and drag force. Fflat and Fry are the lateral tire force in the front and rear wheel, respectively, which is obtained by simplifying the Pacejka tire model [18]. Longitudinal force on the front wheel is considered to be negligible since no brake nor torque is applied to it. The forces are given by (2) Frx=(Cm0−Cm1vx)D−C0vx−C1−CDAρvx22 (3) Fflat=2Cafδ−arctanvy+lfθ˙vx (4) Fry=−2Cararctanvy−lrθ˙vx where δ and D are two control inputs, steering angle and duty cycle, respectively. Some of the system parameters are obtained by physical measurement, and the remaining ones by least-squares optimization. The obtained values and description of the parameters are listed in Table 2. 2.1.1. Construction of TS Model The goal is to derive a TS model from the nonlinear vehicle dynamics (1) as if the response of the TS model exactly matches the response of a nonlinear system within a specified domain with the same input u. To form the TS model, the varying nonlinear terms in the equations are set as fuzzy variables or scheduling variables (ϑi(t)). The set of fuzzy (or scheduling) variables is represented in Table 3. These are the state and control variables on which the nonlinear dynamics are dependent. These variables represent the physical limit of the vehicle, for example, the maximum and minimum longitudinal velocities or maximum and minimum steering angle that a vehicle can achieve. From the nonlinear model (1), the TS model (5) is obtained by embedding schedule variables inside the matrix:(5) vx˙vy˙ω˙X˙Y˙θ˙︸x˙=A11A120000A21A220000A3100000A41A420000A51A520000001000︸A(ϑ)vxvyωXYθ︸x+B11B120B220B32000000︸B(ϑ)Dδ︸u where Ai,j and Bi,j are the varying sub-elements of matrix A(ϑ) and matrix B(ϑ), respectively, and can be expressed as (6) A11=−1mC0+C1vx+CDAρvx2,A12=ω,A21=−ω,A22=−2Carmvyarctanvy−lrθ˙vx,A31=2CarlrvxIzarctanvy−lrθ˙vx,A41=cos(θ),A42=−sin(θ),A51=sin(θ),A52=cos(θ)B11=1m(Cm0−Cm1vx),B12=−2Cafmδδ−arctanvy+lfθ˙vxsin(δ)B22=2Cafmδδ−arctanvy+lfθ˙vxcos(δ),B32=2CaflfIzδδ−arctanvy+lfθ˙vxcos(δ) The scheduling variables can be further represented as a set of membership function ϑi, as follows:(7) ϑi(t)=μi1(ϑi(t))·ϑi_)+μi2(ϑi(t))·ϑi¯,i∈[1,2,…,5] (8) μi1(ϑi(t))+μi2(ϑi(t))=1, where ϑi_, ϑi¯ are the minimum and maximum scheduling variables, respectively, and the μij(ϑi(t)) are the fuzzy sets with two membership functions for each scheduling variable being in total 32 combinations. For each membership function, a system vertex or polytopic system represented by Aix(t)+Biu(t) can be obtained, and by applying the fuzzy rules, a nonlinear system can be modeled as:(9) x˙(t)=∑i=132hi(ϑ(t)){Aix(t)+Biu(t)} where, (10) ϑ(t)=[ϑ1(t),ϑ2(t),ϑ3(t),ϑ4(t),ϑ5(t)] and (11) hi(ϑ(t))=μ1j(ϑ1(t))·μ2j(ϑ2(t))·μ3j(ϑ3(t))·μ4j(ϑ4(t))·μ5j(ϑ5(t)),j∈[1,2] The polytopic system representation will be used to formulate the LMIs to obtain offline gain in Section 4.1. Additionally, the fuzzy representation (9) of the TS model will also allow the computation of online gain using the interpolation technique without optimizing the LMIs at each time step. 2.1.2. Measurement Model The rough position of the vehicle is obtained from the scan matching algorithm, and other information is directly obtained from various sensors listed in Table 4. The discretized state-space measurement model for the system is defined by yk=Cxk+Duk+Evvk, where vk∈Rny is the measurement noise and ny=5, Ev is the distribution matrix with appropriate dimension. D=05×2 is taken as there is no interaction from control input. The C matrix relates the system state to the output measurement yk. (12) Ev=1000001000001000001000001,C=100000001000000100000010000001 The noise vector vk=[σvx2,σω2,σX2,σY2,σθ2]T has noise of each sensor with the covariance listed in Table 4. 2.1.3. Observability Analysis Before designing the estimator, the observability of Equation (5) needs to be analyzed for singularity. This equation consists of nx=6 state variables and the observability matrix defined by:(13) O=C,CAd,CAd2,···,CAdnx−1T If the rank of observability is equal to nx, then the system is observable. To check the observability, Equation (5) is discretized using Euler approximation Ad=I+A·Δts. During the analysis, at certain states, the rank of the observability matrix O reaches singularity, precisely when any of the vx=0,vy=0 and δ=0. To resolve this issue whenever this state variable attains this value, a bias ϵ=0.0001 is added to this variable. 3. SLAM Algorithm The measurement from the LIDAR provides the environment information through building an occupancy grid probability map using Bayes inference theory which is later on converted to occupancy grids for detecting obstacles and free zones within the map. Using the occupancy probability map at time {t} and previous probability maps {t−1:0}, a robust matching is performed using Gauss–Newton optimization to obtain the relative pose. This approach allows to map and estimate the pose without any prior information about the environment or landmarks. Later on, this pose and map will be cured using the TS estimator due to corrupted matching as the LIDAR has an error in the measurement. For robust matching between occupancy probability maps, the sub-grid-level probability is accessed for sub-grid-level accuracy [6]. The algorithm has two main steps: (a) mapping the obtained map into sub-grid level accuracy, and (b) localization using the optimization process. 3.1. Mapping The map used for matching is developed using an interpolation scheme to overcome the discrete nature of the map through bilinear filtering, which allows sub-grid-level cell accuracy for estimating occupancy probabilities and derivatives. The occupancy value M(Pm) can be approximated by linear interpolation of a scan end-point Pm, similar to Figure 3, in a continuous map coordinate given as:(14) M(Pm)≈Y−Y0Y1−Y0X−X0X1−X0M(P11)+X1−XX1−X0M(P01)+Y1−YY1−Y0X−X0X1−X0M(P10)+X1−XX1−X0M(P00) where P00,P01,P10, and P11 are the closest integer point coordinates in a grid map. The partial derivatives along the X and Y axis can be approximated by:(15) ∂M∂X(Pm)≈Y−Y0Y1−Y0(M(P11)−M(P01))+Y1−YY1−Y0(M(P10)−M(P00))∂M∂Y(Pm)≈X−X0X1−X0(M(P11)−M(P10))+X1−XX1−X0(M(P01)−M(P00)) 3.2. Localization The localization algorithm is based on the matching of scan end-points shown in Figure 4 with the existing maps to find the rigid transformation ξ=(X,Y,θ)T that minimizes (16) ξ*=argminξ∑i=1n[1−M(Si(ξ))]2 where Si(ξ) is the location of scan end-points in the world coordinate frame, given by:(17) Si(ξ)=cos(θ)−sin(θ)sin(θ)cos(θ)si,xsi,y+XY where si=(si,x,si,y)T is the location of end-points in the LIDAR coordinate frame. The X,Y,andθ are the pose of the LIDAR in the world coordinate frame. The function M(Si(ξ)) provides the map value at the coordinate given by Si(ξ). Let us suppose the robot started at initial pose ξ and control input is given to move by Δξ. At position ξ, the map is built from the measurement, and at ξ+Δξ, a new map is registered. To estimate the Δξ, the optimizer minimizes the error according to:(18) ∑i=1n[1−M(Si(ξ+Δξ))]2→0 Solving for Δξ, using first-order Taylor expansion of M(Si(ξ+Δξ)) and setting the partial derivative with respect to ξ, yields the Gauss–Newton minimization equation:(19) Δξ=H−1∑i=1nΔM(Si(ξ))∂Si(ξ)∂ξT[1−M(Si(ξ))] where (20) H=ΔM(Si(ξ))∂Si(ξ)∂ξTΔM(Si(ξ))∂Si(ξ)∂ξ and from Equation (17), the gradient of ∂Si(ξ)∂ξ can be represented as (21) ∂Si(ξ)∂ξ=10−sin(θ)si,x−cos(θ)si,y01cos(θ)si,x−sin(θ)si,y and the map gradient ΔM(Si(ξ)) can be approximated from Equation (15). The obtained pose might be inaccurate due to sensor noises and errors which need to be corrected before building the map. The estimator which is discussed in the next section will be used to correct this pose. The corrected pose is applied to Equation (17) to relocate the scan end-points Si, which eventually updates the occupancy probability grid map. 4. Takagi–Sugeno Kalman Filter Design For the development of the proposed estimator design, first, in Section 4.1, LMIs for the TS Kalman filter design are formulated. Second, in Section 4.2, the measurement noise and system perturbation matrix are provided. Finally, the implementation of the proposed approach and its improvement are discussed in Section 4.3. 4.1. LMI Design Procedure The following Kalman filter to obtain the x^ estimation is required:(22) x^˙(t)=(A(ϑ)−L(ϑ)C)x^+(B(ϑ)−L(ϑ)D)u+L(ϑ)y where A(ϑ),B(ϑ) can be obtained by using TS model (5), L(ϑ) is the online Kalman gain, and D=05×2. The above continuous Kalman estimator is discretized for implementation on a real-time system. Now, the aim is to find the optimal L(ϑ) which converges to the estimation ground truth in the presence of sensor noise and system disturbance. To obtain an optimal Kalman gain L(ϑ), first, LMIs are offline optimized to obtain gain Loff. Second, the obtained Loff is interpolated in real time to obtain L(ϑ) by exploiting relation (9). To formulate LMIs for the discretized system, the continuous fuzzy model Equation (9) can be discretized using Euler approximation with sampling time Δts:(23) x(k+1)=x(k)+∑i=132hi(ϑ(t)){Aix(k)+Biu(k)}Δts (24) =∑i=132hi(ϑ(t)){(I+AiΔts)︸Adix(k)+(BiΔts)︸Bdiu(k)} The LQR optimal control objective is to find a state feedback control u(k)=−Kx(k), where K is the gain for the system matrix, that minimizes the following objective function:(25) J=∑k=0∞[xT(k)Qx(k)+uT(k)Ru(k)] Introducing a Lyapunov function for each vertex of the polytopic model:(26) V(x(k))=xT(k)Px(k) that satisfies the following conditions:(27) V(x0)<γ (28) V(x(k+1))−V(x(k))+xT(k)Qx(k)+uT(k)Ru(k)<0 Integrating the Equation (28) and substituting u(k)=−Kx(k) yields (29) ∑k=0∞[xT(k)Qx(k)+uT(k)Ru(k)]<V(x0)=x0TPx0<γ The above equation ensures that LQR objective function is bounded by the optimal value γ for all the polytopes. The increment of Lyapunov function (26):(30) ΔV(k+1)=xT(k)Ad(ϑ)TPAd(ϑ)x(k)−xT(k)Px(k) For the closed-loop system the Lyapunov function becomes (31) ΔV(k+1)=xT(k)(Ad−BdK)TP(Ad−BdK)x(k)−xT(k)Px(k) The previous inequality Equations (29) and (31) can be rewritten as follows:(32) x0TPx0<γ (33) (Ad−BdK)TP(Ad−BdK)−P+Q+KTRK<0 Applying duality principle Ad→AdT,Bd→CdT,K→LT [19] to LQR inequality Equation (33) results:(34) (AdT−CdTLT)TP(AdT−CdTLT)−P+Q+LRLT<0 By applying some algebraic manipulations to Equations (32) and (34), some changes of variables (Q=HTH,Y=P−1) and Schur complement lead to the following LMIs optimization:(35) γIIIY>0−YYAd−WTCdYHTWTAdTY−CdTW−Y00HY0−I0W00−R−1<0 The optimal gain can be found by L=(WY−1)T. The solution involves optimization at each time step, due to the varying system matrices (Ad,Bd), which are computationally expensive and slow. Instead, we exploit the method developed in Section 2.1.1. Any nonlinear system can be represented in the form (24), which means the offline gain will be found at the system vertices (polytopes) of these systems and later on interpolated online using fuzzy membership grade function (11) for the TS model (5). Therefore, for the 5 scheduling variables, 32 system matrices can be obtained at the polytopes, resulting in 32 LMIs and offline gain. The gain obtained from solving LMIs will be called Lioff∈[1,···,32]. All the steps for obtaining the LMIs are mentioned in Algorithm 1. 4.2. Disturbance and Noise Matrix The disturbance of the vehicle model and sensor noise is modeled by Gaussian distribution whose mean is set to zero, and covariance is obtained experimentally. The disturbance and the noise of the model are found experimentally, and some of the sensor noise from the manufacturer’s data sheet. The scan matching covariance is obtained via a sampling-based covariance estimate. However, a second method based on the Hessian matrix can also be used [20]. The disturbance in the model is considered to be higher than the measurement noise to compensate for uncaptured dynamics during parameter estimation. The following covariances (m2) for the experiment were set for the disturbance (Q) and noise (R) matrices, respectively:(36) Q=diag(0.15,0.05,0.15,0.25,0.25,0.1) (37) R=diag(0.04,0.0187,0.0225,0.0225,0.01) 4.3. Switching Estimator Design During the experiment phase, the scheduling variable θ:[−π,0] does not yield a better approximation of the nonlinear function. This can be seen by substituting the values in element A41,A42,A51,andA52 in Equation (5) and taking only these subelements:(38) A4:5,1:2(θ)=cos(θ)−sin(θ)sin(θ)cos(θ) (39) A4:5,1:2(−π)=−100−1,A4:5,1:2(0)=1001 The above polytopic systems are true considering the minimum and maximum vertices in cos function, but it is not valid for sin function. To accurately model this effect, the θ scheduling variable is chosen for each quadrant ϑ4j=[0,π2,−π,−π2],j∈[1,..,4]. This improvement provides the system vertex to reach all the possible values:(40) A4:5,1:2(0)=1001,A4:5,1:2π2=0110 (41) A4:5,1:2(−π)=−100−1,A4:5,1:2−π2=0−1−10 For the implementation of this system, every four sections of the quadrant are considered and the LMIs for all the quadrants are computed offline, following the steps mentioned in Algorithm 1.    Algorithm 1: Offline Gain Optimization Algorithm 1. Define maximum and minimum scheduling variables for four sectors: (a) [[ϑ_1,ϑ¯1],[ϑ_2,ϑ¯2],[ϑ_3,ϑ3¯],[ϑ_41,ϑ¯41],[ϑ_5,ϑ¯5]] (b) [[ϑ_1,ϑ¯1],[ϑ_2,ϑ¯2],[ϑ_3,ϑ3¯],[ϑ_42,ϑ¯42],[ϑ_5,ϑ¯5]] (c) [[ϑ_1,ϑ¯1],[ϑ_2,ϑ¯2],[ϑ_3,ϑ3¯],[ϑ_43,ϑ¯43],[ϑ_5,ϑ¯5]] (d) [[ϑ_1,ϑ¯1],[ϑ_2,ϑ¯2],[ϑ_3,ϑ3¯],[ϑ_44,ϑ¯44],[ϑ_5,ϑ¯5]] 2 Aij,Biji∈[1,2,⋯,32],j∈[1,2,⋯,4]← Using Equation (5) obtain 32 polytopic systems for each scheduling variables (a)–(d); 3. Adij,Bdij← Discretize using sampling time (Δt); 4. Lijoff← Optimize formulated LMIs Equation (35); The estimator gains Lijoff,j∈[1,..,4] are obtained for each quadrant, and then particular gains are applied according to the region in which the previous yaw estimate (θ^wrap) lies. For example, in Figure 5, θ lies in the first quadrant, then Li1off gain will be weighted using the TS fuzzy interpolation function depending on the scheduling variable [0,π2] to obtain the current online gain. Likewise, for each quadrant, the Lijoff is interpolated for the current states using the membership function. Algorithm 2 presents the method to obtain the fuzzy gain interpolation scheme. Algorithm 2: Gain Interpolation Algorithm L_interpolation(ϑ(k),Lijoff); Input: Scheduling variable ϑ(k) Output: Interpolated gain L ϑi¯,ϑi_←Loadschedulingvariables; μi1(k), μi2(k)← Compute fuzzy sets for all the ϑ(k) using Equations (7) and (8); hi(ϑ(k))← Compute fuzzy interpolation function using Equation (11); L←∑i=132hi(ϑ(k))Lijoff;    The algorithm for the proposed state estimation technique can be referred from Algorithm 3. The wrap() function is used to discretize the θ value between [−π,π], and the unwrap() function is used to change the θ value to continuous value. This is required as the yaw measurement from the sensor provides a measurement between [−π,π], which is discontinuous. The measurement discontinuity results in wrong estimates due to the lagging or leading effect of the measurement. In Figure 6, the wrong estimation of states is depicted. Due to the abrupt change in yaw measurement in Figure 6b, the robot position is wrongly estimated in Figure 6a. It can be seen in Figure 6b that at around 12.5 s, the estimated yaw slightly leads the measurement, due to which the estimator tries to track the measurement data, which results in the full turn of the robot at location [−1.8,4]. The same phenomenon at 19 s also occurs when the estimated yaw lags behind the measurement data. Changing the discontinuity using the unwrap() function provides an accurate estimation of all the states, which is shown in Figure 6c,d. Algorithm 3: State Estimation Algorithm 5. Results To validate the estimator performance, experiments were performed in the simulator as well in a real environment. First, the estimator is tested using manual control input, and once it is proved to be working, an NMPC controller is utilized to follow a track. The vehicle is 41 cm long and 21 cm wide, and the track is 50 cm in width. The setting of the controller is tuned to complete two laps while achieving a longitudinal velocity of 0.8 m/s, keeping the vehicle inside the track and closer to the center track, shown as a dotted line in Figure 7. The objective of the validation is to (i) estimate full states correctly, including unmeasured state vy, in the simulator as well as real vehicle, and (ii) validate real-time performance. For the simulator, the NRMSE evaluation metric is used to compare the error between the simulated and estimated state, which can be computed by:(42) NRMSE=∑i=1N(x−x^)2xmax−xmin 5.1. Simulation The vehicle simulator is developed using the vehicle full state dynamics obtained in Section 2.1. The perturbations and noises are injected into the vehicle model and sensor model, respectively, to simulate the real world. The perturbation and noises are kept a little higher than the actual one to ensure the estimator works even in the worst cases. By analyzing Figure 7, the estimated position is very close to the simulator state of the vehicle. The NRMSE error for all the estimated states is presented in Table 5. The rest of the estimated states are compared in Figure 8. The lateral velocity (vy), which cannot be measured directly, has an NRMSE value of 0.0323 (Figure 8b). The errors of all the dynamic estimated states are within a certain range, which indicates that the estimator is fully working and ready to be tested on the real vehicle. 5.2. Real Experiment Note that the RMSE error is not used here due to a lack of ground truth measurement. The validation for this experiment is performed visually. The estimated X–Y position for the real experiment is shown in Figure 9. Some snapshots for visual validation are shown in Figure 10 and compared to Figure 9; it can be noticed that the estimated position of the vehicle matches the real position of the vehicle in the snapshots. For properly substantiated illustration, the media attached (https://youtu.be/Oey2ZxsxlnY (accessed on 29 March 2022)) shows the estimated states and NMPC controller performance. The media validates the performance of the estimator design. Additional estimated states are shown in Figure 11. The velocity obtained from the motor encoder is very noisy and inaccurate (see Figure 11a), but the TS Kalman filter can provide quite a clean estimation. Similarly, this happens with the angular velocity. The lateral velocity was not measured and is successfully estimated. The final corrected map after completing the laps is shown in Figure 12b. Figure 12a represents the environment which includes fixtures, obstacles, and free space. There are some unoccupied cells, particularly when the LIDAR scan is hindered by the fixtures present in the environment. For this reason, the error between ground truth and map is only calculated for properly scanned areas, i.e., the fixture occlusion region is excluded (see Figure 12a). The intersection-over-union (IOU) score of 0.9388 is obtained between the ground truth and the corrected map. 5.3. Simulated vs. Real Experiment It is worth noting that the time taken for completing two laps in the simulator and real experiments are 38 s and 32 s, respectively. The real experiment completed the lap faster than the simulation due to strictly maintaining a longitudinal velocity of 0.8 m/s. There are discrepancies between some estimated states which are due to different control input in simulation and real experiment phase or mismatch of the simulator model from the real vehicle model. Table 6 shows the minimum and the maximum absolute differences between estimated and measured states, as well as their standard deviations for both the simulator and real platform experiment. The minimum deviations for the real experiment are similar to those of the simulator, while there is a deviation of about 10 cm for real experiment values compared with simulator values; this is because the standard deviation of simulation measurements is set higher than the real standard deviation. This can also be validated from the standard deviation values in the simulator and real experiments. Even though the performance of the estimator in the real experiment cannot be verified due to the lack of ground truth, the values in the real experiment appear to be similar to those in the simulator, suggesting that the estimator performs similarly. 6. Conclusions This paper has presented an approach for full-state estimation of an autonomous vehicle using a TS Kalman filter as well as imprecise scan matching and measurements. Further, this technique can also correct the map obtained from the noisy LIDAR scan end-points matching. The proposed approach provides a stable and optimal solution in the presence of model disturbance and measurement noises at a high update rate of 100 Hz for state estimation, and simultaneous correction of the obtained map. The update rate can go much faster than 100 Hz; the only limitation is the update rate of the sensor measurements. The computational cost of the proposed approach is fairly low if compared with EKF SLAM, which depends on the number of landmarks. The result produced motivates the usage of the TS-based state estimation and mapping in the field of autonomous vehicles and robotics. The proposed estimation technique depends on the model of the system and the measurement model with certain disturbance and noise, respectively. This requirement is sometimes hard to fulfill, and also, for some systems, the parameters change within a certain range, for example, the tire coefficient of the vehicle. For such a kind of system, online model learning techniques need to be incorporated [21,22]. The proposed method can also be scaled to a high-DoF system if the system model is known, and extra sensors can be installed, for example, a height sensor, such as a barometer, or range sensor can be used to detect z-pose. The proposed scan matching technique can be applied to an unknown environment but the accuracy may degrade in a highly dynamic environment. For such cases, robust scan matching techniques need to be applied, such as in [23]. As future work, the proposed approach will be benchmarked against competing approaches. Author Contributions Conceptualization, S.C. and V.P.; methodology, S.C. and V.P.; software, S.C.; writing—original draft preparation, S.C.; writing—review and editing, V.P.; supervision, V.P. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement All the required data is included in the paper. Conflicts of Interest The authors declare no conflict of interest. Figure 1 The architecture of the software modules, including information flow, and a conceptual overview of the interconnections. Software framework also includes low-level sensor measurement acquisition units. Figure 2 A representation of the bicycle model in 2D space for deriving the equations of motion. The vehicle schematic shows the inertial frame (O), the center of gravity (CoG) frame (C) attached to the center of gravity of the vehicle. The forces Frx and Fry on the rear wheel are the longitudinal and lateral force, respectively. The front wheel forces Fflong and Fflat are the longitudinal and lateral forces, respectively, the forces Ffx and Ffy are the result of these forces in frame C. The lengths lf and lr are the distance from CoG to the front wheel and rear wheel, respectively. Figure 3 Calculation of laser end-point probability at sub-grid level by using occupancy grid probability map. Figure 4 Translation of scan information [r,α]T to scan end-points [siX,siY]T in LIDAR frame of reference. Figure 5 A defined sector that is designed to capture kinematic system matrices to obtain offline gain. Later on, using the same sector approach, online interpolated gains are obtained. Figure 6 Estimation comparison between wrapped yaw measurement and unwrap measurement. Panels (a,b) represent the position of vehicle and yaw measurement in the global frame, respectively, both of these states are estimated using yaw measurement between [−π,π]. Panels (c,d) represent the position of vehicle and yaw measurement in the global frame, respectively, both of these states are estimated using continuous yaw measurement. (a) X–Y global position on yaw measurement between [−π,π]. (b) Yaw sensor measurement. (c) X–Y global position on yaw measurement between [−∞,∞]. (d) Yaw transformed to continuous measurement. Figure 7 Vehicle estimated trajectory in the presence of sensor disturbance during the simulation. As per the controller policy, the vehicle tried to follow two laps of the center-line track. Figure 8 Estimated states on vehicle simulator for two laps. Figure 9 Vehicle estimated trajectory in the presence of sensor disturbance during the real experiment. As per the controller policy, the vehicle tried to follow two laps of the center-line track. Figure 10 Snapshots of the vehicle following the center line of the oval track. The vehicle motion is represented by a sequence of images clockwise from the upper-left to the bottom-left. Figure 11 Estimated states on real vehicle for two laps. Figure 12 Comparison of ground truth and final map obtained. (a) The ground truth image represents the environment where the testing is performed. (b) The final map formed after completion of two laps. sensors-22-03399-t001_Table 1 Table 1 The list of symbols used in this article. Symbol Description vx Longitudinal velocity of vehicle in center of gravity (CoG) frame (C), see Figure 2. vy Lateral velocity of the vehicle in CoG frame (C). ω Angular velocity of the vehicle in CoG frame (C). X Global position of the vehicle in X-axis frame (O). Y Global position of the vehicle in Y-axis frame (O). θ Orientation of the vehicle with respect to the x-axis of the frame (O). D Duty Cycle of motor, normalized between [0,1]. δ Steering angle. sensors-22-03399-t002_Table 2 Table 2 Vehicle model parameters. Parameters Values Description m 2.424 kg Mass of the vehicle lf 0.1377 m Distance from CoG to front wheel lr 0.1203 m Distance from CoG to rear wheel ρ 1.225 kg/m3 Air density Cm0 9.4685 N Motor parameter 1 Cm1 0.6672 kg/s Motor parameter 2 C0 2.6104 kg/s Resistive driveline parameter C1 −0.00213 N Static friction force CDA 0.466 m2 Coefficient of drag multiplied with area Caf 1.2354 N/rad Front wheel cornering stiffness Car 1.4532 N/rad Rear wheel cornering stiffness Iz 0.02 kg·m2 Moment of inertia sensors-22-03399-t003_Table 3 Table 3 Scheduling variables, i.e., states or inputs on which the system matrix A(ϑ) and B(ϑ) are dependent. The maximum and minimum value of these variables can define any polytopic system. Variables (ϑi(t)) min(ϑi(t))=ϑi¯ max(ϑi(t))=ϑi¯ vx −5.0 5.0 vy −3.0 3.0 ω −1.5 1.5 θ −π π δ −0.35 0.35 sensors-22-03399-t004_Table 4 Table 4 Noise of dedicated sensors or algorithms used in the experimental platform to measure or estimate the rough information. States Sensors Covariance (σ2) [m2] vx Motor encoder 0.04 ω IMU 0.0187 X Scan matching 0.0225 Y Scan matching 0.0225 θ IMU 0.01 sensors-22-03399-t005_Table 5 Table 5 NRMSE evaluation of estimated states. States vx vy ω X Y θ NRMSE 0.0781 0.0323 0.0913 0.0157 0.0175 0.0198 sensors-22-03399-t006_Table 6 Table 6 Evaluation between estimated states and measured states for the experiment in the simulator and real platform. The min and max header represent the minimum and maximum absolute difference between estimated and measured states, respectively. The std header represents the standard deviation between estimated and measured states. States min [m] max [m] std [m] Sim Real Sim Real Sim Real X 0.124 0.094 0.405 0.317 0.159 0.116 Y 0.137 0.119 0.344 0.241 0.138 0.126 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Litman T. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095207 ijerph-19-05207 Article Motivational Factors Are Varying across Age Groups and Gender Sigmundsson Hermundur 12* Haga Monika 3 Elnes Magdalena 4 Dybendal Benjamin Holen 1 Hermundsdottir Fanny 5 Tchounwou Paul B. Academic Editor Lawn Sharon Academic Editor 1 Department of Psychology, Norwegian University of Science and Technology, 7491 Trondheim, Norway; benjamhd@stud.ntnu.no 2 Research Center for Education and Mindset, University of Iceland, 101 Reykjavík, Iceland 3 Department of Teacher Education, Norwegian University of Science and Technology, 7491 Trondheim, Norway; monika.haga@ntnu.no 4 Department of Primary and Secondary Teacher Education, OsloMet—Oslo Metropolitan University, 0167 Oslo, Norway; magdalen@oslomet.no 5 Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway; fanny.hermundsdottir@ntnu.no * Correspondence: hermundur.sigmundsson@ntnu.no 25 4 2022 5 2022 19 9 520725 3 2022 11 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The aim of the current study was to explore differences in passion for achievement, grit, and mindset across age and gender, by using a cross-sectional design. The sample consisted of 1548 participants including 931 females and 617 males aged from 13 to 77 years (Mage 26.53 years, SD = 11.77). The eight-item Passion for Achievement Scale was used to assess general passion and the Grit-S scale was used to assess grit. Mindset was assessed using the eight-item Theories of Intelligence Scale (TIS). The results indicated significant differences between the three factors related to age, age groups, and gender. For the total sample, there was a significant gender difference in passion, where males score higher, and growth mindset, where females score higher. With age, passion decreases until the age of 50–59, and slightly increases for the remaining age groups. After a decrease in grit between the first (13–19 years) and the second (20–29 years) age group, grit increases with age. Mindset scores decline strongly after the age of 40–49. Generally, the patterns show that mindset and passion decrease across the life-span, while grit increases. Indeed, these attributes seems to be different from each other, and how they change varies across age groups. passion grit mindset life-span development cross-sectional ==== Body pmc1. Introduction A way to improve insights in how individuals face challenges, pursue long-term goals, and maintain effort in school and life is to delve into the factors of growth mindset, passion, and grit. In the last few decades, there has been a growing interest towards these potential predictors to high achievement and excellence [1,2,3,4,5,6], and promising findings are found regarding their association to motivation and success [2,4,5,6,7,8,9,10]. Despite increased interest in these factors, our knowledge of their developmental patterns throughout the course of life are still scarce. Hence, seeking to explore passion, grit, and mindset through a cross-sectional design is a novel approach in discovering variations related to age and gender within these concepts. Passion for achievement. Most experts justify their exceptional motivation to a strong interest or passion [11], and passion can just be defined as “a strong feeling toward a personally important value/preference that motivates intentions and behaviors to express that value/preference” [12] (p. 1). Passion is often put in relation to dedication, enthusiasm, persistence, goal orientation, liking, and even love [13]. Moreover, it is an intense affective state, which may produce beneficial effects on skill development based on its key mechanism “immersion” i.e., a deep mental involvement [12]. Consequently, passion may be providing the focus necessary for long-term goal achievement [9,14]. Usually, passion has been assessed in relation to specified activities, and thus been domain-specific [15]. However, a recently developed scale has operationalized passion independent from a specific activity, and focused more on achievement in general [8]. In this context, passion can represent a trait, or a tendency, to develop strong interests toward areas, themes, or skills in general. Gender differences have been found in passion for achievement, males scoring higher [7,16]. Grit. When striving against a long-term goal, some activities or situations may be experienced as quite challenging or even boring for an individual. Consequently, one needs persistence of effort and grit in order to stay on course. Duckworth et al. [17] define grit as “perseverance and passion towards long-term goals” (p. 1). Generally, grit is described as a trait that entails working diligently toward a challenging goal through thick and thin, over many years, and even decades [18]. It consists of two underlying facets: “Consistency of Interests” reflecting the stability of a person’s interests over longer periods of time, and “Perseverance of Effort” which means diligence and effort despite difficulties or failure [17]. Although grit may have similarities to resilience in terms of striving through adversity [19], (it is to a greater extent characterized by long-term goals and consistent interest) [20,21,22]. As a result, gritty people manage to develop high skills through hard work and zeal to achieve their long-term goals. Furthermore, grittier individuals tend to attain higher levels of education and earn a higher Grade Point Average [17]. However, the concept has received a lot of reasonable criticism concerning its facets, predictability, and similarity to other existing concepts such as Conscientiousness in the “big five” [23,24,25]. The “big five” are broad categories of personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) [26], and strong associations are found between grit and conscientiousness) [17,27,28]. In addition, grit can be an effective measure to assess proactive conscientiousness [29]. Using the original 12-item grit scale, gender differences have been found; female score higher [25,30]. When using the Grit-S scale, gender differences are not found [30]. Mindset. Grit has also been related to possessing a “growth mindset” [4]. Having a growth mindset means believing in the development of skills by practice and experience [31]. In contrast, people with a “fixed” mindset assume they are born with a certain amount of talent or intelligence, and that it cannot be changed [31]. Thus, growth mindset may play a key role in the motivation and achievement of an individual, and influence how a person approaches learning opportunities, challenges, and goals [31], which in turn, can influence motivation and effort. Mindset and motivation are important factors in improving math performance in high school students [32]. Park et al. [22] argue that the two attributes mindset and grit seem to mutually reinforce each other. Furthermore, people with a growth mindset have a smaller tendency to worry about learning outcomes, and invest more time and energy into learning [33]. A short online growth mindset intervention was found to improve grades among lower-achieving students [6], yet it seems that the concept has a general weak effects [34], indicating that having a growth mindset is even more effective in combination with other relevant traits. Previous findings show that females and males either do not differ in mindset on average [35] or that females hold a higher growth mindset than males [36,37]. Passion, grit, and mindset. Passion, grit, and mindset are intertwined constructs that carry advantages for high achievement. It can be argued that growth mindset may be an underlying factor for both passion and grit, indeed, individual’s belief about the malleability of personal attributes and abilities affects an individual’s behavior in terms of goals and actions. However, the opposite might also be true, and therefore passion, grit, and growth mindset seem to be attributes whose development are mutually reinforcing [5,7,9,22]. Most studies concerning passion, grit, and mindset have focused on each specific concept in relation to performance and motivation [6,11,18], while little is known about the development of these traits across the life-span. Although some studies have explored the associations between other factors and these traits in different age groups, such as grit and work life [38,39], (specified passion in art performance [11] and mindset and academic performance [40,41], there is limited knowledge about variations in passion, grit, and mindset, related to age and gender. Studies indicate that grit increases with age, suggesting that grit changes as the individual acquires different experiences throughout the life-span) [17,19]. However, these possible variations and changes should be further investigated to explore their significance. The current study is part of a larger project focusing on these motivational factors, i.e., passion, grit, and mindset. Earlier studies have explored the patterns of association between these three concepts across the life-span, as related to age and gender [9,16]. In this current paper, we continue to study passion, grit, and mindset, with a special interest in the development of these three variables across the life-span in a cross-sectional sample among seven age groups from 13 to 79 years (N = 1548). Hence, this study is further exploring how these central variables are characterized in different age groups between gender and through different periods in life. As far as we know, this has not been earlier investigated and will add new understanding about these variables. Both gender- and age differences across life are probably influenced by different life experiences that affects in different ways [42]. It is therefore hypothesized that passion, grit and mindset might be independent of each other, and vary through life-span. The aim of this study is to explore age and gender differences in passion, grit, and mindset using a cross-sectional design in the age span from 13–77. 2. Method 2.1. Participants A sample of 1548 participants from 13 to 77 years (M = 26.53, SD = 11.77), completed scales for passion, grit, and mindset (dependent variables) during 2019/2020. Participants were recruited from two Nordic countries, Norway (N = 838) and Iceland (N = 710). Mean age for the female sample (N = 931) was 26.70 years (SD = 11.80), and 26.27 years (SD = 11.71) for the male sample (N = 617). Adolescents from 13 to 19 years (N = 242) were recruited from mainstream secondary schools and high schools. The entire sample reflected the population of adolescents attending schools in these areas and included adolescents from a wide range of socio-economic backgrounds. The adults aged 20–77 years (N = 1306) were recruited from a university student population (tested at university campus in a group setting in ), sports clubs (football players, female and males at different levels), and group of visitors to a public building (tested individually). The participants were divided into seven age groups based on chronological age: 13–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79. Participants aged 19 years and younger (high school students) as well as participants over 70 years (people with pensions in Norway) were divided in two separate groups (13–19 years and 70–79 years). The age range from 20 to 70 years was divided into five groups at 10-year intervals (a decade apart). The information registered about the participants was anonymous (except age and gender). The sample can be described as a convenience sample. 2.2. Measurements 2.2.1. Passion Participants completed the Passion scale [8] as a measure of passion for achievement. Participants indicated their responses to eight items on a 1 (not like me at all) to 5 (very much like me) scale. The maximum score on this scale is 5 (extremely passionate) and the lowest is 1 (not at all passionate). The Passion for achievement scale has demonstrated good internal consistency (Cronbach’s alpha value of 0.86) and high levels of test-retest reliability. Intra class correlation coefficient (ICC) between test and retest total scores was 0.92 (N = 21, mean age 23. 67, SD =2.41). Construct validity: Pearson’s correlation coefficient between the total score of the Passion and Grit S Scale was 39 for adults, mean age 21.23 (SD = 3.45) (N = 107) [8], and 0.54 for adolescents, mean age 17.85 (SD = 1.47) (N = 242) (this study). For this particular study, the Chronbach’s alpha value was 0.92, indicating a high internal consistency (N = 1548). Principal component analysis: An exploratory principal component analysis was used in this study (N = 1548) to investigate the component structure of the 8-item passion-scale [8]. A one-component structure was extracted based on the inspection of eigenvalues, scree plot, and theoretical sensitivity [43]. The items characterized a component that was named “Passion for achievement” and had an eigenvalue of 5.23. In addition, the component explained approximately 65.48% of the variance. Factor loadings ranged from 0.73 to 0.87, which indicates good loadings with the latent component. KMO was 0.91 which indicated an adequate sample size, and the significance of the Bartlett’s test suggested that the variance was the same in each group. A good dimensionality and adequate factor structure of the passion scale has been confirmed through exploratory factor analysis and confirmatory factor analysis in a Turkish sample [44]. 2.2.2. Grit Participants completed a Norwegian version of the Grit S Scale [19,45] as a measure of level of grit. The scale has two dimensions: consistency of interests (COI) (e.g., “I often set a goal but later choose to pursue a different one”) (reverse-scored), and perseverance of effort (POE) (e.g., “I finish whatever I begin”). All eight items were measured on a 5-point Likert scale, wherein 1 would mean “not like me at all” and 5 would mean “very much like me”. The maximum score on this scale is 5 (extremely gritty), and the lowest score is 1 (not at all gritty). Grit-S has shown good internal consistency in several studies, α = 0.82 and α = 0.84 [19] (p. 170), and provided evidence for the predictive validity, consensual validity, and test-retest stability of the Grit-S. For this particular study, the Chronbach’s alpha value was 0.73, indicating a good internal consistency (N = 680). 2.2.3. Mindset Participants completed a Norwegian version of Dweck’s [31] Theories of intelligence Scale (TIS) as a measure of mindset [46]. The self-form for adults of this measure was used to ensure that the students focused on their ideas about their own intelligence and not their ideas about people in general. In completing the scale, participants indicated their agreement or disagreement using a 6-point scale (1 = strongly agree to 6 = strongly disagree) on a variety of items related to the malleability and stability of intelligence and talent. The scale consists of two subscales, and the items were presented so that agreement indicated either support for an entity theory, i.e., fixed mindset (e.g., You have a certain amount of intelligence, and you can’t really do much to change it) or an incremental theory, i.e., a growth mindset (e.g., No matter who you are, you can significantly change your intelligence level). Before summing all items, the incremental scale items were reversed. Therefore, higher average scores indicate a greater amount of incremental beliefs about intelligence i.e., growth mindset. The reliability data for the scale comes from Dweck et al. [47] and is based on the 8-item scale. The scale showed good internal consistency (α = 0.85) and test-retest reliability at two weeks (r = 0.80). Additionally, the scale showed a good construct validity, with scores predicting a meaningful relationship with several variables [47]. The Norwegian version of TIS has been found to be reliable as well, with Cronbach’s α of 0.86 for entity items and 0.88 for the incremental items [46]. For this particular study, the Chronbach’s alpha value was 0.93. (N = 680), indicating a high internal consistency. 2.3. Procedure The study was carried out in accordance with the regulations set out by the Norwegian Centre for Research Data and the Icelandic Data Protection Authority. Before data collection, participants in the adolescents group (i.e., younger than 16 years) and their parents or guardians were given written information about the study. For the adolescent group, written permission was obtained from parents or guardians before involvement in the study. According to the Norwegian Centre for Research Data and the Icelandic Data Protection Authority, passive consent was sufficient for participants older than 16 years, as no sensitive personal data were collected. Research assistants carried out data collection. The data collection was both conducted by using online survey (mainly adolescents and adults) and distribution and collection in person (the youngest and oldest groups). 2.4. Data Analysis For the statistical analysis, SPSS Version 25 for Windows was used (SPSS Inc., Chicago, IL, USA). Pearson’s correlations analyses were used to analyze the relationship between age and the three factors, and t-test was used to analyze the difference between genders. Multivariate analyses of variance (MANOVA) were used to analyze the difference between the three factors related to age, seven age groups, and gender. To counteract the problem of multiple comparisons, the Bonferroni’s correction was used for analyzing difference between groups within each factor: passion, grit, and mindset. The magnitude of partial eta squared was determined following the thresholds: η2 = 0.01 indicates a small effect; η2 = 0.06 indicates a medium effect; η2 = 0.14 indicates a large effect. Statistical significance was set to p < 0.05. 3. Results 3.1. Demographic Differences As a first step, demographic differences among variables of interest were explored. Age correlated significantly with mean total score Passion (r = −0.135, that is the higher the age the lower the passion score) and mean total score Grit (r = 0.144, i.e., higher age—higher grit score). No significant correlation was found between age and mean total score Mindset (r = −0.009, i.e., higher age—lower mindset) (Pearson’s correlation, p < 0.01). For the whole sample, the mean score for passion was 3.95, for grit 3.39, and for mindset 4.21. There was a significant difference between females and males for the average total score of passion for achievement (t-test, p < 0.001), males having a higher score. A significant gender difference was also found in total score for mindset, females having a higher score (t-test, p = 0.023). For grit, no gender difference was revealed. (see Table 1). The correlation between passion and grit was r = 0.330, the correlation between passion and mindset was r = 0.158, and the correlation between grit and mindset was r = 0.177. The correlation was significant (p < 0.01). MANOVA indicated a difference between the three factors related to: (1) age (F(189, 4225) = 1.735, p < 0.001), with a medium effect size (partial η2 = 0.071); (2) age groups (F(18, 4274) = 9.540, p < 0.001), with a small effect size (partial η2 = 0.035); and (3) gender (F(3, 1409) = 5.875, p < 0.001), with a small effect size (partial η2 = 0.014) (see Figure 1). There was a significant interaction effect between gender (2) and age (F(153, 4224) = 1.217, p = 0.038), with a small effect size (partial η2 = 0.042). There was also a significant interaction effect between gender (2) and age groups (seven age groups) (F(18, 4274) =1.841, p = 0.016, with a micro effect size (partial η2 = 0.007). 3.2. Passion There was a significant effect of age (F(63, 1411) = 1.589, p = 0.003), with a medium effect size (partial η2 = 0.064), and of age groups (F(6, 1513) = 6.986, p < 0.001), with a small effect size (partial η2 = 0.026) (see Figure 1). Post hoc test indicated a significant higher score in passion for group 1 (13–19) compared to group 2 (20–29), group 3 (30–39), and group 5 (50–59). Furthermore, there was a significantly higher score in passion for group 2 than group 5. A significant effect of gender was found (F(1, 1513) = 6.141, p = 0.013) with a small effect size (partial η2 = 0.004) There was no significant interaction between gender (2) and age-groups (seven age groups) (F(6, 1513) = 1.364, p = ns) (see Figure 2), with a micro effect size (partial η2 < 0.005). t-test indicated significant gender differences in age group 13–19 (p < 0.001) and 20–29 (p < 0.001), males having higher scores. 3.3. Grit There was a significant effect of age (F(63, 1411) = 1.589, p = 0.003), with a medium effect size (partial η2 = 0.069), and of age groups (F(6, 1513) = 9.895, p < 0.001), with a small effect size (partial η2 = 0.037) (see Figure 1). Post hoc test indicated a significantly higher score in group 1 compared to group 2, and significantly lower than group 5 and group 7. Furthermore, there were significantly lower scores in group 2 compared to group 4, 5, and 7. Additionally, group 3 scores were significantly lower than group 5. There was no significant effect of gender (F(1, 1513) = 0.040, p = ns.) and no significant interaction between gender (2) and age groups (seven age groups) (F(6, 1513) =1.224, p = ns.) (see Figure 3) with a micro effect size (partial η2 < 0.0001). t-test indicated significant gender differences in age group 13–19, males having a higher score (p = 0.048). 3.4. Mindset There was a significant effect of age (F(63, 1411) = 1.540, p = 0.005), with a medium effect size (partial η2 = 0.069), and of age groups (F(6, 1513) = 9.895, p < 0.001), with a small effect size (partial η2 = 0.024) (see Figure 1). Post hoc test indicated a significantly higher score in group 1 compared to group 7. Additionally, group 2 had a significantly higher score in mindset compared to group 7, and group 3 scored significantly higher in mindset compared to group 7. There was a significant effect of gender (F(1, 1513) = 11.629, p < 0.001) with a micro effect size (partial η2 = 0.007) (see Figure 4), and a significant interaction between gender (2) and age groups (seven age groups) (F(6, 1513) = 2.886, p = 0.006), with a small effect size (partial η2 = 0.012). t-test indicated significant gender differences in age group 60–69, females having higher scores (p = 0.037). 4. Discussion The aim of the study was to explore the significance of age and gender related differences in passion, grit, and mindset across the life-span. The three previously described scales were administered to 1548 subjects, covering females and males aged from 13 to 77 years. Previous research has argued that these factors are important for achievement) [1,2,4,5,8,9,10,48,49]. However, very little is known about the development of passion, grit, and mindset across different age groups and during aging. Seeking to explore these factors through a cross-sectional approach is a novel approach in discovering differences related to age and gender within these concepts. The overall results show that with age, passion decreases until the age of 50–59 years, and slightly increases for the remaining age groups. After a decrease in grit between the first (13–19) and the second (20–29) age group, grit increases with age, as observed in earlier studies [17]. Mindset scores decrease clearly after the age of 40–49 years. Generally, the different development in patterns in the three concepts across the seven included age groups may support that passion, grit, and mindset can be seen as different constructs [9,50]. The only similarities in patterns are found between mindset and grit in the four youngest age groups, with a decrease from 13–19 to 20–29 years, followed by an increase. Furthermore, both passion and grit increase slightly after the age of 50–59. Generally, the patterns show that mindset and passion decrease across the life-span, while grit increases. Indeed, these attributes seems to be different from each other, and how they change varies across age groups. 4.1. Passion Studying the development of passion from an ecological perspective, the individuals in the age interval of 13–19 years could be freer to interact with the microsystems including their passion [51], making their average scores high (4.11). Decrease in mean passion scores until the age of 50–59 could be a result of restraints in the environment. Often great life events occur during the ages of 20–29 and 30–39, such as studies, the start of working careers, and establishing families. This could possibly restrict the availability of microsystems that involve the individuals’ passion, and consequently, their passion could decrease due to new/great commitments to other arenas. Similarly, when passion increases in the age interval 50–59, it could be a result of children moving out, retirement, and the availability of microsystems including their passions such as hobbies and leisure activities [8]. Such changes in the average passion scores may question Swanson [52] claiming that interests stabilize over time. The analysis of gender differences revealed that on average, males tend to score higher in passion compared to females, across all age groups. This might indicate that passion is a stronger driving force for males [9,50]. In this respect, it might be argued that passion provides the focus that is important in achieving goals [4]. Passion can therefore be of special importance, and guide an individual toward a specific area of interest [8,9]. Among males, passion scores decline between the age interval of 13–19 to 50–59, followed by increases in the two oldest age groups. Females show the same trend, with a weaker decline in passion scores compared to males appearing between the age interval of 13–19 to 50–59. The scores slightly increase among the remaining, older age groups. These findings could potentially be caused by both sociocultural factors and neurobiological differences between males and females [53,54,55]. Sociocultural expectations may contribute to males becoming more passionate about the activities in which they engage [9,16]. Moreover, the availability of activities capturing males’ interests (such as gaming) could also explain the significant gender difference in passion. Generally, males show higher addictive tendencies towards not only alcohol, but also towards activities such as gambling, television, and internet use [56]. Consequently, it is tempting to speculate whether there is a relationship between passion, addiction, and the interaction between dopamine, serotonin, and sex hormones [57,58]. In this context, studies have indicated higher levels of dopamine in males compared to females [59]. 4.2. Grit Average grit scores are low in the age interval 14–19 year compared to older age groups. As proposed in earlier studies, this might indicate that grit is developed in interaction with various microsystems during the life course, for example by facing challenges and difficulties that could develop grit [17]. Indeed, in the interval from 20–29, the mean grit scores increase, which could be a result of individuals interacting with different microsystems, including attending to universities, and participating in work life [17]. Gender differences in grit scores are only found in the youngest group (13–19), where males score significantly higher on grit than females (3.56 vs. 3.41). Some studies report on no gender differences in grit scores, and that the trait increased with age in a sample of adults from 25 to 65 years [17]. On the contrary, others have shown that females score generally higher in grit compared to males) [30], and they were unable to detect a linear relation between grit and age, both with and without statistically controlling for the effects of education [42]. Considering that various studies contain different samples, the observed variability of the results can be caused by sociocultural factors, and grit might be adaptable. 4.3. Mindset Results showed that growth mindset decreases throughout the life-span, from the age of 13–19 (mean score 4.22) to 70–79 (mean score 3.37). However, there is a small raise in mindset at the age interval ranging from 20–29 to 40–49, followed by a decrease among the older age groups. People with a growth mindset believe that human attributes are changeable, and that intelligence can develop through effort, practice, and education [60], and students’ beliefs and goals can powerfully influence their learning success [61]. In this context, although it is shown that grey matter tends to decrease with age and throughout life [62], it is linked to plasticity and to the process of maintaining and creating new neural networks due to practice and experience [63]. However, as the individual ages groups’ grey matter decreases, it may result in limitations in attainment of new knowledge and skills [3,31]. As a result, individuals might experience a decrease in growth mindset once they realize their abilities (both physically and cognitive) are not as adaptable as they used to be, and that learning new skills or knowledge may demand more effort and take a longer time. Such relationships might explain the decrease in growth mindset due to age. However, a limitation of this study concerns the fact that we do not use repeated measures. As different participants are measured in different ages, the results do not show actual individual development of the concepts across the life-span. Consequently, the significant differences between the younger age groups (13–19, 20–29, 30–39) and the oldest group (70–77) may indicate a change in learning and development approaches, which may be cultural. The younger generation may have been more exposed to information concerning intelligence change due to practice, through slogans such as “use it or lose it” and “use it and improve it” based on contemporary theories in neural development) [64,65,66,67]. As a result, younger people may have developed growth mindsets to a higher extent, compared to the older age groups. Furthermore, it is interesting to notice the curves for mindset as they are differing among males and females after the age group 30–39. Males’ mindsets increase on average until the age of 30–39, followed by a decrease until the age of 70–79, unlike in females, where mindset increases until the age 40–49 and decreases until the age of 70–79. In this context, one can notice the significant difference between the genders for the age group 60–69 years, which shows that females score significantly higher on growth mindset, compared to males. The significant difference (p = 0.023) between females and males for the whole sample is also interesting, underlining the importance of both age and gender associated to the development of mindset. According to Schlender et al. [55]), gender differences may exist because of their different socialization process, for example resulting in varying levels of academic mindsets. Additionally, the ability to adapt to age-related changes and losses, both physical and cognitive, is acted upon differently in male and females [68]. 4.4. Limitations and Future Research The current study has some limitations. As mentioned, repeated measures would have been more appropriate for the investigation of trait development, instead of a cross-sectional study. Based on this approach, the results may reveal generational differences, rather than development of the traits. Another limitation concerns the unbalanced number of participants in each age group, and a small sample size among the older age groups, which were more difficult to recruit. This could have caused non-significant results in patterns or trends we potentially could have discovered among the groups with smaller sample sizes. Furthermore, as this current study only focuses on a limited cultural context, i.e., Iceland and Norway, the generalizability of the findings may be reduced. Further studies should investigate if ethnic and socioeconomic factors relate to these variables. There is also a need to study these factors in relation to other achievement measures, as well as possible effects of interventions increasing these traits, across different age groups. 5. Conclusions This study sought to explore the development of passion, grit, and mindset across life-span in relation to age and gender in a sample from 13 to 77 years. Generally, the patterns show that mindset and passion decrease across the life-span, while grit increases. The study finds gender differences (total sample) in passion (males score higher), and in mindset (females scores higher). These findings suggest that the three variables studied might be independent of each other, and partly related to age and gender. This knowledge might potentially be of relevance for future research exploring developmental patterns and associations between these factors. Broadening our knowledge about how passion, grit, and mindset fluctuate across the life-span could be of relevance for theoretical development within this area. The results can also reveal phases in life that may be more sensitive for interventions towards these qualities. The practical field should focus on the development of these concepts across the life-span, as they seem related to learning, achievement, and well-being. Gaining a better picture of these qualities can improve our understanding of how to enhance motivation and effort, as well as promote happiness and successful aging. Author Contributions Conceptualization, H.S., M.H., M.E., B.H.D. and F.H.; Data curation, H.S., M.H., B.H.D. and F.H.; Formal analysis, H.S., M.H., M.E., B.H.D. and F.H.; Investigation, H.S. and F.H.; Methodology, H.S., M.H. and F.H.; Project administration, H.S.; Writing—original draft, H.S., M.H., M.E., B.H.D. and F.H. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and in accordance with the regulations set out by the Norwegian Centre for Research Data and the Icelandic Data Protection Authority. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data that support the findings of this study are available from the corresponding author, [HS], upon reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Passion, grit, and mindset across life-span in the seven age groups. Error bars represent 95 CI. Figure 2 Passion across life-span in the seven age groups in relation to gender. Error bars represent 95 CI. Figure 3 Grit across life-span in the seven age groups in relation to gender. Error bars represent 95 CI. Figure 4 Mindset across life-span in the seven age groups in relation to gender. Error bars represent 95 CI. ijerph-19-05207-t001_Table 1 Table 1 Mean scores for age, passion, grit, and mindset for age groups and gender. Age Passion Grit Mindset Group N Mean (SD) Mean (SD) Mean (SD) Mean (SD) Total sample 1548 26.53 (17.77) 3.95 (0.65) 3.39 (0.60) 4.21 (0.99) Female 931 26.70 (11.81) 3.85 (0.66) 3.40 (0.59) 4.26 (0.93) Male 617 26.27 (11.71) 4.09 (0.61) * 3.39 (0.60) 4.14 (0.06) ** 13–19 Group 242 17.85 (1.47) 4.11 (0.63) 3.47 (0.60) 4.22 (0.91) Female 140 18.30 (1.11) 3.95 (0.63) 3.41 (0.57) 4.20 (0.89) Male 102 17.25 (1.68) 4.33 (0.56) 3.56 (0.63) 4.25 (0.93) 20–29 Group 1014 22.68 (2.38) 3.95 (0.65) 3.32 (0.60) 4.18 (0.98) Female 606 22.46 (2.32) 3.86 (0.66) 3.34 (0.59) 4.20 (0.90) Male 408 23.02 (2.43) 4.10 (0.60) 3.29 (0.61) 4.14 (1.08) 30–39 Group 112 33.57 (2.79) 3.85 (0.63) 3.45 (0.56) 4.51 (1.01) Female 67 34.00 (2.86) 3.82 (0.67) 3.43 (0.60) 4.50 (0.98) Male 45 32.93 (2.85) 3.89 (0.59) 3.47 (0.50) 4.51 (1.07) 40–49 Group 62 44.39 (2.90) 3.89 (0.64) 3.58 (0.48) 4.66 (1.04) Female 43 43.72 (2.75) 3.89 (0.64) 3.58 (0.49) 4.83 (1.03) Male 19 45.89 (2.73) 3.86 (0.65) 3.73 (0.44) 4.25 (0.98) 50–59 Group 57 55.07 (2.68) 3.56 (0.68) 3.74 (0.55) 4.41 (0.99) Female 42 54.86 (2.72) 3.53 (0.69) 3.76 (0.58) 4.61 (0.92) Male 15 55.67 (2.55) 3.64 (0.66) 3.71 (0.44) 3.84 (1.00) 60–69 Group 38 63.29 (2.54) 3.82 (0.72) 3.56 (0.42) 3.96 (1.04) Female 20 62.95 (2.54) 3.78 (0.79) 3.58 (0.45) 4.42 (0.89) Male 18 63.67 (2.54) 3.88 (0.65) 3.58 (0.45) 3.44 (0.98) 70–79 Group 23 72.87 (2.46) 3.86 (0.71) 3.73 (0.51) 3.37 (0.84) Female 13 73.46 (2.60) 3.69 (0.83) 3.78 (0.58) 3.37 (0.87) Male 10 72.10 (2.13) 3.95 (0.52) 3.67 (0.44) 3.37 (0.83) Group: group as a whole; * significant difference between genders in favor of males (t-test, p < 0.001); ** significant difference between genders in favor of females (t-test, p = 0.02). 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095634 ijerph-19-05634 Article Are Personal Resources and Perceived Stress Associated with Psychological Outcomes among Israeli Teachers during the Third COVID-19 Lockdown? https://orcid.org/0000-0003-1340-2909 Shinan-Altman Shiri 1* https://orcid.org/0000-0003-1582-3889 Levkovich Inbar 2 Tchounwou Paul B. Academic Editor 1 Louis and Gabi Weisfeld School of Social Work, Bar Ilan University, Ramat-Gan 52900, Israel 2 Oranim Academic College of Education, Kiryat Tiv’on 36006, Israel; inbar.lev2@gmail.com * Correspondence: shiri.altman@biu.ac.il; Tel.: +972-37384546 05 5 2022 5 2022 19 9 563427 3 2022 01 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Teachers’ psychological responses during a pandemic, such as COVID-19, play an important role in their adaptation to the new routine. This research aimed to explore the association between personal resources (sense of control, social support), perceived stress, and three psychological outcomes (resilience, depression, loneliness) among teachers during the third COVID-19 lockdown in Israel. A cross-sectional study was conducted among 208 teachers. Significant associations were found between perceived stress, resilience, depression, and loneliness. Sense of control was significantly associated with perceived social support. The research model was tested using Structural Equation Modeling. The model yielded appropriate indices of fit (χ2(10) = 10.31, χ2/df = 1.03, p = 0.413, NFI = 0.970, NNFI = 0.997, CFI = 0.999, RMSEA = 0.012, 95%CI RMSEA = 0.077), demonstrating that the model fits the data well. Findings suggest that in order to improve teachers’ psychological health during a virus outbreak, it is recommended to pay attention to their personal resources and perceived stress. coping resources COVID-19 depression loneliness perceived stress resilience teachers This research received no external funding. ==== Body pmc1. Introduction During the COVID-19 pandemic, several studies have reported elevated levels of stress, anxiety, and depression among individuals [1,2,3]. Specifically in the third lockdown, which lasted 42 days, there were strict lockdown regulations in Israel. These included remaining within 1000 m of one’s home, prohibitions against visiting others in their homes and the banning of gatherings of more than 20 people. By this time, the number of fatalities from COVID-19 in Israel had reached 3346, and the third wave had peaked with 1444 deaths in the former month [1]. Studies conducted during the third lockdown in Israel have found depression, anxiety, and psychological distress among the general population as a result of COVID-19 [1,4]. In Israel, the education system was closed down during the three lockdowns that were imposed in the country, following the outbreak of the COVID-19 pandemic. Each of these lockdowns lasted for about eight weeks. During the three lockdowns, and also in periods between them, teachers had to shift from frontal teaching to online distance teaching. While schools were closed during the lockdown periods, questions arose regarding how teachers could best provide students with help and support during these challenging times and how to maintain successful teacher -student relationships. Many teachers indicated that the transition to the use of remote learning technology and digital platforms—often with a lack of technical support and without prior training—added to their perceived stress [5]. Teachers reported that preparing for the lessons became complicated and challenging, as they had to create or find new suitable materials for distance teaching, whilst overcoming technical problems [6,7]. An additional source of teachers’ perceived stress stemmed from the need to create a balance between home space and professional space [8]. Many teachers had to share their home-based workspace with other family members studying or working from home. The need to simultaneously support their students as well as their own children was another source of stress among teachers [5,8]. This prolonged exposure to a stressful situation may create negative psychological outcomes among teachers [9]. In this study, we concentrated on three psychological outcomes among teachers during COVID-19: resilience, depression, and loneliness. Resilience is one’s ability to cope with difficulties and to be flexible enough in adapting to new demands in stressful situations [10]. It was suggested that resilience predicts better coping with a variety of stressful situations, such as COVID-19 [11]. A study conducted in Israel among 300 participants examined the effect of resilience and well-being on symptoms of distress during the first lockdown of COVID-19. This study found that higher resilience and well-being contributed to reduced distress symptoms and a decreased sense of danger [12]. Depression is one of the most common psychiatric disorders in the Western world. The prevalence of depression in the general population ranges from 3% to 10% [13]. Among teachers, studies conducted during COVID-19 indicated higher levels of depression, anxiety, and distress [6,14]. A study conducted in Spain among 1600 teachers found symptoms of depression in 32.2% teachers [15]. Loneliness is defined as a subjective feeling of dissatisfaction following a lack of satisfying interpersonal relationships [16]. The COVID-19 crisis has forced many countries to implement social distancing policies and lockdowns at different times [2,17]. Studies examining the impact of loneliness on people’s lives during quarantine periods have reported that loneliness was associated with suicidal thoughts among adults in the United States [18,19]. In addition, a study conducted in April 2020 during the COVID-19 period, which examined 634 language teachers from Europe and North America, found positive associations linking loneliness with sadness, and anger [5]. It was further found that young people, women, single people, divorced people, and people with emotional regulation difficulties reported a higher level of loneliness [20,21]. This study was based on the stress and coping theory [22]. According to this theory, coping involves persistently changing cognitive and behavioral efforts aimed to manage internal and/or external demands that are assumed as exceeding or taxing one’s resources. This model proposes that the psychological and physical health outcomes of coping with chronic and intense stressors are influenced by personal resources, stressor appraisals, and psychological and physical outcomes. Following this theory [22], we assumed that adjustment to the abovementioned psychological outcomes (resilience, depression, loneliness) depends mainly on coping resources and perceived stress. According to the revised version of the job demands–resources model it is important to consider internal resources (called ‘personal resources’) in addition to external resources used to meet the job demands [23]. Accordingly, in this study, two coping resources were examined among teachers: sense of control (internal resource) and perceived social support (external resource) because these two coping resources have been found in the literature to be associated with psychological outcomes among teachers [8,24]. Sense of control refers to the real or perceived control of individuals over their life and their perceptions regarding their ability to cope with stressors [25]. Perceived social support is defined as the perception that one is part of a social network of people (e.g., friends, family) who care for one’s safety [26]. Apart from the direct influence of coping resources and perceived stress on adjustment to stressful events, several complex models have been suggested. One of these models is the mediating model [27], known also as the deterrence model. According to this model, internal and external coping resources decrease perceived stress, which in turn decreases maladaptive psychological responses to stressful situations. To sum up, the COVID-19 crisis, together with the intermittent closure of the education system and the shift to online distance teaching, have raised the levels of emotional distress among some teachers [6]. To date, the Israeli education system has undergone three such closures. To our knowledge, no study has yet examined how Israeli teachers coped during the third lockdown, after nine months of coping with the COVID-19 crisis in Israel. Therefore, the aim of this study was to examine a comprehensive model that integrates the interrelationships among perceived stress, personal resources (sense of control, perceived social support), and three psychological outcomes (resilience, depression, and loneliness) among teachers during the third COVID-19 lockdown in Israel. In addition, we examined indirect relationships, involving perceived stress as an independent variable, personal resources as mediators, and resilience, depression, and loneliness as the outcome variables. Specifically, the following hypotheses were tested: H1.  Higher levels of perceived stress, and lower levels of personal resources (sense of control, perceived social support) will be directly associated with lower resilience, higher depression, and higher loneliness. H2.  Personal resources (sense of control, perceived social support) would mediate the association between perceived stress and three psychological outcomes (resilience, depression, and loneliness). 2. Materials and Methods 2.1. Participants A total of 208 teachers working in public schools throughout Israel participated in the study; 180 were women (86.5%) and 28 were men (13.5%). Sample size was calculated using the G*Power software (version 3.1.9.7) [28,29]. For a regression analysis with 7 predictors, an effect size of f2 = 0.145 (equals the minimum R2 of 0.17 that was found), α = 0.05, and power = 0.95, the required sample size is 158 participants. All higher effect sizes (i.e., higher R2), require smaller samples. The criterion for inclusion was being a teacher working in the Israeli school system. The criterion for exclusion was not filling the whole questionnaire. The participants ranged in age from 24 to 65 (Mean = 43.4, SD = 10.1). Their mean teaching experience was 14.8 years (SD = 10.5). Most of the participants were married (n = 174, 83.7%) and had an average of 2.5 children (SD = 1.1). Most of the teachers (59.1%) had a Master’s degree; 135 participants worked in middle and high schools (64.9%). 2.2. Procedure The present study was approved by the university’s Ethics Committee (Approval no. 99-21). A convenience sampling method was used to recruit the study participants. The research participants were invited to take part in a study focused on teachers’ coping during the third COVID-19 lockdown. The teachers were recruited mainly through internet forums and social media outlets (Facebook pages for teachers). A total of 208 teachers visited the online survey and filled out the whole questionnaire. Another nine were excluded due to incomplete questionnaires. The study was conducted between January 2021 and February 2021. This period of time reflects the third lockdown in Israel. During this time, all schools were closed and teachers were asked to teach via online platforms. The survey’s introductory page stated explicitly that proceeding to the questionnaire would signify consent to participate. 2.3. Measures 2.3.1. Dependent Variables Resilience was measured using the Brief Resilience Scale (BRS) [30], which assesses one’s ability to ‘bounce back’ after experiences of distress and includes positive and negative items. Participants were asked to indicate the extent to which they agreed or disagreed with each item on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree (e.g., “It does not take me long to recover from a stressful event”). After reversing the negative statements, a mean score was calculated; a high score indicated greater levels of resilience (Cronbach’s α = 0.83). Depression was assessed using the Symptoms of Depression Questionnaire (Center for Epidemiological Studies Depression, CESD–10) [31], which is composed of 10 items and is a shortened version of the original 20-item instrument [32]. Participants were asked to rate the intensity of their experiences during the previous week on a 4-point Likert-type scale ranging from 0 = never to 4 = to a great extent (e.g., “I felt depressed”). After reversing the positive statements, a mean score was calculated; a high score indicated higher levels of depression (Cronbach’s α = 0.85). Loneliness was assessed using the Revised U.C.L.A. loneliness scale (R-UCLA) [33], a shortened version of the original questionnaire [34], which was validated in Hebrew [35]. The questionnaire includes three items relating to the severity of general loneliness. Participants were asked to rate each statement on a 5-point Likert-type scale, ranging from 0 = not at all to 4 = very much (e.g., “To what extent do you feel isolated from others?”). A mean score was calculated; a high score indicated higher levels of loneliness (Cronbach’s α = 0.91). 2.3.2. Independent Variables Sense of control was assessed using a 7-item scale measuring the ability to have control over issues in one’s lives [25]. Participants were asked to rate the extent to which they agreed or disagreed with each statement on a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree (e.g., “I can do almost anything I decide”). A mean score was calculated; a high score indicated greater levels of sense of control (Cronbach’s α = 0.73). Perceived social support was assessed using the Multidimensional Scale of Perceived Social Support [36], a 12-item scale validated in Hebrew. Participants were asked to rate the extent to which they agreed or disagreed with each statement on a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree (e.g., “My friends really try to help me”). A mean score was calculated; a high score indicated greater levels of perceived social support (Cronbach’s α = 0.92). Perceived stress was measured using a single item that seeks to assess participants’ subjective stress experience: “Please rate your stress level during the past week”. This item has been widely used in past studies [37]. Participants were asked to indicate the extent of their stress on a 10-point Likert scale ranging from 1 = no stress to 10 = very high stress; a high score indicated higher levels of perceived stress. Personal and professional background included gender, age, years of education, marital status, number of children, and teaching seniority. 2.4. Statistical Analyses Data were analyzed using SPSS and AMOS (ver. 27). Descriptive statistics were used to describe the participants’ demographic characteristics and the research variables. Pearson correlations were calculated to assess the associations between the research variables. No missing data were noted. The variable of social support was negatively skewed (skewness= −1.64, SE = 0.17) and was thus exponentially transformed. The variable of loneliness was positively skewed (skewness = 0.53, SE = 0.17) and was thus logarithmically transformed. No meaningful outliers were noted. Independent continuous variables were standardized. The research model was tested with path analysis, with maximum likelihood estimation (ML), using AMOS 27. Model fit was assessed with five indices. Chi-square and the normed chi-square (χ2/df) tests were used to assess the model’s overall fit and parsimony. Normed chi-square values of ≤2.0 indicates a good fit. We used also comparative fit index (CFI), normed fit index (NFI), and non-normed fit index (NNFI), which are incremental fit indices. We employed the root mean-square error of approximation (RMSEA and its confidence interval), which measures the discrepancy per degree of freedom and indicates the model’s absolute fit. CFI, NFI, and NNFI scores of >0.95 and RMSEA values of <0.06 indicate a good model fit [38]. In addition, indirect effects were evaluated, within the path analysis model, by using a bootstrapping test (5000 bootstrap samples), and 95% bias-corrected confidence intervals (CI), in order to evaluate the statistical significance of the indirect paths. 3. Results 3.1. Descriptive Statistics and Correlations between Study Variables As can be seen in Table 1, the mean score for perceived social support was relatively high and the mean scores for sense of control and resilience were relatively moderate to high. The mean score for perceived stress was relatively moderate, and the mean scores for depression and loneliness were relatively moderate to low. According to Table 1, loneliness was significantly and positively associated with depression—namely, the more the participants experienced a sense of loneliness, the more depression they reported feeling. In addition, the higher the participants’ sense of control was, the greater their perceived social support, and the lower their perceived stress—the lower their feelings of depression and loneliness were. Significant negative moderate associations were found linking depression and loneliness with resilience—namely, the lower the participants’ feelings of depression and loneliness were, the higher their resilience was. In addition, participants with higher levels of sense of control and lower levels of perceived stress, reported higher levels of resilience. Finally, higher levels of sense of control were related with higher levels of perceived social support, and with lower levels of perceived stress. No significant associations were found linking perceived social support with either resilience or perceived stress. Prior to assessing the research model, the associations between the background characteristics of the teachers and the research variables were examined, to identify control variables for the model. Significant correlations were found between the teachers’ ages and: perceived stress (r = −0.29, p < 0.001), depression (r = −0.23, p < 0.001), and loneliness (r = −0.19, p = 0.006), so that younger teachers reported higher levels of perceived stress, depression, and loneliness. Thus, the research model was assessed while controlling for the teachers’ ages. Controlling for the teachers’ ages controls for their seniority in teaching as well, as the correlation between these two variables was high (r = 0.79, p < 0.001). In addition, depression scores were found higher for teachers working in middle and high schools (M = 1.16, SD = 0.53) than for teachers working in elementary schools (M = 0.98, SD = 0.57) (t(206) = 2.29, p = 0.023). Other differences were not found significant, and thus teachers’ ages and students’ age levels (0—elementary school; 1—middle and high school) were controlled for. 3.2. The Study Model The research model presented in Figure 1 yielded appropriate indices of fit (χ2(10) = 10.31, χ2/df = 1.03, p = 0.413, NFI = 0.970, NNFI = 0.997, CFI = 0.999, RMSEA = 0.012, 95%CI RMSEA = 0.0, 0.077), demonstrating that the model fits the data well. As can be seen in Figure 1, the regression model explained 17% of the variance in resilience, 46% of the variance in depression, and 21% of the variance in loneliness. A significant association was found between sense of control and perceived stress, such that a higher sense of control was related with lower perceived stress. No relationship was found between perceived social support and perceived stress. Further, significant associations were found between sense of control and the three dependent variables—namely, the higher the participants’ sense of control was, the higher their resilience was, and the lower their depression and loneliness levels were. Similarly, significant negative direct associations were found linking perceived social support with depression and loneliness. No significant association was found between perceived social support and resilience. Finally, significant associations were found between perceived stress and the three dependent variables—namely, participants with higher levels of perceived stress reported lower resilience levels and higher levels of depression and loneliness. As can be seen in Figure 1 and Table 2, perceived stress served as a mediating variable between sense of control and the three dependent variables. That is, higher sense of control was related with lower perceived stress, which in turn was related with higher resilience, lower depression, and lower levels of loneliness. No significant association was found between perceived social support and perceived stress. Therefore, no mediation was examined for perceived social support and the outcome variables. 4. Discussion The aim of this study was to explore the associations among personal resources (sense of control, perceived social support), perceived stress, and three psychological outcomes (resilience, depression, and loneliness) among teachers during the third COVID-19 lockdown in Israel. According to our findings, a negative association was found between both coping resources and teachers’ depression and loneliness—namely, participants with higher levels of coping resources reported lower levels of depression and loneliness. These findings are supported by previous studies suggesting that coping resources may be protective factors in times of stress and may help promote individuals’ psychological health [39]. In a study conducted among teachers in Jordan during the COVID-19 crisis, about one-third of the participants reported experiencing anxiety, depression, and stress, noting that social and family support helped them and were a vital resource in their coping [40]. Similarly, a study conducted among teachers in China found that sense of control and perceived social support were associated with participants’ emotional state [41]. It should be noted that the COVID-19 crisis belongs to the phenomenon of “Shared Traumatic Reality” (STR). This state refers to the shared reality of professionals in the field of education and care: that they, together with their students or patients, live and/or work in shared areas that endanger their personal safety [42]. The COVID-19 pandemic is a new STR for teachers who experience the fear and danger of the virus and are concerned for their family members, and at the same time must provide support and help for their students. In this situation, these teachers need a great deal of coping resources—both for themselves and in order to provide others with support. The findings of the current study indicate that the more teachers reported a sense of control, the higher their reported resilience levels. Sense of control is an internal resource which allows to cope with stressful events more effectively and to experience fewer negative consequences (e.g., depression, anxiety) caused by the exposure to the stressor [22]. A sense of control helps maintain a sense of resilience when coping with stressful situations and is positively associated with positive mental well-being [43]. A qualitative, longitudinal study conducted among U.S. teachers showed that teachers felt a better sense of control over time during COVID-19 [44]. Researchers suggest that resilience develops and strengthens over time dynamically, as a result of the individual’s positive coping strategies [45,46,47]. Our findings indicted that perceived stress served as a mediating variable in the relationship linking sense of control with resilience, depression, and loneliness. Perceived stress is the result of a person’s appraisal of a stressor as threatening or non-threatening. The person evaluates what can be done, if at all, to overcome the stressful situation and improve his/her mental well-being [22]. Assessing the stressful situation largely defines the coping strategies a person chooses as well as the coping outcomes [22]. The mediating role of perceived stress found in this study is in line with a study conducted among 1100 Israelis during the second lockdown period, which found that perceived stress mediated the association between participants’ perceived state of health and their emotional state [48]. It might be that people with a higher sense of control may experience lower perceived stress, perceive the situation as less challenging, and experience a lower sense of threat. These perceptions may create lower levels of depression and loneliness and higher levels of resilience. This finding is consistent with the stress and coping model [22], indicating the central role of both resources and perceived stress. In this study, age was found to have a significant negative association with perceived stress, depression, and loneliness. Thus, younger teachers reported higher levels of these variables compared to older teachers. This finding is consistent with studies conducted in Israel and in other countries. In a study that included 46,000 participants aged 16–99 from 237 countries, it was found that loneliness levels were higher for younger individuals compared to older individuals during COVID-19 [49]. A study conducted among teachers in Spain indicated that the highest levels of depression and stress were found among teachers aged 23–35 [15]. Indeed, younger teachers may have less experience with difficult life events, and at the beginning of their professional career their work is more intensive and demanding, which may affect them emotionally [15,49]. In addition, during the quarantine period younger teachers were required to provide care for their young children and/or to cope with spouses whose employment status sometimes changed abruptly (working from home, furloughed, or unemployed). These factors may also have affected younger teachers’ psychological status. Several potential shortcomings should be acknowledged. First, we used online questionnaires because of the limitations of social distancing restrictions; therefore, the response rate and reasons for refusal are unknown. Second, we applied a cross-sectional design and, therefore, bidirectionality of the associations among the variables cannot be ruled out and caution is advised when inferring causality. Caution is also advised in this regard when interpreting the results of the mediation model. Longitudinal studies with larger groups of teachers are therefore needed in order to develop a deeper understanding of the nature of the relationships examined in this study. Third, we have no information whether the associations between study variables found during the pandemic would be different (or probably the same) during the normal time before the pandemic. Additionally, probably the same associations could be found in a general population or other occupational groups. Fourth, only one instrument was validated in Hebrew. Fifth, other aggravating factors that may be responsible for the negative effects, such as a history of mental disorders, were not investigated in this study. Finally, because the current pandemic is dynamic and its impact is lasting, the emotional state of teachers should be examined across time. 5. Conclusions This study is the first to identify factors contributing to psychological outcomes among teachers during a third lockdown of COVID-19, after nine months of coping with COVID-19 in Israel. Practically, the study findings emphasize that teachers need control during a pandemic. In addition, due to the research findings indicating higher levels of emotional distress among younger teachers, it is suggested to provide more support for younger teachers and appoint senior teachers who can educate them and help them cope in times of crisis. Intervention programs should include means for increasing sense of control and social support among teachers, in order to improve their coping, and ultimately decrease negative psychological outcomes during a pandemic outbreak. In order to develop a sense of understanding and advocacy, stakeholders, including parents, school district officials, and community partners need to know the mechanism that may lead to decreases in teachers’ well-being. In order to improve teachers’ psychological health during a virus outbreak, it is recommended to pay attention to their personal resources and perceived stress. Author Contributions Formal analysis, S.S.-A.; investigation, I.L.; writing—original draft, S.S.-A.; writing—review and editing, I.L. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The present study was approved by Oranim College Ethics Committee (Approval no. 99-21). Informed Consent Statement The survey’s introductory page stated explicitly that proceeding to the questionnaire would signify consent to participate. Data Availability Statement The data that support the findings of this study are available from the authors upon reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 The study model. Note: All independent and mediating parameters are standardized. * p < 0.01, *** p < 0.001. ijerph-19-05634-t001_Table 1 Table 1 Correlates, means, SDs, and ranges of study variables (n = 208). Variables Mean (SD) 2 3 4 5 6 1. Resilience 3.54 (0.74) −0.42 *** −0.25 *** 0.32 *** 0.07 −0.35 *** 2. Depression 1.10 (0.55) 0.37 *** −0.44 *** −0.34 *** 0.39 *** 3. Loneliness 1.42 (1.13) −0.31 *** −0.33 *** 0.29 *** 4. Sense of control 5.04 (0.90) 0.28 *** −0.29 *** 5. Perceived social support 4.45 (0.60) −0.07 6. Perceived stress 5.51 (2.57) *** p < 0.001. ijerph-19-05634-t002_Table 2 Table 2 Indirect effects between sense of control and the dependent variables (n = 208). Dependent Variable Indirect Effect SE 95%CI p Resilience 0.08 0.03 0.03, 0.14 <0.001 Depression −0.13 0.04 −0.20, −0.07 <0.001 Loneliness −0.06 0.02 −0.12, −0.02 <0.001 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Adar T. Davidof M. Elkana O. Social Support Mediates the Association between Attachment Style and Psychological Distress during COVID-19 in Israel Viruses 2022 14 693 10.3390/v14040693 35458423 2. Shinan-Altman S. Levkovich I. 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==== Front Nutrients Nutrients nutrients Nutrients 2072-6643 MDPI 10.3390/nu14091981 nutrients-14-01981 Article Associations between Frequency of Culinary Herb Use and Gut Microbiota https://orcid.org/0000-0001-5126-0601 Vita Alexandra Adorno 1* McClure Ryan 2 Farris Yuliya 2 Danczak Robert 2 Gundersen Anders 1 Zwickey Heather 1 Bradley Ryan 1 Melini Valentina Academic Editor Ruzzi Maurizio Academic Editor 1 Helfgott Research Institute, National University of Natural Medicine, Portland, OR 97201, USA; agundersen@nunm.edu (A.G.); hzwickey@nunm.edu (H.Z.); rbradley@nunm.edu (R.B.) 2 Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA; ryan.mcclure@pnnl.gov (R.M.); yuliya.farris@pnnl.gov (Y.F.); robert.danczak@pnnl.gov (R.D.) * Correspondence: alexandra.adorno.vita@gmail.com 09 5 2022 5 2022 14 9 198118 4 2022 07 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). While evidence suggests that culinary herbs have the potential to modulate gut microbiota, much of the current research investigating the interactions between diet and the human gut microbiome either largely excludes culinary herbs or does not assess use in standard culinary settings. As such, the primary objective of this study was to evaluate how the frequency of culinary herb use is related to microbiome diversity and the abundance of certain taxa, measured at the phylum level. In this secondary data analysis of the INCLD Health cohort, we examined survey responses assessing frequency of culinary herb use and microbiome analysis of collected stool samples. We did not observe any associations between frequency of culinary herb use and Shannon Index, a measure of alpha diversity. Regarding the abundance of certain taxa, the frequency of use of polyphenol-rich herbs and herbs with certain quantities of antibacterial compounds was positively associated with Firmicutes abundance, and negatively associated with Proteobacteria abundance. Additionally, the total number of herbs used with high frequency, defined as over three times per week, was also positively associated with Firmicutes abundance, independent of adjustments, and negatively associated with Proteobacteria abundance, after adjusting for dietary factors. Frequency of culinary herb use was not associated with Bacteroidota or Actinobacteria abundance. microbiota culinary herbs phytochemicals National Center for Complementary and Integrative Health (NCCIH) of the National Institutes of Health5R90AT008924 1K24AT011568 This research was supported by the National Center for Complementary and Integrative Health (NCCIH) of the National Institutes of Health via grants 5R90AT008924 and 1K24AT011568. ==== Body pmc1. Introduction The gastrointestinal (GI), or gut, microbiome is highly diverse and includes microbiota from more than 1000 species of bacteria, as well as some archaea, protists, viruses, and fungi. The most predominant residents, however, are bacteria from two major phyla—Firmicutes and Bacteroidetes—which comprise over 90% of the microbiota [1,2,3]. While every individual’s microbiome is unique, certain predominant “enterotypes” have emerged, each characterized by its own distinctive compilation of microbiota and dominant genera [4,5]. The gut microbiota and their downstream products (e.g., metabolites, synthesized vitamins, amino acids, and neurotransmitters, etc.) directly affect the regulation of immune responses, coordination of neuroendocrine and cardiovascular activities, facilitation of host nutrition and metabolism, and maintenance of intestinal barrier function [3,6]. Due to these wide-ranging physiological effects, it is perhaps unsurprising that recent evidence highlights a role for the gut microbiome in the pathophysiology of numerous inflammatory, metabolic, and neurological diseases [7]. More specifically, the generation and severity of many such diseases have been linked to dysbiosis of the microbiome, which is characterized by the increased and/or decreased abundance of specific microbes and their metabolites to an extent that disrupts homeostatic conditions within the body [7]. 1.1. Diet and the Microbiome While there are numerous intrinsic and extrinsic variables impacting microbial composition within the gut, diet has emerged as one of the predominant influencing factors that is also modifiable, and thus has therapeutic potential. The ability of diet to shape microbial communities, both long-term and short term, is largely in response to available macro and micronutrients [8]. Macronutrients (i.e., proteins, fats, complex carbohydrates including fiber) can either selectively promote or inhibit growth, depending on the preferred and available metabolic pathways of specific bacteria, as well as microbial sensitivity to macronutrient-induced changes within the gut, such as bile acid production and shifting pH [5,8]. Additionally, micronutrients (i.e., vitamins and minerals) and other physiologically relevant plant-derived bioactive compounds (i.e., secondary metabolites, such as polyphenols, alkaloids, terpenoids, etc.) can impact microbial metabolism, enzyme function, and gene expression [9,10,11,12]. Additionally, certain macronutrients, such as fiber [8], and certain micronutrients, such as polyphenols [13], act as energy sources for bacterial growth, and are considered prebiotics. 1.2. Culinary Herbs and Spices Culinary herbs and spices contain micronutrients and bioactive compounds (e.g., polyphenols, alkaloids, etc.) [11,14,15], yet have not been widely studied regarding their impact on the GI microbiome in human populations, and thus represent a confounder in research investigating diet and microbiota. There is much pre-clinical evidence regarding the impact of culinary herbs and spices, or their active constituents, on GI microbiota [12,16,17,18,19]. However, these often do not utilize the herbs and spices (or their active constituents) in doses, combinations or frequency of administration relevant to typical culinary use. In human samples, some herbal mixtures, containing culinary herbs and spices, have been used as interventions in clinical investigations, such as a single capsule of mixed curry spices [20], a single capsule of mixed culinary spices (cinnamon, oregano, ginger, black pepper, and cayenne pepper) [21], and an herbal formula containing both culinary spices and specific nutrients [22], among others. There have also been evaluations of specific herbs and/or known active constituents on the microbiome (e.g., oregano [23], capsaicin [24], curcumin [25], etc.,). Many report findings in which the abundance of beneficial bacteria increase while that of opportunistically pathogenic bacteria decrease. While such studies may help elucidate a causal relationship between the use of specific herbs and changes in microbiota, and are thus important, they are not representative of how these products are consumed during typical cooking practices. 1.3. Research Objective Due to the emerging role of the microbiome in health and disease [26], along with existing evidence that culinary herbs have the potential to modulate gut microbiota, evaluating associations between cooking with herbs and the gut microbiome may be hypothesis-generating for future experimental research, and provide insight into prospective therapeutic strategies. As such, the primary objective of this study is to evaluate if the frequency of culinary herb use is associated with microbiome diversity and abundance of certain phyla. 2. Materials and Methods 2.1. Study Design This study is a secondary analysis of data collected during the INCLD Health longitudinal cohort study, the methods of which have been previously published [27]. Briefly, data from participants was collected over the course of three time points—baseline, 6 months, and 1 year—and included various evaluations of health, wellness, lifestyle, diet, and the microbiome. In this ancillary study, only select data from the baseline visit was used. Survey data was collected and managed using REDCap [28] electronic data capture tools hosted at the National University of Natural Medicine. Nutritional data was collected with VioScreen (by VioCare, Princeton, NJ, USA), a validated food survey that was administered online, and calculated by the Nutrient Coordinating Center. 2.2. Participants Participants in the original study were students enrolled in a complementary and integrative medicine education program and were previously recruited through methods outlined in the INCLD Health protocol [27]. While data was collected from 197 participants during the INCLD Health study, data from only 96 of those participants were used in the statistical analyses of this ancillary study for reasons outlined under the “Statistical models” sub-section. The final working sample was roughly 15% male and 85% female, with an average age of 29. About 75% of the sample was White/Caucasian, 5% Asian, 2% Black/African American, 2% Middle Eastern, 1% Native Hawaiian/Pacific Islander, 1% Native American/Alaskan, 6% Mixed, and 4% other/unknown. 2.3. 16S rRNA Gene Sequencing and Processing All 16sRNA gene sequencing and processing was carried out at the Pacific Northwest National Laboratory (Richland, WA, USA). DNA was extracted from participant fecal samples using the Quick-DNA Fecal/Soil Microbe Microprep Kit (Zymo, Irvine, CA, USA). The hypervariable V4 region of the 16S rRNA gene was sequenced on an Illumina MiSeq using the 515F-806R primer set. The resulting 16S rRNA amplicon dataset was processed using QIIME2 (v2021.4) [29]. Within the QIIME2 environment, DADA2 (q2-dada2) [30] was used to both denoise and cluster amplicon sequence variants (ASVs), which were then taxonomically classified (q2-feature-classifer) using the SILVA database (v138) [31]. Processed data was then exported from QIIME2 and converted into a comma-delimited file. 2.4. Microbial Ecology Analyses Ecological analyses were performed using the statistics program R (v4.1.0) [32] with figures generated using the ggplot2 R package [33]. Communities were first rarefied down to 4000 ASV counts per sample (rrarefy; vegan package v2.5-7). Using this rarefied dataset, Shannon’s diversity [34] (diversity; vegan package v2.5-7) [35] and richness (sum of all ASVs in a sample) were calculated to obtain a measure of alpha diversity. Multivariate differences (e.g., beta-diversity) were measured by first transforming the rarefied dataset following a Hellinger transformation (decostand; vegan package v2.5-7), calculating a Bray-Curtis dissimilarity (vegdist; vegan package v2.5-7), and finally ordinated the data using a principal coordinate analysis (PCoA; pcoa; ape package v2.5-7) [36]. Phylum abundances were calculated by summing the rarified counts of all ASVs common to a given phylum. 2.5. Variables 2.5.1. Outcome Variables Microbial alpha-diversity (Shannon Index) and Phylum Abundance, which were calculated as described above, were the two outcome measures. The abundance of four phyla were used for analysis in this study: Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. 2.5.2. Exposure Variables The exposure variables were derived from frequency of culinary herb use data, which was surveyed during the INCLD Health study. The culinary herbs surveyed included cumin, garlic, onion, cinnamon, thyme, ginger, basil, rosemary, cilantro, parsley, sage, oregano, mint, dill, clove, cayenne, allspice, nutmeg, paprika, saffron, cardamom, tarragon, chives, bay leaf, coriander, red chili, black pepper, and fennel seed. Frequency was reported in the survey as never, once per month, 2–3 times per month, once per week, twice per week, 3–4 times per week, 506 times per week, or daily. Frequency was recoded into low frequency (never to 2–3 times per month), medium frequency (once to twice per week), or high frequency (at least 3 times per week). Dr. Dukes Phytochemical and Ethnobotanical Database [37] was used to identify chemical constituents of culinary herb, the parts per million (PPM) of each constituent, and which constituents had reported antibiotic properties. The Phenol-explorer 3.0 database [38,39] was then used to identify which chemical constituents were polyphenols. Following this identification, the culinary herbs were then grouped by certain phytochemical characteristics. The Alliums group contained garlic, onions, chives, and the Capsaicin group contained chili pepper, paprika, and cayenne. Herbs containing at least 30,000 PPM of eugenol were grouped together (Eugenol) and included allspice, clove, and cinnamon. Two groups of herbs containing polyphenolic compounds were formed: A group with herbs containing over 50,000 PPM polyphenols (>50,000 PPM Polyphenols) included clove, cinnamon, fennel seed, thyme, oregano, onion, rosemary; a group with herbs containing over 30,000 PPM polyphenols (>30,000 PPM Polyphenols) contained those previously mentioned, as well as ginger, tarragon, cumin, basil, and allspice. Two groups of herbs containing compounds with reported antibiotic properties were formed: A group with herbs containing at least 90,000 PPM of compounds with reported antibiotic properties (>90,000 PPM Antibiotic) included clove, cinnamon, fennel seed, thyme, oregano, rosemary, black pepper, nutmeg, and cardamom; A group with herbs containing at least 30,000 PPM of compounds with reported antibiotic properties (>30,000 PPM Antibiotic) included those previously mentioned, as well as sage, bay leaf, mint, parsley, cumin, and allspice. The frequency of use of each of these herb groups (high, medium, and low frequency, as previously described) was then used as the exposure variables. Finally, the total number of herbs used with high frequency (Total High Frequency Herbs), defined as use at least 3–4 times per week, was used as an additional exposure variable. The final groups were not discrete, as there was some overlap between groups; however, each group was utilized in its own regression. 2.5.3. Adjustment Variables Adjustment measures included age, sex assigned at birth, race, and ethnicity, grouped as Demographic Factors; estimated daily intake of protein, fat, and fiber, grouped as Dietary Factors; the total number of supplements used, reported as Total Supplements; and the total number of medications used, reported as Total Medication. Regarding demographic information, age was reported continuously; Sex assigned at birth was reported as male, female, or intersex; Ethnicity was reported as either Hispanic or Latino/Latina/LatinX, not Hispanic or Latino/Latina/LatinX, or unknown/not reported; Race was reported as Black or African American, Asian, Middle Eastern, Native Hawaiian or Pacific Islander, American Indian/Alaska Native, White/Caucasian, more than one race, or other/unknown. Any supplements and medications that were currently being used at the time of the baseline INCLD Health visit were reported and the total number of each were calculated. Estimated daily intake of protein, fat, and fiber were all reported in grams (g). 2.6. Statistical Models To investigate the association between alpha-diversity and culinary herb use, linear regression models were used: Model 1 includes Frequency of Use of a specific culinary herb grouping variable, as described above; Model 2 adjusts for Demographic Factors; Model 3 adds adjustment for Dietary Factors; Model 4 adds adjustment for Total Supplements; Model 5 also adjusts for Total Medication. To investigate the association between phylum abundance and culinary herb use, the same Models 2–4 are used as described above. Data was excluded from statistical analysis for the following purposes: (1) incomplete demographic data; (2) participants reported the combined use of a culinary herb with its matching supplement; for example, if a participant reported using turmeric as a culinary herb and reported using it as a supplement, that participant’s data was excluded from statistical analysis; (3) incomplete microbiome analysis data. All regression analyses were performed using SPSS Version 28.0.1.0 (IBM Corp, Armonk, NY, USA) [40]. 3. Results 3.1. Descriptive Statistics Average values for alpha diversity and phylum abundance are reported in Table 1. Baseline characteristics, regarding demographics, medications and supplements used, and intake of dietary factors, are reported in Table 2. 3.2. Frequency of Culinary Herb Use Black pepper was the most frequently used spice, being used on average about 4 times per week, followed by onion and garlic, which were used on average about 3 to 4 times per week (Figure 1). Cinnamon and ginger were the next most frequently used spices, being used between once to twice per week (Figure 1). All other spices were used on average less than once per week. No spice was used daily, on average (Figure 1). When considering the frequency of use of grouped herb categories, the Alliums group was the most frequently used, with an average use of about 2 times per week (Figure 2). Conversely, the Eugenol group was the least frequently used, with an average use of about 2–3 times per month (Figure 2). 3.3. Association between Frequency of Culinary Herb Use and Alpha Diversity No statistically significant associations were observed between the frequency of culinary herb use and Shannon Index (Table 3). Additionally, there were no statistically significant associations observed between the total number of herbs used with high frequency and Shannon Index (Table 4). 3.4. Association between Frequency of Culinary Herb Use and Phylum Abundance There were no significant associations observed between any of the exposure variables and Actinobacteria (Table 5) or Bacteroidota abundance (Table 6). However, a significant positive association was observed between Firmicute abundance and frequency of use of herbs in the >30,000 PPM antibiotic category, after adjusting for the total number of supplements and medications used (Table 7), and Firmicute abundance and the of use of herbs in the >90,000 PPM antibiotic category, independent of adjustments (Table 7). Additionally, significant positive associations were observed between Firmicute abundance and frequency of use of herbs in both the >30,000 PPM and >50,000 PPM polyphenol categories (Table 7), and between Firmicute abundance and the total number of herbs used with high frequency (Table 4), independent of adjustments. Significant inverse associations were observed between Proteobacteria abundance and frequency of use for herbs the >30,000 PPM polyphenol category, after adjusting for dietary factors (Table 8); Non-significant inverse trends (p-values > 0.1) were observed in Models 1, 2, and 4. This same pattern was detected between Proteobacteria abundance and total number of herbs used with high frequency (Table 3). Significant inverse associations were also observed between Proteobacteria abundance and frequency of use for herbs the >50,000 PPM polyphenol category and >90,000 PPM antibiotic category, independent of adjustments (Table 8). 4. Discussion This secondary data analysis of the INCLD Health cohort aimed to evaluate associations between the frequency of culinary herb use and microbiome diversity and phylum abundance. Our results indicate that the frequency of culinary herb use, particularly herbs with high phenolic content, as well as the total number of herbs used with high frequency, may be associated with changes in Firmicute and Proteobacteria abundance. Polyphenols are metabolized by numerous gut microbes and thus act as prebiotics to certain taxa, such as commensal Roseburia spp., Faecalibacterium spp., Lactobacillus spp., which belong to the Firmicutes phylum [13]. While polyphenols can act as prebiotics for commensal bacteria, they many also interfere with virulence factors present in certain pathogenic bacteria, many of which are from the Proteobacteria phylum. For example, polyphenols from various sources decreased quorum sensing activity, motility, and biofilm formation of Pseudomonas aeruginosa [41,42]. Several polyphenols have also been shown to directly interfere with membrane-bound ATP synthase activity on Escherichia coli [43,44,45], thus disrupting energy production by these bacteria. It is possible these mechanisms may contribute to the associative patterns observed in this study, in which the frequency of high polyphenol-containing herb use was positively associated with Firmicute abundance and negatively associated with Proteobacteria abundance. Clinical investigations into the impacts of polyphenol-based interventions further support the associative patterns observed in this study between high polyphenol-containing herbs and Firmicute and Proteobacteria abundance. When a polyphenol rich diet was administered to individuals with increased gut permeability [46], for example, serum metabolites of polyphenol catabolism were positively associated with bacteria in the order Clostridiales, as well as in the genera Roseburia, Butyricicoccus and Faecalibacterium, all of which are included in the Firmicutes phylum. Additionally, an inverse association was present between polyphenol-derived serum metabolites and the Proteobacteria of the genera Desulfovibrio and Enterobacteriaceae [46]. At the phylum level, a polyphenol-rich diet in individuals at high risk for cardiometabolic disorders resulted in increased Firmicutes abundance, as well an increase in members of the Clostridial cluster IV [47]. Similarly, a separate study observed that the administration of red wine polyphenols were associated with increases in Firmicutes phylum members, such as Lactobacillus, Roseburia, Feacalibacterium, and decrease in Proteobacteria phylum members, such as Escherichia coli and Enterobacter spp. [48], within fecal samples of study participants. Regarding the use of herbs specifically, independent of phenolic content, the administration of a multi-herb formula in adults with digestive disorders resulted in increased Lactobacillus, Clostridium, and Faecalibacterium prausnitzii; all members of the Firmicutes phylum [22]. A similar pattern of association was also observed between the frequency of herbs in the antibiotic categories, particularly those in the >90,000 PPM antibiotic category, and Firmicutes and Proteobacteria abundance. Studies looking specifically at the impact of select herbs on the gut microbiome reveal that some might perhaps reduce the abundance of opportunistic pathogens while increasing the abundance of commensal bacteria. Oregano, for example, contains numerous compounds reported to have antibiotic properties (thymol, carvacrol, p-cymene, y-terpinene, etc.) [37]. Oregano has been shown in vivo to reduce Streptococcus sp. and increase Enterococcus sp. when used in powdered form [49], and increase Lactobacillus sp., Clostridium sp., and the Firmicutes phylum when used in essential oil form [50]. Additionally, oregano use resulted in decreased opportunistically pathogenic members of the Proteobacteria phylum, such as the Klebsiella and Proteus genera [49]. Comparable results were observed with other herbs in the >90,000 PPM antibiotic category, or their constituents, demonstrating in previous studies an ability to increase commensal bacteria from the Firmicutes phylum and decrease opportunistically pathogenic bacteria from the Firmicutes and/or Proteobacteria phyla [19,51,52,53,54]. It is important to note, however, that there was overlap between the herbs in the antibiotic and polyphenol categories, as some herbs contained high quantities of both. Since these were not discrete categories, the observed positive association with Firmicute abundance may be an artifact of using herbs that also contain high quantities of polyphenols. The pattern of positive association with Firmicute abundance, independent of adjustments, and inverse association with Proteobacteria abundance, after adjusting for dietary factors, was also observed for the total number of herbs used with high frequency. This category was originally included to investigate whether associations between frequency of culinary herb use and microbiome outcomes were due to the total number of different unique herbs used with high frequency, as opposed to specific biochemical profiles. Indeed, Macdonald et al. [55], previously described alpha diversity to be impacted by the consumption of 30 or more unique plants per week, regardless of plant type. However, due to the associative pattern shared with polyphenol-containing herb groups (i.e., associations with Firmicutes and Proteobacteria abundance, but not Shannon Index or the abundance of other phyla), we question whether the observed associations were impacted by participants consuming more herbs high in polyphenols when also using a great number of herbs with high frequency. Finally, we did not observe associations between any of the exposure variables and the Shannon Index, a measure of alpha diversity. A recent randomized placebo-controlled trial investigating the effects of a daily measured dose of mixed culinary spices (cinnamon, oregano, ginger, black pepper, and cayenne pepper) on the gut microbiome also witnessed changes in the abundance of specific taxa, while not observing changes in alpha diversity [21]. At the phylum level, individuals taking the mixed spice intervention experienced an increase in the abundance of Firmicutes and a decrease in the abundance of Bacteroidetes. Despite detectable changes in the abundance of specific bacteria, however, alpha diversity and fecal SCFA concentrations, remained unchanged when compared to the placebo group [21]. Additionally, as previously mentioned, alpha diversity has been shown to be impacted by the consumption of 30 or more plant-based products per week, regardless of plant type [55]. As such, since the maximum total herbs used with high frequency by a participant was 17 herbs, it is possible participants did not reach the 30 plant per week threshold and/or consumed herbs in a large enough quantity to induce changes in diversity, even after adjusting for dietary factors. Moreover, it appears that our participants already entered the study with Shannon Index scores either above average or on the upper end of average, when compared to reported values from other studies. On average, our participants had an overall Shannon Index value of 4.13. While some previously reported values are between about 2 to 3.7 [56,57], others are more akin to our own observation, with values between 4 to 5 [58,59]. One study reporting an average Shannon Index of 4.63 in participants, which is similar to our own results, considered that value to be high [58]. Strengths and Limitations While phenolic compounds from various sources have previously been associated with changes in abundance of a variety of bacteria under both the Firmicutes and Proteobacteria phyla, this current study is the first to our knowledge which examines associations between microbiota and herbs with high phenolic content during typical culinary use. Additionally, the inclusion of medications, supplements, and dietary factors as adjustment variables, which are known to impact gut microbiota, allow for a detailed evaluation of how these factors might impact the associations between gut microbiota and culinary herb use. This study, however, is not without limitations. The assessment of culinary herb use, which only considers frequency, leaves out other factors which may be important when considering how these herbs interact with the microbiome, such as herb preparation (e.g., fresh, dried) and quantity. Obtaining more detailed information on preparation and quantity could possibly allow for estimations of polyphenol ingestion. Additionally, as diet provides numerous sources of polyphenols, the meaningful correlations observed could be a compounded effect of polyphenol-rich herbs adding to the polyphenol content already derived from other dietary sources (e.g., certain fruits and vegetables). An important future direction will be to estimate polyphenol content derived from non-herb dietary sources to determine how much, if any, it contributes to the associations observed. Finally, due to the vastly disproportionate number of White/Caucasian participants, and the lack of robust representation of other racial and ethnic groups, a more granular statistical analysis of how such factors may impact the observed associations was not conducted. A larger sample size, coupled with community-based recruitment, as opposed to recruitment from a naturopathic college (i.e., the INCLD Health cohort), could provide a more diverse and generalizable sample for such an analysis. A larger sample size may also allow for a more detailed evaluation of bacterial abundance at the genus level as well as offer higher statistical power. 5. Conclusions The current study provides evidence for associations between the frequency of culinary herb use and Firmicutes and Proteobacteria abundance at the phylum level, and that this relationship may be impacted by the phenolic content of culinary herbs. This novel insight supports further investigation into the role of culinary herbs in modulating the microbiome and may have future implications for dietary recommendations in the context of health and disease. As such, we intend to carry out a follow-up study, with a modified culinary herb use questionnaire, estimations of polyphenol intake, and a wider participant recruitment pool. Acknowledgments All research investigators involved in facilitating the INCLD Health cohort study. Author Contributions Conceptualization, A.A.V., R.B. and H.Z.; methodology, R.B., A.A.V., R.M. and Y.F.; software, R.M., R.D. and A.G.; validation, R.B., H.Z. and R.M.; formal analysis, A.A.V., R.M. and R.D.; investigation, A.A.V., R.M. and Y.F.; resources, R.B., R.M., R.D. and A.G.; data curation, A.A.V., A.G. and R.D.; writing—original draft preparation, A.A.V.; writing—review and editing, A.A.V., R.B., H.Z. and R.M.; visualization, A.A.V.; supervision, R.B. and H.Z.; project administration, R.B.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of National University of Natural Medicine (IRB #: RB091218l). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data presented in this study are available within this article. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Average frequencies of culinary herb and spice use. Shown are the average frequencies of culinary herb and spice use with standard error bars, ranging from: Never, once per month, 2 to 3 times per month, once per week, twice per week, 3 to 4 times per week, and 5 to 6 times per week. No herbs averaged daily use. Herbs are displayed from least frequently used (top) to most frequently used (bottom). Figure 2 Average frequencies of culinary herb use by phytochemical grouping. Shown are the average frequencies of culinary herb and spice use with standard error bars, ranging from: Never, once per month, 2 to 3 times per month, once per week, twice per week, 3 to 4 times per week, and 5 to 6 times per week. No herb groups averaged daily use. nutrients-14-01981-t001_Table 1 Table 1 Characteristics of exposure and outcome variables (n = 96). Variables Value Alpha Diversity M (SD) Shannon Index 29.34 (6.13) Phylum Abundance M (SD) Firmicutes 2837.28 (398.21) Bacteroidota 738.44 (309.27) Actinobacteria 150.59 (172.71) Proteobacteria 181.93 (243.70) Frequency of Herb Use M (SD) Allium 3.86 (2.16) Eugenol 1.87 (1.67) Capsaicin 2.68 (1.42) >30,000 PPM Polyphenol 2.43 (1.40) >50,000 PPM Polyphenol 2.40 (1.54) >30,000 PPM Antibiotic 2.07 (0.33) >90,000 PPM Antibiotic 2.55 (1.44) Shown are the average values for the frequency of herb use for each herb category used as an exposure variable, as well as the overall average value for Shannon Index and phylum abundance. nutrients-14-01981-t002_Table 2 Table 2 Characteristics of study participants (n = 96). Variable Value Age M (SD) 29.34 (6.13) Sex Assigned at Birth n (%) Male 14 (14.6%) Female 81 (84.4%) Intersex 1 (>1%) Race n (%) White/Caucasian 75 (78.1%) Asian 5 (5.2%) African American 2 (2%) Middle Eastern 2 (2%) Native Hawaiian/Pacific Islander 1 (1%) American Native/Alaska Native 1 (1%) Mixed 6 (6.3%) Other/Unknown 4 (4.2%) Ethnicity n (%) Hispanic/LatinX 9 (9.4%) Non-Hispanic/LatinX 83 (86.5%) Unknown 4 (4.2%) Dietary Factors M (SD) Est. daily fat intake (g) 80.6 (37.5) Est. daily protein intake (g) 64.7 (30.7) Est. daily fiber intake (g) 27.2 (10.8) Medications M (SD) Total Used 0.6 (1.0) Supplements M (SD) Total Used 5.7 (4.9) Shown are the demographic factors (sex, age, race, ethnicity), dietary factors (estimated daily intake of fat, protein, and fiber in grams), and medication and supplement usage associated with study participants. nutrients-14-01981-t003_Table 3 Table 3 Association between frequency of culinary herb use and Shannon Index. Exposure: Allium Capsaicin Eugenol Antibiotic Antibiotic Polyphenol Polyphenol Freq. of Use >30,000 PPM >90,000 PPM >30,000 PPM >50,000 PPM β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) p-Value p-Value p-Value p-Value p-Value p-Value p-Value Model 1 −0.067 0.009 0.015 0.079 0.136 0.056 −0.054 (−0.068, 0.035) (−0.063, 0.068) (−0.054, 0.062) (−0.037, 0.082) (−0.021, 0.107) (−0.047, 0.83) (−0.076, 0.044) 0.517 0.931 0.888 0.446 0.187 0.587 0.6 Model 2 −0.042 0.018 0.039 0.104 0.135 0.048 −0.042 (−0.065, 0.043) (−0.059, 0.071) (−0.048, 0.070) (−0.030, 0.090) (−0.021, 0.106) (−0.050, 0.082) (−0.073, 0.049) 0.696 0.858 0.709 0.32 0.189 0.646 0.69 Model 3 −0.055 −0.078 −0.048 0.063 0.109 0.044 −0.109 (−0.068, 0.041) (−0.094, 0.045) (−0.076, 0.049) (−0.047, 0.0.79) (−0.031, 0.099) (−0.057, 0.085) (−0.098, 0.033) 0.614 0.477 0.667 0.576 0.296 0.693 0.33 Model 4 −0.045 −0.094 −0.041 0.066 0.119 0.036 −0.109 (−0.067, 0.044) (−0.101, 0.041) (−0.077, 0.054) (−0.047, 0.063) (−0.029, 0.104) (−0.062, 0.085) (−0.099, 0.035) 0.688 0.403 0.725 0.564 0.266 0.754 0.342 Shown are the beta coefficients and p-values associated with each regression model. Model 1: Exposure variable (frequency of culinary herb use); Model 2: Model 1 + Demographic factors; Model 3: Model 2 + Dietary factors; Model 4: Model 3 + Total number of supplements and medications used. nutrients-14-01981-t004_Table 4 Table 4 Association between total number of herbs used with high frequency and outcome variables. Exposure: Shannon Index Firmicutes Bacteroidota Proteobacteria Actinobacteria Freq. of Use β (CI) β (CI) β (CI) β (CI) β (CI) p-Value p-Value p-Value p-Value p-Value Model 1 0.042 0.289 −0.154 −0.194 −0.037 (−0.012, 0.018) (11.2, 59.2) (−33.8, 4.6) (−29.5, 0.572) (−12.8, 8.9) 0.684 0.004 ** 0.135 0.059 0.725 Model 2 0.046 0.286 −0.153 −0.198 −0.029 (−0.012, 0.018) (9.50, 59.7) (−34.0, 5.8) (−30.4, 0.940) (−12.6, 9.0) 0.66 0.007 ** 0.152 0.064 0.783 Model 3 −0.022 0.301 −0.137 −0.235 −0.015 (−0.018, 0.014) (9.0, 63.9) (−34.1, 9.8) (−34.9, −0.590) (−13.3, 10.5) 0.842 0.009 ** 0.243 0.044 * 0.897 Model 4 −0.021 0.294 −0.145 −0.216 −0.005 (−0.018, 0.015) (8.4, 62.2) (−35.3, 9.5) (−32.8, 0.462) (−13.0, 11.2) 0.855 0.009 ** 0.223 0.055 0.968 Shown are the beta coefficients and p-values associated with each regression model. Model 1: Exposure variable (frequency of culinary herb use); Model 2: Model 1 + Demographic factors; Model 3: Model 2 + Dietary factors; Model 4: Model 3 + Total number of supplements and medications used. * p-value < 0.05; ** p-value < 0.01. nutrients-14-01981-t005_Table 5 Table 5 Association between frequency of culinary herb use and Actinobacteria abundance. Exposure: Allium Capsaicin Eugenol Antibiotic Antibiotic Polyphenol Polyphenol Freq. of Use >30,00 PPM >30,000 PPM >90,000 PPM >50,000 PPM β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) p-Value p-Value p-Value p-Value p-Value p-Value p-Value Model 1 −0.044 −0.017 −0.033 −0.077 −0.102 −0.119 0.032 (−46.61, 30.06) (−52.64, 44.46) (−48.98, 36.55) (−60.72, 27.69) (−71.35, 24.04) (−77.05, 20.34) (−37.79, 51.77) 0.669 0.867 0.773 0.46 0.327 0.251 0.757 Model 2 0.01 −0.015 0.039 −0.03 −0.107 −0.09 0.059 (−37.12, 40.80) (−50.68, 43.68) (−35.09, 51.13) (−50.23, 37.31) (−71.48, 21.53) (−69.94, 26.03) (−31.25, 57.07) 0.926 0.883 0.713 0.771 0.289 0.379 0.563 Model 3 −0.001 0.01 0.071 −0.031 −0.112 −0.12 0.072 (−40.83, 40.40) (−49.57, 54.39) (−32.31, 61.68) (−55.23, 41.75) (−74.56, 22.32) (−83.90, 21.70) (−33.30, 64.43) 0.992 0.927 0.536 0.783 0.287 0.287 0.528 Model 4 −0.002 0.024 0.095 −0.033 −0.105 −0.106 0.083 (−41.70, 40.99) (−47.70, 58.99) (−29.78, 69.31) (−55.46, 42.46) (−74.38, 25.54) (−82.87, 26.36) (−31.77, 67.81) 0.986 0.834 0.43 0.792 0.334 0.36 0.474 Shown are the beta coefficients and p-values associated with each regression model. Model 1: Exposure variable (frequency of culinary herb use); Model 2: Model 1 + Demographic factors; Model 3: Model 2 + Dietary factors; Model 4: Model 3 + Total number of supplements and medications used. nutrients-14-01981-t006_Table 6 Table 6 Association between frequency of culinary herb use and Bacteroidota abundance. Exposure: Allium Capsaicin Eugenol Antibiotic Antibiotic Polyphenol Polyphenol Freq. of Use >30,000 PPM >90,000 PPM >30,000 PPM >50,000 PPM β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) p-Value p-Value p-Value p-Value p-Value p-Value p-Value Model 1 −0.067 −0.164 −0.046 −0.135 −0.087 −0.13 −0.067 (−81.07, 56.26) (−155.98, 16.58) (−93.79, 59.26) (−126.90, 30.64) (−121.66, 49.39) (−142.59, 31.56) (−106.24, 53.87) 0.517 0.113 0.655 0.191 0.404 0.209 0.518 Model 2 −0.042 −0.171 −0.054 −0.124 −0.074 −0.125 −0.061 (−92.79, 51.68) (−158.38, 14.22) (−100.00, 60.02) (−132.96, 28.18) (−117.66, 55.68) (−141.26, 36.43) (−105.67, 58.35) 0.696 0.101 0.621 0.253 0.497 0.238 0.568 Model 3 −0.055 −0.157 −0.015 −0.106 −0.053 −0.118 −0.025 (−89.54, 61.65) (−161.90, 29.66) (−93.28, 82.15) (−136.83, 42.75) (−112.84, 68.56) (−145.72, 51.50) (−101.10, 81.31) 0.614 0.174 0.9 0.364 0.629 0.31 0.83 Model 4 −0.045 −0.16 −0.032 −0.128 −0.064 −0.127 −0.032 (−93.48, 60.67) (−165.91, 31.08) (−104.71, 80.83) (−138.61, 43.00) (−120.34, 66.80) (−152.21, 51.70) (−105.68, 80.64) 0.688 0.177 0.799 0.298 0.571 0.292 0.79 Shown are the beta coefficients and p-values associated with each regression model. Model 1: Exposure variable (frequency of culinary herb use); Model 2: Model 1 + Demographic factors; Model 3: Model 2 + Dietary factors; Model 4: Model 3 + Total number of supplements and medications used. nutrients-14-01981-t007_Table 7 Table 7 Association between frequency of culinary herb use and Firmicute abundance. Exposure: Allium. Capsaicin Eugenol Antibiotic Antibiotic Polyphenol Polyphenol Freq. of Use >30,000 PPM >90,000 PPM >30,000 PPM >50,000 PPM β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) p-Value p-Value p-Value p-Value p-Value p-Value p-Value Model 1 0.113 0.195 0.127 0.198 0.347 0.255 0.279 (−39.3, 136.6) (−3.5, 216.1) (−37.2, 158.5) (−1.7, 198.7) (78.2, 275.0) (26.3, 214.7) (44.8, 261.9) 0.275 0.058 0.221 0.054 0.001 ** 0.013 * 0.006 ** Model 2 0.106 0.198 0.114 0.192 0.342 0.247 0.272 (−48.2, 139.2) (−4.3, 219.2) (−48.8, 158.4) (−8.5, 199.3) (71.4, 276.6) (19.1, 214.4) (37.4, 261.8) 0.337 0.059 0.296 0.071 0.001 ** 0.020 * 0.010 * Model 3 0.101 0.221 0.084 0.193 0.33 0.284 0.327 (−53.9, 140.6) (−18.3, 227.7) (−72.6, 153.3) (−4.3, 224.2) (61.8, 274.4) (27.6, 241.2) (57.7, 301.8) 0.378 0.059 0.479 0.094 0.002 ** 0.014 * 0.004 ** Model 4 0.131 0.142 0.089 0.225 0.302 0.266 0.286 (−38.7, 151.0) (−44.9, 199.6) (−72.2, 157.1) (0.86, 222.2) (46.0, 262.1) (18.4, 233.2) (34.6, 279.5) 0.242 0.212 0.463 0.048 * 0.006 ** 0.022 * 0.013 * Shown are the beta coefficients and p-values associated with each regression model. Model 1: Exposure variable (frequency of culinary herb use); Model 2: Model 1 + Demographic factors; Model 3: Model 2 + Dietary factors; Model 4: Model 3 + Total number of supplements and medications used. * p-value < 0.05; ** p-value < 0.01. nutrients-14-01981-t008_Table 8 Table 8 Association between frequency of culinary herb use and Proteobacteria abundance. Exposure: Allium Capsaicin Eugenol Antibiotic Antibiotic Polyphenol Polyphenol Freq. of Use >30,000 PPM >90,000 PPM >30,000 PPM >50,000 PPM β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) β (CI) p-Value p-Value p-Value p-Value p-Value p-Value p-Value Model 1 −0.057 −0.091 −0.102 −0.141 −0.347 −0.193 −0.258 (−69.05, 39.07) (−98.67, 37.79) (−89.96, 30.15) (−104.81, 19.05) (−143.73, −19.82) (−132.80, 2.99) (−140.21, −18.03) 0.583 0.378 0.325 0.072 0.010 ** 0.061 0.012 * Model 2 −0.037 −0.087 −0.099 −0.137 −0.342 −0.203 −0.262 (−67.29, 47.57) (−98.39, 40.25) (−92.27, 34.40) (−105.61, 22.36) (−147.92, −19.74) (−137.90, 1.36) (−143.44, −17.42) 0.734 0.407 0.366 0.199 0.011 * 0.055 0.013 * Model 3 −0.038 −0.11 −0.113 −0.189 −0.227 −0.257 −0.326 (−70.01, 50.09) (−113.10, 39.90) (−120.49, 36.14) (−128.23, 13.17) (−152.76, −19.71) (−164.08, −11.1) (−169.30, −30.88) 0.742 0.344 0.344 0.109 0.012 * 0.025 * 0.005 ** Model 4 −0.063 −0.048 −0.098 −0.191 −0.225 −0.201 −0.311 (−74.68, 41.29) (−90.86, 59.93) (−98.41, 40.78) (−125.60, 9.73) (−137.05, −3.03) (−143.45, 8.16) (−162.44, −28.34) 0.568 0.673 0.413 0.092 0.041 * 0.078 0.006 ** Shown are the beta coefficients and p-values associated with each regression model. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095582 ijerph-19-05582 Brief Report Impact of Oral Rinsing with Octenidine Based Solution on SARS-CoV-2 Loads in Saliva of Infected Patients an Exploratory Study Smeets Ralf 12*† https://orcid.org/0000-0001-7489-6557 Pfefferle Susanne 3† https://orcid.org/0000-0002-5086-4961 Büttner Henning 3 https://orcid.org/0000-0002-2591-6387 Knobloch Johannes K. 3 https://orcid.org/0000-0002-9468-7944 Lütgehetmann Marc 3* Rodríguez Rocío Barrios Academic Editor 1 Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany 2 Department of Oral and Maxillofacial Surgery, Division of Regenerative Orofacial Medicine, University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany 3 Center for Diagnostics, Institute of Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany; s.pfefferle@uke.de (S.P.); h.buettner@uke.de (H.B.); j.knobloch@uke.de (J.K.K.) * Correspondence: r.smeets@uke.de (R.S.); mluetgeh@uke.de (M.L.) † These authors contributed equally to this work. 04 5 2022 5 2022 19 9 558231 3 2022 28 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Objective: In this study, the in-vivo effect of an antiseptic mouth rinse with Octenisept plus phenoxyethanol (OCT + PE) on the oral SARS-CoV-2 load was investigated. Material and Methods: In eight COVID-19 patients, saliva samples were obtained before mouth rinsing and at five time points post rinsing with OCT + PE (n = 47 saliva samples in total). SARS-CoV-2 RNA was detected and quantified by RT-qPCR and virus isolation in cell culture was performed to assess for infectivity. Results: Immediately after mouth rinsing (1 min), a significant reduction of the SARS-CoV-2 RNA loads in saliva was achieved (p = 0.03) with 7/8 participants having SARS-CoV-2 RNA levels undetectable by RT-qPCR. At later time points, RNA levels returned to baseline levels in all study participants. Infectivity of saliva samples was demonstrated by successful virus isolation from saliva samples collected at later time points. Conclusions: This study highlights that saliva samples from COVID-19 patients are infectious and demonstrates that mouth rinsing with OCT + PE temporarily leads to a significant reduction of the SARS-CoV-2 load in saliva. Clinical relevance: Mouth rinsing with OCT + PE could provide a simple, rapid, and efficient method for SARS-CoV-2 infection prevention, particularly in the field of dental and respiratory medicine SARS-CoV-2 octenidine oral rinsing Schülke & Mayr GmbHThe study was financially supported by the Schülke & Mayr GmbH. The funder had no influence on the collection, evaluation and interpretation of the data. ==== Body pmc1. Introduction The pandemic coronavirus SARS-CoV-2 can be transmitted via direct or indirect contact with infected individuals through aerosol formation, saliva, respiratory secretions, or respiratory droplets released by coughs, sneezes, talking, or singing [1]. It is shown that transmission occurs in the early phase of the SARS-CoV-2 associated coronavirus disease 2019 (COVID-19) and often before symptom onset [2], thus posing a major challenge for disease prevention and infection control measures. Accordingly, healthcare workers in the field of dental and respiratory medicine, such as dentists, maxillofacial surgeons, and ENT physicians are at high risk for SARS-CoV-2 transmission [3,4]. Besides personal protective equipment, safety precautions, and hand hygiene, pre-procedural antiseptic oral rinsing immediately before oral care procedures have been recommended by various health authorities worldwide [4,5,6,7]. Recently published in vitro experiments reveal that octenidine dihydrochloride (OCT) plus phenoxyethanol (PE) or povidone-iodine (PI) reduce infectious SARS-CoV-2 within 30 s by more than 4 log10 [8,9,10], thus indicating a >10,000-fold reduced infectivity of virus-containing supernatants after incubation with the substances. OCT belongs to the group of bispyridines and displays activity against bacteria, fungi, and enveloped viruses. Th eaddition of PE leads to a fast onset of action after 15 s. However, current recommendations are mainly based on the general ability of the various compounds to disrupt lipid membranes of pathogens and thus assumed effectiveness against enveloped viruses such as SARS-CoV-2. The World Health Organization (WHO) and Chinese health authorities recommend the use of hydrogen peroxide (H2O2) or 0.2% PI. The Center for Disease Control and Prevention (CDC) recommend chlorhexidine digluconate (CHX), Cetylpyridinium chloride (CPC), or PI and essential oils [11]. German health authorities suggest gargling with 0.2% PI before dental treatment [12,13], while the German Working Group for Hygiene in Dentistry (Deutscher Arbeitskreis für Hygiene in der Zahnmedizin, DAHZ) states that OCT-based rinses can be used [5]. The aim of the present study in COVID-19 patients was to analyze the in-vivo antiviral effect in the saliva by oral rinsing with a commonly used OCT-based antiseptic rinsing solution. Viral RNA quantification in saliva samples before and after oral rinsing and assessment of infectivity by virus isolation experiments are performed. 2. Materials and Methods 2.1. Study Design, Ethics, and Patients This exploratory study evaluates the short-term effect of rinsing the oral cavity with commonly used OCT plus PE (OCT + PE) based antiseptic rinsing solution (octenisept® Schülke & Mayr, Norderstedt, Germany) to reduce the SARS-CoV-2 burden in the saliva. The primary study outcome was SARS-CoV-2 RNA load. The null hypothesis for the primary outcome was the reduction of less than 1 log after mouth-rinsing with OCT plus PE. The secondary study outcome was to analyze infectivity by virus isolation. The inclusion criteria comprised male or female individuals between 18 and 90 years of age with SARS-CoV-2 infection as confirmed by Reverse-Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) within the last 48 h. Exclusion criteria were applied e.g., the inability to understand instructions, inflammation in the oral cavity, respiratory symptoms, or fever at enrolment in the study. The study was performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Research and Ethics Committee of the Medical Board Hamburg (PV7415). Overall, n = 8 patients with active SARS-CoV-2 infection are included in this study. 2.2. Sample Collection In total, six saliva samples were collected for each participant by spitting into a sterile tube (before rinsing and at time points 1 min, 30 min, 60 min, 240 min, and 360 min after rinsing). Subjects were briefed to fast 30 min prior to collection of the initial saliva sample (1.2 mL) and were then instructed to rinse their mouth with 20 mL of the antiseptic rinsing solution for 20 s in accordance with the product information leaflet. Successful adherence to the study protocol was observed and documented by the research physician via ClickDoc Videosprechstunde® (CompuGroup Medical, Koblenz, Germany). 2.3. Molecular Diagnostic SARS-CoV-2 RNA in saliva was quantified and detected by qPCR. Briefly, saliva was 1:1 diluted using Cobas PCR media (Roche) and samples were loaded on the fully automated cobas6800/8800 system (Roche, Mannheim, Germany) using the IVD SARS-CoV-2 Cobas PCR assay (see also [14,15]). Standard SARS-CoV-2 RNA reference material (obtained from INSTAND e.V., Düsseldorf, Germany) was used for quantification. To calculate log10 RNA copies/mL (y) based on ct-values, the following targets and conversion formulae were used y = −0.308x + 13.81 (cobas SARS-CoV-2, target T2). To analyze the potential interference of Octenidin rinsing solution with the quantitative SARS-CoV-2 RNA detection, we performed a method comparison experiment. Briefly, a highly positive SARS-CoV-2 RNA saliva sample diluted in either SARS-CoV-2 negative saliva or SARS-CoV-2 negative saliva + 5% octenident to generate two dilution series with 6 levels (1:10) and 5 repeats at each dilution level covering the whole linear range (<103 to 107 copies/mL), samples were analyzed by the IVD SARS-CoV-2 Cobas PCR assay. For method comparison, Ct values (n = 60; target 2: E-gene) were analyzed. The mean bias over the whole range was 0.13 Ct. Non parametric Passing-Bablok regression analysis (samples +/− octenidin solution) showed high correlation r2 = 0.991 with a slope of y = 0.364 + 0.99 * x). These results indicate that the octenident solution does not interfere with the quantitative qPCR detection of SARS-CoV-2 RNA in saliva samples. 2.4. Cell Culture and Virus Isolation Virus isolation experiments were performed for all available samples (n = 47 samples in total). For infection, Vero E6 cells (ATCC-CRL-1008) seeded in 24-well tissue culture plates were inoculated with 500 µL of the saliva samples. After 72 h of incubation at 37 °C, supernatants of cultures were harvested and virus growths were quantitatively assessed by SARS-CoV-2 Cobas PCR assay. An increase of at least 1log SARS-CoV-2 RNA compared to baseline viral load was used to identify samples with successful virus isolation [16]. 2.5. Statistic Statistical analysis was performed using Graphpad 7 (San Diego, CA, USA), Rstudio v1.4.1103 and Validation manager software V 2022.3.3 (Finbiosoft, Espoo, Finland). 3. Results 3.1. SARS-CoV-2 RNA Quantification In the initial pre-mouthwash samples of the RT-qPCR positive participants (n = 8), a median SARS-CoV-2 RNA level of 2.68 × 104 copies/mL (range 2.09 × 103–1.81 × 105 copies/mL) was detected. One minute after mouth rinsing with OTC + PE containing solution, SARS-CoV-2 RNA levels significantly dropped compared to the initial RNA levels (unpaired t-test, p = 0.031, see Figure 1). At this time point, SARS-CoV-2 RNA levels were below the limit of detection (LoD) of the RT-qPCR in 7/8 (87.5%) participants (median SARS-CoV-2 RNA level < LoD, range 0–7.47 × 103 copies/mL). In one participant, only a slight SARS-CoV-2 RNA reduction of 66% compared to the initial RNA level was observed at 1 min after rinsing, in this participant, the lowest detected RNA level was 7.43 × 103 copies/mL. SARS-CoV-2 RNA levels in the saliva of 8 subjects as quantified by RT-qPCR pre-rinsing with OCT plus phenoxyethanol (0 min) and 1, 30, 60, 240, and 360 min after rinsing with OCT plus phenoxyethanol are illustrated. RNA levels < LoD were set to 1 × 100 copies/mL to allow for logarithmic presentation. Median RNA levels for each time point and 95% CI are indicated. Significant differences are indicated by asterisk (* = p < 0.05, unpaired t-test). Circles represent individual participant values with circles highlighted in light turquoise representing infectious samples as proven by successful virus isolation. At 30 min after rinsing, SARS-CoV-2 RNA could again be detected in the saliva of all participants with available samples of that time point (7/8, one sample missing). The median SARS-CoV2 RNA level at this time point was 3.08 × 104 copies/mL (range 5.63 × 103–2.77 × 105 copies/mL). In 4 of these 7 participants (57%), the RNA levels at 30 min after rinsing were below the initial value before the mouth rinse. At all later time points after rinsing, SARS-CoV-2 RNA levels in the saliva samples of all participants were in the range of the baseline RNA levels and remained at stable levels until the end of the observation period (see Figure 1, for individual kinetics refer to Figure 2). 3.2. Infectivity of the Saliva Samples To determine infectivity, virus isolation in cell culture was attempted from all saliva samples of the 8 RT-qPCR positive participants. Overall, n = 47 samples collected at six time points were assessed (one sample of time point 30 min after rinsing was missing). Infectious virus was successfully isolated from 2/47 (4%) samples. Both infectious samples were obtained at late time points (at 240 min and 360 min after mouth rinsing). SARS-CoV-2 RNA levels in the infectious samples were 3.19 × 105 and 5.06 × 104 copies/mL, respectively (see Figure 1 and Figure 2 for individual courses). 4. Discussion 4.1. Based on the Study Results, the Null Hypotheses for the Primary Outcome (SARS-CoV-2 RNA Reduction of Less than 1 Log) by Oral Rinsing with OCT plus Phenoxyethanol Was Rejected The implementation of oral rinsing as a preventive method to reduce SARS-CoV-2 levels in the saliva of patients, and thus protect health care workers from possible virus transmission, is recommended by national and international health authorities [4,5,6,7,11,12,13]. However, clinical evidence and in-vivo data are lacking. In the present study, we provide evidence that one of the commonly used substances for oral rinsing in the health sector (OCT plus PE), leads to a rapid and effective reduction of SARS-CoV-2 RNA levels in the saliva of the user. We were able to show that the viral RNA levels drop significantly (p = 0.03) in the saliva within the first minute after the mouth rinse and in almost all participants (7/8). Notably, while most participants responded very uniformly to the substance, in one participant, only a minor SARS-CoV-2 RNA reduction in the saliva was observed after rinsing. This phenomenon cannot be conclusively explained and the possibility of incorrect application or ineffectiveness of the substance arises. However, it is conceivable that SARS-CoV-2 containing material from the deep respiratory tract of that participant (e.g., coughed up or entering the oral cavity through sneezing) may have falsified an OCT + PE effect that was actually present. The fast onset of OCT + PE effects (<1 min) as demonstrated here in the saliva is relevant for pre-procedural application in clinical practice, e.g., in dentists’ or oral and maxillofacial surgeons’ offices, and represents an advantage over other oral rinses tested in-vivo so far with longer, unreliable times until onset of effects [17,18]. In this study, we assessed the infectivity of the saliva samples by cell culture experiments and were indeed able to prove infectivity for two of the saliva samples obtained at late observation time points underlining the risk of virus transmission. Notably, all initial samples collected before the OCT + PE rinsing were culture-negative. These samples were first examined in RT-qPCR and the virus cultivation was only carried out after the result was obtained, therefore the infectivity in these samples could already have been significantly reduced. However, as only comparatively mildly ill persons were enrolled at a considerable time after their COVID-19 diagnosis, SARS-CoV-2 RNA levels in all samples analyzed were at the lower range of RNA levels in the respiratory tract of severely ill or even asymptomatic patients in the early phase of the disease [2]. Moreover, it is known that the probability of virus isolation decreases with the increasing duration of the disease [2]. Additionally, saliva is not the common material used for virus isolation and it is quite conceivable that besides possibly containing antibodies (IgA, IgG), enzymes contained in saliva may interfere with virus isolation. Nevertheless, we believe that our results with a proof of infectivity only in samples obtained late after mouth rinsing indicate that OCT + PE not only reduces the amount of RNA in the saliva, but is in line with the in-vitro data available [8,9,10], might reduce the burden of SARS-CoV-2 infectious particles in the oral cavity in-vivo. 4.2. Limitations Despite the promising results of the present study, certain limitations should be noted. Firstly, the study was conducted without a control group. Therefore, it cannot be ruled out that the reduction effects are partly based on a dilution by the mouth rinse. Yet it has been shown that mouth rinsing with tap water has no impact on viral load [19]. Secondly, possibly remaining OCT + PE compound in the saliva sample was not inactivated before the sample was added to cell culture and the low number of individuals may cause statistical bias, hence a larger number of participants in studies is required for valid statements. The study design was an exploratory study therefore power calculation to determine the study’s sample size was not performed. Possible effects of the OCT + PE rinse benefit on clinical outcomes need to be evaluated in future randomized, placebo-controlled clinical trials. Given the early onset effect, OCT + PE might be primarily indicated for interventions of short duration, however, repeated use during prolonged dental procedures might be conceivable and could also be investigated in follow-up studies. 5. Conclusions Results of the present study provide clinical data revealing that OCT + PE might temporarily reduce the SARS-CoV-2 RNA burden in the oral cavity with a rapid onset of effects. Antiseptic oral rinsing with OCT + PE might thus represent a simple and safe intervention to reduce the risk of SARS-CoV-2 transmission, particularly in the field of dental and respiratory medicine, especially during short-lasting examinations or in the initial phase of the procedures, respectively. Based on the encouraging results, further studies should be conducted to prove the clinical efficacy of the compounds. It must be stated that the transfer of material from the deep airways can mitigate the observed effect. Thus, the use of OCT + PE can be a useful component in infection prevention. However, other infection prevention measures (personal protective equipment for acting personnel) cannot be replaced by mouth rinsing in patients. Author Contributions R.S., S.P. and M.L. planned and conceived the study, S.P. and M.L. performed experiments, S.P. and M.L. collected and analysed data, H.B. and J.K.K. analysed data, R.S. and S.P. prepared the initial draft of the manuscript. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was approved by the Research and Ethics Committee of the Medical Board Hamburg (PV7415). Compliance with ethical standards. Informed Consent Statement Written informed consent was obtained from all participants. Conflicts of Interest M.L. received speaker honoraria and related travel expenses from Roche Diagnostics. All other authors report no conflict of interest. Figure 1 Octenidine dihydrochloride (OCT) plus phenoxyethanol effectively reduces detectable viral RNA in the saliva of SARS-CoV-2 positive individuals. SARS-CoV-2 RNA levels in the saliva of 8 subjects as quantified by RT-qPCR pre-rinsing with OCT plus phenoxyethanol (0 min) and 1, 30, 60, 240 and 360 min after rinsing with OCT plus phenoxyethanol are illustrated. RNA levels < LoD were set to 1 × 100 copies/mL to allow for logarithmic presentation. Median RNA levels for each time point and 95% CI are indicated. Significant differences are indicated by asterisk (* = p < 0.05, unpaired t-test). Circles represent individual participant values with circles highlighted in light turquoise represent infectious samples as proved by successful virus isolation. Figure 2 SARS-CoV-2 RNA kinetics of the included 8 subjects: SARS-CoV-2 RNA load [copies/mL] in saliva samples at the analyzed time points is illustrated. The dashed light grey line corresponds to the limit of detection of the RT-qPCR used [14]. The points marked with a green asterisk (time points 240 h subject #5, 360 h subject #6) correspond to infectious samples from which virus isolation was successful. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Schijven J. Vermeulen L.C. Swart A. Meijer A. Duizer E. de Roda Husman A.M. Quantitative Microbial Risk Assessment for Airborne Transmission of SARS-CoV-2 via Breathing, Speaking, Singing, Coughing, and Sneezing Environ. Health Perspect. 2021 129 47002 10.1289/EHP7886 33793301 2. Jones T.C. Biele G. Muhlemann B. Veith T. Schneider J. Beheim-Schwarzbach J. Bleicker T. Tesch J. Schmidt M.L. Sander L.E. Estimating infectiousness throughout SARS-CoV-2 infection course Science 2021 373 eabi5273 10.1126/science.abi5273 34035154 3. Heinzerling A. Stuckey M.J. Scheuer T. Xu K. Perkins K.M. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091765 polymers-14-01765 Article Application of Carbon–Flax Hybrid Composite in High Performance Electric Personal Watercraft https://orcid.org/0000-0001-8031-8366 Zouhar Jan 1* https://orcid.org/0000-0002-9162-0066 Slaný Martin 1 Sedlák Josef 1 https://orcid.org/0000-0001-6474-789X Joska Zdeněk 2 https://orcid.org/0000-0002-7098-3693 Pokorný Zdeněk 2 Barényi Igor 3 https://orcid.org/0000-0002-6577-1987 Majerík Jozef 3 Fiala Zdeněk 1 Masłowski Marcin Academic Editor 1 Institute of Manufacturing Technology, Faculty of Mechanical Engineering, Brno University of Technology, 61669 Brno, Czech Republic; slany.m@fme.vutbr.cz (M.S.); sedlak@fme.vutbr.cz (J.S.); fiala.z@fme.vutbr.cz (Z.F.) 2 Department of Mechanical Engineering, Faculty of Military Technology, University of Defence in Brno, 66210 Brno, Czech Republic; zdenek.joska@unob.cz (Z.J.); zdenek.pokorny@unob.cz (Z.P.) 3 Department of Engineering Technologies and Materials, Faculty of Special Technology, Alexander Dubček University of Trenčín, 91101 Trenčín, Slovakia; igor.barenyi@tnuni.sk (I.B.); jozef.majerik@tnuni.sk (J.M.) * Correspondence: zouhar@fme.vutbr.cz 26 4 2022 5 2022 14 9 176527 3 2022 25 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Within the herein presented research, we studied the applicability of flax fabrics for composite parts in personal watercrafts in order to enhance damping of vibrations from the engine and noise reduction (which is relatively high for contemporary carbon constructions). Since the composite parts are intended to be exposed to humid environments requiring high levels of mechanical properties, a carbon–flax composite was selected. Samples of carbon, fiberglass, flax, and hybrid carbon–flax twill and biax fabrics were subjected to tensile and three-point bending tests. The mechanical properties were also tested after exposure of the samples to a humid environment. Damping was assessed by vibration and noise measurements directly on the complete float for samples as well as real parts. The hybrid carbon–flax material exhibited lower values of tensile strength than the carbon material (760 MPa compared to 463 MPa), but, at the same time, significantly higher than the other tested materials, or flax itself (115 MPa for a twill fabric). A similar trend in the results was observed for the three-point bending tests. Vibration tests and noise measurements showed reductions in vibration amplitude and frequency when using the carbon–flax hybrid material; the frequency response function for the watercraft part assembled from the hybrid material was 50% lower than for that made of carbon. Testing of samples located in a humid environment showed the necessity of surface treatment to prevent moisture absorption (mechanical properties were reduced at minimum by 28%). The tests confirmed that the hybrid material is satisfactory in terms of strength and its contribution to noise and vibration damping. flax hybrid composite personal watercraft Project for the Development of the OrganizationDZRO Military autonomous and robotic systemsFSI-S-22-7957 European Regional Development FundThis research work was supported by the Project for the Development of the Organization “DZRO Military autonomous and robotic systems”, with the grant “Modern technologies for processing advanced materials used for interdisciplinary applications”, FSI-S-22-7957. Advancement and support of R&D for “Centre for diagnostics and quality testing of materials”, in the domains of the RIS3 SK specialization, ITMS2014+:313011W442, based on the Operational Programme Integrated Infrastructure and funded from the European Regional Development Fund. ==== Body pmc1. Introduction Natural fibre reinforced composites have become popular, especially in the automotive and construction industry. Given their favourable price and weight, they are very attractive for the production of automobile and aerospace components, bicycle frames, window frames, sports equipment, etc. [1]. Furthermore, environmental efforts encourage the use of extremely lightweight materials to decrease the fuel consumption of vehicles, leading to reduction of carbon dioxide emissions. Therefore, glass fibres (very popular in the past) are becoming less attractive because of their weight and recycling difficulty. On the other hand, flax and other natural fibres open new opportunities for biocomposites with high stiffness/weight ratio and better recyclability [2,3]. Compared to carbon fibre composites, they also offer better damping properties. The greatest weakness of natural fibre composites is, however, their lower mechanical performance, which is limiting for their application [4,5]. Peças et al. [6] presented a comprehensive review on properties of selected natural fibres used as reinforcements within composite materials. They identified flax fibres to be among the most suitable for the automotive industry, sports equipment, or civil engineering. Flax fibre reinforced composites have been used since the late 1930s, but their industrialization increased over the last years. In addition, the manufacturing steps—flax growing and cultivation, agricultural methods, retting methods, fibre extraction techniques—have evolved in time, which enhanced the properties of final products [7]. Kandemir et al. [8] studied physical, thermal, and mechanical properties of four types of natural fibres–namely jute, kenaf, curaua and flax–and reported that curaua and flax exhibited advantageous mechanical properties comparable with those of glass fibre reinforced polymer composites. Researchers often discuss the advantages of glass and flax fibre reinforced composites, not only from the environmental viewpoint. Poilâne et al. [9] investigated the room-temperature yield point of these two very popular fibres, and documented that flax fibre reinforced polymers exhibited plastic behaviour after a short period of elasticity. Furthermore, flax fibre reinforced composites feature specific characteristics which cannot be characterized by unique values due to their strong dependence on the measurement method (e.g., density) [10]. Therefore, the selected testing method should take into consideration the real conditions under which the product is intended to be used. Although natural fibre reinforced composites feature (not only) the abovementioned positive aspects, they are mostly produced from twisted yarns of short natural fibres, which cause problems with their impregnation. Poor impregnation increases porosity and deteriorates mechanical properties [11]. Besides water sensitivity, variability in mechanical performance is a disadvantage of natural fibres. Nevertheless, researchers have developed treatments to eliminate the mentioned problems, e.g., Whitacre et al. [12] presented positive effects of zein protein treatments on improving mechanical properties of flax fibre reinforced composites. Another solution is hybridization; hybridization of natural reinforced fibre composites by incorporation of carbon fibres has positive effects on properties of the final composites. Fehri et al. [13] proved that carbon plies applied near the surface decrease porosity; the study showed that a single carbon ply placed on the surface decreased the diffusion coefficient to half its value and the water content by 40%. Generally, hybridization enhances properties of reinforced composites by using more than one reinforcement under the same matrix [14]. Atmakuri et al. [15] analysed mechanical properties and wettability of hybrid composites, and discovered that hybrid composites showed better mechanical properties–especially higher flexural properties–than pure flax composites. Bolcu et al. [16] confirmed these findings while studying mechanical properties of composites with dammar based hybrid matrices. They also reported advantageous vibration damping of the analysed composites. Another important but often neglected characteristics is water ageing behaviour, which can decrease composite stiffness, cause matrix crosslinking, or layer interfaces release. This behaviour is generally more favourable for hybrid composite than for flax fibre reinforced ones [17]. The ability of hybrid carbon–flax reinforced composites to improve damping properties of vehicles opens new possibilities in automotive, marine, and vehicle engineering in general. Fairlie and Njuguna [18] discovered that adding an external flax layer can increase the damping ratio of a composite by more than 50%, while adding two layers imparts increase by more than 90%. However, Mahmoudi et al. [19] documented that damping properties are related to fibres’ orientations. Among natural fibres, pre-impregnated flax fibre textiles (also called prepregs) featuring enhanced mechanical properties can also be used [20]. There are numerous applicable production techniques for flax fibre reinforced composites. Compression moulding, often combined with hot pressing, is a widely used technology for manufacturing natural fibre reinforced composites. Symington et al. [21] presented a vacuum infusion rig—another technology ensuring consistent quality of composites. Vacuum Assisted Resin Transfer Moulding (VATRM) and Seemann Composite Resin Infusion Moulding Process (SCRIMP) are other technologies with positive impacts on mechanical properties of natural fibre reinforced composites. Especially VATRM has become very popular in recent years due to its high productivity and low operational costs. However, VATRM introduces defects, such as voids, which deteriorates quality and mechanical properties of produced composite parts [22]; the content of voids is especially important since it can negatively influence mechanical properties of fibre reinforced composites. Therefore, from a quality viewpoint, monitoring this parameter is crucial, especially due to the development of modern manufacturing techniques (e.g., VATRM, out-of-autoclave (OaA), automated prepreg laying, etc.) introducing lower production costs and shorter manufacturing times [23,24]. For example, Kedari et al. [25] suggested to increase mould temperature and vacuum and appropriately reduce inlet pressure to produce high quality VARTM parts. Testing and evaluation of properties of both fibres and matrix according to a wide range of criteria is important for the manufacturers to select the most suitable combinations of reinforcements and matrices for their final composite products. AL-Oqla et al. [26] emphasized the importance of combined economic, environmental, and technical viewpoints to achieve better composite performance. Researchers have massively studied tensile and compressive mechanical properties of flax fibre reinforced composites. Besides standard mechanical tests, damaged response through SEM (Scanning Electron Microscope) is often observed in order to describe in-plane modulus and inelasticity evolution [27,28,29,30,31]. SEM is suitable to perform morphological analyses of composites reinforced with wires and fibres [32,33], and can advantageously be combined with other methods such as Y-ray microcomputed tomography to analyse influence of water absorption on the behaviour of flax and glass reinforced hybrid composites [34]. With regards to mechanical testing, the single fibre tensile test, dry fibre bundle test, or impregnated fibre bundle test (IFBT) are common methods [35,36,37]. The IFBT method is also suitable to test the effects of individual extraction and refining steps on stiffness and strength of natural fibres in composites [38]. For deeper understanding of mechanical behaviour and damage modes, acoustic emission evaluation–possibly combined with other modern methods such as post-mortem microscopy or neutron diffraction–can be used [30,39,40,41]. The output is a frequency response function (FRF) based on vibration response data signals acquired by one or more microphones [42]. The FRF offers a set of linear equations that are solved by a bounded-variables least-squares algorithm. The minimum number of natural frequencies and mode shapes used to compute FRF matrices is three [43,44]. The real component that the presented material is planned to be used for is a part of Jetsurf Electric, an electric-powered motor float by MSR Engines. Currently, the float structure is made of carbon fabric and consists of approximately 30 parts that are glued or mechanically joined together. The requirement is to reduce vibrations and noise affecting the rider and the surrounding environment while maintaining mechanical properties and favourable weight. Available studies primarily focused on the effects of drive system on vibrations [45,46,47]; however, they did not consider the mechanical bond to the watercraft hull. Due to the internal construction of the float, the battery, and the drive system in which they are located, sandwich structures cannot be used since they are typically very thin and thus prone to quick failure due to the dynamic load and activity of the rider and surrounding waves [48]. Therefore, thin-walled parts with a thickness of up to 2–3 mm have to be used. In case of flax materials and fabrics, the majority of available studies focused on materials with similar thickness, but only evaluated selected material properties [49,50,51]. For example, only a few researchers studied the behaviour of variable twill-like fabrics via mechanical properties and damping characteristics [49,50,51,52,53]. For complex characterization of properties, various kinds of fabrics (twill, biax, UD, etc.) by a single producer should be evaluated from the viewpoint of mechanical properties, and subsequently tested for application in hybrid fabrics. Furthermore, the composite fabric should be symmetrical, since it is planned to be produced using autoclaves—uniform composition is necessary to eliminate temperature stress and subsequent deformation, i.e., springback, after curing [54]. Moreover, the effect of a humid environment should be tested [54,55]. The component selected for the herein-presented study was a cover of the engine compartment of an electro-motor. 2. Materials and Methods 2.1. Materials For the purposes of testing and production of tested parts, a range of materials commonly used in construction applications was chosen. The chosen fabric types were Twill and Biaxial with a basis weight of about 200 g/m2. The area density was chosen based on their spread in practical applications, favourable handling, and availability. The properties of the used fabrics can be found in Table 1. Flax fabrics were by B-Comp Ltd. (Fribourg, Switzerland); the others were by GRM Systems ltd (Olomouc, Czech Republic). Boards for testing purposes were made from these materials via the Vacuum Infusion (VARTM) method [56,57], which is widely used for production of high-quality composites with all types of weaving, and guarantees constant conditions of saturation, curing, control, and processing. Examples of the board production process are shown in Figure 1a,b. LG700 epoxy resin with HG700 hardener (as used by the manufacturer—GRM Systems, Olomouc, Czech Republic) were used for the matrix; this is suitable for Resin Transfer Moulding (RTM) and infusion applications. The resin is used in a weight ratio of 100:30. The mechanical properties of the unreinforced resin are tensile strength of 65–75 MPa, elongation at break of 6–8%, and flexural strength of 110–120 MPa [58]. All the samples were cured at 24 °C for 24 h before demoulding. The samples were cut after five days, and no additional post-cure process was required. Boards with the dimensions of 300 mm × 300 mm, which were subsequently cut to the dimensions of 220 mm × 220 mm to measure vibrations, were produced for attenuation testing. Samples for the tensile tests and three-point bending tests were produced according to the given EN ISO 527-4 and ISO 14125: 1999 standards. To measure vibrations, boards of two thicknesses with four and eight layers of material were made; the layers were laid to form a balanced symmetrical laminate (0/90; ±45) S with quasi-isothropic properties [59]. Thicknesses and other properties are summarized in Table 2. The weight fraction according to [59,60] and other properties were calculated for the resulting laminates. The part for testing in the real environment—engine cover—was also made using the vacuum infusion technology (Figure 1), and can be seen in Figure 2a (the cover of the float engine compartment is located in the sensor area). The composition of the part was determined in accordance with the sample tests; three types of samples (D01-D03) were produced. Sample D01 was made of four layers of 200 g/m2 fabric with a 2 × 200 g/m2 inner flax insert ending 20 mm from the edge to prevent water penetration into the flax area. The edges were complemented with carbon fabric to maintain constant thickness, and the weight was 273 g. Sample D02 was made to be identical to sample D01, but with complete flax liner (reaching to the edges); the sample weight was 223 g. The composition of sample D03 consisted of four layers of GG600T fabric (Deltapreg, Sant’Egidio alla Vibrata, Italy) weighing 600 g/m2; sample weight was 265 g. After production, the samples were cut to required shapes using a KUKA KR 60HA robot with a machining spindle and a sintered carbide tool with diamond coating. 2.2. Testing Equipment Tensile strength measurement was performed using a ZwickRoel Z100 measuring machine according to EN ISO 527-4 standard (the main parameters and size of the sample corresponded to the standard) [61]. The samples were 25 mm wide, 210 mm long, and 1.05 mm thick, and were fixed in the jaws with glass fibre attachments using an epoxy glue. Five valid samples were measured in each measurement series. The loading speed was set to 2 mm/min. Flexural tests were measured according to EN-ISO 14125: 1999 [62] standard with the help of the same ZwickRoel Z100 device using reversal and jig. The length of the samples corresponded to the standard according to the thickness and distance of the supports; the width of the samples was 15 mm Five samples were again measured for each material. The measuring jig allowed to change the distance of the supports as described in the standard, so that samples with different thicknesses (varying according to the production method) could be measured. The loading speed was set to 2 mm/min. Conditioned samples were prepared for testing in a humid environment. The conditioned samples were placed in a Constazo KB300 climate chamber heated to 40 °C with 100% humidity for 100 h. The conditions simulated the above-described real load. The samples were then removed and within a few hours subjected to tensile and deflection testing. Among the results of the measurement were the maximum values of strength, modulus, and strain. As regards morphology and damage assessment, SEM Tescan Mira 4 equipment was used to monitor the fractured surface to characterize failures of the individual materials. In order to study and compare the behaviours of the composite materials, ARAMIS digital image correlation system enabling to accurately evaluate the behaviour of a material under load in time was used [63,64,65]. Evaluations of RMS (Root Mean Square) mechanical vibration, noise levels, and dynamic properties were also made to find out the most suitable composite material for the Electric Personal Watercraft. Measurement of FRF (frequency response function) was chosen to evaluate dynamic properties of the samples. Modal analyses were not performed because investigating the samples’ mode shapes was more suitable to compare the results of real measurements with mathematical models. Brüel & Kjaer Photon + equipment was used to perform the experimental measurements (see Table 3). The FRFs measurements were performed in laboratory conditions. A bench vice was used to clamp the composite samples (see Figure 2b). Keeping identical positions of samples in the vice, the position of accelerometer and hits by modal hammer were emphasized when exchanging the samples. The 4517-type accelerometer was chosen since its low weight does not affect the FRFs results and low sensitivity prevents sensor overloading. Each FRF peak represented a frequency the system in which vibrated excessively. This function could be used to calculate Young’s modulus, loss factor, and damping ratio at different resonant frequencies of each specimen [18]. 3. Results and Discussion 3.1. Tensile Properties The results of tensile tests are summarized in Table 4. The parameters of tensile modulus, tensile strength, and fail strain were further calculated from the tensile test records. The final values are the arithmetic mean values acquired from five samples with the standard deviation N = 5. The C02-carbon composite material exhibited the highest values of both the tensile modulus and strength. The hybrid material also had very favourable properties and exhibited higher values than the glassy and pure flax materials. Tensile strength for all the samples is depicted in Figure 3, whereas the comparison of tensile moduli is shown in Figure 4. From Figure 3, it is evident that the flax composite featured significantly lower properties than glass for both the twill and biaxial materials. In general, the twill–woven material exhibited lower values of both the tensile modulus and strength than the biaxial material. Compared to the unidirectional material flax with fibres in the direction of the applied force, the twill glass fabric achieved less advantageous properties. As regards the tensile modulus (Figure 4), the fabric and bi-axial types of both the flax and glass composite exhibited minimum differences. However, differences are more evident from Figure 5, showing graphical depictions of the tensile tests results, i.e., stress-strain curves, the tensile moduli from which were derived. As for the glass materials, the tensile strength was higher for the biaxial fabric, but the shapes and slopes of the curves revealed that their tensile moduli were comparable. This phenomenon is well-described in the literature and is typical for composite materials, the value of tensile modulus for which is not entirely meaningful without knowing the shape of the stress-strain curve. For example, Zhang [65] documented such mechanical behaviour for glass–flax hybrid materials. In regards to the other materials, the results for the carbon composites and hybrids were evidently the most advantageous, while the lowest values were achieved for the flax (note that the courses of the load curves for the fabric and biaxial materials were almost identical). The values of strain were primarily given by the composition of the samples, which was quasi-isotropic. Therefore, the strain was of higher values for the one-way samples, or samples arranged in ideal directions. All the evaluated samples experienced final fracture, as can be seen from the curves in Figure 5. The values of tensile strength and modulus presented in available literature can significantly differ according to the type of the used fabric, epoxy resin, and fabrication method [20]. The tensile test results herein acquired for the hybrid composite can be compared to those reported by Fairlie et al. [18] and Al-Hajaj [49], who evaluated hybrid composite, pure carbon, and flax. The compositions of the tested samples were comparable, and the differences in the tensile strength were within the standard deviation. The tensile modulus was higher for the base samples, which can be attributed to different fabric composition in each layer. AL-Hajaj [49] studied a flax fabric in an UD form with 0/90 or ±45° layering, and presented differing results for different layering directions of the flax fabric within the composites. Comparing to the herein presented quasi-symmetric flax fabric, the values of both the tensile strength and modulus were higher for the 0/90 layering direction, and comparable or lower for the ±45° layering direction. This can be attributed to usage of UD fabrics instead of twill-like ones. The results acquired for the flax fabric can be compared to studies [15,16,27]. 3.2. Flexural Properties The values acquired based on the three-point bending test, i.e., flexural modulus, flexural strength, and flexural strain, are given in Table 5. The values are again the arithmetic means from five samples with the standard deviation N = 5. Similar to the tensile test results, the carbon sample, together with the hybrid one, exhibited the highest values. However, the hybrid and glass samples exhibited differences. The values for the hybrid sample corresponded to those acquired for the carbon ones, while the values for the glass reached similar values as for the flax (see Figure 6 depicting the tensile strengths and Figure 7 showing the tensile moduli). The results from three-point bending testing reveals that the glass fabric exhibited very low flexural strength—compare 183 MPa to 760 MPa acquired for the carbon and 463 MPa reached for the hybrid fabric, respectively. The trend was similar also for the flexural modulus—compare 12.30 MPa for glass, 46.12 MPa for carbon, and 34.63 MPa for hybrid carbon–flax materials, respectively. Similar values for comparable materials were reported by others [20,65,66]. The comparison of the results also shows that the flexural strength and moduli acquired for the flax UD were comparable to those acquired for the glass bi-axial fabric. From the flexural stress-strain curves depicted in Figure 8, it is evident that after reaching the maximum flexural stress (force), the samples further deformed at lower loads, but with no final fractures. This phenomenon was the most evident for the glass and flax samples [50]. For each sample, the test was stopped after 30% loss of the maximum reached force. The flexural moduli values were derived from the curves—the phenomenon mentioned in Section 3.1 was also evident here; when the flexural stress for the glass–biax sample reached higher value than for the glass-twill sample, its flexural modulus value was lower, given by the slope of the curve acquired for the highest achieved stress. Interesting behaviour was observed for the glass-based fabrics—the values of strain corresponding to the maximum stress values were relatively high, up to approx. 8% (also observed e.g., in [67]). However, such high imposed strain already introduces irreversible changes, and the maximum observed plasticity of the composites is thus not practically useful (typically usable up to 2.5–3% of strain). In regards to the comparison of twill and biax types of fabrics with identical bases, the courses of the stress-strain curves were similar (Figure 8). Carbon–flax composites are sometimes prone to matrix degradation, which is common for composites with resin surplus. This phenomenon is related to the brittleness of epoxy resins, which can only undergo limited deformation before failure, and thus feature low impact strength [66]. Furthermore, cracks spread quickly from the flax core of such hybrid material towards the carbon layers (featuring lower resin fraction than the flax core) [65]. 3.3. Conditioned Samples Conditioned samples were tested only for flax UD, flax-biax, and hybrid materials. Other materials–glass and carbon–are standard, and their environmental degradation values are known [59,60]. When impacted by moisture, the thickness of the tested samples increased as follows: by 11.8% for the flax UD sample, by 13.9% for the flax biax sample, and by 2.34% for the carbon–flax one. The evident difference observed for the carbon–flax sample could be attributed to the effect of the epoxy inter-layer located between the flax core and carbon cover. The inter-layer gets damaged, i.e., micro-delaminated, by surrounding humidity and different wettability of the individual layers (also documented e.g., in [56,67]). Furthermore, fibres soaked with moisture compress the surrounding epoxy resin and contribute to the formation of micro-cracks. From the tensile tests, the results of which are summarized in Figure 9, it is evident that the standard deviation increased for all the flax samples. Moreover, the change in tensile properties was minimal except for the hybrid composites, for which values 28% lower were achieved (compared to standard samples). This can mainly be attributed to the effect of moisture on materials’ interface, i.e., location in which failure was initiated. These conclusions are supported by study [68] reporting a correlation between increase in material thickness, decrease in mechanical properties, and occurrence of failure. The UD flax material and hybrid fabric were tested by the three-point bending test (see Figure 10 for the flexural strength results). Similar to the tensile test results depicted in Figure 9, the results of bending tests showed that moisture decreased properties of the hybrid material. However, the decrease in the values achieved for the Flax UD sample was even more noticeable (more than 60%). Comparable results were acquired by César dos Santos et al. [54], who achieved the flexural strength of 248.02 ± 26.24 MPa for a longitudinal specimen, and the flexural strength of 77.93 ± 8.25 MPa for a specimen placed for four days in a 100% humidity environment (which is in agreement with the herein performed experiment). In the presented study, the hybrid fabric still exhibited higher values given by the carbon layer. The core itself behaved as a sandwich structure primarily loaded by shear stress. Therefore, the drop in the flexural values was not as large as for the flax UD sample [34]. 3.4. Frequency and Noise Characteristics A bench vice is not the most favourable solution to fix samples, however, all the samples were tested under identical conditions. The first FRF measurement showed that the carbon samples featured the highest amplitude values at higher frequencies when compared to the other tested materials exhibiting lower amplitude values at lower frequencies (Figure 11). The thickness and composite density are the most relevant parameters having significant impact on dynamic properties of the sample. Compared to the carbon and glass samples, both the hybrid and flax samples exhibited better attenuation values; the flax sample exhibited the most favourable properties. The results of frequency analyses were in agreement with previously published research by Chinnasamy [69] and [42,43], who evaluated sisal and jute fibres via the FRF function. A hybrid flax–carbon composite was tested by Fairlie [18]. He confirmed the relation of reduction in damping and the amount of flax content within the composite. However, he used different measurement methods and measured samples. Results for different carbon–carbon combinations can also be found in the work by Assar et al. [52]; they performed the comparison of bending modulus vs. specific damping for a material consisting of components identical to those used in the presented work. Dynamic behaviour can be determined via FRF measurement, or by modal analysis. At present, there is no available study reporting the behaviour of functional parts made of various composites from the viewpoint of dynamic properties. Singh et al. [53] used FRF measurement to study the effect of epoxy resin content on frequency and damping characteristics of composite samples. Hassani [70] focused on detection of structure defects within composite materials via FRF measurement. Nevertheless, the published studies mostly deal with theoretical studies and focus on the development of mathematical models to predict and characterize dynamic behaviour of various composite materials (e.g., [71,72,73]). Due to this, the herein acquired results cannot be directly compared with results reported by other researchers dealing with composite materials. Based on the acquired results, the decision to assemble the final part of the prototype from the flax hybrid composite was made. The final prototype of the selected part was tested whilst directly mounted to the electric personal watercraft (see Figure 2a), as well as clamped to the bench vice. The RMS and sound noise levels for the samples were measured when mounted on the watercraft, and FRFs were measured when camped to the bench vice. The FRFs measurement showed that the best results were acquired for the D01 sample; the differences between the amplitudes and dominant frequencies for the D02 and D03 samples were negligible (Figure 12). Final testing of the prototype parts mounted on the watercraft were performed for the maximum revolutions of the engine, i.e., 7200 RPM. The RMS and noise level were calculated for the frequency range of 0–3.2 kHz. The accelerometer was fixed by a special wax in the middle of the tested sample, see Figure 2a. The results of noise level are not conclusive because the prototype part is a small subcomponent that did not affect the results in a greater extent. The problems were instability of engine-speed fluctuations and poor measurement repeatability. The results of RMS measurement are shown in Figure 13a, and the noise levels are depicted in Figure 13b. The most advantageous RMS results were acquired for the D01-1 and D01-2 samples. This fact was in accordance with the results of the FRFs measurements of samples clamped in the bench vice. Assessment of noise level is typically performed under different conditions during on-water operation (especially for diesel engines) [74]. The herein acquired noise level was higher than recommended [75], which can be attributed to the fact that the measurement was performed at high RMPs (more than 80% of maximum) in a closed room. The distance of the microphone and recorded frequencies could also have affected the result. 3.5. Microstructure Microstructure of the fibre–hybrid sample and fractured surface after tensile test are depicted in Figure 14. The up-view on the fractured surface of the sample depicted in Figure 14a also shows saddle clamps on both ends of the sample. Figure 14a,d depict carbon fibres separated from the matrix, which indicates inter-laminar failure and separation of the matrix from the fibres at crack initiation [27]. Figure 14b shows the side surface of the sample with evident delamination in the carbon—flax inter-layer. A crack in the resin propagating along the borders of the fibres is evident at the bottom side of the sample. Figure 15c shows the interfaces of carbon and flax areas. As can be seen, the carbon fibres were not separated from the matrix and there was no tearing of the material. It can be assumed that crack development primarily occurred at inter-laminar carbon regions—at resin–fibre interfaces—and propagated towards the flax region, in which the individual fibres were torn due to their larger size and poor fibre-matrix interfacial properties [31]. The results correspond to those acquired by Dinesh el al. [51], who documented poor interfacial adhesion and delamination for a flax–carbon hybrid composite in tension, compression, and during bending. Increasing the content of flax fibres promotes the occurrence of fraction without delamination. The fractured surface after bending is depicted in Figure 15. Figure 15a depicts a region featuring the delamination and matrix crack of carbon fibres, pointing to the occurrence of tensile stress at the bottom of the sample. On the other hand, compressive stress occurs at the top of the sample (Figure 15b)—its morphology exhibiting brittle fracture of a carbon fibre bundle is shown in detail in Figure 15c. Given their deteriorated cohesion with the matrix [27], the flax fibres were primarily damaged by delamination and massive failure of the matrix with the fibres (evident in Figure 15d). Such separated fibres could be observed in the whole fractured area, as depicted in Figure 15b. 4. Conclusions The study focused on the assessment of the suitability of usage of carbon–flax hybrid materials for prospective production of composite parts for personal watercrafts via fundamental mechanical testing. The hybrid material exhibited advantageous damping of vibration and noise, while maintaining favourable mechanical properties, weight, and transversal dimensions of the part. The carbon–flax composite achieved 72% of tensile strength and 60% of flexural strength when compared to the values acquired for a carbon sample (CF04). Other materials, such as pure flax and glass, achieved much lower values (below 35%). Decrease in the flexural strength of carbon–flax samples exposed to a humid environment (simulating realistic service conditions) was relatively low—only by 40% (for the pure flax UD sample it was up to 64%). This phenomenon requires further study to optimize the surface treatment in order to avoid moisture absorption while maintaining favourable mechanical characteristics. Based on the performed tests, real parts were fabricated and tested in operation. The carbon–flax composites achieved lower RMS and damping for both the samples and real parts during operation. The reduction of noise can be noticed by the electric board rider. Author Contributions Conceptualization, J.Z.; methodology, J.Z., Z.J., Z.P.; validation, Z.P., J.M. and J.S.; formal analysis, J.Z., J.S., I.B.; investigation, J.S, J.Z., M.S.; sources, J.S., M.S.; curation of data, M.S.; writing-preparation of original draft, J.Z, J.S.; writing-revision and editing, J.S., Z.P., J.M.; supervision, Z.P., J.M.; project administration, J.Z.; fundraising, J.S., Z.P., J.M.; arrangement of testing, I.B., Z.J., Z.F. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Vacuum infusion process: (a) prepared auxiliary materials; (b) product during curing. Figure 2 Frequency measurement: (a) laboratory measurement setup; (b) real measurement of float setup. Figure 3 Tensile strengths of test samples. Figure 4 Tensile moduli of test samples. Figure 5 Tensile test results, stress-strain curves for test samples. Figure 6 Flexural moduli of test samples. Figure 7 Flexural strengths of test samples. Figure 8 Flexural test results stress-strain curves for test samples. Figure 9 Tensile strengths of conditioned samples. Figure 10 Flexural strengths of conditioned samples. Figure 11 FRF measurement of composite samples: CO—Carbon, CL—hybrid carbon–flax, S—glass, L—Flax. Figure 12 FRF values for final prototypes. Figure 13 RMS measurement results (a); noise level measurement results (b). Figure 14 Microstructure of cracked tensile test sample—optical microscopy and SEM analysis, (a) top view, (b) side view (c) SEM image fracture area carbon–flax, (d) SEM image carbon bundle–matrix. Figure 15 Microstructure of cracked flexural test sample—optical microscopy and SEM analysis, (a) side view, (b) detail of compressive stress area, (c) SEM image of carbon bundle, (d) SEM image flax fibre. polymers-14-01765-t001_Table 1 Table 1 Fabrics used for samples. Fabric Style Weight [g/m2] Thickness [mm] E-Glass Bi-Ax 200 0.33 E-Glass Twill 2/2 200 0.29 Flax Bi-Ax 350 0.62 Flax Twill 2/2 200 0.45 Flax UD 280 0.35 Flax–Carbon UD 225 0.32 Carbon Twill 2/2 200 0.25 polymers-14-01765-t002_Table 2 Table 2 Laminates and their properties. Laminate 1 Style Stacking Sequence Thickness [mm] Composite Density [g/cm3] Fibre Volume Fraction [%] C01 Twill 2/2 8×C 2.02 ± 0.03 1.47 42.12 C02 Twill 2/2 4×C 1.05 ± 0.02 1.39 42.74 CF01 UN 8×C+F (hybrid) 2.65 ± 0.02 1.30 31.74 CF02 Twill 2/2 C+C+F+C+C 1.48 ± 0.05 1.29 49.19 CF03 Twill 2/2 C+C+1/2F+C+C 1.03 ± 0.04 1.50 55.91 CF04 Twill 2/2 C+C+F+F+C+C 2.25 ± 0.04 1.42 51.20 G01 Twill 4×G 1.10 ± 0.03 1.55 38.51 G02 Bi-axial 4×G 1.31 ± 0.02 1.76 46.13 G03 Twill 2/2 8×G 2.02 ± 0.03 1.70 38.86 F01 Twill 4×F 1.8 ± 0.05 1.11 31.12 F02 Bi-ax 4×F 2.65 ± 0.06 1.19 39.64 F03 Twill 8×F 3.50 ± 0.07 1.21 29.11 F04 UD 8×F 2.60 ± 0.04 1.22 51.10 1 C—Carbon; CF—Carbon–flax hybrid; G—Glass; F—Flax. polymers-14-01765-t003_Table 3 Table 3 Brüel & Kjaer measuring equipment. Analyzer Accelerometer Microphone Modal Hammer Photon+ 4517 mini ACC 4189 8204 Analog channels: 4 input/2 output Frequency range: 84 kHz Dynamic range: 115 dB Sensitivity: 1.02 mV/g Frequency range: 20 kHz Frequency range: 20 kHz Sensitivity: 50 mV/Pa Sensitivity: 22.7 mV/N Full-scale force range: 220 N polymers-14-01765-t004_Table 4 Table 4 Tensile properties of samples. Material Style Tensile Modulus [GPa] Tensile Strength [MPa] Fail Strain [%] C02 Twill 2/2 18.21 ± 1.83 511.26 ± 18.31 2.8 ± 0.22 CF04 Twill 2/2 12.24 ± 0.88 368.44 ± 32.90 2.96 ± 0.34 G02 Bi-axial 7.77 ± 0.29 339.24 ± 13.33 4.6 ± 0.18 G01 Twill 6.14 ± 0.21 216.22 ± 3.51 4.03 ± 0.28 F01 Twill 2.86 ± 0.27 93.87 ± 4.44 3.34 ± 0.31 F02 Bi-axial 2.89 ± 0.15 99.9 ± 1.15 3.57 ± 0.09 F04 UD 6.07 ± 0.31 259.12 ± 30.04 4.21 ± 0.24 polymers-14-01765-t005_Table 5 Table 5 Flexural properties of samples. Material Style Flexural Modulus [GPa] Flexural Strength [MPa] Flexural Strain [%] C02 Twill 2/2 46.12 ± 1.24 760.38 ± 18.64 2.01 ± 0.10 CF04 Twill 2/2 34.63 ± 1.91 463.94 ± 23.86 1.58 ± 0.05 G01 Twill 12.30 ± 0.55 183.24 ± 11.01 2.49 ± 0.14 G02 Bi-axial 9.61 ± 0.70 266.41 ± 6.71 3.10 ± 0.67 F01 Twill 4.59 ± 0.13 115.01 ± 4.64 3.08 ± 0.26 F02 Bi-axial 4.28 ± 0.11 146.43 ± 3.21 3.94 ± 0.39 F04 UD 8.96 ± 0.11 257.50 ± 4.91 3.15 ± 0.06 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Ngo T.D. Natural Fibers for Sustainable Bio-Composites Natural and Artificial Fiber-Reinforced Composites as Renewable Sources InTech London, UK 2018 110 138 2. Koronis G. Silva A. Fontul M. Green composites: A review of adequate materials for automotive applications Compos. Part B 2013 44 120 127 10.1016/j.compositesb.2012.07.004 3. Khalfallah M. Abbès B. Abbès F. Guo Y. Marcel V. Duval A. Vanfleteren F. 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==== Front Nutrients Nutrients nutrients Nutrients 2072-6643 MDPI 10.3390/nu14091875 nutrients-14-01875 Article Effect of Cold Storage on the Viable and Total Bacterial Populations in Human Milk https://orcid.org/0000-0003-2799-2727 Stinson Lisa F. 1* Trevenen Michelle L. 2 Geddes Donna T. 1 Demmelmair Hans Academic Editor 1 School of Molecular Sciences, The University of Western Australia, Perth, WA 6000, Australia; donna.geddes@uwa.edu.au 2 Centre for Applied Statistics, The University of Western Australia, Perth, WA 6000, Australia; michelle.trevenen@uwa.edu.au * Correspondence: lisa.stinson@uwa.edu.au 29 4 2022 5 2022 14 9 187525 2 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Expression and cold storage of human milk is a common practice. Current guidelines for cold storage of expressed milk do not take into account the impact on the milk microbiome. Here, we investigated the impact of cold storage on viable bacterial populations in human milk. Freshly expressed milk samples (n = 10) were collected and analysed immediately, stored at 4 °C for four days, −20 °C for 2.25 months and 6 months, and −80 °C for 6 months. Samples were analysed using propidium monoazide (PMA; a cell viability dye) coupled with full-length 16S rRNA gene. An aliquot of each sample was additionally analysed without PMA to assess the impact of cold storage on the total DNA profile of human milk. Cold storage significantly altered the composition of both the viable microbiome and total bacterial DNA profile, with differences in the relative abundance of several OTUs observed across each storage condition. However, cold storage did not affect the richness nor diversity of the samples (PERMANOVA all p > 0.2). Storage of human milk under typical and recommended conditions results in alterations to the profile of viable bacteria, with potential implications for infant gut colonisation and infant health. human milk microbiome bacteria storage viability expressed breast milk Medela AG (Switzerland)This research was supported by an unrestricted research grant from Medela AG (Switzerland), administered by The University of Western Australia. ==== Body pmc1. Introduction Human milk contains a number of important bioactive components, including a low biomass of bacteria [1] which contribute to the seeding of the infant oral and gut microbiomes [2,3,4,5,6]. Expression and cold storage of human milk is a commonplace practice. Mothers may express milk for their infants in order to return to work outside the home, share feeding, feed their hospitalised infant, or feed multiple infants. In the U.S. 85–91% of breastfeeding mothers have expressed milk for their infant at some point [7,8]. Similar figures have been reported in Australia, where 69–98% of mothers have expressed milk for their infants [9,10]. However, the effect of typical cold storage on the human milk microbiome is not well understood. The Academy of Breastfeeding Medicine (ABM) provides guidelines for storage of expressed milk: four hours at room temperature (16–29 °C), four days in the refrigerator (4 °C), and six months in the freezer (≤−4 °C) [11]. However, these recommendations are based on bacterial growth and degradation of milk components, including immune cells [12], lipids [13], and antioxidants [14], and do not take into account the effect of cold storage on the viability of the milk microbiome. The stability of the human milk microbiome under typical cold storage conditions has been poorly characterised. To date, limited studies have attempted to answer this question, and have arrived at conflicting conclusions. Marín et al. analysed 34 milk samples fresh (processed immediately), and after two, four, and six weeks of storage at −20 °C [15]. They found no difference in bacterial colony counts across a range of culture media at any time point, suggesting that storage at −20 °C has no discernible effect on the milk microbiome. Ahrabi et al. collected 40 milk samples over 1–2 pumping sessions that occurred within four hours of one another [16]. Baseline samples were frozen at −80 °C, while stored samples were frozen immediately at −20 °C or stored at 4 °C for 72 h then frozen at −20 °C for one, three, six, or nine months. Both total bacterial and gram-positive bacterial colony counts approached zero after just three months of storage, with a greater decline seen in samples that were refrigerated and then frozen. However, an initial period of up to four hours of storage at room temperature during collection, and the use of a frozen sample to represent the baseline community may have influenced these results. Slutzah et al. analysed 36 milk samples fresh (processed immediately), and after storage at 4 °C for one, two, three, and four days [17]. There was no change in total nor gram-negative bacterial colony counts; however, a decline in gram-positive bacterial colony counts over the course of storage was observed. Collectively, the evidence on the effect of cold storage on bacterial communities in human milk is contradictory. To better characterise the effect of cold storage on the human milk microbiome, a molecular approach is required. This will allow a higher resolution analysis of human milk bacterial communities under various storage conditions. However, DNA-based methods, such as 16S rRNA gene sequencing, cannot differentiate viable bacterial cells from dead cells, potentially leading to spurious results. Here, a combination of propidium monoazide (PMA; a cell viability dye [18]) and full-length 16S rRNA gene sequencing was used to analyse the effect of cold storage on both the total bacterial DNA profile, and the viable bacterial profile of human milk. To ensure relevance of the data, expressed milk samples were stored according to ABM guidelines (four days at 4 °C and six months −20 °C [11]), typical lab conditions (−80 °C for six months), and under home storage conditions (according to the results of a survey of milk storage practices). 2. Materials and Methods 2.1. Survey of Milk Storage Practices Breastfeeding mothers of healthy infants aged 1–12 months who expressed milk for their infant were invited to fill out a survey of their milk storage practices. This survey was approved by the University of Western Australia’s Human Research Ethics Committee (RA/4/1/2369) and all participants provided informed consent. The survey consisted of four questions:Do you store your expressed breast milk in the fridge? If yes, what is the usual amount of time that you store your milk in the fridge? Do you store your expressed breast milk in the freezer? If yes, what is the usual amount of time that you store your milk in the freezer? 2.2. Sample Collection and Storage Milk samples were collected from lactating women (n = 10; 1–12 months post-partum), as previously described [19]. These participants were not the same as those who answered the questionnaire. All participants provided informed consent and The University of Western Australia’s Human Research Ethics Committee approved the study (RA/4/1/2369). An amount of 50 mL of expressed milk was collected using a Symphony electric breast pump (Medela AG, Baar, Switzerland) and sterilized pump kits. Each sample was split into ten 1 mL aliquots and either processed immediately, or stored at 4 °C for four days, −20 °C for a representative “home storage” quantity of time (according to the results of the survey), −20 °C for six months, or −80 °C for six months. At each time point two aliquots from each mother were analysed: one with a PMA pre-treatment to assess the viable microbiome, and one without PMA to assess the total bacterial DNA profile. 2.3. PMA Treatment Human milk samples were centrifuged at 10,000× g for 10 min at 4 °C. For the non-PMA treated aliquots, DNA was immediately extracted from the cell pellet. For the PMA-treated aliquots, samples were treated with PMA (PMAxx™, Biotium) as previously described [19], followed by DNA extraction from the cell pellet. 2.4. DNA Extraction DNA was extracted as previously described [19] within a sterile laminar flow hood. Certified sterile and DNA-free plasticware was used for all steps. 2.5. PacBio Sequencing The full-length 16S rRNA gene was amplified using the primers 27F (5′-AGRGTTYGATYMTGGCTCAG-3′) and 1492R (5′-RGYTACCTTGTTACGACTT-3′), as previously described [19,20]. PCR reagents were decontaminated using the ArcticZymes PCR decontamination kit prior to use. Certified sterile and DNA-free plasticware was used for all steps, and all work was carried out in a sterile laminar flow hood. Primary PCR products were barcoded as previously described [19,20]. Barcoded samples were pooled in an equimolar concentration and the pool was gel purified using the QIAquick Gel Extraction Kit. The purified, barcoded DNA pool was sequenced at the Australian Genome Research Facility (University of Queensland, QLD, Australia) using the PacBio Sequel II system. 2.6. Sequence Processing Sequence data was processed using mothur version 1.44.1 [21], as previously described [19]. 2.7. Statstistical Analysis 2.7.1. Survey Responses Survey responses are summarised as means, standard deviations (SDs), and ranges. 2.7.2. DNA Quantification DNA quantity was skewed so analysis was performed after a log-transformation. Medians and interquartile ranges (IQR) are reported. A linear mixed model was performed with an outcome of (log-transformed) DNA quantity, fixed factors of PMA treatment, storage condition and their interaction, and a random effect of participant ID. Pairwise comparisons were back-transformed to ratios, with associated 95% confidence intervals (CIs), such that interpretation may be on the original DNA quantity scale. p-values of pairwise comparisons from this model, and all further models in this paper, were adjusted across all outcomes to control the false discovery rate using the Benjamini–Yekutieli correction [22]. As such, adjusted p-values are presented. 2.7.3. Alpha Diversity Alpha diversity was assessed using Shannon diversity and richness (number of OTUs). Means and standard deviations (SDs) are reported for the Shannon diversity, whilst medians and IQRs are reported for the richness. Linear mixed models were performed with outcomes of Shannon diversity and log-transformed richness. Fixed effects of PMA treatment, storage condition, and their respective interaction were included in the models, along with a random effect of participant ID. Adjusted p-values are presented. 2.7.4. Beta Diversity Differences in beta diversity were assessed by performing PERMANOVA on Bray–Curtis distances. Fixed effects of PMA treatment, storage condition, and their interaction, as well as a random effect of participant ID were included in the model. Adjusted p-values are provided. A principal correspondence analysis (PCoA) was performed, and the first two principal component axes are plotted. 2.7.5. Relative Abundance Analysis For the relative abundance analysis, results from OTUs which made up ≥1% of the total relative abundance in the samples and had a prevalence of >10% amongst mothers, are reported. Analysis was performed at the OTU level, and taxonomic assignments for each OTU were established using BLAST [23]. Relative abundances were analysed using generalised additive models for location, scale, and shape with a zero-inflated beta family. PMA treatment, storage condition, and their interaction were considered as fixed effects, and participant ID was included as a random effect. Adjusted p-values are provided. 3. Results 3.1. Assessment of Milk Storage Practices In order to ascertain the conditions under which expressed human milk was stored in the home, breastfeeding mothers of healthy full-term infants were invited to answer a survey assessing the length of time that expressed milk was stored in the home refrigerator or freezer. The survey received 43 responses. Two responses were discarded due to qualitative answers (e.g., “months” or “days”), leaving 41 for analysis. Of the 41 respondents, two stored their milk in the fridge only, and three stored their milk in the freezer only. On average, participants stored their milk in the fridge for 1.8 days (SD 1.2 days, range 5 h–6 days) and in the freezer for 2.25 months (SD 1.74 months, range 4 days–9 months) (Table 1). Based on these results, 2.25 months at −20 °C was chosen as the home storage condition. 3.2. Cold Storage Reduces the Yield of DNA from Viable Cells The median yield of DNA from the fresh milk samples analysed here was 0.60 ng/µL (IQR 0.55, min: 0.26, max: 3.98). However, a significant portion of this DNA originated from non-viable cells. The total quantity of DNA originating from viable cells (PMA-treated aliquots) was 0.36 ng/µL (IQR 0.34, min: 0.13, max: 2.63). A detailed comparison of fresh PMA-treated and untreated samples is the subject of another publication [19]. Regardless of storage condition, PMA-treated samples yielded significantly less total DNA than untreated samples, suggesting that a significant quantity of DNA in these samples originated from non-viable cells (fresh p = 0.008, all other storage conditions p < 0.0001) (Figure 1). Cold storage of human milk, regardless of temperature or time, resulted in a significant reduction in the concentration of DNA from viable cells (all p < 0.0001 compared to fresh samples). Within non-PMA treated samples (DNA from viable and non-viable cells), cold storage, regardless of temperature or time, did not result in a significant change in the concentration of DNA compared to fresh samples. However, samples stored at −80 °C consistently had almost twice as much DNA as fresh samples (median of fresh: 0.60 ng/µL (IQR 0.55 ng/µL), −80 °C: 1.10 ng/µL (IQR 1.03 ng/µL), unadjusted p-value 0.015, adjusted p-value 0.19) (Figure 2). These −80 °C stored samples had a significantly higher concentration of DNA compared to those stored under other cold storage conditions (all p ≤ 0.003). 3.3. Cold Storage Alters the Composition of the Human Milk Microbiome Cold storage of human milk samples did not alter the alpha diversity (richness or Shannon diversity) of PMA-treated or untreated samples (Figure 2). However, richness was significantly lower in PMA-treated samples compared to non-PMA treated samples under every storage condition (all p ≤ 0.0004). Shannon diversity was also lower in PMA-treated samples compared to non-PMA treated samples; however, this trend did not reach statistical significance. Cold storage did not alter the community structure of PMA-treated or untreated human milk (Figure 3). While samples tended to cluster by mother, no clustering was observed by storage condition, regardless of PMA treatment (PERMANOVA all p > 0.2). However, the overall bacterial community was significantly different in PMA-treated compared to non-PMA treated fresh and −80 °C stored samples (PERMANOVA p = 0.0001 and p = 0.0078, respectively). Ten OTUs made up ≥ 1% total relative abundance within these samples (Table 2). Similar to previous studies, we found that the human milk microbiome was relatively simple, with just three OTUs, mapping to Staphylococcus and Streptococcus spp., making up an average of 61% of the fresh, untreated samples (Figure 4). Within non-PMA treated samples, cold storage significantly altered the relative abundance of four of the top ten OTUs compared to fresh samples (Staphylococcus epidermidis, Finegoldia magna, Peptoniphilus harei, and Veillonella dispar) (all p ≤ 0.0306). However, it should be noted that the size of this effect for all OTUs but one (mapping to Staphylococcus epidermidis) was small (0.1–1.8% change) (Table 2). Differences between different cold storage conditions were observed for seven of the top ten OTUs (all p ≤ 0.0402). However, again, the effect sizes were quite small (0.1–3.6% change). Further studies are required to determine whether this degree of change is biologically impactful. Within PMA-treated samples, two of the top ten OTUs were absent from the fresh samples. Five of the eight remaining OTUs exhibited significant changes in their relative abundance in fresh compared to cold stored samples (all p ≤ 0.0306) (Table 2). Additionally, differences between different cold storage conditions were observed for eight of the top ten OTUs (all p ≤ 0.0398) Effect sizes within PMA-treated samples were larger than those seen in non-PMA treated samples (0.2–13.4% change). 4. Discussion Here we demonstrate that cold storage of human milk has no significant impact on the richness, Shannon diversity, nor Bray–Curtis diversity of the viable bacterial community. The fact that viable bacterial richness was not reduced by cold storage suggests that infants fed stored expressed breast milk are exposed to the same number of different viable bacterial taxa as those fed fresh breast milk. However, the composition of the viable microbiome in these samples was significantly altered by cold storage. We observed significant changes in the relative abundance of viable Streptococcus salivarius, Streptococcus mitis, Cutibacterium acnes, Lactobacillus gasseri, and Veillonella dispar in cold stored samples compared to fresh samples. This finding has implications for storage of expressed breast milk in the home, as such compositional changes may impact infant bacterial colonisation and infant health. Strains of L. gasseri have previously been shown to be shared between paired human milk and infant fecal samples, suggesting that it is vertically inherited via breastfeeding [24,25]. Feeding of stored expressed milk may therefore alter this sharing of L. gasseri between mothers and their breastfed infants. Similarly, a recent study of paired human milk and infant fecal samples from the CHILD cohort reported that taxa such as Streptococcus and V. dispar co-occur in paired human milk and infant fecal samples, and that this co-occurrence is reduced if the infant is fed expressed breast milk [26]. Here, we found that DNA from viable S. mitis, the most dominant Streptococcus species in our fresh PMA-treated samples, was significantly reduced after storage at −20 °C for 2.25 or 6 months (by 85.4% and 92.2%, respectively). Similarly, storage at 4 °C for 4 days, −20 °C for 2.25 months, and −20 °C for 6 months, resulted in a significant reduction in the relative abundance of viable V. dispar (by 72.7%, 69.7%, and 97%, respectively). Cold storage of expressed breast milk may therefore explain the reduced co-occurrence of these taxa in infants fed expressed breast milk from the CHILD cohort study. Together with the results of Fehr et al., our findings suggest that feeding of frozen or refrigerated milk samples could potentially have an impact on infant gut microbiome colonisation dynamics. Our findings also have implications for the storage of human milk samples in a laboratory setting, as cold storage of such samples may lead to compositional shifts in viable bacterial communities, distorting the results of culture-based or transcription-based microbiome studies. Changes in the composition of the viable microbiome were even observed after storage at −80 °C, with significant decreases in the relative abundances of S. salivarius, S. mitis, and C. acnes, and a significant increase in the relative abundance of V. dispar compared to fresh samples. These changes are notable given the widespread reliance on −80 °C storage to preserve microbiome samples. Our finding that cold storage resulted in a significant reduction in the total quantity of DNA from viable cells suggests that human and/or bacterial cells in human milk are lost during cold storage. Similarly, Zonneveld et al. reported that storage of human milk for 2 h at 37 °C, room temperature, 4 °C, or −80 °C resulted in a significant decline in cell viability [27]. Our results also support the findings of Arahbi et al., which showed a significant reduction in total bacterial cell counts after 1, 3, 6, and 9 months of storage at −20 °C [16]. Collectively, the available data suggests that human and bacterial cell viability is adversely affected by both warm and cold storage. Fresh human milk contains the highest titres of viable cells and DNA, with implications for infant feeding practices and human milk studies. In addition to investigating the impact of cold storage on the viable microbiome of human milk using PMA, we also investigated the effect on the total bacterial DNA profile. This is important as metataxonomic studies of the human milk microbiome assess bacterial DNA profiles, without differentiation of DNA from viable and non-viable cells. We were therefore interested in assessing the impact of cold storage on the total bacterial DNA profiles of human milk. There was no difference in the richness, Shannon diversity, nor Bray–Curtis diversity of the total bacterial DNA profile. However, numerous small-scale but statistically significant differences were detected in the relative abundance of the ten most abundant taxa after cold storage. These data suggest that minor compositional differences may arise in metataxonomic studies of the human milk microbiome if the samples have undergone cold storage. Human milk samples that are to undergo metataxonomic analysis should therefore be stored in a uniform manner to minimise the effect of different storage conditions on the bacterial DNA composition. These findings also have implications for infants fed frozen or refrigerated expressed breast milk. A large portion of the taxa in fresh milk samples were non-viable (average 67.3% fewer OTUs in PMA-treated fresh samples compared to untreated fresh samples). These non-viable bacteria may play a biological role in the infant gut, by exposing the developing mucosal immune system to a range of different bacteria in a non-threatening manner. Therefore, differences in the composition of the total bacterial DNA profile in cold stored samples may have implications for early-life exposure to living and dead microbes. Interestingly, total DNA concentration significantly increased after storage at −80 °C in non-PMA treated samples (Figure 1). Compared to fresh samples, −80 °C stored samples contained approximately twice as much dsDNA. This finding was not reflected by similar increases in the PMA-treated samples, suggesting that an increase in DNA from non-viable cells or cell-free DNA had occurred. This additional DNA may be a result of extracellular vesicle (EV) release as a consequence of cellular stress from freezing at such low temperatures. EVs are lipid-membrane bound compartments released by cells into the surrounding environment. EVs can contain various types of nucleic acid cargo, including dsDNA [28,29,30]. Temperature stress can induce cells to release EVs into their environment [31]. In fact, cold storage of human milk at −80 °C has previously been shown to induce EV formation [27]. This is an important finding, since milk samples are typically stored at −80 °C prior to analysis in lab settings. This may result in inflated estimates of dsDNA quantities due to increased production of EVs. Recently, gram-positive bacteria (the major bacteria in human milk) have been shown to release membrane vesicles with DNA on their surface [32], which may have been detected by our dsDNA quantification here. Importantly, these membrane vesicles were shown to contain immunostimulatory DNA, RNA, and peptidoglycan, that activated innate immune receptors [32]. Such bacterial membrane vesicles may therefore be of importance in early-life immune programming, with differences in fresh and cold stored milk possible. 5. Conclusions In summary, here we demonstrate that cold storage alters the composition, but not the richness or diversity, of the viable and total bacterial DNA profiles of human milk. These findings have significant implications future studies of the human milk microbiome. It is important to note that while we observed changes in the composition of the viable microbiome after cold storage, we did not measure the effects of these on infant colonisation or health. Further, cold storage of human milk following the ABM’s recommendations does not impact other important nutrient or bioactive components of human milk. While this study was limited to a small sample size (n = 10 samples, n = 43 survey responses), the results nevertheless provided proof of concept and impetus for larger studies on this topic. Given that this was the first study to use a molecular approach to examine the effect of cold storage on the milk microbiome, duplicate processing of the samples may have strengthened the conclusions; however, we do not expect such a duplicate analysis to change our findings. Due to our limited sample size, we are not able to provide recommendations for storage conditions for expressed breast milk. However, it is notable that milk stored at −20 °C for 6 months (the ABM’s recommended maximum storage time) was strongly dominated by just two species, S. epidermidis and C. acnes. Overall, no cold storage condition tested here preserved the composition of the viable microbiome. Acknowledgments The authors would like to acknowledge Danielle Freeth, Melissa Singer, and Erika van den Dries for their assistance in recruiting participants for this study and collecting the fresh milk samples. We also acknowledge Matthew Payne for the generous use of his lab space and equipment for this study. Author Contributions Conceptualization, L.F.S. and D.T.G.; methodology, L.F.S. and D.T.G.; validation, L.F.S.; formal analysis, M.L.T. and L.F.S.; investigation, L.F.S.; resources, D.T.G.; data curation, M.L.T. and L.F.S.; writing—original draft preparation, L.F.S.; writing—review and editing, D.T.G. and M.L.T.; visualization, M.L.T. and L.F.S.; supervision, D.T.G.; project administration, D.T.G.; funding acquisition, D.T.G. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Human Research Ethics Committee at The University of Western Australia (RA/4/1/2369). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement FASTQ sequences have been deposited to NCBI SRA (PRJNA758749). Conflicts of Interest L.F.S. and D.T.G. are supported by an unrestricted research grant from Medela AG, administered by The University of Western Australia. This funding body had no role in the design of the study, collection/analysis/interpretation of data, writing of the manuscript, or in the decision to publish the results. M.L.T. has no conflict of interest to disclose. Figure 1 DNA concentration (ng/µL) of human milk samples under different storage conditions (n = 10). Dark blue bars represent total DNA from viable and non-viable cells (non-PMA treated samples). Light blue bars represent DNA from viable cells only (PMA-treated samples). Boxes are interquartile range, whiskers are range, and inner lines are medians. Figure 2 Shannon diversity (A) and richness (B) of human milk samples under different storage conditions (n = 10). Dark blue bars represent total DNA from viable and non-viable cells (non-PMA treated samples). Light blue bars represent DNA from viable cells only (PMA-treated samples). Boxes are interquartile range, whiskers are range, and inner lines are medians. Figure 3 PCoA plot of Bray–Curtis distances of PMA-treated and untreated human milk samples stored under different cold storage conditions (n = 10). Figure 4 Relative abundance (%) of the ten OTUs which made up ≥1% of the total relative abundance in these samples. Species assignments for each OTU are indicated in the legend. nutrients-14-01875-t001_Table 1 Table 1 Cold storage times reported in a survey of 43 mothers who expressed and stored milk for their infants. Fridge Freezer Average (SD) 1.8 (1.2) days 2.25 (1.74) months Minimum 5 h 4 days Maximum 6 days 6 months nutrients-14-01875-t002_Table 2 Table 2 Mean relative abundance (%) of the top ten OTUs in PMA-treated and untreated human milk samples stored under various conditions. Species to which these OTUs map are provided. Statistically significant differences between OTUs at different storage conditions are denoted by superscript letters. Asterisks indicate samples which did not meet the >10% prevalence filter. Non-PMA Treated Samples Fresh 4 Days 4 °C 2 Months −20 °C 6 Months −20 °C 6 Months −80 °C Staphylococcus epidermidis 30.2 a,b 43.2 a 33.6 39.5 b 37.2 Streptococcus salivarius 17.4 18.3 18.8 a 16.3 16.0 a Streptococcus mitis 13.4 9.8 a 10.9 13.4 a 11.2 Cutibacterium acnes 4.8 3.4 4.2 3.5 3.7 Lactobacillus gasseri 4.4 3.8 a,b,c 4.6 a,d,e 4.2 b,d,f 4.0 c,e,f Finegoldia magna 2.0 a,b 1.4 a,c 1.9 b,c 1.6 * 1.6 Peptoniphilus harei 1.5 a,b,c 1.4 a,d,e 1.3 b,d,f 1.3 * 1.4 c,e,f Veillonella dispar 3.8 a 2.6 3.3 b 2.0 a,b 2.7 Rothia mucilaginosa 2.8 2.2 a 2.4 b 1.9 c 3.6 a,b,c Streptococcus lactarius 1.5 0.7 1.4 1.1 1.6 PMA treated samples Fresh 4 days 4 °C 2 months −20 °C 6 months −20 °C 6 months −80 °C Staphylococcus epidermidis 19.0 29.6 a 17.9 27.9 16.2 a Streptococcus salivarius 5.6 a,b,c,d 8.3 a,e,f 5.8 b,g,h 0.9 c,e,g,i 0.9 d,f,h,i Streptococcus mitis 10.3 a,b,c 10.3 d,e,f 1.5 a,d,g,h 0.8 b,e,g 3.2 c,f,h Cutibacterium acnes 27.6 a 11.1 b,c 19.1 25.2 b,d 19.0 a,c,d Lactobacillus gasseri 6.3 a,b 4.6 * 7.4 a,c 0.3 b,c 3.1 * Finegoldia magna 3.2 * 2.3 * 3.7 1.3 * 4.4 Peptoniphilus harei 3.6 * 0.2 * 4.0 a 0.3 * 3.8 a Veillonella dispar 3.3 a,b 0.9 * 1.0 a,c 0.1 * 6.4 b,c Rothia mucilaginosa 0.0 * 0.8 * 0.5 0.0 * 0.0 * Streptococcus lactarius 0.0 * 0.3 * 0.5 a 0.1 * 3.3 a Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095493 ijerph-19-05493 Article The Use of Simple Language in Informal Forest Education as a Key to the Correct Interpretation of Sustainable Forest Management—The Experience of Poland https://orcid.org/0000-0002-3663-0579 Korcz Natalia 1* https://orcid.org/0000-0002-8493-2475 Janeczko Emilia 2 https://orcid.org/0000-0002-3042-6577 Kobyłka Agata 3 Buchecker Matthias Academic Editor 1 Department of Natural Foundations of Forestry, Institute of Soil Science and Environment Management, University of Life Sciences in Lublin, Akademicka 13, 20-950 Lublin, Poland 2 Department of Forest Utilization, Institute of Forest Sciences, University of Life Sciences in Warsaw, Nowoursynowska 159, 02-776 Warsaw, Poland; emilia_janeczko@sggw.edu.pl 3 Department of Tourism and Recreation, University of Life Sciences in Lublin, Akademicka 13, 20-950 Lublin, Poland; agata.kobylka@up.lublin.pl * Correspondence: natalia.korcz@up.lublin.pl 01 5 2022 5 2022 19 9 549321 3 2022 28 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In view of the increasing conflict between society and forest management and a significant increase in the social functions of the forest, informal forest education is becoming increasingly important. In Poland, it is carried out mainly based on the field educational infrastructure, which consists, among other things, of forest educational paths equipped with educational boards. The paper presents the results of research on the assessment of the availability of texts presented on educational boards. The study was conducted on the basis of photographs of educational boards located on six educational paths in the forests of the Regional Directorate of State Forests in Lublin. Using the Google Lans application, the main text from each board was read and then analyzed in the Promovolt software program to determine the level of text accessibility using the Fog Index. The results were then compared with the opinion of respondents using an online survey, which indicated that most of the boards are written in language that is either simple, understandable to middle/high school students, or quite difficult but understandable to first-degree students. On the other hand, the respondents generally indicated the level of accessibility of the text to be easier because, in their opinion, it is enough to have a primary education to understand the content of the boards. This observation leads to the conclusion that in order for education to be more effective, simple language should be used, which can be understood by the less educated members of the population. educational board forestry text accessibility educational infrastructure fog index promovolt This research received no external funding. ==== Body pmc1. Introduction Many studies indicate that the social functions of forests are becoming increasingly important [1,2,3,4], which is the result of numerous socio-economic processes related to an increase in leisure time, increased knowledge, and better education as well as environmental awareness, changing lifestyles, and greater concern for health [3]. At the same time, with the observed increase in the importance of the social functions of the forest, there are increasing expectations from society in terms of participation, especially active participation in matters relating to the management of nature, including forests [5,6]. Decisions on forest management must be made rationally and responsibly [7]. Many researchers, both in Poland and abroad [8,9], point out that society does not yet possess adequate knowledge of the principles of environmental functioning. Many people have an emotional approach to nature [10], and their knowledge is based on media reports [11], generating additional conflicts between decision-makers and various social groups [12,13,14]. Systematic and effective forestry education of the public is, therefore, necessary to prevent such situations. Leskinen [15] and Janse and Konijnendijk [16] emphasize that people’s participation in forestry can improve public environmental awareness and social acceptance of sustainable forest management. Forest education also affects the state of understanding of objects, processes, and laws that have always governed nature, thereby appreciating nature and human actions to protect it [17,18]. However, for this to be possible, it must be conducted in a simple way, using plain language [3,18]. 1.1. Non-Formal Education in Poland Informal forest education in Poland is developing thanks to the activities of national parks, but also, and most of all, thanks to institutions such as the State Forests which manage more than 80% of all forests in Poland [19]. In order to disseminate knowledge to the public, the State Forests undertake numerous initiatives in cooperation with national parks, local government organizations, non-profit organizations, and local groups with common interests [20]. According to the State Forests Education Activity Report, 1,852,129 [20] or 4.82% of the Polish population participated in forest education classes in 2019. The preferred form of classes was field meetings with a guide/educator on educational paths. The main recipients of active forms of education were children aged 7–15 years (43.57%) and adults over 19 years (34.64%) [20]. Educational trails are used both in active forms of education such as walks with an educator and passive forms such as walks in the forest without an educator/guide. The basic element of the development of such routes is educational boards. With respect to tourist–recreational forest management in Poland, there have already been attempts to inventory educational routes, paying particular attention to errors in the ergonomics of entire routes [21,22,23]. However, there is no information concerning the inventory of the educational boards themselves, although numerous authors have drawn attention to this topic [24]. 1.2. Educational Boards Used in Informal Education The design of educational infrastructure influences the way information is conveyed and emotions are aroused in people [25]. Effective interpretation gives visitors a greater sense of curiosity and delight. It leaves the visitor better informed and wanting to learn more [26,27]. According to Tsang et al. [28], signs and proper interpretation of the content on them can play a key role in changing visitor attitudes and behaviors. The work of Ballantyne et al. [29] and Walker and Moscardo [30] indicates that the use of educational boards can go beyond the specific tourist experience, contributing to the broader sustainability of education. On the other hand, poorly designed boards may be a disturbing factor for people who want to commune with nature (e.g., boards that are too distinctive, not consistent with the environment) [31,32]. They may contain various types of errors, both factual and technical, related to the form or content of the board, its use or location, which may affect the quality of education and the overall perception of the institution managed by the area [24]. Seretny [33] points out that difficult texts with new words stacked on top of each other can significantly reduce the motivation level of learners. A poorly designed board, instead of stimulating the learning process and allowing the acquisition of new information, extinguishes engagement, creating resentment or a sense of failure. Educational boards in the forest can also be a factor that interferes with mental recovery, for example, during forest bathing [34]. In the scientific world, there have already been attempts to study the opinions of forest, trail, and museum users about educational boards [35,36,37,38]. There have also been attempts to analyze the power of attraction and the retention of focus of forest users [39], attempts to analyze the factors affecting these qualities in people stimulated by the educational boards as well as attempts to analyze the impact of the subject matter of the boards on public opinion [35,40], but still very little is known about the accessibility of the texts that are posted on them. One example describing this problem is the work of Janeczko et al. [41]. 1.3. Plain Language in Educational Boards Properly designed educational displays enable accurate interpretation—that is, a communication process designed to show the public the meanings and relationships of cultural and natural heritage through direct engagement with an object, artifact, landscape, or place [27]. Pettersson [42] suggests that the solution to the many problems of environmental interpretation is plain language. Plain Language makes it easy to communicate with people of different levels of education or age, resulting in effective communication. The global social phenomenon “Plain Language” has become known through the dissemination of a variety of official writing and news from different areas of life, which, when written in a professional language, was problematic for many people to understand, so they were “translated” using simple, short, common phrases [43,44,45,46]. The purpose of this research is to determine the accessibility of the main texts posted on educational boards, which are part of the equipment of educational trails. In order to better explore the possibility of using educational boards for more effective forest education and improving understanding of sustainable forest management principles, the following research hypotheses were adopted:the text of educational boards on educational trails is written in difficult language. the correct determination of the level of accessibility of the text appearing on the educational boards requires a higher education. 2. Materials and Methods 2.1. Study Sites The study was carried out in the Regional Directorate of State Forests in Lublin (RDSF), which consists of 25 forest districts. The RDSF manages an area of 426 thousand ha, including 408.4 thousand ha of forest land. The forest cover in the region is 24.9%, which is one of the lowest in the country. Six educational paths located in the following Forest Districts were selected for the experiment: Chotyłów, Mircze, Sarnaki, Świdnik, Janów, and Kraśnik (Scheme 1). These were the paths on which the largest number of educational activities were conducted in 2018 and which are the most popular among individual forest users (information received from RDSF Lublin). All routes are in the form of a loop (they start and end in the same place). Detailed analyses concerning the selection of routes for the study are described in Korcz and Janeczko [35]. Table 1 presents information on the length of routes and the number of boards. 2.2. Procedure 2.2.1. Analysis of the Accessibility of Text and Graphics on Educational Boards The first stage of the study was to check the current technical condition of the educational boards on the selected trails in the field [49]. All educational boards from the 6 educational trails were in good technical condition with no signs of vandalism. Photographic documentation was also made as part of the inventory. Scheme 2 shows examples of educational boards on the routes. Next, the text of the educational boards was analyzed in detail as part of the in-camera work. For this purpose, the main text of each board was read using the Google Lens application, then verified for linguistic correctness and analyzed using the Promovolt web application (Promovolt), available free of charge at http://www.promovolt.com [50]. Promovolt allows for the analysis of both text and images in terms of their usability, e.g., in advertising campaigns or for marketing activities [51]. This application was used to determine the number of sentences, words, multisyllabic and multi-label words, and syllables, as well as to assess the level of text comprehensibility using the Fog index (text fogginess index). The Fog index value indicates the number of years of education necessary for text comprehension [41,43,51]. The Fog index is one of the most popular indices of readability, and at the same time is adjusted to the Polish language. Its value is determined by the formula:Fog = 0.4 × (ASL + PDW) where: ASL (Average Sentence Length)—is the average sentence length in words, PDW (Percentage of Difficult Words)—means the percentage of multisyllabic words (4 and more for Polish) [51]. The Fog index value can be interpreted as follows:− 1–6: very simple text, understandable for elementary school students; − 7–12: simple text, understandable for middle/high school students; − 13–17: quite difficult text, understandable for first-degree students; − 18 and above: difficult text, understandable by post-graduate students, aged over 24 years [51]. A detailed analysis of the text using the Fog index made it possible to determine the level of education needed to understand it. Based on the analyzed features, it was possible to assess the accessibility of the content of the boards. In addition, texts were also analyzed for the presence of specialized forest terminology [52], the presence of Latin names, or the presence of numerical data (units of measurement, mass, dates, etc.) [41]. The analysis was conducted in April 2019. 2.2.2. An Analysis of the Accessibility of Text and on Educational Boards as Perceived by the Public A total of 540 individuals participated in the survey. Detailed characteristics of the study participants are presented in Table 2. For organizational reasons, it was decided to take the survey using a Google form. Surveys were distributed directly from the main profile of the authors of the paper using social media. The survey used the snowball effect [53], whereby participants were asked to forward the survey link to a minimum of two other adults (over 18 years of age), which streamlined the study and allowed it to reach as wide a range of stakeholders as possible. The survey questionnaire, in addition to the metric questions (gender, age, education level, and place of residence), included 10 questions regarding various photos on education boards (authors’ own questions and photos—see Tables 6–9 in Results). Only one answer could be selected for each question. Based on the results of the text accessibility assessment, each board was assigned to one of four categories, according to the scale proposed by Janeczko et al. [41]:group 1: very simple text, understandable by elementary school students (Board 1); group 2: simple text, understandable by middle/high school students (Boards 2–4); group 3: rather difficult text, understandable for first-degree students (Boards 5–7); group 4: difficult text, understandable for post-graduate students (Boards 8–10). Photos depicting the educational boards used in the questionnaire were attached to the paper as Appendix (Figure A1). On the basis of the questionnaire, it was possible to determine the respondents’ opinions on the level of accessibility of the text. In this way, it was possible to compare the results obtained with those obtained using Promovolt. The authors give their assurance that all the procedures performed in this study were in accordance with the ethical standards of the Polish Committee on Ethics in Science and the 1964 Declaration of Helsinki, as amended. 2.3. Data Analysis The individual parameters of the text on the boards were characterized using descriptive statistics (mean, standard deviation). The relationship and strength of dependence between them were also tested by calculating correlations. Pearson’s chi-2 test was used to test the significance of intergroup differences between independent samples [54]. 3. Results 3.1. An Analysis of the Accessibility of Texts on the Educational Boards The most common level of accessibility of the main text on the educational boards was simple, being understandable for middle/high school students, found on 38 boards (42.70%), and fairly difficult but understandable to first-degree students, also found on 38 boards (42.70%). The exception to this was one educational board only (1.12%), where the level of text accessibility was very simple and understandable for elementary school students. A detailed analysis of the Fog Index scores for each educational board, as determined through the use of Promovolt, is presented in Table 3. Analysis of the results showed a negative correlation between the Fog index value and the number of sentences in the text. In other cases, the correlations were positive. As the Fog index increases, the number of sentences in the main text decreases. As the Fog index increases, the number of multisyllabic and multi-label words increases (Table 4). As a result of the analysis, it was found that in texts rated as quite difficult but understandable for upper/high school students and difficult texts understandable for post-graduate students, forest terminology was used most often. In the case of the simple texts, understandable for middle/high school students, Latin language and numerical data were added most often to the educational boards (Table 5). 3.2. Analysis of the Accessibility of the Texts by the Respondents and Comparison of the Results Using Promovolt Ratings of the accessibility of texts on educational boards were statistically significant relative to demographic factors. Table 6 shows the differences in the evaluation of the difficulty of the text on educational boards made by both genders. Statistically significant differences were obtained for board numbers 9 and 10, which were assigned to the fourth group (boards with difficult text, understandable by post-graduate students). The highest number of correct answers concerning the indication of the level of accessibility of the text concerned boards number one (group one) and two and four (group two). In both groups, the results obtained were comparable. In the case of board one, where the level of accessibility of the text is very simple and understandable for elementary school students, as many as 44.94% of females and 42.86% of males indicated the correct level of accessibility of the text. In the case of board two, where the level of accessibility of the text is simple and understandable for middle/high school students, as many as 44.30% of females gave the correct answer. In the case of board four, as many as 45.54% of males also correctly identified the level of accessibility of the text (Table 6). Considering the educational level of the respondents, statistically significant results were obtained for the first group of boards (very simple, understandable for elementary school students), the third group (quite difficult text, understandable for first-degree students, boards 5–7), and the fourth group (difficult text, understandable for post-graduate students). Those who had a college education were significantly more likely to indicate a score adequate to that indicated by Promovolt for the third and fourth groups. The only exception is board eight (group three) (Table 7). The age of the respondents was statistically significant for all educational boards; respondents in the 45–53 age range and those over the age of 54 most often indicated the level of accessibility of the text correctly. The exceptions are board one (group one) and board six (group three) where 55.71% of 36–44 year-olds and 14.81% of 37–35 year-olds correctly indicated the level of accessibility of the text (Table 8). In the case of respondents’ place of residence, statistically significant results were obtained for all educational boards. Most often, people living in cities with a population of 25,000 to 100,000 correctly indicated the level of accessibility of the text (third and fourth group of educational boards). The exceptions are the boards from groups one and two. In the case of board one (group one), as many as 53.03% of respondents from cities with a population of up to 25 thousand correctly indicated the level of accessibility of the text. In the second group, in the case of boards two and three, 41.93% of respondents from cities with over 100,000 inhabitants gave the correct answer. The exception was board four (group 2), where 46.15% of respondents from a city of 25 to 100 thousand inhabitants gave a correct answer (Table 9). 4. Discussion One of the key principles of creating educational materials should be using simple text, eliminating unnecessary words, and replacing scientific terms with commonly used words, or avoiding complex sentences [41,44,55]. This is especially important when creating educational materials. As Ballantyne and Hughes [56] emphasize, educational boards need to be especially effective for communication due to the presence of many random sensory stimuli that are not conducive to concentration. In addition, people’s motivation to learn, and thus to engage in interpretive media, may be greater in museums and other indoor settings compared to open-air settings [57]. Ballantyne and Uzzell [58] point out that adults and children have very different interpretations of how the natural world is perceived and understood. Therefore, the forms and methods of communication in forest education should be adjusted to maximize the effectiveness of educational boards for the general public [29,30]. Simple, easy, and brief verbal messages are better assimilated by people [59], including children [60]. According to Kim [61], it is also necessary to develop some standard elements for the design attributes of signs, which will allow more effective and reliable implementation of evaluation work related to the use of educational boards in education. According to Taylor [62] and Munksgaard et al. [63], the Fog Index tool can be used to measure the level of simplicity of language in most types of documents as well as educational materials. The Fog Index is calculated based on, among other things, the number of long words, and statistical analysis showed a significant correlation between the scores generated by Promovolt and both the number of multisyllabic words and the average number of words in a sentence. As Pankowska and Rostkowska [52] point out, in Polish, specialized terms are in many cases four-syllable words or more, which may also explain the correlation between the number of specialized vocabulary terms and the evaluation value generated by Promovolt. Specialized forestry terminology is a factor that reduces the level of accessibility of the texts. Munksgaard et al. [63], Korcz and Janeczko [35], and Snopek [24] pointed out this aspect in their works, emphasizing the fact that educational materials must be simple and the content related to forest terminology can only be an addition or curiosity in this type of educational material. In Poland, a worldwide social phenomenon, Plain Language, is gaining more and more supporters, whose advocates promote the idea of writing in “simple language”—the most important features of which are comprehensibility, effectiveness, and universality [44,45]. The use of plain language has also received attention in the field of medicine, creating more accessible questionnaires for sick people (a common method of health communication) [64] or in the legal context, to write more easily understandable legal documents [55,65]. The proponents of the plain language concept assume that public information does not reach the majority of society because it is transmitted in too exclusive a language—the language of a few well-educated people, too difficult to understand for the average person. This hypothesis was also partially confirmed by the results of our study. Most of the educational boards are written in relatively simple language, understandable for middle/high school students, or 38 boards (42.70%), and fairly difficult text but understandable to first-degree students, also 38 boards (42.70%) (Table 3). Interestingly, studies conducted in the urban forests of Warsaw also give similar results [41]. The difficult level of accessibility of educational boards can be a significant problem because the largest target group of educational activities on the trails is children [20]. Among the analyzed boards, only one was adjusted to the level of education of young children. This may significantly affect the communication and education of people participating in forest education classes. However, another important problem is that the respondents incorrectly indicated the level of accessibility of the texts on the boards analyzed. Mostly, post-graduate students, over 36 years old, from larger cities, correctly indicated the level of accessibility of the text, which indicates that to interpret them correctly, one should be an educated person (Table 8 and Table 9). This is a natural phenomenon because, as Hammet and Patterson [66] and Janse and Ottitsch [67] point out, people with higher education indicate higher participation in various forms of recreation and education in forested areas and better interpret natural behaviors and natural phenomena. On the other hand, Thilden [68] emphasizes that the majority of recreational users in parks and forests did not have knowledge of the particular topics interpreted by the educational boards. Work by Burns [69] and Evans and Durant [70] indicate that the public has difficulty understanding scientific materials. This may be due to increasing literacy problems among the public [71,72]. A way to solve the problem with the level of text appearing on educational boards is, for example, to simplify educational texts. In social communication, “acting on the text”, which consists of adapting the text to strictly defined norms of accessibility (comprehensibility), has become more and more popular; a given text will gain the widest possible social range, that is, it will be understood by the so-called average citizen [67,73]. Difficult, incomprehensible professional terms are replaced by colloquial synonyms, thereby improving communication [74]. Another solution can be the replacement of written words with pictograms, figures, or pictures, which allow communication in a faster and more effective way [75]. Visual representation of content can not only evoke emotions [63], it can also portray the status of creatures, objects, or scenes occurring in nature in a realistic way, in a more attractive, objective, and appealing manner as opposed to purely textual form [76]. The application of these principles is very important today due to the increasing number of social conflicts resulting from the public’s lack of understanding of the basic principles of sustainable forest management. 5. Conclusions Educational boards located on educational trails in forests of the Regional Directorate of State Forests in Lublin are not well adapted to the general public because they are written in somewhat difficult language. Most of the texts on educational boards use specialized forestry terminology, which can hinder the interpretation of the content. Respondents’ responses in relation to the level of accessibility of the main texts on educational boards accompanying educational trails were statistically significant in relation to their demographic characteristics. Overall, respondents misinterpreted the content on the educational boards, indicating that the text on the boards is very simple and understandable for elementary school students. Respondents with higher education, over 36 years of age, and living in a city of 25–100 thousand inhabitants, most often indicated the correct level of text accessibility. The results of our study should be taken into account when creating this type of educational material to educate the public more effectively. In both theory and practice, our research can also contribute to mitigating social conflicts in forestry. Appropriate board design, using simple language, can also affect the costs associated with board preparation as well as increase other people’s interest in recreation in natural areas. 6. Limitations Limitations in our work may include the relatively small group of randomly selected educational boards analyzed from a small number of routes. Nevertheless, this number was sufficient to observe some important issues that should be investigated more extensively. In the next stages of the research, the target audience should be expanded to include younger people, including children. Another important element is to investigate whether the subject matter of the educational boards is directly related to the accessibility of the texts included on those boards. Then, an attempt should be made to investigate whether the graphic design as well as the graphics themselves, contained on the educational boards, have a significant impact on the level of understanding of the content contained on the boards by the public. In addition, this type of research should be conducted in different locations (indoors and directly on educational trails) to see if the natural environment can have any influence on the interpretation of the content contained on the boards. Author Contributions Conceptualization N.K. and E.J.; methodology, N.K., E.J. and A.K; software, N.K.; validation, N.K., E.J. and A.K; formal analysis, A.K.; investigation, N.K.; resources, N.K.; data curation, A.K.; writing—original draft preparation, N.K.; writing—review and editing, E.J.; visualization, N.K.; supervision, N.K. and E.J.; project administration, N.K. and E.J.; funding acquisition, N.K. and E.J. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not available. Conflicts of Interest The authors report no conflict of interest. Appendix A Figure A1 Photos of the educational boards used in the online survey. Schemes and Tables ijerph-19-05493-sch001_Scheme 1 Scheme 1 Map with location of educational routes in analyzed forest districts. ijerph-19-05493-sch002_Scheme 2 Scheme 2 Examples of educational boards located on the analyzed educational routes. ijerph-19-05493-t001_Table 1 Table 1 Technical and environmental parameters of educational pathways. Number Forest Districts Route Name Length of the Educational Route Number of Boards The Predominant Theme of the Boards Predominant Function * Forest Habitats ** 1 Chotyłów Educational route Leśna Kłoda 2 km 8 25% forest management commercial forest fresh mixed broadleaved forest 25% animals in the forests 25% plants in forests 25% environmental protection 2 Mircze Educational route Witków 1.5 km 17 58.82% forest management commercial forest fresh broadleaved forest 3 Sarnaki Educational route Mierzwice 3 km 30 23.33% forest management commercial forest fresh mixed coniferous forests 23.33% plants in forests 4 Świdnik Educational route Rejkowizna 3.5 km 11 63% forest management commercial forest fresh broadleaved forest 5 Janów Lubelski Educational route Porytowe Wzgórze 4.7 km 10 30% animals in the forests protection forest fresh mixed broadleaved forest 6 Kraśnik Educational route Kleniewo 2.8 km 13 30.77% forest management commercial forest fresh mixed coniferous forests 30.77% plants in forests Total 17.5 km 89 - - - * Forest functions have been determined thanks to the Forest Data Bank website [47]; ** Forest habitat types according to the European Forest Types-European Environment Agency [48]. ijerph-19-05493-t002_Table 2 Table 2 Detailed characteristics of the respondents. Distribution of Respondents—Demographics n % Gender Female 316 58.52 Male 224 41.48 Age 18–26 162 30.00 27–35 148 27.41 36–44 140 25.93 45–53 54 10.00 >54 years old 36 6.67 Educational level Primary education 40 7.41 High school 238 44.07 University 262 48.52 Place of residence village 154 28.52 city up to 25 thousand inhabitants 132 24.44 city of 25–100 thousand inhabitants 130 24.07 city of over 100,000 inhabitants 124 22.96 ijerph-19-05493-t003_Table 3 Table 3 Detailed analysis of primary texts on educational boards determined using Promovolt. Variable * µ ± SD Very Simple, Understandable for Elementary School Students Simple, Understandable for Middle/High School Students Fairly Difficult Text, but Understandable to First Degree Students Difficult Text, Understandable for Post-Graduate Students 1 Board 38 Boards 38 Boards 12 Boards Number of sentences 8.00 11.08 ± 4.97 8.26 ± 4,56 4.50 ± 2.15 Number of words 120.00 136.71 ± 63.81 157.84 ± 88.09 121.83 ± 51.15 Number of multisyllabic words 12.00 19.39 ± 9.24 26.97 ± 16.48 26.25 ± 16.15 Number of multi-label words 16.00 31.84 ± 15.50 39.16 ± 20.22 40.83 ± 19.38 Number of syllables 130.00 308.79 ± 136.36 348.92 ± 194.67 307.42 ± 126.50 µ ± SD—mean ± standard deviation; * Results from Promovolt. ijerph-19-05493-t004_Table 4 Table 4 Correlation analysis between detailed parameters of the text. Variable Fog Index Number of Sentences Number of Words Number of Multisyllabic Words Number of Multi-Label Words Number of Syllables Fog Index 1.00 Number of sentences −0.43 *** 1.00 Number of words 0.01 0.74 *** 1.00 Number of multisyllabic words 0.29 ** 0.54 *** 0.82 *** 1.00 Number of multi-label words 0.23 * 0.58 *** 0.88 *** 0.87 *** 1.00 Number of syllables 0.09 0.72 *** 0.96 *** 0.80 *** 0.89 *** 1.00 * Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed); *** Correlation is significant at the 0.001 level (2-tailed). ijerph-19-05493-t005_Table 5 Table 5 Analysis of additional elements present in the texts of the main educational boards. Variable Very Simple, Understandable for Elementary School Students Simple, Understandable for Middle/High School Students Fairly Difficult Text, but Understandable to First Degree Students Difficult Text, Understandable for Post-Graduate Students 1 Board 38 Boards 38 Boards 12 Boards [%] Use of forestry terminology 100.00 57.89 68.42 66.67 Use of the Latin language 100.00 28.95 13.16 0.00 Use of numerical data 100.00 39.47 34.21 16.67 ijerph-19-05493-t006_Table 6 Table 6 Differences in the evaluation of the accessibility of the text on educational boards, taking into account the gender of respondents. Group Board Gender I Think the Text Is: Statistics Very Simple, Understandable for Elementary School Students Simple, Understandable for Middle/High School Students Fairly Difficult Text, but Understandable to First Degree Students Difficult Text, Understandable for Post-Graduate Students Chi^2 Pearson p * 1 1 Female 44.94 36.71 14.56 3.80 1.915 0.590 Male 42.86 37.50 13.39 6.25 2 2 Female 40.51 44.30 10.76 4.43 7.172 0.067 Male 47.32 33.04 14.29 5.36 3 Female 29.11 39.87 22.15 8.86 1.235 0.744 Male 33.04 38.39 21.43 7.14 4 Female 31.01 39.24 20.89 8.86 2.137 0.545 Male 27.68 45.54 18.75 8.04 3 5 Female 66.46 24.05 5.06 4.43 5.606 0.132 Male 66.96 19.64 9.82 3.57 6 Female 39.87 39.87 13.92 6.33 1.073 0.784 Male 41.96 36.61 13.39 8.04 7 Female 36.08 24.68 29.11 10.13 6.083 0.108 Male 31.25 33.93 24.11 10.71 4 8 Female 31.01 34.81 21.52 12.66 0.261 0.967 Male 33.04 33.93 20.54 12.50 9 Female 18.35 27.85 25.95 27.85 12.714 0.005 * Male 23.21 37.50 22.32 16.96 10 Female 14.56 25.95 30.38 29.11 15.418 0.001 * Male 25.00 31.25 20.54 23.21 Percentage of correct assessments. * Statistically significant differences (p < 0.05). ijerph-19-05493-t007_Table 7 Table 7 Differences in ratings of text accessibility on educational boards by educational level. Group Board Educational Level I Think the Text Is: Statistics Very Simple, Understandable for Elementary School Students Simple, Understandable for Middle/High School Students Fairly Difficult Text, but Understandable to First Degree Students Difficult Text, Understandable for Post-Graduate Students Chi^2 Pearson p * 1 1 Primary education 35.00 35.00 20.00 10.00 21.982 0.001 * High school 47.06 38.66 7.56 6.72 University 42.75 35.88 19.08 2.29 2 2 Primary education 30.00 40.00 20.00 10.00 10.797 0.095 High school 43.70 41.18 9.24 5.88 University 45.04 28.17 13.74 3.05 3 Primary education 25.00 35.00 20.00 20.00 10.881 0.092 High school 31.09 37.82 21.85 9.24 University 31.30 41.22 22.14 5.34 4 Primary education 25.00 40.00 20.00 15.00 5.533 0.477 High school 31.93 42.86 16.81 8.40 University 28.24 41.22 22.90 7.63 3 5 Primary education 50.00 30.00 5.00 15.00 18.863 0.004 * High school 66.39 21.01 9.24 3.36 University 69.47 22.14 5.34 3.05 6 Primary education 30.00 55.00 0.00 15.00 17.415 0.008 * High school 44.54 34.45 13.45 7.56 University 38.93 39.69 16.03 5.34 7 Primary education 25.00 20.00 30.00 25.00 28.352 0.000 * High school 36.97 32.77 18.49 11.76 University 32.82 25.95 34.35 6.87 4 8 Primary education 5.00 50.00 20.00 25.00 25.808 0.000 * High school 36.13 36.13 15.97 11.76 University 32.06 30.53 25.95 11.45 9 Primary education 15.00 25.00 40.00 20.00 41.483 0.000 * High school 28.57 37.82 18.49 15.13 University 13.74 27.48 27.48 31.30 10 Primary education 5.00 25.00 50.00 20.00 45.426 0.000 * High school 24.37 36.13 21.85 17.65 University 16.03 21.37 26.72 35.88 Percentage of correct assessments. * Statistically significant differences (p < 0.05). ijerph-19-05493-t008_Table 8 Table 8 Differences in ratings of text accessibility on educational boards by age group. Group Board Age I Think the Text Is: Statistics Very Simple, Understandable for Elementary School Students Simple, Understandable for Middle/High School Students Fairly Difficult Text, but Understandable to First Degree Students Difficult Text, Understandable for Post-Graduate Students Chi^2 Pearson p * 1 1 18–26 44.44 41.98 8.64 4.94 71.160 0.000 * 27–35 48.65 35.14 16.22 0.00 36–44 55.71 28.57 11.43 4.29 45–53 25.93 33.33 22.22 18.52 >54 years-old 5.56 61.11 27.78 5.56 2 2 18–26 39.51 43.21 12.35 4.94 75.134 0.000 * 27–35 45.95 44.59 8.11 1.35 36–44 58.57 27.14 12.86 1.43 45–53 25.93 33.33 18.52 22.22 >54 years-old 16.67 61.11 16.67 5.56 3 18–26 27.16 37.04 25.93 9.88 43.413 0.000 * 27–35 32.43 48.65 14.86 4.05 36–44 42.86 30.00 21.43 5.71 45–53 22.22 33.33 25.93 18.52 >54 years-old 5.56 55.56 27.78 11.11 4 18–26 27.16 41.98 20.99 9.88 37.473 0.000 * 27–35 32.43 45.95 18.92 2.70 36–44 38.57 37.14 17.14 7.14 45–53 22.22 37.04 18.52 22.22 >54 years-old 5.56 50.00 33.33 11.11 3 5 18–26 62.96 25.93 7.41 3.70 40.149 0.000 * 27–35 66.22 27.03 6.76 0.00 36–44 77.14 12.86 4.29 5.71 45–53 55.56 18.52 11.11 14.81 >54 years-old 61.11 27.78 11.11 0.00 6 18–26 41.98 40.74 12.35 4.94 56.628 0.000 * 27–35 40.54 44.59 14.86 0.00 36–44 48.57 28.57 14.29 8.57 45–53 29.63 29.63 14.81 25.93 >54 years-old 22.22 55.56 11.11 11.11 7 18–26 32.10 30.86 23.46 13.58 48.941 0.000 * 27–35 31.08 40.54 25.68 2.70 36–44 44.29 18.57 28.57 8.57 45–53 29.63 14.81 29.63 25.93 >54 years-old 22.22 27.78 38.89 11.11 4 8 18–26 35.80 37.04 14.81 12.35 48.830 0.000 * 27–35 29.73 41.89 20.27 8.11 36–44 40.00 22.86 24.29 12.86 45–53 25.93 29.63 18.52 25.93 >54 years-old 0.00 44.44 44.44 11.11 9 18–26 23.46 38.27 24.69 13.58 53.970 0.000 * 27–35 14.86 40.54 22.97 21.62 36–44 24.29 22.86 17.14 35.71 45–53 25.93 14.81 29.63 29.63 >54 years-old 5.56 27.78 50.00 16.67 10 18–26 18.52 39.51 24.69 17.28 81.865 0.000 * 27–35 17.57 33.78 24.32 24.32 36–44 25.71 15.71 22.86 35.71 45–53 18.52 22.22 14.81 44.44 >54 years-old 0.00 11.11 72.22 16.67 Percentage of correct assessments. * Statistically significant differences (p < 0.05). ijerph-19-05493-t009_Table 9 Table 9 Differences in ratings of text accessibility on educational boards by place of residence. Group Board Place of Residence I Think the Text Is: Statistics Very Simple, Understandable for Elementary School Students Simple, Understandable for Middle/High School Students Fairly Difficult Text but Understandable to First-Degree Students Difficult Text, Understandable for Post-Graduate Students Chi^2 Pearson p * 1 1 village 46.75 37.66 11.69 3.90 24.703 0.003 * city up to 25 thousand inhabitants 53.03 33.33 9.09 4.55 city of 25–100 thousand inhabitants 41.54 35.38 13.85 9.23 city of over 100,000 inhabitants 33.87 41.94 22.58 1.61 2 2 village 41.56 44.16 9.09 5.19 129.50 0.021 * city up to 25 thousand inhabitants 56.06 30.30 10.61 3.03 city of 25–100 thousand inhabitants 33.85 41.54 16.92 7.69 city of over 100,000 inhabitants 41.94 41.94 12.90 3.23 3 village 31.17 42.86 18.18 7.79 24.604 0.003 * city up to 25 thousand inhabitants 43.94 31.82 16.67 7.58 city of 25–100 thousand inhabitants 21.54 40.00 26.15 12.31 city of over 100,000 inhabitants 25.81 41.94 27.42 4.84 4 village 33.77 41.56 14.29 10.39 35.189 0.000 * city up to 25 thousand inhabitants 42.42 36.36 16.67 4.55 city of 25–100 thousand inhabitants 18.46 46.15 21.54 13.85 city of over 100,000 inhabitants 22.58 43.55 29.03 4.84 3 5 village 62.34 31.17 2.60 3.90 24.413 0.004 * city up to 25 thousand inhabitants 72.73 16.67 9.09 1.52 city of 25–100 thousand inhabitants 61.54 20.00 10.77 7.69 city of over 100,000 inhabitants 70.97 19.35 6.45 3.23 6 village 44.16 40.26 11.69 3.90 24.540 0.004 * city up to 25 thousand inhabitants 46.97 36.36 9.09 7.58 city of 25–100 thousand inhabitants 35.38 30.77 21.54 12.31 city of over 100,000 inhabitants 35.48 46.77 12.90 4.84 7 village 35.06 32.47 24.68 7.79 31.763 0.000 * city up to 25 thousand inhabitants 46.97 24.24 19.70 9.09 city of 25–100 thousand inhabitants 18.46 32.31 32.31 16.92 city of over 100,000 inhabitants 35.48 24.19 32.48 8.06 8 village 42.86 29.87 15.58 11.69 27.883 0.001 * city up to 25 thousand inhabitants 34.85 34.85 19.70 10.61 city of 25–100 thousand inhabitants 18.46 36.92 24.62 20.00 city of over 100,000 inhabitants 29.03 37.10 25.81 8.06 9 village 35.06 29.87 16.88 18.18 44.204 0.000 * city up to 25 thousand inhabitants 21.21 27.27 24.24 27.27 city of 25–100 thousand inhabitants 9.23 33.85 26.15 30.77 city of over 100,000 inhabitants 12.90 37.10 32.26 17.74 10 village 31.17 28.57 20.78 19.48 35.141 0.000 * city up to 25 thousand inhabitants 18.18 31.82 21.21 28.79 city of 25–100 thousand inhabitants 10.77 24.62 29.23 35.38 city of over 100,000 inhabitants 12.90 27.42 35.48 24.19 Percentage of correct assessments. * Statistically significant differences (p < 0.05). 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PMC009xxxxxx/PMC9099818.txt

==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091733 polymers-14-01733 Article A Degradable Difunctional Initiator for ATRP That Responds to Hydrogen Peroxide https://orcid.org/0000-0003-2331-8057 Hill Lawrence 1* Sims Hunter 12 Nguyen Ngoc 1 Collins Christopher 1 Palmer Jeffery 1 Wasson Fiona 13 Chen Yung-Chung Academic Editor 1 Department of Chemistry, Western Kentucky University, Bowling Green, KY 42101, USA; sims75@purdue.edu (H.S.); ngocng@stanford.edu (N.N.); chrischasecollins@gmail.com (C.C.); jeffery.palmer672@topper.wku.edu (J.P.); wassonfj@mail.uc.edu (F.W.) 2 Department of Chemistry, Purdue University, West Lafayette, IN 47907, USA 3 Department of Chemistry, University of Cincinnati, Cincinnati, OH 45221, USA * Correspondence: lawrence.hill@wku.edu; Tel.: +1-270-745-2136 24 4 2022 5 2022 14 9 173320 3 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Mid-chain degradable polymers can be prepared by atom transfer radical polymerization from difunctional initiators that include triggers for the desired stimuli. While many difunctional initiators can respond to reducing conditions, procedures to prepare difunctional initiators that respond to oxidizing conditions are significantly less available in the literature. Here, a difunctional initiator incorporating an oxidizable boronic ester trigger was synthesized over four steps using simple and scalable procedures. Methyl methacrylate was polymerized by atom transfer radical polymerization using this initiator, and the polymerization kinetics were consistent with a controlled polymerization. The polymer synthesized using the difunctional initiator was found to decrease in molecular weight by 58% in the presence of hydrogen peroxide, while a control experiment using poly(methyl methacrylate) without a degradable linkage showed a much smaller decrease in molecular weight of only 9%. These observed molecular weight decreases were consistent with cleavage of the difunctional initiator via a quinone methide shift and hydrolysis of the methyl ester pendent groups in both polymers, and both polymers increased in polydispersity after oxidative degradation. boronic ester degradable polymer difunctional initiator ATRP ==== Body pmc1. Introduction Controlled radical polymerization techniques have allowed the design and preparation of increasingly intricate polymer architectures [1,2,3,4,5,6,7,8,9,10,11,12,13]. One major challenge is that controlled radical polymerization of vinyl monomers gives rise to polymer chains consisting of carbon–carbon bonds which are challenging to break apart, and making these polymers degradable has potential biomedical applications that could benefit from the structural diversity achievable with controlled radical polymerization. A large body of research has been devoted to this question, but one simple approach is to include a cleavable difunctional initiator to allow for the formation of vinyl polymer chains with a central degradable functionality [14,15]. This approach provides a balance between the structural control afforded by controlled radical polymerization of vinyl monomers while still resulting in a molecular weight decrease of approximately 50% in the presence of the appropriate stimuli, which was previously shown to be sufficient to trigger the release of cargo from polymer nanoparticles for drug delivery [14]. Boronic esters have also received much attention for biomedical applications due to their biocompatibility, rapid response rates, and long-term stability when stored [16,17], and boronic acid pinacol esters were recently used to synthesize block copolymers by controlled Suzuki–Miyaura cross-coupling polymerization combined with atom transfer radical polymerization (ATRP) [18]. However, to our knowledge, there is no example in the literature of a degradable difunctional initiator for controlled radical polymerization that uses a boronic ester trigger. Given the broad range of multi-stimuli responsive polymers that can be prepared from difunctional initiators using controlled radical approaches, a difunctional initiator with a boronic ester trigger may find use in a number of biomedical applications [19,20]. Here, we demonstrate the synthesis of a degradable difunctional initiator (named DFI here) for ATRP that is responsive to hydrogen peroxide by including a boronic ester trigger. We targeted a boronic ester based on the notable lack of a difunctional initiator for ATRP with this functional group in the literature despite the extensive work that was carried out on boronic ester-containing polymers for drug delivery. We began our investigation based on the report by Almutairi et al., where they synthesized degradable polyester nanoparticles via polycondensation using diol monomers with boronic ester triggers [21]. Simply adding one step to a slightly modified version of their monomer synthesis allowed for the synthesis of the DFI (Figure 1, compound 4). This DFI incorporating an oxidizable boronic ester trigger was synthesized over four steps using simple procedures. Methyl methacrylate was then polymerized by ATRP using this initiator, and the polymerization kinetics were consistent with a controlled polymerization. Polymer synthesized using the DFI was found to decrease in molecular weight by 58% in the presence of hydrogen peroxide when dissolved in dimethylformamide, while a control experiment using poly(methyl methacrylate) without a degradable linkage showed a decrease in molecular weight of only 9%. These observed molecular weight decreases were consistent with cleavage of the DFI via a quinone methide shift and hydrolysis of the methyl ester pendent groups, and both polymers increased in polydispersity after oxidative degradation. 2. Materials and Methods 1H-NMR spectra were obtained using a 500 MHz nuclear magnetic resonance spectrometer (JEOL, Peabody, MA, USA), and 13C-NMR spectra were obtained using a 400 MHz nuclear magnetic resonance spectrometer (JEOL, Peabody, MA, USA). Relative polymer molecular weights were determined using an Acquity Advanced Polymer Chromatography system (size-exclusion chromatography, SEC) purchased from Waters Corporation (Milford, MA, USA) versus poly(methyl methacrylate) standards using tetrahydrofuran as the mobile phase. Imidazole (99%), anisole (99%), and methyl methacrylate (MMA) (98%) were purchased from Acros Organics through Fisher Scientific (Pittsburgh, PA, USA). Methyl methacrylate was passed through a column of basic alumina to remove the polymerization inhibitor immediately before use. Ethyl α-bromoisobutyrate (ebib) (98%) and p-toluenesulfonic acid monohydrate (98%) were purchased from Alfa Aesar through Fisher Scientific (Pittsburgh, PA, USA). 4-bromomethylphenylboronic acid pinacol ester (97%) was purchased from AmBeed (Arlington Heights, IL, USA). Copper(I) chloride (98%), tert-butyldimethylsilyl chloride (TBS-Cl) (99%), and tetrahydrofuran (THF) (99.0%, HPLC grade) were purchased from Oakwood Chemical (Estill, SC, USA). 2,6-bis(hydroxymethyl)-p-cresol (95%), anhydrous dichloromethane (DCM) (99.8%), anhydrous dimethylformamide (DMF) (99.8%), anhydrous methanol (99.8%), α-bromoisobutyryl bromide (98%), N,N,N′,N″,N″-pentamethyldiethylenetriamine (PMDETA) (99%), and hydrogen peroxide (30 wt.%) were purchased from Sigma Aldrich (Milwaukee, WI, USA). Copper (I) bromide (98%) was purchased from Strem Chemicals (Newburyport, MA, USA). Hexane, anhydrous sodium sulfate, glacial acetic acid, basic alumina, and anhydrous potassium carbonate were standard reagent grade, and anhydrous potassium carbonate was ground in a mortar and pestle and dried for 2.5 h at 100 °C before use. All liquids were handled in a ventilated fume hood. Copper(I) chloride and copper(I) bromide were stirred in glacial acetic acid overnight, washed with ethanol, filtered, dried under vacuum, and stored under argon before use. Compound 1. Compound 1 was synthesized based on Almutairi et al [21]. 2,6-bis(hydroxymethyl)-p-cresol (2.5130 g, 14.94 mmol) and imidazole (2.270 g, 32.71 mmol) were dissolved in anhydrous DMF (7.5 mL) in a 100 mL round bottom flask with stir bar and chilled in an ice bath for 10 min resulting in a yellow solution with black flecks. A solution of TBS-Cl (4.9416 g, 32.79 mmol) dissolved in anhydrous DMF (12.5 mL) was added dropwise via syringe to the cooled, stirring, solution for approximately 8 min, and then stirred in the ice bath for an additional 10 min. The flask was then removed from the ice bath and placed in a sonication bath for 10 min to break up the precipitate. This mixture was then stirred at room temperature for an additional 2 h. The cloudy yellow mixture with white solids was filtered and the white solids were rinsed with hexane (40 mL) into a separatory funnel. The two liquid phases were mixed by shaking and the hexane phase was collected. The remaining DMF phase was washed with additional hexane (20 mL, 2×), the hexane phases were combined, and the hexane solution was placed on a rotary evaporator at 45 °C until approximately 8 mL of bright yellow solution remained. This solution was washed with saturated potassium carbonate (3 × 10 mL, 0.25 M) and dried over sodium sulfate for 20 min before removing the hexane under reduced pressure (45 °C) to give a yellow oil. This oil was dried under vacuum at room temperature overnight to give compound 1 as a slightly yellow oil (5.2334 g, 88%). Compound 2. Compound 2 was synthesized based on Almutairi et al [21]. Anhydrous DMF (15 mL) was combined with anhydrous potassium carbonate (1.1760 g, 8.51 mmol) in a 100 mL round bottom flask, sealed with septum, and placed in a sonication bath at 50 °C for 1.5 h resulting in a cloudy yellow mixture with visible white solids. The round bottom flask was cooled to 0 °C in an ice bath and compound 1 (2.8167 g, 7.10 mmol) was added quickly by syringe before an additional 2 mL anhydrous DMF was used to wash the syringe. The mixture was stirred for 10 min at 0 °C before 4-bromomethylphenylboronic acid pinacol ester (2.2140 g, 7.46 mmol) was added. The mixture was stirred for another 10 min at 0 °C, allowed to warm to room temperature, and left to stir overnight (18 h) at room temperature. The crude reaction mixture was filtered, and the solids were rinsed with hexane (70 mL) into a separatory funnel. The two liquid phases were mixed by shaking and the hexane phase was collected. The remaining DMF phase was washed with additional hexane (20 mL, 1×). The hexane phases were combined, washed into a separatory funnel with 5 mL hexane, and washed with brine (20 mL, 2×, 29 g NaCl/100 mL H2O). The hexane solution was dried over sodium sulfate for 45 min and the solvent evaporated under reduced pressure at 45 °C. The resulting oil was dried under vacuum overnight at 50 °C to give a partially solidified, cloudy oil (3.8875 g, 89%). Compound 3. Compound 3 was synthesized based on Almutairi et al [21]. Compound 2 (1.5024 g, 2.45 mmol) was dissolved in anhydrous methanol (8 mL) in a 20 mL glass vial with stir bar, and p-toluenesulfonic acid monohydrate (0.094 g, 0.49 mmol) was added. The vial was placed in a sonication bath for 20 min and then stirred at room temperature for 2 h. The solvent was removed under vacuum (50 °C) and the resulting oil was dissolved in 2 mL anhydrous methanol and purified using column chromatography (silica, hexane/ethyl acetate = 1:1) to give a cloudy oil that was dried under vacuum at 50 °C for 4 h to give compound 3 (0.9302 g, 99%). We also had success purifying compound 3 by liquid/liquid extractions with methanol and hexane instead of column chromatography. In these cases, the methanol phase was collected and evaporated under reduced pressure. The resulting oil was then dissolved in DCM, washed with 0.25 M potassium carbonate solution, dried over sodium sulfate, and the solvent evaporated under reduced pressure at 50 °C to give a yellow oil. This procedure gave NMR spectra with some small amounts of impurities that did not interfere with the synthesis of compound 4. Compound 4 (DFI). Compound 3 (0.6251 g, 1.63 mmol) was combined with triethylamine (0.54 mL, 4.24 mmol) in a 250 mL round bottom flask with stir bar and anhydrous dichloromethane (20 mL) was added. The flask was cooled in an ice bath for 10 min before a solution of α-bromoisobutyryl bromide (0.48 mL, 3.41 mmol) dissolved in anhydrous dichloromethane (5 mL) was added and dropwise over 10 min. The solution was stirred for another 10 min in the ice bath and then left to stir at room temperature overnight. The crude product was washed into a separatory funnel with 5 mL dichloromethane and then washed with water (30 mL, 2×). The organic phase was collected and evaporated under reduced pressure to obtain a dark yellow oil. This oil was combined with 2 mL methanol and placed in a sonication bath for 30 min to give a white precipitate. The solids were collected by vacuum filtration and washed with an additional 2 mL methanol before drying under vacuum at room temperature overnight to give compound 4 (0.7147 g, 65%). 1H-NMR (500 MHz, Chloroform-D) δ 7.84 (d, J = 7.6 Hz, 2H), 7.46 (d, J = 8.5 Hz, 2H), 7.25 (s, 2H), 5.24 (s, 4H), 5.01 (s, 2H), 2.34 (s, 3H), 1.93 (s, 12H), 1.35 (s, 12H). 13C-NMR (101 MHz, Chloroform-D) δ 171.52, 153.94, 139.94, 135.17, 134.43, 131.71, 128.84, 127.01, 83.95, 77.31, 63.06, 55.74, 30.87, 24.97, 21.00. The procedure above represents our best yield obtained for the synthesis of compound 4, but we found that the precipitation from methanol was unsuccessful for some reactions. The reason for this is unknown at this time, but samples that failed to precipitate from methanol were instead evaporated, dissolved in dichloromethane, and purified by column chromatography (silica, hexane/ethyl acetate = 4:1) which resulted in lower yields (15–27%) than precipitation from methanol. pMMA-DFI-pMMA. A stock solution of the ligand N,N,N′,N″,N″-pentamethyldiethylenetriamine (PMDETA) was prepared in anisole (0.38 M). All solutions and neat liquids were sparged with argon for 20 min prior to use. In a typical reaction, copper (I) chloride (29.3 mg, 0.296 mmol) and the DFI (compound 4, 84.5 mg, 0.124 mmol) were added to a 100 mL Schlenk flask with stir bar, the flask was sealed with a septum secured by parafilm, and the air was removed by vacuum and replaced with argon for a total of five cycles. The PMDETA/anisole stock solution (0.65 mL, 0.25 mmol PDMETA) was injected under argon with gentle stirring to form a green/yellow solution, followed by an additional 5 mL deoxygenated anisole. Methyl methacrylate (5.3 mL, 49.8 mmol) was then injected under argon, followed quickly by an additional 5 mL deoxygenated anisole added along the interior sides of the flask to rinse the contents to the bottom of the flask. The flask was then lowered into a heated oil bath and maintained at approximately 55–65 °C with vigorous stirring. Small aliquots were periodically removed under argon to monitor the reaction progress for initial kinetic studies. These samples were passed through a small plug of basic alumina followed by a 0.45 µm PTFE syringe filter before characterization by 1H-NMR and size exclusion chromatography. These kinetic studies allowed us to correlate molecular weight with reaction time. Subsequent polymerization reactions were then conducted without removing samples, and these reactions were quenched at a predetermined time by cooling to room temperature, removing the septum, and diluting with dichloromethane. The contents of the Schlenk flask were then poured through basic alumina in a fritted funnel with additional dichloromethane, and the filtrate was transferred to a round-bottom flask before removing the solvent under reduced pressure to near dryness. The visible solids were dissolved in a minimal amount of dichloromethane and added dropwise to a large excess of vigorously stirred methanol. The poly(methyl methacrylate) precipitated as white strands in the beaker that were collected by vacuum filtration and dried under vacuum overnight to give a white solid. ebib-pMMA. Stock solutions of the ligand N,N,N′,N″,N″-pentamethyldiethylenetriamine (PMDETA) and the initiator ethyl α-bromoisobutyrate (ebib) were prepared separately in anisole at concentrations of 0.38 M. All solutions and neat liquids were sparged with argon for 20 min prior to use. In a typical reaction, copper(I) chloride (39 mg, 0.39 mmol) was added to a 100 mL Schlenk flask with stir bar, the flask was sealed with a septum and parafilm, and the air was removed by vacuum and replaced with argon for a total of five cycles. The PMDETA stock solution (0.75 mL, 0.28 mmol PDMETA) was injected under argon with gentle stirring to form a green solution, followed by adding anisole (12 mL) and then methyl methacrylate (6.0 mL, 56 mmol). The ebib stock solution (0.75 mL, 0.28 mmol ebib) was injected last under argon. The flask was then lowered into a heated oil bath and maintained at approximately 55–65 °C with vigorous stirring. Small aliquots were periodically removed under argon to monitor the reaction progress for initial kinetic studies. These samples were passed through a small plug of basic alumina followed by a 0.45 µm PTFE syringe filter before characterization by 1H-NMR and size exclusion chromatography. These kinetic studies allowed us to correlate molecular weight with reaction time. Subsequent polymerization reactions were then conducted without removing samples, and these reactions were quenched at a predetermined time by cooling to room temperature, removing the septum, and diluting with dichloromethane. The contents of the Schlenk flask were then poured through basic alumina in a fritted funnel with additional dichloromethane, and the filtrate was transferred to a round-bottom flask before removing the solvent under reduced pressure to near dryness. The visible solids were dissolved in a minimal amount of dichloromethane and added dropwise to a large excess of vigorously stirred methanol. The poly(methyl methacrylate) precipitated as white strands in the beaker that were collected by vacuum filtration and dried under vacuum overnight to give a white solid. Methyl methacrylate was also polymerized from ethyl α-bromoisobutyrate using copper(I) bromide, which resulted in a less controlled polymerization than using copper(I) chloride [22,23]. However, changing the halide did not affect the outcome of polymer degradation experiments of ebib-pMMA. Polymer degradation experiments. Conditions for the polymer degradation experiments were adapted from Almutairi et al [21]. Approximately 10 mg of purified polymer was weighed into a vial, and for each milligram of polymer, 1 mL of N,N-dimethylformamide (DMF), and 10 μL of 30 wt.% hydrogen peroxide were added, giving a peroxide concentration of approximately 100 mM. In order to allow the polymer to fully degrade, the solution was stirred for four days before the DMF and leftover hydrogen peroxide were removed under vacuum on a rotary evaporator with a heat gun in a chemical fume hood. The resulting residue was dissolved in tetrahydrofuran for molecular weight characterization. 3. Results and Discussion 3.1. Synthesis of the DFI The DFI was synthesized over four steps (Figure 1). Compounds 1–3 were synthesized based on the procedure given by Almutairi et al [21]. In the first step, the benzyl alcohol groups of 2,6-bis(hydroxymethyl)-p-cresol were protected with TBS-Cl using imidazole in DMF to form compound 1 in good yields. Compound 1 was then coupled to 4-bromomethylphenylboronic acid pinacol ester by reaction of the phenolic oxygen with the benzyl bromide in DMF with dry potassium carbonate to afford compound 2 in good yields. The TBS protecting groups were then removed from compound 2 using TsOH in methanol to afford compound 3 in nearly quantitative yields. Esterification of the benzylic alcohols of compound 3 with α-bromoisobutyryl bromide in DCM with triethylamine afforded the DFI compound 4. The synthesis of the DFI was straightforward and all the reactions were conducted by undergraduate and high school student researchers. The most reproducible procedures that resulted in our best yields are given in the Materials and Methods section, though these yields varied slightly. We found the synthesis of compounds 1–3 to be easily scaled up without other changes to the procedure. While we initially used a column to purify compound 3, we found that the need for column chromatography could be reassessed after the reaction to form the DFI (compound 4), which facilitated increasing the scale of the reactions. The reaction to form compound 4 was also scalable, and we had some success recrystallizing this product to make the entire synthesis free of chromatography at scale. However, for reasons still unknown to us, some reactions to form compound 4 did not readily form a precipitate of the product and required column chromatography, which limited us to approximately 1 g per purified batch in these cases. The simplicity of the synthesis should allow others to easily prepare this initiator for their own use. The 1H-NMR spectrum of the DFI with corresponding spectral assignments is shown in Figure 2, and the 13C-NMR spectrum of the DFI with corresponding spectral assignments is shown in Figure 3. Integration values and chemical shifts of the 1H-NMR spectrum matched expected values for the DFI with no discrepancies. Similarly, the chemical shifts observed in the 13C-NMR spectrum were all consistent with the expected structure. Additionally, the 13C-NMR spectrum, shown in Figure 3, was obtained from a sample of the DFI that had been stored in a vial in a dark cabinet for approximately 2 years, which indicates the stability of this initiator when stored as a solid. 3.2. Polymerization Using the DFI Methyl methacrylate was polymerized using the DFI with copper(I) chloride, PMDETA, and anisole (Figure 4a). Copper chloride was used to obtain a polymer with lower polydispersity via halogen exchange [22,23]. We did not conduct end group analysis on these materials, but we expect a preferential exchange from bromine to chlorine in the polymer based on previous studies with small molecule model systems [24]. A single, mostly symmetrical SEC trace was obtained for most samples, providing evidence that the polymerization was controlled using the DFI, though a high molecular weight shoulder began to appear at higher monomer conversion (Figure 4b). This high molecular weight shoulder was small compared to the overall molecular weight distribution, but it continually shifted to higher molecular weight with increasing monomer conversion. One possible explanation for this shoulder is that there may be a small amount of coupling between growing polymer chains. Because each polymer chain has two living ends, any coupling of two chains forms a new polymer chain with two living ends that can continue to grow as the reaction proceeds. Here, the presence of this high MW shoulder did not interfere with the experiments that were central to this work, but the polymerization conditions could be optimized further to limit this coupling if desired. Radical termination of pMMA occurs preferentially through disproportionation over chain coupling, but some amount of chain coupling is still expected to occur [25]. A linear correlation was observed between Mn and monomer conversion and the polydispersity decreased with conversion, indicating that the polymerization was controlled (Figure 4c). This is consistent with termination being a minor contributor to the overall polymerization process. The polymerization kinetics were first-order, which also indicated a constant radical concentration during the polymerization (Figure 4d). The initiator efficiency was estimated to be 79% by comparing the measured molecular weight of 24,670 g/mol at 47% conversion with the theoretical molecular weight of 19,463 based on the initial ratios of monomer to the initiator. This estimated initiator efficiency is comparable to that observed for other ATRP initiators [5]. The polymer obtained was purified by passing through basic alumina followed by precipitation from methanol to remove unreacted monomer before further use in oxidative degradation studies. 3.3. Oxidative Degradation of pMMA with and without the DFI With the polymerization conditions in hand, we synthesized pMMA-DFI-pMMA and ebib-pMMA to use for oxidative degradation tests (Figure 5a,b). For these studies, polymers were stirred in DMF with approximately 100 mM H2O2 for approximately 90 h before the solvent was removed and the residue was dissolved in THF and analyzed using size exclusion chromatography. These two polymers responded very differently to this oxidation condition as expected from the inclusion of a mid-chain degradable linkage in pMMA-DFI-pMMA that was not present in ebib-pMMA. The number average molecular weight of pMMA-DFI-pMMA decreased by 58%, which is consistent with degradation through oxidative cleavage of the central linkage due to rearrangement of the boronic ester trigger in addition to hydrolysis of the methyl ester pendent groups. By comparison, only a 9% decrease in Mn was observed for ebib-pMMA, which is consistent with the hydrolysis of the methyl ester pendent groups without cleavage of the polymer backbone. This result demonstrated that the DFI was capable of acting as a degradable linkage under these conditions. The mechanism for the oxidative degradation of aryl boronic esters with peroxides was extensively studied by others [17,21,26,27,28]. This mechanism begins with the insertion of oxygen into the bond between boron and the aryl carbon atom and the subsequent formation of a phenoxide ion. A quinone methide rearrangement then cleaves the benzyl ester bonds in the polymer backbone resulting in a decrease in polymer molecular weight. We did not examine the mechanism further in our work which was focused on communicating the synthesis of the DFI and demonstrating its utility for synthesizing mid-chain degradable polymers by ATRP. 4. Conclusions A difunctional initiator for ATRP was synthesized and methyl methacrylate was polymerized using this initiator. Polymer synthesized using the difunctional initiator was found to decrease in molecular weight in the presence of hydrogen peroxide when dissolved in dimethylformamide. The molecular weight decrease was consistent with both the cleavage of the boronic ester via a quinone methide shift and the hydrolysis of the methyl ester pendent groups. Poly(methyl methacrylate) synthesized using a monofunctional initiator (ebib) showed a much smaller decrease in molecular weight in the presence of hydrogen peroxide when dissolved in dimethylformamide that was consistent with the hydrolysis of the methyl ester pendent groups. Both polymers increased in polydispersity after oxidative degradation. Future planned studies include determining the degradation rates of these polymers, optimizing degradation conditions to accentuate differences between degradation rates of different initiators, and incorporating antioxidant pendent groups into polymers to perturb the degradation rate of this initiator. The DFI reported here can, in principle, be used as a difunctional monomer for polyaddition reactions to incorporate many degradable linkages per polymer chain [29,30,31,32,33,34,35,36]. Author Contributions Conceptualization, L.H.; investigation, H.S., N.N., C.C., J.P., F.W. and L.H.; writing—original draft preparation, L.H. All authors have read and agreed to the published version of the manuscript. Funding This research was supported by the American Chemical Society Petroleum Research Fund (PRF# 59123-UNI7), the National Science Foundation under Cooperative Agreement No. 1355438 (KY NSF EPSCoR), and through the sponsorship of the Western Kentucky University Research & Creative Activities Program (RCAP). The 400 MHz NMR was purchased using the National Science Foundation Major Research Instrument program, grant number 1919501. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 1 Synthesis of the degradable difunctional initiator (DFI) over four steps. Figure 2 1H-NMR spectrum of the difunctional initiator (DFI) compound 4 in CDCl3. Figure 3 13C-NMR spectrum of the difunctional initiator (DFI) compound 4 in CDCl3. Figure 4 Monitoring the evolution of molecular weight and polydispersity in the ATRP of methyl methacrylate using the difunctional initiator (DFI): (a) Polymerization of methyl methacrylate using the DFI, T = 55 °C, [MMA]0/[DFI]0/[CuCl]0/[PMDETA]0 = 400/1/2.24/2; (b) SEC traces obtained from samples taken at 15, 50, 95, 120, 160, and 200 min for a representative synthesis of pMMA-DFI-pMMA (versus pMMA standards in THF); (c) Evolution of molecular weight (solid black squares) and polydispersity (hollow red circles) versus monomer conversion in the ATRP of MMA during a representative polymerization; (d) First-order kinetic plot for the ATRP of MMA using the DFI. Figure 5 Molecular weights and polydispersity indexes for poly(methyl methacrylate) synthesized by ATRP using two different initiators before and after oxidative degradation experiments: (a) pMMA-DFI-pMMA; (b) ebib-pMMA. Plots of relative molecular weight distributions were obtained versus poly(methyl methacrylate) standards in tetrahydrofuran. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Kato M. Kamigaito M. Sawamoto M. Higashimura T. Polymerization of Methyl Methacrylate with the Carbon Tetrachloride/Dichlorotris-(triphenylphosphine)ruthenium(II)/Methylaluminum Bis(2,6-di-tert-butylphenoxide) Initiating System: Possibility of Living Radical Polymerization Macromolecules 1995 28 1721 1723 10.1021/ma00109a056 2. Wang J.-S. Matyjaszewski K. Controlled/“Living” Radical Polymerization. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095172 ijms-23-05172 Article Sulforaphane Suppresses the Nicotine-Induced Expression of the Matrix Metalloproteinase-9 via Inhibiting ROS-Mediated AP-1 and NF-κB Signaling in Human Gastric Cancer Cells https://orcid.org/0000-0003-4535-098X Li Shinan 1† Khoi Pham Ngoc 2† Yin Hong 3 Sah Dhiraj Kumar 1 Kim Nam-Ho 1 https://orcid.org/0000-0002-7295-9216 Lian Sen 3* https://orcid.org/0000-0003-1209-6786 Jung Young-Do 14* Stefanachi Angela Academic Editor 1 Research Institute of Medical Sciences, Chonnam National University Medical School, Gwangju 61469, Korea; 156103@chonnam.edu (S.L.); 197784@chonnam.edu (D.K.S.); nhk111@jnu.ac.kr (N.-H.K.) 2 Faculty of Basic Medical Sciences, Pham Ngoc Thach University of Medicine, Ho Chi Minh City 740500, Vietnam; khoicnsh@gmail.com 3 Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; graceyinh@126.com 4 Department of Biochemistry, Chonnam National University Medical School, Hwasun 58128, Korea * Correspondence: senlian@i.smu.edu.cn (S.L.); ydjung@chonnam.ac.kr (Y.-D.J.); Tel.: +82-61-379-2772 (S.L.); +86-20-6278-9385 (Y.-D.J.); Fax: +82-81-379-2781 (S.L.); +86-20-62-789-385 (Y.-D.J.) † These authors contributed equally to this work. 05 5 2022 5 2022 23 9 517211 4 2022 02 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Sulforaphane, a natural phytochemical compound found in various cruciferous vegetables, has been discovered to present anti-cancer properties. Matrix metalloproteinase-9 (MMP-9) plays a crucial role in gastric cancer metastasis. However, the role of sulforaphane in MMP-9 expression in gastric cancer is not yet defined. Nicotine, a psychoactive alkaloid found in tobacco, is associated with the development of gastric cancer. Here, we found that sulforaphane suppresses the nicotine-mediated induction of MMP-9 in human gastric cancer cells. We discovered that reactive oxygen species (ROS) and MAPKs (p38 MAPK, Erk1/2) are involved in nicotine-induced MMP-9 expression. AP-1 and NF-κB are the critical transcription factors in MMP-9 expression. ROS/MAPK (p38 MAPK, Erk1/2) and ROS functioned as upstream signaling of AP-1 and NF-κB, respectively. Sulforaphane suppresses the nicotine-induced MMP-9 by inhibiting ROS-mediated MAPK (p38 MAPK, Erk1/2)/AP-1 and ROS-mediated NF-κB signaling axes, which in turn inhibit cell invasion in human gastric cancer AGS cells. Therefore, the current study provides valuable evidence for developing sulforaphane as a new anti-invasion strategy for human gastric cancer therapy. sulforaphane nicotine metalloproteinase-9 gastric cancer cell invasion National Research Foundation of Korea (Ministry of Education, Science, and Technology)2018R1D1A1B07049918 Guangdong Basic and Applied Basic Research Foundation2020A1515011433 Guangzhou Basic and Applied Basic Research Foundation202102021052 This study was supported by the Basic Science Research Program grant through the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology (2018R1D1A1B07049918); the Guangdong Basic and Applied Basic Research Foundation (2020A1515011433); the Guangzhou Basic and Applied Basic Research Foundation (202102021052). ==== Body pmc1. Introduction Gastric cancer is the most common gastrointestinal cancer with high mortality worldwide. The vast majority of gastric cancer cases detected are in the advanced stage [1]. The poor prognosis and treatment of gastric cancer are highly relative to metastasis [2]. Therefore, exact studies on the mechanisms underlying gastric cancer may contribute to the development of improved treatments. Tobacco abuse is strongly associated with gastric cancer progression [3]. Nicotine, a psychoactive alkaloid contained in tobacco, is a known carcinogen, related to cancer metastasis, including gastric cancer [4]. It has been reported that the pathophysiological roles of nicotine are mediated by nicotinic acetylcholine receptors [5]. In addition, nicotine promotes invasion and angiogenesis by the induction of cyclooxygenase-2 and vascular endothelial growth factor receptor-2 in gastric cancer [6]. Tumor metastasis includes the cancer progression of proliferation, migration, invasion, and the following progression of adhesion and angiogenesis in a distant tissue [2]. Most cancer-related mortalities are due to tumor metastasis. Studies indicate that cell invasion is one of the fundamental properties of malignant cancer cells [7]. The breakdown of the extracellular matrix by proteinases is an essential step in cancer cell invasion [8]. Matrix metalloproteinases (MMPs), a family of extracellular-matrix-degrading proteinases, induce cancer cell invasion through the degradation of the extracellular matrix and the basal membrane [9]. Among the MMPs, MMP-2, and MMP-9 play crucial roles in cancer metastasis [10]. Normally, MMP-2 is constitutively expressed in highly malignant tumors, whereas MMP-9 is induced by growth factors [11]. High MMP-9 expression is observed in tumor extracts in gastric cancer; furthermore, aberrant expression of MMP-9 can increase tumor cell detachment and metastasis, which are related to malignancy and poor prognosis in gastric cancer [12]. Therefore, agents with the ability to suppress MMP-9 expression may be useful for the development of treatment strategies for gastric cancer. Sulforaphane, a natural compound that is abundant in cruciferous vegetables, exhibits several beneficial properties, including anti-inflammatory, antioxidant, and anticancer activities. Recently, the anticancer effect of sulforaphane has attracted much attention [13]. Intake of cruciferous vegetables has been proven to prevent gastric cancer. It has been reported that sulforaphane induces cell cycle arrest and apoptosis in human colorectal cancer cells [14]. Sulforaphane was also suggested to sensitize hepatoma cancer cells to TRAIL-mediated apoptosis by reactive oxygen species (ROS)-mediatedDR5 expression [15]. Our recent studies show that sulforaphane inhibits bladder cancer cell proliferation via suppression of HIF-1α-mediated glycolysis in hypoxia [16]. Moreover, sulforaphane shows a protective effect on gastric mucosa via the Nrf2 mechanism [17]. The effects of MMP-9 inhibitors on the treatment of gastric cancer have been widely reported. However, the potential mechanisms by which sulforaphane inhibits MMP-9 expression are not fully understood in gastric cancer. In this study, we examined the effect of sulforaphane on nicotine-mediated induction of MMP-9 and explored the underlying mechanisms. Based on our results, we reported that sulforaphane suppresses the nicotine-induced MMP-9 by inhibiting ROS-mediated MAPK (p38 MAPK, Erk1/2)/AP-1 and ROS-mediated NF-κB signaling axes, which in turn inhibit cell invasion in human gastric cancer AGS cells. 2. Results 2.1. Sulforaphane Suppresses Nicotine-Induced MMP-9 Expression in AGS Cells To determine the effect of sulforaphane on nicotine-induced MMP-9 expression, AGS cells were pretreated with sulforaphane and treated with nicotine. The expression levels of MMP-9 mRNA and protein were measured, respectively. As shown in Figure 1A,B, treatment with nicotine significantly induced MMP-9 expression at both the mRNA and protein levels, which can be abrogated by sulforaphane. In addition, suppression of nicotine-induced MMP-9 promoter activity was also observed in sulforaphane-pretreated AGS cells (Figure 1C). These results showed that sulforaphane inhibits nicotine-induced MMP-9 expression in human gastric cancer AGS cells. 2.2. Sulforaphane Suppresses Nicotine-Induced MMP-9 Expression by Inhibiting ROS Generation Due to the important role of oxidative stress in the pathogenesis of cancer [18], the ROS production levels were determined by H2DCFDA treated with nicotine in the presence or absence of sulforaphane. As shown in Figure 2A,B, sulforaphane suppressed the nicotine-induced ROS production levels. N-Acetylcysteine (NAC) was used as a positive control. NAC treatment abrogated nicotine-induced MMP-9 expression (Figure 2C). These results indicate that sulforaphane suppressed the nicotine-induced MMP-9 via regulating ROS generation in human gastric cancer AGS cells. 2.3. Sulforaphane Suppresses Nicotine-Induced MMP-9 Expression by Inhibiting p38 MAPK and Erk1/2 Activation MAPKs have well-established roles in the progression of human cancers [19,20]. To determine the role of MAPKs on nicotine-induced MMP-9 expression, pharmacological inhibitors of MAPKs, SB203580 (a p38 MAPK inhibitor), and PD98059 (a MEK inhibitor) were used along with nicotine treatment in AGS cells. As shown in Figure 3A, both SB203580 and PD98059 inhibited the nicotine-induced MMP-9 expression at the transcriptional level. Transfection of dominant-negative mutant constructs mP38 (p38 MAPK) or K97M (MEK-1) attenuated nicotine-induced MMP-9 promoter activity (Figure 3B). Additionally, we found that sulforaphane suppressed nicotine-induced p38 MAPK and Erk1/2 (Figure 3C,D). These results suggest that sulforaphane suppressed nicotine-induced MMP-9 expression by inhibiting p38 MAPK and Erk1/2 activation in AGS cells. 2.4. Sulforaphane Suppresses Nicotine-Induced MMP-9 Expression by Inhibiting Reporter Activities of AP-1 and NF-κB Studies showed that AP-1 plays a pivotal role in tumor carcinogenesis [21]. Curcumin (an AP-1 inhibitor) pretreatment significantly suppressed the nicotine-induction MMP-9 protein expression and promoter activity (Figure 4A,B). Furthermore, sulforaphane treatment resulted in significant inhibition of nicotine-induced c-fos and c-jun phosphorylation. (Figure 4C). Moreover, NF-κB is also a key transcription factor in tumor carcinogenesis [22]. BAY 11-7082 (an NF-κB inhibitor) pretreatment decreased the nicotine-induced MMP-9 protein expression and promoter activity (Figure 4D,E). It is observed that sulforaphane suppressed the nicotine-enhanced phosphorylation of NF-κB and IκBα (Figure 4F). These results demonstrated that sulforaphane inhibited nicotine-induced MMP-9 expression via suppressing AP-1 and NF-κB activation. 2.5. ROS/(p38 MAPK, Erk1/2) and ROS Functioned as Upstream Regulators of AP-1 and NF-κB Respectively To dissect the relevant signaling pathways contributing to AP-1 activation induced by nicotine, we performed inhibitor studies with luciferase activity assay and Western blot. As shown in Figure 5A, SB203580 partially suppressed the AP-1 transcription, while both PD98059 and NAC significantly blocked the AP-1 transcription. Similar results are shown at protein levels (Figure 5B). In addition, the data presented in Figure 5C indicated that the ROS inhibitor, NAC, decreases p38 MAPK and Erk1/2 activation. These results indicate that ROS/(p38 MAPK, Erk1/2) is the upstream regulator of AP-1 in nicotine-induced MMP-9 expression in AGS cells. Next, we examined which relevant regulator contributed to AP-1 activation induced by nicotine. To determine whether the ROS contributed to NF-κB activation induced by nicotine, the effects of an inhibitor of ROS on nicotine-induced NF-κB activation were examined. The inhibitor of ROS, NAC, inhibited nicotine-mediated NF-κB reporter activity (Figure 5D). Pretreatment of NAC attenuated nicotine-media activation of p65 (Figure 5E). These findings supported that ROS functioned as upstream signaling of NF-κB in nicotine-induced MMP-9 expression in AGS cells. 2.6. Sulforaphane Attenuates the Invasiveness of AGS Cells by Suppressing MMP-9 Expression It is well known that high expression of MMP-9 is important for the invasive phenotype of cancer cells. The effect of sulforaphane on nicotine-induced cell invasion was examined by performing a matrigel invasion assay. AGS cells incubated in nicotine resulted in increased activity of the cell invasive phenotype. However, in the presence of sulforaphane or an MMP-9 antibody, the number of invaded cells decreased, suggesting that sulforaphane suppressed the nicotine-induced invasive phenotype by inhibiting MMP-9 expression (Figure 6A). We further counted the invading cells and the cell invasion results showed with statistically significant values that the sulforaphane pretreatment significantly reduced the nicotine-induced cell invasive activity as well as the neutralizer, MMP-9 antibody (Figure 6B). These results further indicated that sulforaphane inhibited the AGS cell invasive activity by downregulating the MMP-9 expression. 3. Discussion Gastric cancer ranks as the fourth most common cancer and is one of the leading causes of cancer-related death worldwide [23]. Phytochemicals, derived from plants, have become an important source of anticancer medicines, with antioxidant activities [24]. Sulforaphane, 1-isothiocyanato-4-(methylsulfinyl)butane, a natural compound that includes the isothiocyanate group of organosulfur compounds, is one of the major phytochemicals found in cruciferous vegetables [25]. Many studies have been directed at defining the role of sulforaphane as an anticancer medicine in humans, due to various reasons. Firstly, cruciferous vegetables, particularly broccoli, are rich in sulforaphane, which can prevent cancer risk [26]. Sulforaphane may protect against various types of cancer. In breast cancer, combination therapy with sulforaphane has been shown to improve the outcome [27]. Sulforaphane can inhibit breast cancer stem cells via downregulation of the Wnt/β-catenin self-renewal pathway in the xenograft mice model [28]. In colorectal cancer, sulforaphane inhibits the stemness of cancer stem cells both in vitro and in vivo by targeting TAp63α [29]. Rutz et al. reported that sulforaphane acts as a histone deacetylase (HDAC) inhibitor to prostate cancer cell progression [30]. In addition, sulforaphane has a potential therapeutic application in the treatment and prevention of gastric cancer by induction of apoptosis of gastric cancer cells [31]. Our earlier studies indicated that sulforaphane decreased glycolytic metabolism in a hypoxia microenvironment by inhibiting hypoxia-induced HIF-1α and HIF-1α trans-localization in non-muscle-invasive bladder cancer cell lines [16]. Moreover, it has been known that sulforaphane has many health benefits. Sulforaphane could prevent memory dysfunction and improve cognitive function [32]. Sulforaphane prevents type 2 diabetes-induced cardiomyopathy by activating the lipid metabolic pathway and enhancing NRF2 activation [33]. Sulforaphane presents anti-inflammation properties by suppression of cyclooxygenase-2 expression [34]. Clinical and epidemiological research has revealed that smokers are more likely to develop cancer progression as compared to non-smokers [35]. Cigarette smoke caused many diseases and cancers, and nicotine is a major poison in cigarette smoke [36]. Nicotine caused more harm to human organs and tissues than other compounds of cigarette smoke [37]. Recently, we demonstrated that nicotine promotes gastrointestinal cancer progression through IL-8 upregulation [38]. Aberrant processes of wound healing contribute to cancer progression [39]. Matrix metalloproteinases (MMPs) have been identified as the main factors in both acute and chronic wounds and the excess protease activity can lead to a chronic nonhealing wound [40]. Reiss et al. reported that when MMP-9 is expressed at excessive levels, it prevents the reestablishment of the dermal/epidermal junction and, thereby, limits epithelial migration and wound closure in a murine wound model [41]. In the present study, we attempted to explore the role and potential mechanisms of sulforaphane in nicotine-challenged gastric cancer cells. We revealed that nicotine induces MMP-9 expression and cell invasiveness in gastric cancer AGS cells. Sulforaphane effectively suppressed ROS, p38 MAPK, Erk1/2, AP-1, and NF-κB activation by inhibiting MMP-9 expression in gastric cancer AGS cells. Healthy bodies and tissues are often subjected to sublethal doses of various oxidants [42]. There is considerable evidence suggesting oxidative stress has been associated with the development of cancer [18]. Increased ROS generation was observed in cancer cells compared with normal cells [43]. ROS function as secondary messengers and control various signaling cascades in cells [44]. Nicotine promotes atherosclerosis by the induction of ROS in endothelial cells [45]. The present study suggested that nicotine induces ROS generation in gastric cancer AGS cells, and NAC abrogated nicotine-induced MMP-9 expression. Sulforaphane suppresses ROS production to inhibit nicotine-induced MMP-9 expression. NADPH oxidases were identified as upstream signal molecules of ROS in AGS cells. NADPH oxidase activation is regulated by several processes such as phosphorylation of its components, exchange of GDP/GTP on Rac2, and binding of p47phox and p40phox to phospholipids [46]. Membrane translocation of p47phox plays a critical role in the activation of NADPH oxidase [47]. Nicotine can trigger the generation of ROS through NADPH oxidase [48]. Sulforaphane decreases ROS and inhibits carcinogenesis by the activation of NRF2 [49]. Sulforaphane was also reported to induce HO-1 in microglia [50]. In T24 bladder cancer cells, sulforaphane upregulates ROS to induce cell apoptosis [51]. Sulforaphane induces ROS generation to promote tumor necrosis factor-related apoptosis-inducing ligand sensitivity [52]. In this respect, the mechanisms involved in sulforaphane inhibited nicotine-activated ROS are revealed in this study. MAPK cascade plays a vital role in various cancer progression [20]. MAPK-regulated MMP-9 in cancer cells has been reported in many studies [7]. Here, nicotine stimulated the phosphorylation of p38 MAPK and Erk1/2 to induce MMP-9 expression in AGS cells. The aberrant activation of EGFR has been implicated in tumor growth [53]. Previously, we observed that EGFR is involved in MMP-9 expression in human endothelial cells [54]. One study reported that Akt and PKCδ are associated with TPA-induced MMP-9 expression [55]. MAPKs are studied as the downstream of PKCα/β [56,57]. Experiments on colorectal tumor cells documented that MAPK signaling may directly depend on ROS [58]. Treatment with sulforaphane significantly reduced the amount of phosphorylated Akt and phosphorylation of the mTOR subunit [59]. In this study, sulforaphane inhibited p38 MAPK and Erk1/2 activation to suppress nicotine-induced MMP-9 expression. Thus, many additional signaling modulators should be explored to define sulforaphane suppression of nicotine-induced MMP-9 expression in AGS gastric cancer cells. Our previous study revealed the important role of AP-1 and NF-κB in regulating MMP-9 by cadmium in endothelial cells [54]. AP-1 is composed of members of the c-fos and c-jun families, which have been shown to regulate the expression of several genes involved in tumor development. Here, enhanced phosphorylation of c-fos and c-jun was observed in nicotine-treated cells. Our results showing that AP-1 inhibitor ameliorated MMP-9 expression indicated that AP-1 contributed to nicotine-mediated induction of MMP-9. Sulforaphane’s inhibition of c-fos and c-jun phosphorylation accompanied by a reduction in AP-1 transcription factor activity, therefore, suppressed MMP-9 expression. To further determine the underlying mechanisms, we treated AGS cells with the inhibitors of ROS, p38 MAPK, and Erk1/2. We found that inhibitors of ROS, p38 MAPK, and Erk1/2 suppressed the nicotine-mediated c-fos and c-jun activation and the AP-1 reporter activity. ROS inhibitor reduced the nicotine-induced activation of p38 MAPK and Erk1/2. Our results indicated that ROS/MAPK (p38 MAPK, Erk1/2) functioned as the upstream signaling molecules in the nicotine-activated AP-1 pathway. Src tyrosine was reported to be upstream of AP-1 [60]. We observed that JNK1/2 mediates AP-1 activation in AGS cells [61]. Increased NF-κB translocation is usually associated with its phosphorylation and IκB proteasomal degradation in many types of cancer progression. In our study, we found that IκBα and p65, the two subunit elements of NF-κB, play vital roles in the nicotine-mediated induction of MMP-9 in AGS cells. It was observed that sulforaphane inhibited the nicotine-induced NF-κB p65 and IκBα in a dose-dependent manner. ROS contribute to the upstream signaling of NF-κB [62]. Our results showed that ROS are the upstream molecules of NF-κB in nicotine-induced MMP-9 in AGS cells. A prior study suggested that the EGFR signaling activates NF-κB via mTORC2 [63]. We found that Erk1/2 and JNK were critical for NF-κB in bladder cancer cells [64]. 4. Conclusion Figure 7 illustrates that sulforaphane suppresses the nicotine-induced MMP-9 by inhibiting ROS-mediated MAPK (Erk1/2, p38 MAPK)/AP-1 and ROS-mediated NF-κB signaling axes, which in turn inhibit cell invasion in human gastric cancer AGS cells. These findings demonstrate that sulforaphane might be a potential functional food ingredient with the feature of gastric cancer therapy. 5. Materials and Methods 5.1. Reagents RPMI-1640, OPTI-modified Eagle’s medium, fetal bovine serum (FBS), phosphate-buffered saline, and penicillin–streptomycin solution were obtained from HyClone (Logan, UT, USA). TrypLE™ Express was obtained from Gibco (Grand Island, NY, USA). Sulforaphane, nicotine, DMSO, curcumin, and all other chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA). BAY11-7082, PD98059, and SB203580 were purchased from Calbiochem (San Diego, CA, USA). Antibodies against MMP-9, phos-Erk1/2, Erk1/2, phos-p38, p38, phos-c-jun, phos-c-fos, phos-p65 (Ser536), phos-IκBα (Ser32), and IκBα were purchased from Cell Signaling Technology (Danvers, MA, USA). 5.2. Cell Culture The AGS human gastric cancer cell line was obtained from American Type Culture Collection (Manassas, VA, USA) and cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 0.6% penicillin–streptomycin at 37 °C in a 5% CO2 humidified incubator. In these experiments, stimulants such as nicotine were added to serum-free media for the indicated time intervals. When the inhibitors were used, they were added 1 h before the nicotine treatment. 5.3. Reverse Transcription PCR AGS cells were treated with 100 µg/mL nicotine for 4 h. Then, total RNA was extracted from the AGS cells using TRIzol reagent (Invitrogen). One µg of total RNA was used for first-strand complementary DNA synthesis using random primers and M-MLV transcriptase (Promega). The complementary DNA was subjected to PCR amplification with primer sets for GAPDH and MMP-9 using a PCR master mix solution (iNtRON, Korea). The specific primer sequences were as follows: GAPDH sense, 5′-TTG TTG CCA TCA ATG ACC CC-3′, and GAPDH antisense, 5′-TGA CAA AGT GGT CGT TGA GG-3′ (836 bp); and MMP-9 sense, 5′- AAG TGG CAC CAC CAC AAC AT -3′ and MMP-9 anti-sense, 5′-TTT CCC ATC AGC ATT GCC GT-3′ (497 bp). The PCR conditions were as follows: denaturation at 94 °C for 30 s, annealing at 52 °C for 20 s, and extension at 72 °C for 30 s, 28 cycles. 5.4. Western Blot Analysis AGS cells were treated with 100 µg/mL nicotine for 12 h to detect the MMP-9 changes and were treated with 100 µg/mL nicotine for 30–60 min to detect the signal molecule changes. After each experiment, cells were washed twice with cold PBS and were harvested in 100 µL of protein extraction solution (iNtRON, Seongnam, Korea). Cell homogenates were centrifuged at 10,000× g for 20 min at 4 °C. Equal amounts of total cellular protein (50 µg) were electrophoresed in sodium dodecyl sulfate (SDS)-polyacrylamide gels, and the protein was then transferred to polyvinylidene difluoride membranes (Millipore, Billerica, MA, USA). Nonspecific binding sites on the membranes were blocked with 5% nonfat dry milk in 15 mM Tris/150 mM NaCl buffer (pH 7.4) at room temperature for 2 h. Membranes were incubated with the target antibody. The membranes were then probed with a secondary antibody labeled with horseradish peroxidase. The bands were visualized using an enhanced chemiluminescence kit (Millipore, Billerica, MA, USA) and were scanned by a luminescence image analyzer (Vilber Lourmat, Collégien, France). 5.5. Transient Transfection with Dominant Negative Mutants The plasmids encoding dominant-negative mutants of MEK-1 (pMCL-K97M) and p38 MAPK (pMCL-mP38) were kindly provided by Dr. N. G. Ahn (University of Colorado, Boulder, CO, USA) and Dr. J. Han (Scripps Research Institute, CA, USA), respectively. All mutants were prepared by using TIANGEN (Beijing, China) plasmid DNA preparation kits. Dominant-negative mutants (1 µg) were carried out using Lipofectamine 3000 from Invitrogen (Carlsbad, CA, USA). 5.6. Measurement of MMP-9, AP-1 and NF-κB Luciferase Activity The transcriptional regulation of MMP-9 was examined by the transient transfection of an MMP-9 promoter–luciferase reporter construct (pGL4-MMP-9). The plasmid pGL4-MMP-9 promoter (spanning nucleotides from −925 to +13) was kindly provided by Dr. Young-Han Lee (Konkuk University, Korea). The NF-κB and AP-1 luciferase reporter plasmid were purchased from Clontech (Palo Alto, CA, USA). The effects of sulforaphane on MMP-9 promoter activity were determined by pretreating cells with sulforaphane for 1 h prior to the nicotine treatment. Cells were collected with cell culture lysis reagent (Promega, Madison, WI, USA) and the luciferase activity was determined using a luminometer (Centro XS lb960 microplate luminometer, Berthold Technologies, Oak Ridge, TN, USA) according to the manufacturer’s protocol. 5.7. Detection of ROS by H2DCFDA ROS production levels were performed by modifying the method described by our previous study [65]. Briefly, H2DCFDA (MCE, Romulu, MI, USA), a cell-permeable probe, was used to detect changes in intracellular ROS produced by AGC cells in the sulforaphane and nicotine treatment group (30 min) and control, which were incubated with H2DCFDA at 37 °C with 5% CO2 for 30 min, digested with trypsin, and suspended in PBS. Images were acquired using the Laser Scanning Microscope 5 PASCAL program (Carl Zeiss) and a confocal microscope. DCF fluorescence was excited at 488 nm with an argon laser, and the evoked emission was filtered with a 515 nm long-pass filter. 5.8. Matrigel Invasion Assay The cell invasion assay was carried out according to our previous study [66] using 10-well chemotaxis chambers (Neuro Probe, Gaithersburg, Maryland, USA) with an 8-µM pore membrane (Neuro Probe) in RPMI-1640 with 10% FBS as the chemoattractant in the lower chamber. Briefly, AGS cells were added to the upper chamber with nicotine for 24 h, the non-invading cells on the upper surface of each membrane were removed from the chamber by using cotton swabs, and the invading cells on the lower surface of each membrane were stained using the Quick-Diff stain kit (Becton-Dickinson, Franklin Lakes, NJ, USA). After two washes with water, the chambers were allowed to air dry. The number of invading cells was counted using a phase-contrast microscope. 5.9. Statistics Analysis Quantitative data were analyzed using a one-way analysis of variance followed by Tukey’s honestly significant difference tests between individual groups. Data are expressed as the mean ± SEM. A value of p < 0.05 was considered to be significant. The statistical software package Prism 5.0 (GraphPad Software, La Jolla, CA, USA) was used for analysis. Author Contributions Conceptualization: S.L. (Shinan Li), S.L. (Sen Lian) and Y.-D.J.; investigation and methodology: S.L. (Shinan Li) and P.N.K.; software and formal analysis: P.N.K., H.Y. and D.K.S.; resources: H.Y. and N.-H.K.; validation: P.N.K. and Y.-D.J.; data curation: S.L. (Shinan Li) and Y.-D.J.; writing—original draft: S.L. (Sen Lian); writing—review and editing: S.L. (Shinan Li) and Y.-D.J.; project administration: Y.-D.J.; funding acquisition: S.L. (Sen Lian) and Y.-D.J. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available in the article. Conflicts of Interest The authors declare that they have no conflict of interest. Figure 1 Sulforaphane inhibits nicotine-induced MMP-9 expression in human gastric AGS cells. AGS cells were pretreated with sulforaphane (10, 30, 50 µM) for 1 h, followed by treatment with 100 µg/mL nicotine for 4 h or 12 h, and MMP-9 expression was analyzed by performing RT-PCR (A), Western blot (B), and luciferase activity assay (C), respectively. The data represent the mean ± SEM from three experimental trials. * p < 0.05 in comparison with the control; ** p < 0.05 in comparison with nicotine alone. Figure 2 Sulforaphane inhibits nicotine-induced ROS in human gastric cancer AGS cells. (A) AGS cells were pretreated with 30 µM sulforaphane and 5 mM NAC for 1 h prior to nicotine treatment for 30 min. The cells were then incubated in the dark for 10 min with 10 µM H2DCFDA. The H2DCFDA fluorescence was imaged using a confocal laser scanning fluorescence microscope. (B) Relative fluorescence intensities of the ROS production level. (C) Cells pretreated with NAC (2.5, 5, 10 mM) for 1 h were incubated with nicotine (100 µg/mL) for 12 h. After incubation, extracted proteins were analyzed for the induction of MMP-9 expression by Western blot. The data represent the mean ± SEM from three experimental trials. * p < 0.05 in comparison with the control; ** p < 0.05 in comparison with nicotine alone. Figure 3 Sulforaphane inhibits nicotine-induced MMP-9 expression by suppressing p38 MAPK and Erk1/2 signaling pathways. (A) AGS cells were pretreated with 20 µM SB203580 and 20 µM PD 98059 for 1 h and incubated with 100 µg/mL nicotine for 4 h. After incubation, extracted mRNA was analyzed for the induction of MMP-9 expression by RT-PCR. (B) AGS cells were cotransfected with dominant-negative mutants of p38 MAPK (mP38) or MEK-1 (K97M) and the pGL4-MMP-9 promoter-reporter construct. The luciferase activity was determined using a luminometer after incubating the cells with 100 µg/mL nicotine for 4 h. (C) Cells were treated with 100 µg/mL nicotine for 0–60 min, and extracted proteins were analyzed by Western blot. (D) Cells were pretreated with sulforaphane (15, 30 µM) followed by 100 µg/mL nicotine treatment for 30 min, and extracted proteins were analyzed by Western blot. The data represent the mean ± SEM from three experimental trials. * p < 0.05 in comparison with the control; ** p < 0.05 in comparison with nicotine alone. Figure 4 Sulforaphane inhibits nicotine-induced MMP-9 expression by suppressing the transcription factors of AP-1 and NF-κB. (A) AGS cells were pretreated with the indicated concentration of curcumin and treated with 100 µg/mL nicotine for 12 h, and extracted proteins were analyzed for the induction of MMP-9 expression by Western blot. (B) Cells were transfected with the AP-1 luciferase reporter. The luciferase activity was determined using a luminometer after incubating the cells with sulforaphane for 1 h prior to nicotine treatment for 4 h. (C) Cells were pretreated with sulforaphane and treated with 100 µg/mL nicotine for 1 h; the expression of phos-c-fos and phos-c-jun were analyzed by Western blot. (D) Cells were pretreated with the indicated concentration of BAY11-7082 and treated with 100 µg/mL nicotine for 12 h, and extracted proteins were analyzed for the induction of MMP-9 expression by Western blot. (E) Cells were transfected with the NF-κB luciferase reporter. The luciferase activity was determined using a luminometer after incubating the cells with sulforaphane for 1 h prior to nicotine treatment for 4 h. (F) Cells were pretreated with sulforaphane and treated with 100 µg/mL nicotine for 1 h, the expression of phos-p65 (Ser536 and phos-IκBα (Ser32) were analyzed by Western blot. The data represent the mean ± SEM from three experimental trials. * p < 0.05 in comparison with the control; ** p < 0.05 in comparison with nicotine alone. Figure 5 ROS/(p38 MAPK, Erk1/2) and ROS functioned as upstream regulators of AP-1 and NF-κB, respectively. (A) AGS cells were transfected with the AP-1 luciferase reporter. The luciferase activity was determined using a luminometer after incubating the cells with 20 µM SB203580, 20 µM PD 98059, or 5 mM NAC for 1 h prior to nicotine treatment for 4 h. (B) AGS cells were pretreated with 20 µM SB203580, 20 µM PD 98059, or 5 mM NAC and treated with 100 µg/mL nicotine for 30 min; the expression of phos-c-fos and phos-c-jun were analyzed by Western blot. (C) Cells were pretreated with the indicated concentration of NAC and treated with 100 µg/mL nicotine for 30 min; the expression of phos-Erk1/2 and phos-p38 were analyzed by Western blot. (D) Cells were transfected with the NF-κB luciferase reporter. The luciferase activity was determined using a luminometer after incubating the cells with NAC for 1 h prior to nicotine treatment for 4 h. (E) Cells were pretreated with the indicated concentration of NAC and treated with 100 µg/mL nicotine for 30 min; the expression of phos-p65 (Ser5360 and phos-IκBα (Ser32) were analyzed by Western blot. The data represent the mean ± SEM from three experimental trials. * p < 0.05 in comparison with the control; ** p < 0.05 in comparison with nicotine alone. Figure 6 Sulforaphane inhibits nicotine-induced cell invasion in AGS cells. (A) AGS cells (3 × 105) were incubated with nicotine (100 µg/mL) in the presence or absence of sulforaphane (30 µM) or anti-MMP-9 antibody (200 ng/mL) in a Corning Matrigel matrix for 24 h. (B) AGS cells (3 × 105) were incubated with nicotine (100 µg/mL) in the presence or absence of nonspecific IgG, anti-MMP-9 antibody (200 ng/mL), or sulforaphane (10, 30, 50 µM) in a Corning Matrigel matrix for 24 h. Cells invading the undersurface of the membrane were stained using a Diff-Quick stain kit and counted under a phase-contrast light microscope. The data represent the mean ± SEM from three experimental trials. * p < 0.05 in comparison with the control; ** p < 0.05 in comparison with nicotine alone. Figure 7 Schematic representation of the mechanism underlying the inhibition of nicotine-induced MMP-9 expression by sulforaphane in AGS cells. Sulforaphane inhibits nicotine-induced MMP-9 expression via suppression of the ROS/MAPKs(p38 MAPK, Erk1/2)/AP-1 and ROS/NF-κB signaling pathways, which in turn attenuate AGS cell invasiveness. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Ganesh S. Sier C. Heerding M.M. Griffioen G. Lamers C. Verspaget H.W. Prognostic relevonce of the plasminogen activation system in tissue of patients with gastric cancer Neth. J. Med. 1995 47 A38 10.1016/0300-2977(95)97043-O 2. Jin X. Zhu Z. Yi S. Metastasis mechanism and gene/protein expression in gastric cancer with distant organs metastasis Bull. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092891 molecules-27-02891 Article Synthesis and Structural Study of Amidrazone Derived Pyrrole-2,5-Dione Derivatives: Potential Anti-Inflammatory Agents https://orcid.org/0000-0003-1217-879X Paprocka Renata 1* Pazderski Leszek 2 Mazur Liliana 3 Wiese-Szadkowska Małgorzata 4 Kutkowska Jolanta 5 Nowak Michalina 4 Helmin-Basa Anna 4* Sabatier Jean-Marc Academic Editor Kouidhi Soumaya Academic Editor 1 Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Jurasza Str. 2, 85-089 Bydgoszcz, Poland 2 Department of Analytical Chemistry and Applied Spectroscopy, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina Str. 7, 87-100 Torun, Poland; leszekp@chem.umk.pl 3 Institute of Chemical Sciences, Faculty of Chemistry, Maria Curie-Skłodowska University, Pl. Marii Curie-Sklodowskiej 2, 20-031 Lublin, Poland; liliana.mazur@mail.umcs.pl 4 Department of Immunology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, M. Curie-Sklodowska Str. 9, 85-094 Bydgoszcz, Poland; mwiese@cm.umk.pl (M.W.-S.); michalina.nowak2442@gmail.com (M.N.) 5 Department of Genetics and Microbiology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Akademicka Str. 19, 20-033 Lublin, Poland; jolanta.kutkowska@mail.umcs.pl * Correspondence: renata.bursa@cm.umk.pl (R.P.); a.helmin-basa@cm.umk.pl (A.H.-B.) 30 4 2022 5 2022 27 9 289131 3 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 1H-pyrrole-2,5-dione derivatives are known for their wide range of pharmacological properties, including anti-inflammatory and antimicrobial activities. This study aimed to synthesize new 3,4-dimethyl-1H-pyrrole-2,5-dione derivatives 2a–2f in the reaction of N3-substituted amidrazones with 2,3-dimethylmaleic anhydride and evaluate their structural and biological properties. Compounds 2a–2f were studied by the 1H-13C NMR two-dimensional techniques (HMQC, HMBC) and single-crystal X-ray diffraction (derivatives 2a and 2d). The anti-inflammatory activity of compounds 2a–2f was examined by both an anti-proliferative study and a production study on the inhibition of pro-inflammatory cytokines (IL-6 and TNF-α) in anti-CD3 antibody- or lipopolysaccharide-stimulated human peripheral blood mononuclear cell (PBMC) cultures. The antibacterial activity of compounds 2a–2f against Staphylococcus aureus, Enterococcus faecalis, Micrococcus luteus, Esherichia coli, Pseudomonas aeruginosa, Yersinia enterocolitica, Mycobacterium smegmatis and Nocardia corralina strains was determined using the broth microdilution method. Structural studies of 2a–2f revealed the presence of distinct Z and E stereoisomers in the solid state and the solution. All compounds significantly inhibited the proliferation of PBMCs in anti-CD3-stimulated cultures. The strongest effect was observed for derivatives 2a–2d. The strongest inhibition of pro-inflammatory cytokine production was observed for the most promising anti-inflammatory compound 2a. amidrazone pyrrole-2,5-dione cyclic imide anti-inflammatory activity antiproliferative activity antibacterial activity This research received no external funding. ==== Body pmc1. Introduction Five-membered heterocyclic nitrogen-containing rings with two carbonyl groups adjacent to the N atom are present in many organic compounds exhibiting various biological activities, including antipsychotic (perospirone), anxiolytic and antidepressant (tandospirone), antiepileptic (ethosuximide), antiproliferative, immunomodulating and antineoplastic (thalidomide, pomalidomide) activities. Similar to many species studied recently, these well-known drugs contain variously substituted 1H-pyrrolidine-2,5-dione rings [1,2]. However, some interest has focused on 1H-pyrrole-2,5-dione derivatives during the last decade. In particular, the latter ring system is present in some anti-inflammatory compounds [3], e.g., those inhibiting lipopolysaccharide (LPS)-induced PGE2 production in RAW 264.7 macrophage cells [4,5] and cyclooxygenases (COX-1 and COX-2 enzymes) [5]. A similar inhibitory activity against COX-1 and COX-2 was exhibited for the N-Mannich bases derived from pyrrolo [3,4-c] pyrrol-1,3-dione [6]. Thereafter, some natural 1H-pyrrole-2,5-dione derivatives, called aquabamycins, were described as antibacterial agents [7], while 3-bromo-1H-pyrrole-2,5-dione and 3,4-dibromo-1H-pyrrole-2,5-dione were described as antifungal and cytotoxic agents [8]. 1H-pyrrole-2,5-diones also play a role in cholesterol absorption as they are HMG-CoA reductase inhibitors [9]. Finally, many N(1)-substituted 1H-pyrrole-2,5-dione derivatives possess anti-fungal and insecticidal (larvicidal) [10], as well as anti-tumor [11] and antiviral [12] activities. The simplest representatives of this class of chemicals are 1H-pyrrole-2,5-dione, also referred to as maleimide (i.e., maleic acid imide) [13,14,15], 3-methyl-1H-pyrrole-2,5-dione [16,17,18,19,20], 3,4-dimethyl-1H-pyrrole-2,5-dione [14,16,21,22], 3,4-diethyl-1H-pyrrole-2,5-dione [23], and 3,4-diphenyl-1H-pyrrole-2,5-dione [21,24,25]. Then, there are their N(1)-methyl derivatives (1-methyl-1H-pyrrole-2,5-dione, 1,3-dimethyl-1H-pyrrole-2,5-dione, 1,3,4-trimethyl-1H-pyrrole-2,5-dione and 1-methyl-3,4-diphenyl-1H-pyrrole-2,5-dione) [16,19,26,27,28,29,30,31,32,33], as well as various N(1)-amino derivatives (containing the moiety of –NH–: 1-phenylamino-, 1-(4-methylphenyl)amino-, 1-(4-methoxyphenyl)amino-, 1-(4-bromophenylamino)-, or of –N <: 1,1-dimethylamino-, 1,1-diphenylamino-, 1-(piperidyn-1-yl)-, 1-(morpholin-4-yl)-, 1-(4-methylpiperazin-1-yl)-, dimeric species consisting of two identical N(1),N(1′)-bonded 1H-pyrrole-2,5-dione ring systems) [34,35,36,37,38] and N(1)-amido (containing the moiety of –NH–CO–: 1-benzamido-, 1-(4-methoxybenzamido)-, 1-(4-bromobenzamido)-, and 1-(4-nitrobenzamido)-, or of –NH–CO–O–: 1-methoxycarbonylamino-) [39,40] analogues which were widely studied by 1H and 13C NMR, and by single crystal X-ray diffraction (see Table S1, Supplementary Materials, part A). In contrast, the analogous N(1)-imino species (containing the –N= moiety) are less known; the 1H and 13C NMR data are available only for two series of variously substituted (in the phenyl ring of the imino substituent) derivatives of 1-((E-4-phenylbut-3-en-2-ylidene)imino)-1H-pyrrole-2,5-dione and 1-((E-4-phenylbut-3-en-2-ylidene)imino)-3-methyl-1H-pyrrole-2,5-dione [10]. Their X-ray structures have never been reported. The aim of this study was the synthesis of six new 3,4-dimethyl-1H-pyrrole-2,5-diones, N(1)-substituted by the imino moieties derived from N3-substituted amidrazones (1a–1f, Figure 1) [41] which have the general formula shown in Figure 2 (2a–2f). Thereafter, the main goal was the investigation of their structural and spectroscopic (1H, 13C NMR) properties in their solid state and solution, together with the evaluation of their biological activity. This was done to gain insight into the influence of the R1 and R2 substituents on the molecular conformation and intermolecular interactions and the anti-inflammatory and antibacterial properties of the compounds. Nitrogen-containing heterocycles, specifically cyclic imides, could be synthesised from amidrazones and dicarboxylic acid anhydrides [42,43]. The uncertainty in using this method is related to the fact that the behaviour of the best known N3-substituted amidrazones in reactions with such cyclic anhydrides is largely dependent on the type of R1 and R2 substituents, the applied anhydride and the reaction conditions. For example, the reactions of N3-substituted amidrazones (also including 1a–1f) with maleic anhydride led to 1,2,4-triazole derivatives [44], whereas those with itaconic anhydride led to either acyclic compounds (in the case of 1b–1f) [45,46] or 1,2,4-triazole derivatives (among others, in the case of 1a–1b and 1d–1f) [47]. In contrast, the reactions of 1d with succinic, trans- and cis-1,2-cyclohexanedicarboxylic, maleic, phthalic, cis-1,2,3,6-tetrahydrophthalic, pyridine-2,3-dicarboxylic and pyridine-3,4-dicarboxylic anhydrides resulted only in acyclic species [48,49]. On the other hand, although a number of heterocycles containing the N(1)-substituted –CO–N(1)–CO– moiety was obtained in the reactions of 1a–1c with cis-1,2-cyclohexanedicarboxylic anhydride, these were derivatives of 1,2-cyclohexanedicarboximide (i.e., hexahydrophtalimide or hexahydroisoindole-1,3-dione), possessing 1H-pyrrolidine-2,5-dione and not the 1H-pyrrole-2,5-dione moiety. Moreover, in the same syntheses, some acyclic compounds (the case of 1b–1d and 1f) and/or 1,2,4-triazole derivatives (the case of 1a and 1d–1e) were also formed, sometimes even simultaneously [42,43]. Hence, the selective preparation of 2a–2f seemed to be a challenge. Nevertheless, it was achieved after performing a series of successful reactions of 1a–1f with 2,3-dimethylmaleic anhydride (3), as shown in Scheme 1. 2. Results and Discussion 2.1. The Syntheses of Compounds 2a–2f In this work, a series of N(1)-substituted derivatives of 3,4-dimethyl-1H-pyrrole-2,5-dione 2a–2f were prepared from the respective N3-substituted amidrazones 1a–1f and 2,3-dimethylmaleic anhydride 3 (Scheme 1). The syntheses of 2a–2c and 2e–2f were carried out in toluene, chloroform or diethyl ether. The best yields (75–95%) were obtained at the boiling points of chloroform or toluene in a much shorter time than at room temperature. The exception was compound 2d, which was obtained only in diethyl ether at room temperature. Possibly the presence of two 2-pyridyl substituents hinders the formation of this product. The detailed dependencies of 2a–2f yields with the solvent, temperature and time are presented in Tables S2–S7 (Supplementary Data, part B). In contrast to our previous results [42,43,44,45,46,47,48,49], compounds 2a–2f account for the first case where N3-substituted amidrazones (1a–1f) react with a cyclic anhydride, exclusively forming 1H-pyrrole-2,5-dione derivatives, independent of the reaction conditions. Thus, one can suppose that 2,3-dimethylmaleic anhydride (3) facilitates just this course of a reaction. In fact, such behaviour is totally different from that observed during the reactions of 1a–1f with maleic anhydride in diethyl ether (at room temperature for 48 h), leading to 3,4-disubstituted 1,2,4-triazol-5-yl β-derivatives of acrylic acid (as proved, for R1 = 2-pyridyl and R2 = 4-nitrophenyl; CSD refcode: QAHPIZ) [44]. It also differs from the one reported for the reaction of 1d with maleic anhydride in toluene (at ambient conditions for 10 min), where the respective N1-acylamidrazone (with R1 = R2 = 2-pyridyl) derivative was formed [48]. The structures of 2a–2f were confirmed by elemental analyses, mass spectra and 1H, 13C NMR spectra with the application of two-dimensional HMQC and HMBC techniques (Supplementary Data, parts C–D, including Figures S25–S48). The 1H-13C NMR correlation spectroscopy allowed us to assign all 1H and 13C signals for each 2a–2f molecule exhibiting the presence of two isomeric forms (denoted generally as A and B; these symbols correspond to the species with the higher and the lower chemical shift of the most deshielded H(8) proton, i.e., NH) in DMSO-d6 solutions. The assigned 1H and 13C NMR chemical shifts for A and B isomers of 2a–2f, compared to those for the parent amidrazones 1a–1f (Supplementary Data, parts C–D, including Figures S1–S24) are summarized in Tables S8 and S9 (Supplementary Data, part E). A detailed description of our attempt to identify and attribute the A and B forms of 2a–2f, based on the comparative analysis of their 1H and 13C NMR spectra in solution and partly on the single-crystal X-ray data for 2a (R1 = R2 = phenyl) and 2d (R1 = R2 = 2-pyridyl), is presented, in the form of comments below Tables S8 and S9. The resulting general conclusion is that A and B are most likely geometric isomers differing in the position of R1 and N(8)H-R2 substituents at the C(7) carbon. The lack of rotation around the N(6)=C(7) double bond probably results in cis-/trans- isomerism: in one stereomer, R1 is trans to N(1), and N(8)H-R2 is cis to N(1), whereas in the other one R1 is cis to N(1) and N(8)H-R2 is trans to N(1). Taking into account the spatial orientation of the N(1) and N(8) atoms, these are Z and E stereomers (Figure 3). The hypothesis of Z/E isomerism is supported by the fact that, in the solid phase of 2a or 2d, where only one stereomer is observed, the crystal structures correspond to such distinct isomeric species: 2a to Z and 2d to E. 2.2. X-ray Crystallography The molecular plots of 2a and 2d with the atom labelling schemes (modification of the general numbering presented in Figure 2) are shown in Figure 4. The selected geometric parameters of 2a and 2d are listed in Table S11 (Supplementary Data, part F) together with those for the previously reported, closely related derivative of hexahydro-2H-isoindole-1,3-dione (CSD refcode: LUZGUJ) [43,50], corresponding to the already mentioned analogue of 2a. The yellow (2a) and orange (2d) prismatic crystals, suitable for diffraction studies, were grown by recrystallization of the originally synthesized compounds from pure ethanol (99.8%) using the standard solvent evaporation technique. The single-crystal X-ray diffraction analysis revealed that both 2a and 2d crystallize in the same centrosymmetric space group P21/c with one molecule in the asymmetric part of the unit cell. Both 2a and 2d have the same –N(6)=C(7)–N(8)H– bond system, as exhibited by the bond lengths proving the presence of the N(6)=C(7) double bond and the C(7)–N(8) single bond (Table S11, Supplementary Data, part F). This conclusion is consistent with the 1H-13C NMR correlation analysis results for all 2a–2f compounds in the DMSO-d6 solutions (paragraph 2.1). However, these two molecules adopt different configurations in the solid state: Z for 2a and E for 2d (Figure 3), as confirmed by the corresponding N(1)−N(6)=C(7)−N(8) torsion angles of −13.0(2)° and 171.5(1)°. The latter value is very close to that found in LUZGUJ (173.1(3)o), which adopted the E geometry in its solid state [43]. The bond lengths in 2a and 2d are comparable, being in good agreement with those in LUZGUJ (Table S11, Supplementary Data, part F). This similarity is mainly observed within the N(1)–N(6)=C(7)–N(8) chain, as exemplified by the clear distinction between the N(1)–N(6) and C(7)–N(8) single bonds versus the N(6)=C(7) double bond. However, the C(7)−C(11) single bond is shorter in 2a and LUZGUJ than in 2d, whereas the N(8)-C(21)bond is longer in 2a than in 2d and LUZGUJ (Table S11, Supplementary Data, part F). On the other hand, in 2a and 2d, one can observe the elongation of the N(1)−N(6) and N(6)=C(7) bonds and the shortening of the C(7)−N(8) bond in comparison to those in the eight previously reported N1-acylamidrazones derived from 1d (PAZDIF [48] and RIBVEG, RICGUI, RICHAP, RICHET, RICHIX, RICHOD, and RICHUJ [49] (Table S12, Supplementary Data, part G). These phenomena are well-exemplified when compared to 2a vs. N1-acylamidrazones (as all have the same Z geometry, see Table S12) or 2d vs. N1-acylamidrazones (as all contain the same R1 = R2 = 2-pyridyl substituents). Thus, the respective bond lengths are as follows (in the order: 2a and 2d vs. N1-acylamidrazones): N(1)−N(6) 1.409(1) Å and 1.419(2) Å vs. N(1)−N(2) 1.371(3)-1.388(3) Å; N(6)=C(7) 1.307(2) Å and 1.301(2) Å vs. N(2)=C(2a) 1.284(3)-1.301(5) Å [48,49], reflecting the above relationships as predominant. Moreover, in both 2a and 2d, the N(6)=C(7) bonds are longer, and the C(7)−N(8) bonds are shorter than the respective standard Nsp2=Csp2 (1.28 Å) and Csp2−NH(−Car) (1.38 Å) bonds [51]. This suggests an extended π-electron delocalization in 2a and 2d molecules and can explain the propensity of all 2a–2f compounds to exist in the solutions as various geometric (Z/E) isomers. The bond angles within the N(1)–N(6)=C(7)–N(8) chain in 2a, 2d and LUZGUJ are largely variable (Table S11, Supplementary Data, part F). From these data, it can be seen that there is a greater similarity between 2d and LUZGUJ (having the same E geometry) than between 2a and LUZGUJ (having the same R1 = R2 = phenyl substituents). Thus, the spatial arrangement of substituents seems to depend mainly on the molecule configuration. On the other hand, an important role is also played by the type of a substituent at N(1), as the differences between the N(1)-N(6)-C(7) and N(6)-C(7)-N(8) bond angles in 2a or 2d and the corresponding ones in already mentioned N1-acylamidrazones derived from 1d (Table S12, Supplementary Data, part F) are even more evident. Generally, both parameters in these N1-acylamidrazones are almost always greater than those in 2a and 2d: N(1)-N(2)-C(2a) 118.1(1)-120.3(3)o vs. N(1)-N(6)-C(7) 113.5(1)o and 112.6o; N(2)-C(2a)-N(3) 125.1(1)-136.9(2)o vs. N(6)-C(7)-N(8) 128.8(1)o and 120.8(1)o, respectively. Hence, the steric crowding of substituents at N(1), N(6) and C(7) causes a change in the valence angles around these atoms. The 3,4-dimethyl-1H-pyrrole-2,5-dione ring system in 2a and 2d is essentially planar but with slight distortions, as revealed by the N(1) atom displacement from the N(1)>>C(5) best plane (0.033 Å in 2a and 0.047 Å in 2d) and the torsion angles inside the pyrrole ring varying from −5.2(1)o to 6.0(1)o (2a) and from −7.7(2)o to 8.1(2)o (2d) (Table S11, Supplementary Data, part F). The bond lengths and angles in the 3,4-dimethyl-1H-pyrrole-2,5-dione moiety of 2a and 2d are typical of this ring system; for comparison, seethe mean N(1)-C(2)/C(5) and C(2)-O(1)/C(5)-O(2) bond lengths, as well as the C(2)-N(1)-C(5) bond angles with those in other 1H-pyrrole-2,5-dione derivatives [15,24,35,37,39,40] (Table S13 Supplementary Data, part F). The formal sp2 hybridization of N(1) in 2a results in near co-planarity of the N(6) atom with the 3,4-dimethyl-1H-pyrrole-2,5-dione ring, as revealed by only a slight N(6) displacement from the N(1)>>C(5) best plane, being 0.059 Å. In contrast, the same parameter in 2d is much greater, being as much as 0.380 Å due to the partial sp3 N(1) hybridization. This difference between N(1) atoms in both compounds is also reflected by the sum of bond angles around this atom, which in 2a is 359.2(1)o, whereas in 2d, it is only 353.4(1)o. The steric crowding of the 3,4-dimethyl-1H-pyrrole-2,5-dione ring system and the R2 substituent, as observed in 2a, results in significant conformational adjustment by the simultaneous rotation around the N(1)−N(6), C(7)−N(8) and N(8)−C(21) single bonds. In consequence, the 1H-pyrrole-2,5-dione ring in 2a is significantly twisted with respect to the N(6)−C(7)−N(8) moiety, while in 2d, it is almost perpendicular, as shown by the dihedral angle between the N(1) >> C(5) best plane and the N(6)−C(7)−N(8) plane, being 64.8° for 2a and 85.6° for 2d. Similarly, in 2a, the R1 and R2 substituents are noticeably twisted with respect to the N(6)−C(7)−N(8) moiety, as shown by the dihedral angles between the C(11) >> C(16) best plane or the C(21) >> C(26) best plane and the N(6)−C(7)−N(8) plane, being 29.5° and 61.4°, respectively. In 2d, the R1 substituent is even more twisted, but the R2 one is much less twisted, as revealed by the relevant dihedral angles of 74.3o and 10.2o. Therefore, the great level of co-planarity of the C(21) >> C(26) and N(6)−C(7)−N(8) moieties in 2d enables the formation of the intramolecular C(26)−H(26)⋅⋅⋅N(6) hydrogen bond (d(H⋅⋅⋅N) = 2.23 Å, <(C−H⋅⋅⋅N) = 121°) (Figure 4, Table S14, Supplementary Data, part F), resulting in the S(6) ring motif [52]. Finally, in both 2a and 2d, the phenyl or 2-pyridyl substituents are almost perpendicular to each other, as shown by the dihedral angle between the C(11) << C(16) and the C(21) << C(26) best planes, which are 88.9° and 81.3°, respectively. The studied molecules are proton-deficient, as each possesses one HB donor (N(8)−H(8)) and three or five potential HB acceptors (O(1), O(2) and N(6), as well as N(12) and N(22), optionally). The presence of numerous acceptor atoms, aromatic rings and ‘active’ methyl groups stimulates the formation of weak hydrogen bonds. Among the intermolecular interactions involved in the stabilization of 2a and 2d crystals, a number of weak C−H⋅⋅⋅O/N/π hydrogen bonds (their full list, including geometric parameters and the symmetry codes, together with the selected C−H⋅⋅⋅C short contacts, is presented in Table S14 (Supplementary Data, part F)) and dipolar C=O⋅⋅⋅C contacts play an important role. Generally, it must be noted that some substantial differences in molecular packing occur between 2a and 2d (Figure 5 and Figure 6). In 2a, the primary supramolecular motif is hydrogen-bonded chains (Figure 5) parallel to the b axis. Within each chain, the adjacent 21-axis-related molecules are connected by strong, directional N(8)−H(8)···O(2) (−x + 1, y − 1/2, −z + ½) hydrogen bonds (Table S14, Supplementary Data, part F); the additional stabilization of the chain motif is provided by weak C(26)−H(26)···N(6) (−x + 1, y − 1/2, −z + 1/2) and C(22)−H(22)···O(1) (−x + 1, y + 1/2, −z + 1/2) hydrogen bonds. The neighbouring, inversion-related and hence antiparallel chains are connected by weak C(9)−H(9b)···O(1) (−x + 1, −y, −z) and C(10)−H(10b)···N(6) (−x + 1, −y + 1, −z) hydrogen bonds, resulting in a three-dimensional architecture. It is noteworthy that apart from the already-mentioned strong N−H···O hydrogen bond, the O(2) atom is also engaged in short C=O···Car and dipolar C=O···C=O interactions (Figure 5a). Taking into account the geometry of these contacts (dO(2)…C(2#) = 2.974(2) Å, θC(5)-O(2)…C(2#) = 146.5°, −x + 1, y + 1/2, −z + 1/2; dO(2)…C(5##) = 3.048(2) Å, θC(5)-O(2)…C(5##) = 89.6°, -x + 1, -y + 1, -z), the former can be classified as the ‘edge-on’ C=O···π interactions [53], while the latter represents a classic example of the antiparallel carbonyl−carbonyl contacts [54]. The main forces promoting the self-assembly of molecules in the crystal lattice of 2d seem to result from hydrogen bonding involving amine and pyridine functions (Figure 6). The presence of the additional N(22) acceptor atom and the E configuration enables the adjacent, inversion-related molecules to interact by strong, relatively short (dH(8)…N(22) = 2.24(2) Å) N(8)−H(8)···N(22) (−x + 1, −y, −z) hydrogen bonds (Table S14, Supplementary Data, part F), creating the R22 (8) ring motif. The additional stabilization of the resulting dimers is provided via the C−H···π contacts involving the highly polarized C(23)−H(23) group and the pyridyl C(11) << C(16) ring (−x + 1, −y, −z). The directionality of this contact with the C−H vector oriented towards the centre of the aromatic ring and all H···C/N distances below the sum of the van der Waals radii of the respective atoms are worth noting. The interactions linking the dimers into the three-dimensional supramolecular net are numerous weak C−H⋅⋅⋅O/N/π hydrogen bonds (Figure 6b), π-stacking contacts between the overlapping C(21) << C(26) pyridyl rings, and electrostatic C=O⋅⋅⋅π interactions involving the 1H-pyrrole-2,5-dione system. 2.3. Toxic Activity of 2a–2f The effect of different concentrations of 2a–2f or ibuprofen (as a reference drug) on the viability of PBMCs in 24 h cell culture was studied. Compounds 2a–2f and ibuprofen induced no apoptosis or necrosis of the analyzed cells at low (10 µg/mL) or medium (50 µg/mL) concentrations (data not shown). However, in the highest dose (100 µg/mL), 2a and 2f appeared to be slightly toxic (79% and 64% of viable cells, respectively), as shown in Figure S49 (Supplementary Data, part G). 2.4. Anti-Inflammatory Activity of 2a–2f 2.4.1. Antiproliferative Activity of 2a–2f The effect of different concentrations of 2a–2f or ibuprofen on soluble anti-CD3 antibody-induced PBMC proliferation in 72 h cell culture is shown in Figure 7. Generally, all compounds 2a-2f inhibited this process (except for 2c in the lowest 10 µg/mL dose). Derivative 2d significantly suppressed PBMC proliferation in each dose (39–77% of inhibition compared to 18–39% for ibuprofen). Significant differences were obtained for compounds 2a–2c in the selected concentrations, while derivatives 2e and 2f inhibited PBMC proliferation only in the medium dose. The strongest inhibitory effect was observed for 2c in the highest 100 µg/mL concentration (85% inhibition). 2.4.2. The Effects of Compounds 2a–2f on Pro-Inflammatory and Anti-Inflammatory Cytokine Production The effect of different concentrations of 2a–2f or ibuprofen (as a reference compound) on the LPS-induced production of pro-inflammatory (IL-6 and TNF-α) and anti-inflammatory (IL-10) cytokines in 24 h PBMC culture is presented in Figure 8, Figure 9 and Figure 10. LPS is an endotoxin of Gram-negative bacteria, used extensively for inducing an immune response in vitro. It promotes cytokine production in PBMC cultures, including pro-inflammatory TNF-α and IL-6 and anti-inflammatory IL-10 [33]. TNF-α is the early pro-inflammatory cytokine produced by monocytes, macrophages and lymphocytes in response to inflammatory stimuli, which, together with IL-6, has a broad spectrum of action. Production of TNF-α and IL-6 induces basic symptoms of inflammation such as heat, swelling, redness and pain. In contrast, IL-10, also produced by monocytes, macrophages and lymphocytes (especially type 2 T helper cells, regulatory T and B cells), has anti-inflammatory properties, and LPS could also mediate its production. The strongest inhibition of pro-inflammatory IL-6 production in LPS-stimulated PBMC culture (Figure 8) was also observed for 2a in the highest 100 µg/mL dose (64% of inhibition compared to 11% for ibuprofen). At this concentration, 2b and 2c exhibited a tendency to inhibit IL-6 production (by 28% and 18%, respectively). In regards to pro-inflammatory TNF-α production (Figure 9), a strong inhibitory effect in LPS-stimulated PBMC culture was observed for 2a, only in the highest 100 µg/mL dose (65% inhibition, in comparison to 6% for ibuprofen). In contrast, 2c produced a 19% inhibition of TNF-α, while 2b and 2d–f revealed only small or even negligible impacts in all doses compared to LPS alone or ibuprofen. Finally, we observed a significant inhibition of anti-inflammatory IL-10 production (Figure 10) for derivatives 2a–2c, 2e and 2f in medium (50 µg/mL) or their highest (100 µg/mL) doses (76–92% and 71–95% inhibition, in comparison to 57% and 77% for ibuprofen). However, compound 2d showed a similar inhibitory profile to ibuprofen (42 and 75% inhibition, respectively). All tested derivatives and ibuprofen elevated IL-10 production in the lowest concentration. 2.5. Antibacterial Activity of 2a–2f The results of MIC determination, presented in Table S15 (Supplementary Data, part H), exhibited the best antibacterial activity for 2a and 2c against Staphylococcus aureus, as well as for 2d against Yersinia enterocolitica (all MICs = 128 µg/mL). Moreover, 2b inhibited the growth of S. aureus, 2c inhibited Y. enterocolitica and M. smegmatis, and 2d inhibited Escherichia coli and S. aureus (all MICs = 256 µg/mL). In contrast, 2e and 2f had no impact on any studied strains. 3. Materials and Methods 3.1. General Information 1H and 13C NMR spectra (including 13C DEPT and 1H-13C HMQC and HMBC) were recorded by a Bruker Avance III 400 MHz NMR spectrometer 295–300 K (Bruker Corporation, Billerica, MA, USA) in DMSO-d6. Melting points were measured with the MEL-Temp apparatus (Electrothermal, Stone, UK). Mass spectra were collected on an LCQ Adventage Max (Thermo Finnigan, San Jose, CA, USA). The 1H and 13C chemical shifts were referenced to TMS, with residual 1H and 13C solvent signals as primary references (DMSO-d6: 2.50 ppm and 40.0 ppm, respectively). Elemental analyses were performed on a CHN Vario MACRO analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany). The retention factors were determined in reverse-faced plates (nano-silica gel RP-18W on alu foil with fluorescent indicator, (Merck, Darmstadt, Germany)) using a methanol-water (1:1) mixture as eluent. 3.2. General Method of Syntheses N3-substituted amidrazones 1a–1f were obtained using previously described procedures [41]. A mixture of 1 mmol of 1a–1f and 1 mmol (0.126 g) 2,3-dimethylmaleic anhydride 3 was dissolved in 25 mL of toluene, chloroform or diethyl ether and left for 2–21 days (method A) or dissolved in 25 mL of toluene or chloroform and heated at their respective boiling points for 5 h (method B). The formed 2a–2f solids were collected by filtration at room temperature and purified by crystallization from ethanol. The detailed reaction conditions (solvent, temperature, time) are given in Tables S2–S7 (Supplementary Data, part B). Products 2a–2f are characterised below. The 1H and 13C NMR signals are listed as read from one-dimensional spectra, i.e., with no separation of the overlapping resonances and with only the most obvious proton assignments (all others were done further based on the analysis of two-dimensional 1H-13C HMQC and HMBC spectra; for details see Supplementary Data, parts D-E, including Tables S8 and S9). The NMR spectra of all types for 2a–2f are reproduced in Figures S25–S48 (Supplementary Data, part C). N’-(3,4-dimethyl-2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-phenylbenzene-carboximidamide (2a)—yellow crystals, yield: 91%, m.p. 152–154 °C. 1H NMR (DMSO-d6, 400 MHz): δ 9.54 (s, 0.6H)-NH, 9.23 (s, 0.4H)-NH, 6.74–7.80 (m, 10H)-all phenyl protons in R1, R2, 1.87 (s, 2.4H)-CH3, 1.76 (s, 3.6H)-CH3. 13C NMR (DMSO-d6, 400 MHz): δ 169.4, 168.9, 168.5, 162.8, 140.6, 140.4, 136.2, 135.8, 133.8, 133.5, 131.2, 130.3, 129.6, 3 × 128.9, 128.7, 127.5, 124.0, 123.4, 123.5, 121.0, 9.0, 8.9. Anal. Calcd. for C19H17N3O2: C, 71.46; H, 5.37; N, 13.16%. Found: C, 71.50; H, 5.35; N, 12.97%. MS (m/z): 319; Rf = 0.29 (methanol:water 1:1). N’-(3,4-dimethyl-2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-phenylpyridine-2-carboximidamide (2b)—yellow crystals, yield: 95%, m.p. 177–179 °C. 1H NMR (DMSO-d6, 400 MHz): δ 9.57 (s, 0.4H)-NH, 9.50 (s, 0.6H)-NH, 6.76–8.61 (m, 9H)-all 2-pyridyl/phenyl protons in R1, R2, 1.77 (s, 2.4H)-CH3, 1.73 (s, 3.6H)-CH3. 13C NMR (DMSO-d6, 400 MHz): δ 169.2, 168.2, 165.5, 158.5, 151.6, 151.1, 149.7, 149.0, 140.4, 139.2, 137.8, 137.2, 135.9, 135.7, 129.0, 128.4, 126.2, 125.3, 124.7, 124.4, 124.0, 123.5, 123.4, 120.9, 8.9, 8.8. Anal. Calcd. for C18H16N4O2: C, 67.49; H, 5.03; N, 17.49%. Found: C, 67.31; H, 5.17; N, 17.40%. MS (m/z): 320; Rf = 0.34 (methanol:water 1:1). N’-(3,4-dimethyl-2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-phenylpyridine-4-carboximidamide (2c)—yellow crystals, yield: 92%, m.p. 208–211 °C. 1H NMR (DMSO-d6, 400 MHz): δ 9.71 (s, 0.6H)-NH, 9.46 (s, 0.4H)-NH, 6.76–8.65 (m, 9H)-all 4-pyridyl/phenyl protons in R1, R2, 1.87 (s, 2.4H)-CH3, 1.77 (s, 3.6H)-CH3. 13C NMR (DMSO-d6, 400 MHz): δ 169.3, 168.6, 166.2, 160.9, 150.4, 150.2, 2 × 141.3, 140.2, 139.6, 136.4, 136.2, 2 × 129.1, 124.6, 123.9, 2 × 123.7, 122.2, 121.0, 9.0, 8.9. Anal. Calcd. for C18H16N4O2: C, 67.49; H, 5.03; N 17.49%. Found: C, 67.78; H, 4.95; N, 17.44%. MS (m/z): 320; Rf = 0.32 (methanol:water 1:1). N’-(3,4-dimethyl-2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-(pyridin-2-yl)pyridine-2-carboximidamide (2d)—orange crystals, yield: 67%, m.p. 180–183 °C. 1H NMR (DMSO-d6, 400 MHz): δ 9.95 (s, 0.2H)-NH, 9.84 (s, 0.8H)-NH, 6.80–8.58 (m, 8H)-all 2-pyridyl protons in R1, R2, 1.88 (s, 4.8H)-CH3, 1.78 (s, 1.2H)-CH3. 13C NMR (DMSO-d6, 400 MHz): δ 168.9, 168.4, 164.8, 160.3, 154.0, 152.7, 152.4, 150.7, 149.7, 2 × 148.6, 147.6, 2 × 137.8, 2 × 137.4, 136.4, 136.2, 125.5, 125.0, 2 × 124.3, 119.4, 118.6, 115.3, 115.2, 2 × 9.0. Anal. Calcd. for C17H15N5O2: C, 63.54; H, 4.71; N, 21.79%. Found: C, 63.52; H, 4.70; N, 21.86%. MS (m/z): 321; Rf = 0.39 (methanol:water 1:1). N’-(3,4-dimethyl-2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-(4-methylphenyl)pyridine-2-carboximidamide (2e)—yellow crystals, yield: 84%, m.p. 199–201 °C. 1H NMR (DMSO-d6, 400 MHz): δ 9.47 (s, 0.2H)-NH, 9.42 (s, 0.8H)-NH, 6.65–8.59 (m, 8H)-all 2-pyridyl/phenyl protons in R1, R2, 2.26 (s, 0.6H)-CH3, 2.18 (s, 2.4H)-CH3, 1.76 (s, 1.2H)-CH3, 1.72 (s, 4.8H)-CH3. 13C NMR (DMSO-d6, 400 MHz): δ 169.2, 168.6, 165.4, 158.6, 151.7, 151.1, 149.6, 148.9, 137.9, 137.8, 137.2, 136.4, 135.9, 135.6, 134.3, 132.4, 129.4, 128.9, 126.1, 125.2, 124.7, 123.9, 123.4, 120.9, 20.9, 20.8, 8.9, 8.7. Anal. Calcd. for C19H18N4O2: C, 68.25; H, 5.43; N, 16.76%. Found: C, 68.58; H, 5.52; N, 16.78%. MS (m/z): 334; Rf = 0.28 (methanol:water 1:1). N’-(3,4-dimethyl-2,5-dioxo-2,5-dihydro-1H-pyrrol-1-yl)-N-(4-nitrophenyl)pyridine-2-carboximidamide (2f)—yellow crystals, yield: 70%, m.p. 220–224 °C. 1H NMR (DMSO-d6, 400 MHz): δ 10.26 (s, 0.6H)-NH, 9.94 (s, 0.4H)-NH, 6.79–8.66 (m, 8H)-all 2-pyridyl/phenyl protons in R1, R2, 1.89 (2.4H)-CH3, 1.80 (3.6H)-CH3. 13C NMR (DMSO-d6, 400 MHz): δ 168.8, 168.2, 165.2, 159.4, 150.8, 150.7, 149.9, 149.4, 147.0, 146.5, 142.3, 142.2, 138.1, 137.4, 136.7, 136.3, 126.5, 125.7, 125.3, 125.1, 124.7, 123.7, 121.4, 120.4, 9.0, 8.6. Anal. Calcd. for C18H15N5O4: C, 59.18; H, 4.14; N, 19.17%. Found: C, 59.22; H, 4.54; N, 19.16%. MS (m/z): 365; Rf = 0.24 (methanol:water 1:1). 3.3. Crystal Structure Determination Single-crystal X-ray diffraction data for 2a and 2d were collected using the Oxford Diffraction Xcalibur CCD diffractometer with the graphite-monochromated MoKα radiation (λ= 0.7107 Å). The standard data collection temperature was 100 K, which was maintained using the Oxford Cryosystems nitrogen gas-flow device (Cobra Plus). The CRYSALIS [55] suite of programs was used for data collection, cell refinement and data reduction. A multi-scan absorption correction was applied. The structures were solved by direct methods implemented in SHELXS-97 [56] and refined with the SHELXL-97 program [56] (both operating with WinGX) [57]. All non-H atoms were refined with the anisotropic displacement parameters. The H atoms attached to carbon were positioned geometrically and refined using the riding model with Uiso(H) = 1.2−1.5 Ueq(C). The amine H(8) atoms were found in the Fourier maps and refined with the isotropic displacement parameters. CCDC 2,059,216 (2a) and 2,059,217 (2d) contain the supplementary crystallographic data for this paper. A copy of the data can be obtained free of charge via http://www.ccdc.cam.ac.uk/conts/retrieving.html (accessed on 30 March 2022) or upon application to CCDC, 12 Union Road, Cambridge CB21EZ, UK (fax: +44 1223-336-033; e-mail: deposit@ccdc.cam.ac.uk). Crystal data for 2a (C19H17N3O2, M = 319.36 g⋅mol−1): monoclinic, space group P21/c, a = 10.2634(4) Å, b = 10.2227(4) Å, c = 15.6644(6) Å, β = 98.593(3)°, V = 1625.1(1) Å3, Z = 4, Dcalc = 1.305 g⋅cm−3, μ = 0.087 mm−1, 11,798 refl. measured (2.63 ≤ θ ≤ 27.48°), 3726 unique (Rint = 0.0379), GOF = 1.022. The final R1 = 0.0418 (I > 2σ(I)) and wR2 = 0.1032 (all data). Crystal data for 2d (C17H15N5O2, M = 321.34 g⋅mol−1): monoclinic, space group P21/c, a = 16.4300(9) Å, b = 11.2891(5) Å, c = 8.3230(4) Å, β = 92.472(4)°, V = 1542.3(1) Å3, Z = 4, Dcalc = 1.384 g⋅cm−3, μ = 0.095 mm−1, 13,934 refl. measured (3.04 ≤ θ ≤ 27.48°), 3542 unique (Rint = 0.0425), GOF = 1.029. The final R1 = 0.0444 (I > 2σ(I)) and wR2 = 0.1044 (all data). 3.4. Peripheral Blood Mononuclear Cell Preparation After informed consent, fresh blood (18 mL) was obtained from five healthy donors at the Occupational Medicine Clinic located in Dr. Antoni Jurasz University Hospital in Bydgoszcz, Poland. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation (Lymphosep, BioWest, Nuaille, France). The cells were then washed twice in phosphate-buffered saline (PBS, Biomed Lublin, Poland) and re-suspended in PBS (10–20 cell/mL) or RPMI 1640 medium (Biomed Lublin, Lublin, Poland) supplied with 5% pooled, heat-inactivated AB Rh+ human serum (1 × 106 cell/mL). After isolation, trypan blue assessed cell viability, which was above 90%. The 2a–2f compounds and racemic ibuprofen (Sigma-Aldrich, Burlington, MA, USA) were initially dissolved in dimethyl sulfoxide (DMSO, Sigma-Aldrich), then in a culture medium to obtain concentrations of 10, 50 and 100 μg/mL. The maximum concentration of DMSO in the individual assay was <0.5% and demonstrated no cell lethality. 3.5. In Vitro Toxic Effects on PBMCs by APC Annexin V and Propidium Iodide Staining Assay and Flow Cytometry The effects of compounds 2a–2f on cell viability were studied in PBMCs culture by flow cytometry. The cells (1 × 106 cells/mL) were seeded in 24-well polypropylene, non-adherent plates (Cytogen, Zgierz, Poland). After that, increasing amounts of 2a–2f in DMSO were added to the cells and incubated for 24 h at 37 °C at 5% CO2 conditions. The final concentrations of 2a–2f were 10, 50 and 100 µg/mL. Control samples contained DMSO or ibuprofen. After stimulation, the tubes were centrifuged at 400 g at 4 °C for 5 min and washed once with PBS. Then, the cells were stained with allophycocyanin-conjugated Annexin V (APC Annexin V) and propidium iodide (PI) (both from BD Pharmingen, San Diego, CA, USA) in accordance with the manufacturer’s manual. A total of 10,000 cells were acquired on an FACSCanto II flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA) and analyzed with FlowJo software (v 7.6.1, Tree Star, Ashland, OR, USA). 3.6. Anti-Inflammatory Activity 3.6.1. In Vitro Antiproliferative Effects by VPD-450 Staining Assay and Flow Cytometry Antiproliferative effects were examined by flow cytometry in BD Horizon Violet Proliferation Dye 450 (VPD450, BD Pharmingen)-labeled PBMCs. Flow cytometry assay was employed to find the cytotoxic potential of compounds 2a–2f on the proliferation of soluble anti-human CD3 monoclonal antibody (mouse IgG2a, clone OKT3, Sigma-Aldrich)-induced PBMCs. Briefly, freshly isolated PBMCs at a concentration of 10–20 × 106 cells/mL in PBS were labeled for 11 min with VPD450 (1uM) at 37 °C. The VPD450 labeling reaction was terminated with complete media containing 10% fetal bovine serum (FBS) and then re-suspended at a 1 × 106 cells/mL concentration in 5% FBS/RPMI1640. VPD450-stained cells were cultured in conical polypropylene tubes (BD Bioscience) for 72 h in 37 °C at 5% CO2 atmosphere with anti-CD3 (1 µg/mL, positive control) and/or increasing concentration of 2a–2f in DMSO (10, 50 and 100 μg/mL). Control samples contained DMSO or ibuprofen. The culture tubes were centrifuged at 400× g at RT for 5 min, washed once in PBS, and 10,000 cells from every sample were acquired on a FACSCanto II flow cytometer (Becton Dickinson) and analyzed with FlowJo software (v 7.6.1, Tree Star, Ashland, OR, USA). 3.6.2. In Vitro Anti- and Proinflammatory Cytokine Production Effect by the Enzyme-Linked Immunosorbent Assay (ELISA) The assay was conducted as described earlier [45]. PBMCs were cultured with lipopolysaccharide (LPS, from E. coli, O55:B5, (Sigma-Aldrich), 1 μg/mL, positive control) and/or increasing concentrations of 2a–2f compounds in DMSO (10, 50 and 100 μg/mL) for 24 h in 24-well polypropylene, non-adherent plates (Cytogen). Control cultures contained DMSO or ibuprofen. According to the manufacturer’s instructions, the cytokine levels (TNF-α, IL-6 and IL-10) were measured by means of commercially available ELISA kits (DuoSet, BD Bioscience). The samples were analyzed with iEMS Reader MF (Labsystems, Vantaa, Finland). The contents of analyzed cytokines were calculated by Genesis version 2.2 software. 3.7. Antibacterial Activity The broth microdilution method determined the minimum inhibitory concentration (MIC), defined as the lowest concentration of the compounds 2a–2f that inhibited bacterial growth. The strains used in the study: Staphylococcus aureus ATCC 25923, Enterococcus faecalis ATCC 29212, Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853 came from the American Type Culture Collection (Manassas, VA, USA) and are the recommended reference strains for antibiotic susceptibility testing. Other strains, including Micrococcus luteus, Yersinia enterocolitica O3, Mycobacterium smegmatis and Nocardia corallina (currently Rhodococcus sp.), came from environmental sources, are deposited in the Department of Genetics and Microbiology collection, and have been used by us in previously published experiments [45,46]. Compounds 2a–2f were dissolved in DMSO, diluted tenfold in Mueller–Hinton broth (MHB) to the concentration of 1.024 mg/mL, and then serially diluted in MHB to concentrations ranging from 512 µg/mL to 0.25 µg/mL. The wells were inoculated with bacterial cultures to the final concentration of 104 colony-forming units (CFU) per mL. Bacterial growth was assayed by measuring optical density at OD 550 nm after 18 h incubation at 37 °C. The wells containing only MHB and 2.5% dimethyl sulfoxide were applied as a negative control. All MIC determinations were carried out in triplicates. 3.8. Data Analysis Data were analyzed in Statistica 13.3 software (StatSoft, Cracow, Poland) and graphed in Excel 2016 (Microsoft, Redmond, WA, USA). All p-values represent the nonparametric Mann–Whitney U test. 4. Conclusions Six new 1H-pyrrole-2,5-dione derivatives 2a–2f were selectively obtained in reactions of various N3-substituted amidrazones with 2,3-dimethylmaleic anhydride. In contrast to the previous results, no linear or 1,2,4-triazole products or by-products were formed. The comparative analysis of the 1H-13C NMR spectra of 2a–2f to those for the parent amidrazones 1a–1f demonstrated that they appeared in DMSO-d6 as a mixture of distinct A and B forms, being most likely geometric Z and E isomers, respectively. This is consistent with the results of single-crystal X-ray diffraction studies of 2a and 2d, which revealed the respective Z and E isomers in their solid phase. All studied compounds possess anti-inflammatory properties by inhibiting PBMC proliferation (especially 2c and 2d) as well as TNF-α and IL-6 production (only 2a). Additionally, 2a and 2c exhibit antibacterial activity, particularly against S. aureus. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules27092891/s1. A. Table S1: 1H, 13C NMR and single-crystal X-ray data for selected derivatives of 1H-pyrrole-2,5-dione. B. Tables S2–S7: Details of syntheses of 2a–2f. C. 1H and 13C NMR data of 1a–1f and 2a–2f (A, B isomers). Figure S1. 1H NMR spectrum of 1a (in DMSO-d6). Figure S2. 13C NMR spectrum of 1a (in DMSO-d6). Figure S3. 1H-13C HMQC spectrum of 1a (in DMSO-d6). Figure S4. 1H-13C HMBC spectrum of 1a (in DMSO-d6). Figure S5. 1H NMR spectrum of 1b (in DMSO-d6). Figure S6. 13C NMR spectrum of 1b (in DMSO-d6). Figure S7. 1H-13C HMQC spectrum of 1b (in DMSO-d6). Figure S8. 1H-13C HMBC spectrum of 1b (in DMSO-d6). Figure S9. 13C NMR spectrum of 1c (in DMSO-d6). Figure S10. 1H-13C HMQC spectrum of 1c (in DMSO-d6). Figure S11. 1H-13C HMBC spectrum of 1c (in DMSO-d6). Figure S12. 1H-13C HMBC spectrum of 1c (in DMSO-d6). Figure S13. 1H NMR spectrum of 1d (in DMSO-d6). Figure S14. 13C NMR spectrum of 1d (in DMSO-d6). Figure S15. 1H-13C HMQC spectrum of 1d (in DMSO-d6). Figure S16. 1H-13C HMBC spectrum of 1d (in DMSO-d6). Figure S17. 1H NMR spectrum of 1e (in DMSO-d6). Figure S18. 13C NMR spectrum of 1e (in DMSO-d6). Figure S19. 1H-13C HMQC spectrum of 1e (in DMSO-d6). Figure S20. 1H-13C HMBC spectrum of 1e (in DMSO-d6). Figure S21. 1H NMR spectrum of 1f (in DMSO-d6). Figure S22. 13C NMR spectrum of 1f (in DMSO-d6). Figure S23. 1H-13C HMQC spectrum of 1f (in DMSO-d6). Figure S24. 1H-13C HMBC spectrum of 1f (in DMSO-d6). Figure S25. 1H NMR spectrum of 2a (in DMSO-d6). Figure S26. 13C NMR spectrum of 2a (in DMSO-d6). Figure S27. 1H-13C HMQC spectrum of 2a (in DMSO-d6). Figure S28. 1H-13C HMBC spectrum of 2a (in DMSO-d6). Figure S29. 1H NMR spectrum of 2b (in DMSO-d6). Figure S30. 13C NMR spectrum of 2b (in DMSO-d6). Figure S31. 1H-13C HMQC spectrum of 2b (in DMSO-d6). Figure S32. 1H-13C HMBC spectrum of 2b (in DMSO-d6). Figure S33. 1H NMR spectrum of 2c (in DMSO-d6). Figure S34. 13C NMR spectrum of 2c (in DMSO-d6). Figure S35. 1H-13C HMQC spectrum of 2c (in DMSO-d6). Figure S36. 1H-13C HMBC spectrum of 2c (in DMSO-d6). Figure S37. 1H NMR spectrum of 2d (in DMSO-d6). Figure S38. 13C NMR spectrum of 2d (in DMSO-d6). Figure S39. 1H-13C HMQC spectrum of 2d (in DMSO-d6). Figure S40. 1H-13C HMBC spectrum of 2d (in DMSO-d6). Figure S41. 1H NMR spectrum of 2e (in DMSO-d6). Figure S42. 13C NMR spectrum of 2e (in DMSO-d6). Figure S43. 1H-13C HMQC spectrum of 2e (in DMSO-d6). Figure S44. 1H-13C HMBC spectrum of 2e (in DMSO-d6). Figure S45. 1H NMR spectrum of 2f (in DMSO-d6). Figure S46. 13C NMR spectrum of 2f (in DMSO-d6). Figure S47. 1H-13C HMQC spectrum of 2f (in DMSO-d6). Figure S48. 1H-13C HMBC spectrum of 2f (in DMSO-d6). D. 1H and 13C NMR data of 1a–1f and 2a–2f (A, B isomers). E. Table S8. 1H NMR chemical shifts for A and B forms of 2a–2f, and 1a–1f (in italics), in DMSO-d6 (δ1H, ppm), at 298 K. Table S9. 13C NMR chemical shifts for A and B forms of 2a–2f, and 1a-1f (in italics), in DMSO-d6 (δ1H, ppm), at 298 K. Table S10: 13C NMR chemical shifts for selected N(1)-amino, N(1)-amido and N(1)-imino derivatives of 1H-pyrrole-2,5-diones. F. Table S11: Selected bond lengths (Å), bond angles (°) and torsion angles (°) in the molecules 2a and 2d, and the closely related, CSD-reported X-ray structure LUZGUJ. Table S12: Selected bond lengths in the aliphatic chain of 2a, 2d and of the X-ray reported N1-acylamidrazones. Table S13: Selected bond lengths and angles in the 1H-pyrrole-2,5-dione moiety of 2a, 2d and some other X-ray reported 1H-pyrrole-2,5-dione derivatives. Table S14: Geometries of hydrogen bonds and selected short contacts in the crystals of 2a and 2d. G. Figure S49: The effect of ibuprofen (IBU) and 2a–2f at 100 µg/mL dose on the cell viability in PBMC cultures. H. Table S15: MIC values of 2a–2f, ampicillin and tetracycline against the tested bacterial strains. Click here for additional data file. Author Contributions Conceptualization, R.P.; methodology, R.P., A.H.-B., M.W.-S. and L.M.; formal analysis, L.M. and A.H.-B.; investigation, R.P., L.P., L.M., A.H.-B., J.K. and M.N.; validation: R.P., A.H.-B. and M.N., resources, R.P., L.P. and L.M.; data curation, L.M. and L.P.; writing—original draft preparation, L.P., A.H.-B. and L.M.; writing—review and editing, M.W.-S., J.K. and R.P.; visualization L.M. and R.P.; supervision, R.P.; project administration, R.P.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Collegium Medicum of Nicolaus Copernicus University Bioethical Commission (KB 39/2019). Informed Consent Statement Not applicable. Data Availability Statement Data are available from the authors. Conflicts of Interest The authors declare no conflict of interest. Sample Availability Samples of the compounds 2a–2f are available from the authors. Figures and Scheme Figure 1 N3-substituted amidrazones 1a–1f. 1a: R1 = phenyl, R2 = phenyl. 1b: R1 = 2-pyridyl, R2 = phenyl. 1c: R1 = 4-pyridyl, R2 = phenyl. 1d: R1 = 2-pyridyl, R2 = 2-pyridyl. 1e: R1 = 2-pyridyl, R2 = 4-methylphenyl. 1f: R1 = 2-pyridyl, R2 = 4-nitrophenyl. Figure 2 The studied N(1)-substituted derivatives of 3,4-dimethyl-1H-pyrrole-2,5-dione (2a–2f) together with the numbering scheme. 2a: X = C, Y = C, Z = C, R = H. 2b: X = N, Y = C, Z = C, R = H. 2c: X = C, Y = N, Z = C, R = H. 2d: X = N, Y = C, Z = N, R = H. 2e: X = N, Y = C, Z = C, R = CH3. 2f: X = N, Y = C, Z = C, R = NO2. molecules-27-02891-sch001_Scheme 1 Scheme 1 The synthesis of 2a–2f. Figure 3 Two hypothetical geometric isomers of 2a–2f. Figure 4 Labelling of atoms and the estimation of their thermal motion parameters as ADPs (50% probability level) in the studied crystals. The dashed line indicates the intramolecular C(26)−H(26)⋅⋅⋅N(6) hydrogen bond. Figure 5 Part of the crystal structure of 2a showing: (a) the molecular environment and main intermolecular interactions (symmetry codes: (i) −x + 1, y − 1/2, −z + 1/2; (ii) −x + 1, −y + 1, −z; (iii) −x + 1, −y, −z); (b) antiparallel, helical chains viewed along the a axis. Dashed lines indicate the hydrogen bonds, short C−H⋅⋅⋅O/N/π or C=O⋅⋅⋅C contacts. Figure 6 Part of the crystal structure of 2d showing: (a) the molecular environment and main intermolecular interactions (symmetry codes: (i) −x + 1, −y, −z; (ii) x, −y + 1/2, z−1/2); (b) crystal packing viewed along the c axis. Dashed lines indicate the hydrogen bonds, short C−H⋅⋅⋅O/N/π or C=O⋅⋅⋅C contacts. Figure 7 The effect of 2a–2f on the proliferation of PBMCs induced by the soluble anti-CD3 antibody (the results are shown as a percentage of positive control (anti-CD3 antibody alone), with values expressed as medians from four independent experiments and interquartile ranges (Q1–Q3)). * Significant difference compared to positive control (anti-CD3 antibody alone) at p < 0.05. Figure 8 The effect of 2a–2f on the LPS-induced production of IL-6 in PBMC cultures (the results are shown as a percentage of positive control (LPS alone), with values expressed as medians from five independent experiments and interquartile ranges (Q1–Q3)). * Significant difference compared to a positive control (LPS alone) at p < 0.01. # Significant difference compared to ibuprofen at p < 0.05. Figure 9 The effect of 2a–2f on the LPS-induced production of TNF-α in PBMC cultures (the results are shown as a percentage of positive control (LPS alone), with values expressed as medians from three independent experiments and interquartile ranges (Q1–Q3). Figure 10 The effect of 2a–2f on the LPS-induced production of IL-10 in PBMC cultures (the results are shown as a percentage of positive control (LPS alone), with values expressed as medians from four independent experiments and interquartile ranges (Q1–Q3)). * Significant difference compared to a positive control (LPS alone) at p < 0.05. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093370 materials-15-03370 Article Flexural Strength of Dental Fiber Composite Post Manufactured with a Novel Approach https://orcid.org/0000-0003-2760-9169 Abdelkader Esraa M. 1 https://orcid.org/0000-0002-6976-0679 Cortes Cortes Jose Manuel 2 https://orcid.org/0000-0002-1885-2119 Botella Candela Reyes 34 Nassar Khaled 1 https://orcid.org/0000-0002-9239-294X Rus Guillermo 5 https://orcid.org/0000-0003-4008-5582 Fathy Salma M. 6* 1 Department of Textile, Faculty of Applied Arts, Badr University, Cairo 11829, Egypt; eng.esraa_mahmoud@outlook.com (E.M.A.); khaled.mansour@gmail.com (K.N.) 2 Department of Structural Mechanics, Ultrasonics Lab (TEP-959), University of Granada, 18071 Granada, Spain; jmcortes@go.ugr.es 3 Department of Stomatology, Biomedical Group (BIO277), School of Dentistry, University of Granada, 18071 Granada, Spain; creyes@ugr.es 4 Instituto Investigación Biosanitaria, ibs. Granada, 18012 Granada, Spain 5 Department of Structural Mechanics, Ultrasonics Lab (TEP-959), Biomechanics Group (TEC-12) and Excellence Research Unit “Modeling Nature” MNat, University of Granada, 18071 Granada, Spain; grus@ugr.es 6 Department of Dental Biomaterials, Faculty of Oral and Dental Medicine, Zagazig University, Zagazig 44519, Egypt * Correspondence: salmamf@zu.edu.eg 08 5 2022 5 2022 15 9 337029 3 2022 03 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Thermoplastic resin fiber composites have an easy fabrication process, good mechanical properties, and compatible stiffness to tooth dentin. However, they have not yet attracted much interest in the field of dentistry. The current study was carried out to test a new proposed approach to manufacture a fiber reinforced composite endodontic post and evaluate its flexural strength through a two-point inclined loading test. The proposed fiber post manufacture approach depends upon a braiding technique of the glass fibers’ (GF) reinforcing component with thermoplastic polypropylene (PP) resin fibers that will later represent the resin matrix after thermal melting. Posts were made of different core (70%) and sheath (30%) construction (PP/GF ratios) using three different GF types and seizing pre-treatment to both fiber types. Two-point inclined loading test at 45 °C applied force angle was used to test the posts’ flexural strength. Fiber posts (1.6 mm in diameter) with pure GF (de-sized starch E-GF and pre-silanized S-GF) core, and sheath construction with higher PP/GF ratios, showed the significantly highest two-point bending strength (56.67 ± 4.89 and 53.96 ± 1.81 MPa, respectively), among experimental posts groups (except for the commercial control posts). However, posts with PP core type showed the lowest values (21.59 ± 1.86 and 16.54 ± 1.94 MPa for de-sized and E-glass sheath fibers, respectively). Based on these findings, the proposed approach was reliable in producing fiber-reinforced composite post with the desired dimensions and fiber distribution. Post construction with a pure GF core and higher PP/GF ratio showed considerably higher flexural strength and GF volume fraction comparable to commercial available post types. fiber composite post flexural strength glass fiber silane coupling maleic anhydride thermoplastic resin Ministerio de Educación, Cultura y DeportePID2020-115372RB-I00 PYC20-RE-072-UGR DPI2017-83859-R UNGR15-CE-3664 EQC2018-004508-P Junta de AndaluciaSOMM17/6109/UGR B-TEP-026-UGR18 IE2017-5537 P18-RT-1653 Erasmus + doctoral studiesThis research was funded by Ministerio de Educación, Cultura y Deporte grant numbers PID2020-115372RB-I00, PYC20-RE-072-UGR, DPI2017-83859-R, UNGR15-CE-3664 and EQC2018-004508-P; and Junta de Andalucia grant numbers SOMM17/6109/UGR, B-TEP-026-UGR18, IE2017-5537 and P18-RT-1653. As well as Erasmus + doctoral studies grant. ==== Body pmc1. Introduction Dental posts are used in the restoration of endodontically treated teeth with a substantial loss of coronary dentin to improve the retention of the final restoration [1]. They can be used to support the core build-up material in such teeth when a ferrule is left over [2]. With the growing aesthetic demand in the field of dentistry, non-metallic posts have gained more popularity over metal ones. They show a lower probability vertical root fracture than metallic posts due to their modulus of elasticity (which is similar to tooth dentin) [3,4]. In addition, they possess good bond strength to radicular dentin, which is also in favor of good stress distribution through the root canals, making them more biomechanically compatible to tooth structure [5,6]. They reported a high flexural strength, reaching 800 MPa [7]. Generally, fiber posts are considered as composite materials composed of three unlike constituents: the resin matrix (continuous phase), the fibers (dispersed phase), and the region in between which is the interphase [8]. These fiber-reinforced composite posts (FRCPs) are manufactured with different fiber types, e.g., carbon, polyaramid, polyethylene, and glass [8], and resin matrices such as epoxy-based, di-methacrylate-based cross-linked matrix, Bis-GMA, or less common aromatic polyimides [9,10]. FRCPs are usually fabricated using pre-stretched reinforcement fibers and pre-treated with silane coupling (organofunctional silanes) agent, which are impregnated into the emulsion of the thermoset polymer as the resin matrix [11,12]. Organofunctional silanes are considered the most effective agent to enhance interfacial adhesion with the resin matrix. Resin-impregnated fibers are heat-cured to form blocks with various forms and diameters. The former blocks are then milled to form the post final shape, a process which may expose some of the fibers onto the surface [10]. Although continuous fiber composite materials with a thermoplastic polymer’s matrix have not been commonly used in dental procedures, they have been demonstrated as cutting edge structural materials. They were reported to show good mechanical properties, excellent and easy processability, low density, recyclability, low cost, and excellent corrosion resistance [13,14]. Various thermoplastic polymers are utilized in the former composite structure such as polypropylene (PP) and polyamide [15,16]. PP is one of the most important industrial petrochemical building blocks. It has a closer coefficient of thermal expansion to glass fibers than thermoset-used resin matrices [10]. Fiber-reinforced PP composites, with glass or carbon fibers, have good durability, moisture resistance, and high strength properties. However, due to the fact that polyolefins such as PP are highly non-polar, they can negatively affect the interfacial adhesion with reinforcing GF. Compounds containing anhydride groups need to be used to increase the surface polarity and interfacial adhesion with the reinforcing phase [17]. The current used approach for fabrication of fiber composite posts has been recently discussed [18]. Braiding of three different types of GF (de-sized stash E-GF, pre-silanized E and S-GF) with thermoplastic PP resins yarns representing the resin matrix were prepared and the FRCPs Young’s modulus was evaluated [18]. The current study focuses on testing the flexural strength of the prepared FRCPs with more technique refinement for the experimental post-manufacturing technique. The experimental hypothesis was that the proposed and refined approach for preparing FRCPs, using different types and percentages of glass fibers and thermoplastic PP resin matrices, will yield a product with comparable flexural properties relative to its commercial peers. 2. Materials and Methods The methodology of the current study was carried out through two stages. The first stage was for testing different percentage combinations of glass fiber/PP (GF/PP) thermoplastic fibers braiding. Materials specification of this stage are mentioned in Table 1. Three main materials categories were used: E-GF treated with starch (ECE225, and count of “22 Tex”) and thermoplastic yarns of polypropylene (PP) with a count of 300 denier, all of which were donated by AGY industries, located in the Aiken, SC, USA. The third category was two types of chemical agents for better coupling and adhesion between two matrices as: Maleic anhydride powder with 99% concentration and tri-methoxysilyl propyl methacrylate with 98% concentration, both purchased from Sigma Aldrich, Steinheim am Albuch, Baden-Württemberg, (Germany), for treating PP yarns and GF surfaces, respectively. 2.1. De-Sizing Procedure for Starch Treated E-Glass Fibers (E-GF) The objective of this treatment was to remove the starch coat form E-GF. It was performed through immersion of the fibers in very low concentration of sulfuric acid (1%) while stirring at boiling temperature (100 °C) for 2–3 min. To ensure that no starch residues were left, a starch iodine test was conducted. First, 0.1 wt% aqueous solution of potassium iodide was prepared by adding 10 gm potassium iodide crystals to 100 mL deionized water then stirring until all crystals dissolve. Then, 5 gm iodine were added with stirring to ensure that no blue color formed which is related to starch presence. All procedures were performed in an opaque container and stored in dark cabinet to avoid light degrading action to the solute. 2.2. PP Yarns and E-GF Pre-Treatment (Sizing) before Braiding The thermoplastic PP yarns were first treated with NaOH (3%) with ratio of 1:100 for 2 h. This was carried out to increase surface area of the yarns so that improve adhesion between the yarns (resin matrix) and glass fibers. Then they were placed in a prepared solution of maleic anhydride (1 g/100 mL) and heated 120 °C in an oven (Hobersal, Barcelona, Spain) for two hours. The maleic anhydride/filaments mass proportion was 10% [19,20]. For GF surface treatment, 1 wt% aqueous solution of silane coupling agent, with pH adjusted to 4 using acetic acid, was prepared. GF fibers were dipped within the silane solution then squeezed with squeezing rolls. Afterwards, they were dried in the previously mentioned oven for 10 min at 110 °C [21]. 2.3. Fibers Braiding and Post Fabrication Fiber-reinforced composite posts (FRCPs) with dimensions of 1.6 mm diameter and 2 cm length were fabricated. They consisted from a core (70% of the post’s fibers volume fraction) and sheath (30% of the post’s fibers volume fraction). There were three main groups according to the post core type; pure PP, pure GF and mixed GF/PP (50/50%) cores. Each group was divided into six subgroups according to the sheath braiding construction (fiber volume fraction within post sheath) of PP/GF fibers as: 90/10%, 80/20%, 70/30%, 60/40%, and 50/50%, respectively. Additional two groups of commercial FRCPs were used as control groups; Olipost Light with 1.6 mm diameter (Olident, Cologne, Germany) and SF radiopaque post 1.6 mm in diameter (IndiaMart, Intermesh LTD, India). The different braiding constructions to form different cores and sheaths for the post were performed using the two gears of 42 teeth’s tension gear and 20 teeth’s draw gear meshing. The former gears allow for higher torque transmission to the braiding yarns consequently obtaining the tightest structure of the braid with the most acute and closed braiding angle (45°), and the least radius for the produced braids. The number of working spindles for braiding was 14 ± 1 for creating the suggested ratios of thermoplastic ones (PP) and the reinforcement yarns (GF). Additionally, an extra-sheath of PP 16 thermoplastic yarns was used to cover the whole formed posts external surface. Afterwards, the produced braids were cut and positioned in split aluminum mold containing grooves, each groove would be of 1.6 mm tube diameter when the two mold parts close over each other. The mold containing the post braids was then placed into a digital oven (Hobersal, Barcelona, Spain) for complete melting of thermoplastic PP yarns at 165 ± 5 °C for 40 ± 5 min. After mold complete cooling, the produced posts were removed from the grooves using a thin needle and then they were ready for mechanical testing. The second stage was carried out after mechanical testing of the posts produced from the first stage using the braid construction with best mechanical results. Two additional types of GF were used to construct FCRPs using three types of GF through this stage. They were; the former G-GF de-sized starch type, E-GF (ECDE75) with a count of “66 Tex” and S-GF (SCG75) with a count of “68 Tex”. The latter two types were pre-silanized and donated by AGY industries, located in the Aiken, SC, USA. The post construction procedures, within this stage, was the same as previously described in the first one, using the three core constructions and the sheath braid with PP/GF percentage of 90/10. The former percentage produced the best mechanical results within the first stage. Figure 1 illustrates the steps for experimental post preparation. 2.4. Two-Point Inclined Loading Test (Compression at 45°) 540 experimental fiber-reinforced composite posts (FRCPs), for the first stage testing, and 90 posts, for the second stage, in addition to two groups of commercial FRCPs (n = 10). Specimens’ grips were fabricated using 3-dimentional (3D) printer (UP BOX, Beijing Tiertime Technology Co., Ltd., Beijing, China). Poly-lactic acid polymer filament (1.75 mm, White, ANYCUBIC, China) was used to create the 3D printed holders (Figure 2A). The specimens were fixed in a pre-fabricated hole within the 3D model, which has the same diameter as the tested fiber post, perpendicular to the inclined top-surface of the holder (Figure 2B). The load was applied on the post top surface in a way that the vectorial force forms 45° angle with the post long axis (Figure 2B). The test was conducted using a universal testing machine (Instarus universal testing machine, Spain) using a load cell of 500 N at cross-head speed of 1 mm/min to the incisal surface of the post until fracture of the specimen. The stress (τ in MPa) at fracture was obtained through the following formula [22]:τ = 16. Fmax. cos 45/ 3πD Ç (1) where Fmax is the maximum force at fracture (N); cos 45, refers to a cosine angle of 45°; π is 3.14; and D the diameter of the post at the fulcrum or deflection point (1.6 mm for experimental post). The broken post surfaces were then coated with conductive carbon and examined using SEM (ZEISS Gemini SEM 560, Oberkochen, Germany). 2.5. Statistical Analysis The data were first evaluated for normality through “Shaprio–Wilk” statistical test. The data were tabulated for statistical analysis using statistical, package SAS 9.1.3. Means and standard deviations were calculated and expressed in MPa. Data were statistically analyzed using two and one-ANOVA followed by Tukey’s post hoc test (α = 0.05). 3. Results The results of the current study showed that within the first stage both the type of post core and the interaction between both core type and the sheath composition % had a statistically significant effect on the two-point flexural strength (p-value < 0.0001). On the other hand, the sheath composition % did not have statistically significant effect (p-value = 0.2147) (Table 2 and Figure 3). The results of the first stage through this study showed statistically significant (p-value < 0.0001) highest two-point bending flexural strength for posts with GF cores and core sheath structure of 90/10 and 80/20 % (PP/GF) as 56.67 ± 4.89 and 52.49 ± 2.36 MPa, respectively. Fiber posts with mixed core type showed statistically higher two-point bending strength (p-value = 0.0280) with increasing the GF % within the sheath (60/40 and 50/50 % of PP/GF). Posts with PP core type showed the statistically significant lowest flexural strength values within all core construction % (Table 3 and Figure 3). Within the second stage, again fiber composite posts with GF core showed the statistically significant highest two-point bending flexural strength values (p-value < 0.0001) with the highest values for de-sized starch E-GF and pre-silanized S-GF (56.67 ± 4.89 and 53.96 ± 1.81 MPa, respectively) followed by and pre-silanized E-GF (47.48 ± 2.2 MPa). However, they all showed statistically significant (p-value < 0.0001) lower flexural strength values than two commercial types (66.44 ± 4.27 and 58.73 ± 3.90 for control 2 and control 1, respectively) (Table 4). The SEM images showed cracking within resin matrix on the surface and cutting within GF that is caused under tensile stress during bending stress. They show slight deformation of the GF especially within control 1 (Olipost) which could happened prior to failure under tensile stresses within bending (Figure 4G,H). Other images within Figure 4 (e.g., C–F) showed apparent less volume fraction of GF within the resin matrix which may cause decrease within flexural strength even though they appear parallel to the long axis of the fiber post. 4. Discussion The current study evaluated the flexural strength to fiber-reinforced composite posts (FRCPs) fabricated with new approach. The former approach depends on the braiding of GF with the thermoplastic fibers of the resin matrix which may allow for better controlling of the GF distribution within the matrix. PP thermoplastic resin was used as the thermoplastic resin matrix. It is known for its easy processability and low coefficient of thermal expansion (0.6–1.7 × 10−6/°C) which is closer to E-glass fibers coefficient (8 × 10−6/°C) than the reported thermoset resin matrices (40–80 × 10−6/°C) [10]. That may improve the long-term integrity of the constructed fiber post. The results of the current study showed significantly highest flexural strength values for FRCPs with GF cores and sheath with higher PP/GF ratio in both first and second stages of the research. The lowest flexural strength values were for FRCPs with a PP core. This may be attributed to the highest GF volume fraction in experimental FRCPs with GF core (around 70% of the fiber volume within fiber core) while the total GF volume fraction could reach around 74% of the whole core and sheath in posts with higher PP/GF ratio of the sheath. This former explanation goes hand in hand with what was reported previously, as increasing the fiber density or fiber volume fraction was a contributing factor to increasing mechanical properties such as elastic modulus and yield stress [23]. The current suggested post configuration allowed for loading of such high percentage of GF in the form of pure core made of GF that was then held by the sheath made of both GF and melted PP matrix. The diameter of used GF, within this study, were of 6–9 microns, which are within the range reported previously as 6–21 microns for GF in fiber posts [7,24]. Decreases within the GF diameter allows for higher packing density of the used fibers (up to 70%) [10]. Although the tested commercial types, control 1 and 2, showed the significantly highest flexural strength values, the highest results introduced by experimental FRCPs are comparable to previous study [22]. The later study reported using FRCPs type of up to 80% GF volume fraction within the post. However, the control 1 type (Olipost), as reported by the manufacturer, was composed of 68% GF, 19% nano-zirconia particles and the rest of a 32% resin matrix) [25]. The filler nano-loading may cause further enhancement with the commercial post mechanical properties. That could be considered as limitation with the current proposed FRCPs manufacture approach. It is recommended the one further modify the proposed approach to allow for various filler particles incorporation for higher mechanical performance of FRCPs in future studies. Both the core composition and of the interaction between both the core the sheath composition showed significant impact on the fabricated posts flexural strength. However, the sheath composition alone did not have a significant impact on the post strength. That may be correlated to the proposed structure of the post with 70% volume related to the core part. The groups with higher thermoplastic content, such as in the case of pure PP core and mixed core with higher PP/GF ratio in the sheath, showed the lowest flexural strength. These results are in agreement with recent study which concluded that higher thermoplastic content of PP in the fiber- reinforced composites had more inhomogeneous distribution of reinforcement fibers [26]. This high content caused unexpected rise to viscoelastic properties of the composite with decrease in mechanical properties [26]. Similarly, when different GF types used, within the second stage, posts with a GF core still showed the highest flexural strength values. FRCPs with starch seized E-glass fibers that received surface pretreatment to PP yarns and GF showed almost higher flexural strength than pre-silanized S-glass and E-glass fibers. One of the factors that may improve the adhesion between PP resin matrix and silanized GF was silanization of GF, in aqueous solution, with silane coupling with general structure [X-Si(OR)3]. The R represents an alkyl group and where X group is still available to react with reactive functions of the polymer which is known for interaction with thermoset resins [27,28]. However, as this approach suggests using thermoplastic resin as fiber resin matrix, surface seizing with maleic anhydride to increase polarity of thermoplastic PP (polyolefins) was carried out. The previous treatment was reported to improve adhesive bond with the amine group of the silanized glass surface [29]. From a microscopic point of view, the reinforcing fibers prevent crack propagation by chemically bonding to the polymer matrix with covalent bonds [30]. Nevertheless, posts with seized GF and pre-silanized S-GF showed comparable results to previous two-point bending flexural results [22]. This may be attributed to the higher tensile strength and modulus with higher silica content (up to 64 and 24 wt% silica and alumina, respectively) of S-GF than commercially available E-GF [31]. The limitations of the current study were, first, the absence of introducing filler particles within the resin matrix. Second, using 3D printed polymer to act the substructure surrounding the post during mechanical testing instead of using natural tooth which is not the closer situation to the oral cavity. Therefore, it is suggested that future researchers further improve the technique of the suggested fiber post manufacture. Testing other thermoplastic resin matrices as well as incorporating reinforcing filler particles with nanosized particles are also suggested. 5. Conclusions Based on the results and within the limitations of the current study the following conclusions can be inferred. The proposed approach was successful for fiber post manufacture with the desired dimensions and structure. The fiber posts with a higher GF volume, e.g., pure GF core and higher PP/GF % ratio showed the significantly highest flexural or two-point bending strength values (except for the used commercial types). The de-sized starch E-glass fiber and pre-silanized S-GF showed, with the GF core, the best results for flexural strength for tested posts. Author Contributions Conceptualization, K.N. and G.R.; Formal analysis, C.R.B.; Funding acquisition, G.R.; Investigation, E.M.A., J.M.C.C. and C.R.B.; Methodology, E.M.A. and J.M.C.C.; Supervision, K.N., G.R. and S.M.F.; Writing—original draft, S.M.F.; Writing—review & editing, S.M.F. All authors have read and agreed to the published version of the manuscript. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Schematic representation for the steps of experimental post production, (A–D) represent different core construction (mixed and pure) and the different sheath composition with increase in GF% (red lines) in relation to PP fibers % (blue lines) from (A–D). Figure 2 Two-point bending strength (A) 3D printed specimen holder, (B) The applied force on the fiber post in a way to form a vector force of 45° as two-point bending stress. Figure 3 Bar charts showing the 45° angle compression testing (two-point bending strength) for first stage (A) with highest values were for GF cores in higher PP/GF % core composition and (B) for the second stage where de-sized Gf and S- glass fiber cores showed the closest two-point bending strength values to control 1 and 2 commercial post types. Upper and/or lowercase letters are for Tukey’s post-hoc test. Figure 4 SEM images of FRCPs after two-point bending test showing, (A,B) fiber post with pure GF core and sheath composition of 80/20% (PP/GF) with cracking on the surface and most of fibers appear intact and parallel to long axis of the fiber post (C,D) fiber post with mixed core and S-GF glass fiber showing once breakage within the sheath goes easier within core due to less GF content, (E,F) showing fiber post with pure PP core and sheath with 80/20% (PP/GF). They show almost behavior such as the later type with mixed core, (G,H) are the commercial control type 1 (Olipost) the core showing almost breakage of all-GF after reaching the maximum high bending stress. materials-15-03370-t001_Table 1 Table 1 Glass fibers and polypropylene yarns specifications. Product ECE225 (Starch Treated) SCG75 (Pre-Silanized) ECDE75 (Pre-Silanized) Glass Type E S–2 GLASS E Filament Diameter (microns) 7 9 6 Nominal Yield—yd/lb. 22,500 7295 7500 Tex—g/1000 m 22 68 66.1 Tex tolerance +/− 1.2 6.2 4.3 Nominal Solids % 1.4 1.17 1.42 Solids Tolerance +/− 0.25 0.26 0.17 Nominal Twist TPI (TPM) 0.5Z (Z20) 1.0Z (Z40) 0.7Z (Z28) Twist Tolerance +/− TPI (TPM) 0.15 (6) 0.3 (12) 0.21 (8) Max. Broken Filaments 10 9 10 Approximate Yarn Diameter—in (mm) 0.0065 (0.165) 0.0076 (0.192) 0.106 (0.269) Yarns Type Polypropylene (PP) Count 300 Denier Melting point 165 °C Young’s Modulus (GPa) 1.38 Tensile strength (MPa) 34 TPI, turns per inch; TPM, turns per meter; and yd/lb, yards/pounds. materials-15-03370-t002_Table 2 Table 2 Two-way ANOVA showing the effect of both post core type and sheath composition percentage on two-point bending flexural strength (Compression at 45°) of fiber composite post. Source of Variance DF Sum of Squares Mean Squares F-Value p-Value Post core type 2 5760.796084 2880.398042 251.51 <0.0001 * Sheath composition % 4 70.743676 17.685919 1.54 0.2147 Post core type X Sheath composition % 8 1207.832871 150.979109 13.18 <0.0001 * Error 30 343.576867 11.452562 Total 44 7382.949498 * means statistically significant at p-value ≤ 0.05. materials-15-03370-t003_Table 3 Table 3 Means and standard deviations (SD) of two-point bending flexural strength (2-PBS) (MPa) for first stage. Post Sheath Composition (PP/GF%) 2-PBS p-Value GF Core Type PP Core Type Mixed (50/50%) Core Type Mean ± SD Mean ± SD Mean ± SD 90/10 56.67 ± 4.89 Aa 16.54 ± 1.94 Ba 36.18 ± 1.95 Ca <0.0001 * 80/20 52.49 ± 2.36 Aab 18.29 ± 0.93 Bab 41.14 ± 5.34 Cba 70/30 48.39 ± 2.59 Abc 25.91 ± 4.95 Bb 42.24 ± 4.96 Aba 0.0018 * 60/40 42.17 ± 1.11 Acd 24.78 ± 1.52 Bb 47.81 ± 2.28 Cb <0.0001 * 50/50 36.36 ± 2.28 Ad 21.57 ± 3.34 Bab 48.06 ± 4.95 Cb 0.0004 * p-value <0.0001 * 0.0116 * 0.0280 * Letters are for Tukey’s test, a–d = Means with same small letter in each column are not significantly different, A–C = Means with same capital letter in each row are not significantly different, * means there is significant difference at p-value ≤ 0.05. materials-15-03370-t004_Table 4 Table 4 Means and standard deviations (SD) of two-point bending flexural strength (2-PBS) (MPa) for second stage. Group Core Type/GF Type 2-PBS Mean ± SD GF/De-sized glass 56.67 ± 4.89 ABC GF/S-glass 53.96 ± 1.81 BCD GF/E-glass 47.48 ± 2.22 CDE Mixed/De-sized glass 36.18 ± 1.95 F Mixed/S-glass 45.05 ± 5.55 DEF Mixed/E-glass 41.36 ± 1.77 EF PP/De-sized glass 21.59 ± 1.86 G PP/S-glass 25.54 ± 5.55 G PP/E-glass 16.54 ± 1.94 G Control 1 (Olipost) 58.73 ± 3.90 AB Control 2 (FS type) 66.44 ± 4.27 A p-value <0.0001 Letters are for Tukey’s test, A–G = Means with same letter in are not significantly different. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Garcia P.P. Wambier L.M. de Geus J.L. da Cunha L.F. Correr G. Gonzaga C.C. Do anterior and posterior teeth treated with post-and-core restorations have similar failure rates? A systematic review and meta-analysis J. Prosthet. Dent. 2019 121 887 894.e4 10.1016/j.prosdent.2018.08.004 30617032 2. Monticelli F. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094709 ijms-23-04709 Review Beyond BRCA: The Emerging Significance of DNA Damage Response and Personalized Treatment in Pancreatic and Prostate Cancer Patients https://orcid.org/0000-0002-1534-0521 Dalmasso Bruna 1† https://orcid.org/0000-0002-2492-4043 Puccini Alberto 2† Catalano Fabio 2 https://orcid.org/0000-0001-5403-0916 Borea Roberto 2 Iaia Maria Laura 2 https://orcid.org/0000-0002-0337-0168 Bruno William 13 https://orcid.org/0000-0003-4754-6572 Fornarini Giuseppe 2 Sciallero Stefania 2 https://orcid.org/0000-0003-0546-6304 Rebuzzi Sara Elena 34‡ https://orcid.org/0000-0002-3651-8173 Ghiorzo Paola 13*‡ Basu Ashis Academic Editor 1 IRCCS Ospedale Policlinico San Martino, Genetics of Rare Cancers, 16132 Genoa, Italy; brunasamia.dalmasso@hsanmartino.it (B.D.); william.bruno@unige.it (W.B.) 2 IRCCS Ospedale Policlinico San Martino, Medical Oncology Unit 1, 16132 Genoa, Italy; albertopuccini1@gmail.com (A.P.); catalan.fab@gmail.com (F.C.); roby.borea@gmail.com (R.B.); iaia.mlaura4@gmail.com (M.L.I.); giuseppe.fornarini@hsanmartino.it (G.F.); stefania.sciallero@hsanmartino.it (S.S.) 3 Department of Internal Medicine and Medical Specialties, University of Genoa, 16132 Genoa, Italy; saraelena89@hotmail.it 4 Ospedale San Paolo, Medical Oncology, 17100 Savona, Italy * Correspondence: paola.ghiorzo@unige.it † These authors contributed equally to this work. ‡ These authors contributed equally to this work. 24 4 2022 5 2022 23 9 470921 3 2022 20 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The BRCA1/2 germline and/or somatic pathogenic variants (PVs) are key players in the hereditary predisposition and therapeutic response for breast, ovarian and, more recently, pancreatic and prostate cancers. Aberrations in other genes involved in homologous recombination and DNA damage response (DDR) pathways are being investigated as promising targets in ongoing clinical trials. However, DDR genes are not routinely tested worldwide. Due to heterogeneity in cohort selection and dissimilar sequencing approaches across studies, neither the burden of PVs in DDR genes nor the prevalence of PVs in genes in common among pancreatic and prostate cancer can be easily quantified. We aim to contextualize these genes, altered in both pancreatic and prostate cancers, in the DDR process, to summarize their hereditary and somatic burden in different studies and harness their deficiency for cancer treatments in the context of currently ongoing clinical trials. We conclude that the inclusion of DDR genes, other than BRCA1/2, shared by both cancers considerably increases the detection rate of potentially actionable variants, which are triplicated in pancreatic and almost doubled in prostate cancer. Thus, DDR alterations are suitable targets for drug development and to improve the outcome in both pancreatic and prostate cancer patients. Importantly, this will increase the detection of germline pathogenic variants, thereby patient referral to genetic counseling. DNA damage response BRCA mismatch repair homologous recombination genetics PARP inhibitors pancreatic cancer prostate cancer ==== Body pmc1. Introduction At each cell division, there is a risk of errors occurring in the DNA replication machinery. DNA replication errors occur more frequently in the presence of DNA damaging agents, both endogenous and exogenous. For instance, reactive oxygen species (ROS) generated during metabolism and the inflammatory process can alter the biochemical structure of nucleic acids. Exogenous factors influencing the likelihood of replication errors include UV rays, which alter the structure of nucleic acids and can lead to the formation of pyrimidine dimers. Moreover, ionizing radiation, such as X-rays and γ-rays, can cause both single-strand and double-strand DNA breaks, whereas exposure to alkylating agents can lead to the formation of DNA adducts and DNA crosslinks [1]. To correctly maintain the fidelity of the genetic code, cells have developed sophisticated methods to sense and repair DNA replication errors. When those errors cannot be repaired, mechanisms are put in place to force the cell to undergo senescence and/or eliminate the cell through apoptosis. However, the disruption of the DNA damage response (DDR), resulting in the escape of death/senescence or uncontrolled cell proliferation with DNA replication errors, leads to genomic instability, one of the hallmarks of cancer [2]. 2. DNA Damage Response (DDR) More than 400 proteins and multiple pathways are involved in the regulatory machinery that constitutes the DDR [3]. The main DDR pathways are: base excision repair (BER), nucleotide excision repair (NER), mismatch repair (MMR), homologous recombination (HR), and non-homologous end joining (NHEJ). Although a subset of genes operates within a single pathway, the different DDR pathways are intertwined, and several genes are involved in the correct functioning of multiple pathways [4]. 2.1. Base Damage and DNA Single-Strand Breaks Alterations that change or remove a single base, such as UV-induced cytosine deamination, are addressed by DNA glycosylases belonging to the BER pathway [5]. After the altered base is removed by DNA glycosylases, specific endonucleases, such as Ape1, introduce DNA single-strand (ssDNA) breaks, which are then repaired by DNA polymerase β (POLβ) and XRCC1-DNA ligase IIIa, recruited to the ssDNA break site by the poly(ADP-ribose) polymerase inhibitor (PARP) 1 (PARP1) [6]. Another major DNA excision repair is NER, which removes a broad spectrum of single-strand lesions that impair correct DNA coiling [7]. Unlike BER, NER consists of the removal of an oligonucleotide, followed by the repair of the excision using the opposite DNA strand as a template [8]. DNA base–base mismatches and insertion–deletion loops (IDL) can be generated during DNA replication. These types of DNA errors are identified and repaired by the MMR pathway [9]. The main MMR genes frequently altered in cancer are: MLH1, MSH2, MSH6, and PMS2 genes. The MMR is identified and initiated by the MSH2/MSH6 heterodimer (mutSα), and then completed by another heterodimer (mutLα) formed by MLH1 and PMS2 [10]. In addition to the editing of mismatched base pairs, MMR genes can also regulate the HR pathway, in order to maintain the correct functioning of DNA double-strand (dsDNA) break repair [11]. 2.2. DNA Double-Strand Breaks dsDNA breaks are the most severe form of DNA damage, resulting in DNA replication arrest if not repaired [12]. HR is a high-fidelity pathway involved in the restoration of dsDNA breaks [13]. In HR, DNA damage is sensed by ATM, which activates several proteins, including BRCA1 and BRCA2, after which DNA ends are resected from 5′ to 3′ by the MRN complex (formed by the proteins RAD50, MRE11, and NBS) [14]. An array of other molecules, including the RAD51 paralogs, invade with the stranded filaments the sister chromatid, which is then used as a template by DNA polymerases to elongate the stranded filaments [15]. An alternative dsDNA repair is carried out through the NHEJ pathway, which does not require an intact template [16]. Briefly, through the interaction of the kinase proteins Ku70 and Ku80, DNA-PKcs, and ATM, and the MRN complex, the stranded ends are cleaved to a lesser extent than that of HR and are then ligated together by specific ligases. Although this mechanism repairs dsDNA lesions, it is more error-prone, as it results in deletions with the consequent loss of genetic information [17]. 3. Harnessing DDR Deficiency for Cancer Treatment With the advent of targeted therapies, DDR genes, frequently altered in cancer, have been studied to implement and personalize cancer medical treatment [18]. The rationale of using DDR-targeting agents is to induce cell death through synthetic lethality by blocking a complementary pathway in cells lacking one DDR pathway [19]. Specifically, poly(ADP-ribose) polymerase 1 and 2 (PARP1 and PARP2) enzymes are essential for the normal functioning of BES and its blockade results in the lack of repair of single-strand DNA breaks, with the consequent increased number of errors leading to DNA double-strand breaks that, in the absence of the HR pathway in BRCA1/2 deficient cells, can only be repaired by error-prone mechanisms, such as NHEJ [20]. Moreover, several PARP-inhibitors (PARP-i) also cause an entrapment of PARP at the replication fork, which becomes stalled, and cannot be restarted unless the HR pathway is functioning. There is a growing amount of evidence that this latter mechanism plays a major role in cell death by PARP-i molecules [21,22] Starting from BRCA1/2 studies in breast and ovarian cancer [23,24], DDR-targeting drugs are being studied in other neoplasms with a deficiency of BRCA1/2 or other HR genes, especially pancreatic cancer [25] and castration-resistant prostate cancer [26]. Indeed, clinical data on olaparib, the first PARP-i approved, in pancreatic and prostate cancer were published for the first time in 2014, and even though the size of the study cohort was small (23 pancreatic and 8 prostate), these data led to further clinical investigations [27]. Following the success of PARP-i, novel molecules targeting other DDR genes and pathways are being studied. 4. Pancreatic Cancer and Prostate Cancer Exocrine pancreatic cancer is one of the most lethal malignancies, being the fourth cause of death by cancer considering both sexes together, and predicted to be the second by 2030, with a survival rate at five years from diagnosis lower than 10% [28]. Pancreatic adenocarcinoma, in particular ductal adenocarcinoma, is the most frequent form of pancreatic cancer, constituting more than 85% of all pancreatic cancer cases [29]. Traditional chemotherapy and radiotherapy regimens can hardly overcome the aggressiveness of this disease, and do not guarantee the same response in different treated patients. Therefore, research is ongoing to identify potentially actionable genes and pathways involved in the genesis and progression of this disease, to improve and personalize pancreatic cancer medical treatment [30]. A subset of 5 to 15% of individuals who develop pancreatic cancer are either younger than expected for this malignancy or have a positive family history of pancreatic cancer and/or multi-tumor syndromes [31]. A germline pathogenic variant in a known pancreatic cancer predisposition gene can be found in less than 20% of these patients, depending on selection criteria and genes tested. The recent literature shows that genes associated with breast and ovarian cancer risk are also the most strongly associated with pancreatic cancer risk, with the exception of CDKN2A in some populations [32,33,34]. For example, the DDR genes BRCA1, BRCA2 and ATM, or PALB2, each one usually found mutated in no more than 3.5% of cases, increase pancreatic cancer risk when altered at the germline level [35]. Based on the available literature, it is estimated that 17 to 25% of pancreatic cancer harbor somatic PVs in one of the genes involved in DDR, mainly those implicated in homologous recombination DNA damage response and repair (HR), such as BRCA1, BRCA2, ATM, PALB2, ATRX, and RAD51 [36,37,38,39,40,41,42,43,44]. Prostate cancer is the second most frequent malignancy in males worldwide (the first in western countries and in most African countries), representing 14.3% of all new cancers in males 2020, and the fifth cause of death by cancer in this population [28]. Although the majority of prostate cancers are low risk and/or diagnosed at an early stage, a subset of them displays an aggressive behavior. The initial medical approach to prostate cancer is based on the use of androgen-blocking agents, but a high proportion of metastatic prostate cancers tend to rapidly develop resistance to androgen-blocking agents. Metastatic castration-resistant prostate cancer (mCRPC) patients have a dismal prognosis, as median survival does not exceed two years [45]. The majority of mCRPC samples harbor clinically actionable molecular alterations. With regard to DDR genes, somatic mutations are found in around 23% of mCRPC, and up to 10% of individuals diagnosed with metastatic prostate cancer harbor a germline mutation [46,47]. Of the latter, more than half show loss of heterozygosity in the tumor [48]. In this review, we provide an overview on the DDR genes altered in both pancreatic and prostate cancers. Considering that the mutation rates of DDR genes vary considerably across different studies, and that differences in size cohorts and DNA sequencing methods are likely to be among the reasons of these discrepancies, we only considered original papers, reviews, and systematic reviews/meta-analyses involving at least 200 cases analyzed through multi-gene panel, exome, or genome sequencing, and we set a cut-off of at least 0.2% for reporting mutation rates. In addition, we summarize the clinical implications of targeting these genes in the context of currently ongoing clinical trials. 5. Potentially Actionable DDR Genes in Common between Pancreatic Adenocarcinoma and Castration-Resistant Prostate Cancer (mCRPC) 5.1. BRCA1 and BRCA2 Identified in 1994 and 1995 by positional cloning [47], BRCA1 and BRCA2 are two of the main genes that control chromosomal stability. Indeed, upon phosphorylation by protein kinases, such as ATM, ATR, and CHK2, BRCA1 and BRCA2 become part of the macromolecular complexes necessary to repair DNA double-strand breaks through HR [49]. BRCA1- and BRCA2-deficient cells, lacking both copies of either of the two genes, show a high rate of mutations in multiple genes, including gatekeeper genes, such as CDKN2A, a phenomenon that can lead to neoplastic degeneration. Germline biallelic pathogenic variants (PVs) in BRCA1 or BRCA2 result in different forms of Fanconi Anemia (FA), a syndrome characterized by short stature, multi-organ malformations, neurodevelopmental disorders, and cancer susceptibility [50]. On the other hand, the inheritance of a single allele with a PV predispose to several types of cancers following a second hit, including pancreatic cancer [51]. BRCA1 and BRCA2 PVs can be found in up to nearly 5% of the primary tumors of pancreatic cancer, with the highest frequencies in cohorts enriched for high-risk pancreatic cancer cohorts enriched for familiar cases [52]. In prostate cancer, BRCA1/2 mutations represent around 13% of the of DDR genes alterations in tumor samples and 51% of all germline variants found in individuals affected by prostate cancer, with BRCA2 harboring the majority of variants [46]. Germline BRCA1 and BRCA2 PVs increase the developing prostate cancer with a higher likelihood of aggressive disease for BRCA2 PV carriers [53,54,55,56]. Overall, BRCA1/2 PVs can be found in both germline and somatic samples from pancreatic cancer patients at similar rates. Conversely, BRCA1/2 PVs in prostate cancer occur more frequently as a somatic hit. 5.2. ATM Ataxia–Telangiectasia mutated (ATM) is a large (351KD) PI3/Pi4 kinase with pleiotropic functions. In addition to playing a key role in HR by activating BRCA1 and BRCA2, ATM is involved in DNA double-strand break repair via NHEJ. Moreover, ATM is essential for the correct maturation of lymphocytes [57] and the central nervous system [58]. Indeed, the carriers of biallelic ATM PVs are affected by ataxia–telangiectasia, a rare syndrome characterized by progressive cerebellar ataxia, skin telangiectasias, an increased susceptibility to hematologic and solid tumors, and immunodeficiency. Heterozygous carriers of ATM PVs are at increased risk of several types of cancer, including pancreatic cancer, and it is estimated that up to 3% of high-risk individuals who develop pancreatic cancer harbor an ATM PV [25,35,59,60]. ATM has also been proposed as a prostate cancer predisposition gene and has been found altered at the germline level in both prostate cancer patients with suspected familial cancer syndromes and in apparently sporadic prostate cancer patients [48,61,62] Somatic ATM loss rate can also happen in sporadic pancreatic cancer and prostate cancer [25,36,37,61,63,64]. Indeed, results from a large pan-cancer WGS study include ATM among the top 26 driver genes in these cancers, being found altered in 9 and 7% of pancreatic cancer and prostate cancer samples, respectively [63]. 5.3. ATR The Ataxia–Telangiectasia and Rad3-Related Protein (ATR) encodes for a serine-threonine kinase that acts as a DNA stress sensor. Specifically, in the presence of DNA ionizing and UV radiation and other genotoxic stressors, as well as in the case of the stalling of the replication fork, ATR activates checkpoint inhibitors, such as CHK1, to arrest cell cycle. Moreover, in cells lacking ATR, the inhibition of ATM or proteins in the ATM pathway is synthetically lethal [65]. The rates of ATR PVs, higher in at least 0.2% of the analyzed samples, have been found only as a germline event in pancreatic cancer and prostate cancer patients [35,48]. 5.4. BRIP1 BRCA1-interacting protein (BRIP1) is a helicase that interacts with BRCA1 and promotes its DNA repair activity. It is also known as FANCJ, as it is part of the FA complex J [66]. Similar to BRCA1/2, biallelic germline PVs in the BRIP1 gene are found in children with FA syndrome. Germline monoallelic PVs increase the risk of cancer and are found in up to 1% of pancreatic cancer patients. [35,60]. A similar rate can also be found in sporadic pancreatic cancer samples [36,37]. In prostate cancer, BRIP1 PVs are less frequent, and rates higher that 0.2% are exclusively of germline origin [61,62]. 5.5. CHEK1 and CHEK2 Checkpoint Kinase 1 (CHEK1) and Checkpoint Kinase 2 (CHEK2) encode for two serine-threonine kinases (CHK1 and CHK2, respectively) that are effector kinases acting downstream of ATM and ATR and are involved in cell cycle arrest following DNA damage [67]. ssDNA breaks as well as ssDNA generated following DSB resection in dsDNA break repair or during stalled replication fork ultimately result in the activation of the ATR/ATRIP complex, with subsequent CHK1 phosphorylation [68]. CHK2, on the other hand, is activated through phosphorylation by ATM following dsDNA breaks [67]. Both proteins activate signaling networks, leading to cell cycle arrest [67]. Germline PVs in CHEK2 increase the risk of breast cancer, albeit the penetrance has not yet been defined [69]. Moreover, germline PVs in both genes have been found in several other cancers, but their role those cancers is still under investigation [70,71,72,73]. PVs in one or both of those genes are more frequent at the germline level, both in pancreatic cancer and prostate cancer. In the latter, germline CHEK1/CHEK2 PVs have been found in up to 4.1% of affected individuals [48,61]. 5.6. FANCA The FA Complementation Group A (FANCA) gene is the main causative gene of FA, being altered at the germline levels in at least 60% of the affected children [74]. FANCA is part of the FA core complex, a macromolecular structure with ubiquitin-ligase functions belonging to the FA pathway, involved in DNA crosslinks repair and signaling upon replication stress, and acts in close interaction with BRCA1/2 and RAD51 to protect the replication fork from stalling [75,76]. FANCA has been associated with pancreatic cancer, being among those genes with germline and PV rates higher than 1% [35]. At the somatic level, FANCA has been found mutated in both cancers, with a higher prevalence of PVs in prostate cancer. 5.7. Mismatch Repair (MMR) Genes The mismatch genes are MLH1, MSH2, MSH6, and PMS2. Germline PVs in MMR genes predispose to Lynch syndrome, which is characterized by a higher risk of developing non-polyposis-associated colorectal cancer (HNPCC) as well as extracolonic neoplasms, including pancreatic cancer and prostate cancer [77], whereas microsatellite instability (MSI) due to somatic impairment of MMR can be found in sporadic colorectal cancer. Recently, MMR genes have been implicated in the development of pancreatic and prostate cancers. Indeed, individuals with Lynch syndrome have a higher risk of developing both pancreatic and prostate cancers [78,79]. Germline PVs in MMR genes have been described in both pancreatic cancer and mCRPC unselected for family history [35,60]. MMR PVs are also present in 0.8% of pancreatic cancer samples and in up to 3% of prostate cancer samples [36,61]. 5.8. NBN The Nijmegen Breakage Syndrome 1 gene, also called Nibrin (NBN), is part of the NBN-MRE11-RAD50 complex. Upon activation by dsDNA breaks, the MRN complex participates in both HR and NHEJ [80]. The biallelic absence of NBN characterizes the Nijmegen Breakage Syndrome, a recessive disorder that includes intrauterine growth restriction, microcephaly, increased susceptibility to upper and lower airway infections, and several types of cancer [81,82]. NBN germline PVs range from 0.21% in unselected pancreatic cancer patients to 0.59% in familial pancreatic cancer patients [35], as well as in 2% of prostate cancer patients unselected for family history [48]. Conversely, NBN somatic variants are not found at rates higher than 2% in either of the two cancers. 5.9. PALB2 Partner and Localizer of BRCA2 (PALB2), also known as FANCN, is a moderate-risk breast cancer susceptibility gene involved in both to the FA pathway. Moreover, PALB2 binds BRCA1 and BRCA2, forming a complex necessary for HR [83]. Biallelic loss-of-function PALB2 variants cause a form of Fanconi anemia. PALB2 somatic PV occur in both pancreatic cancer and prostate cancer samples, whereas germline PV have been described in pancreatic cancer patients, at a slightly higher rate in high-risk compared to apparently sporadic patients (0.97% and 0.1–0.65%, respectively) [35,59]. 5.10. RAD51 Paralogs The RAD51 paralogs are: RAD51B, RAD51C, RAD51D, XRCC2, and XRCC3. Each of these proteins work in an intertwined way with the others, forming macromolecule complexes essential for HR. Considering that cells with null RAD51 paralog genes show deficient HR, tumors that lack at least one of those genes could be considered targets for therapy with PARP-I and other drugs directed at HR. Indeed, human cell lines deficient for RAD51 paralogs, with the exception of RAD51B, show marked genomic instability and sensitivity to both mytomicin C and olaparib [84]. Of all RAD51 paralogs, the germline RAD51C and RAD51D variants can be found in both pancreatic cancer and prostate cancer, at a rate lower than 0.5%. Only in prostate cancer, however, RAD51D is altered in up to 4% of somatic samples. 5.11. The Burden of DDR Deficiency in Pancreatic and Prostate Adenocarcinomas Overall, genomic aberrations in 11 non-BRCA DDR genes are shared by pancreatic cancer and prostate cancer. PVs in those genes are found in up to 16% of germline pancreatic cancer samples. The addition of non-BRCA DDR genes triplicates the mutational burden given by BRCA1 and BRCA2 alone. When looking at somatic PVs, the scenario is comparable, as the highest rate of PVs found in the literature increases from 4.8% to 18.5% when adding other DDR genes to BRCA1/2. As for prostate cancer, the addition of the genes included in this review to BRCA1 and BRCA2 more than doubles the mutational burden in both germline and in somatic sample PVs (8.6% to 20.7% and 15.2% to 36.9%). Overall, PVs in both BRCA1/2 and in non-BRCA genes grouped together are slightly more frequent at the somatic level in pancreatic cancer, whereas in prostate cancer, somatic PVs are twice as many as germline PVs. In fact, there is a striking difference between the two types of tumors in what concerns the rate of somatic PVs, which are almost doubled in prostate cancer compared to pancreatic cancer (36.94% vs. 18.5%). A detailed overview of the mutation rates of the above-mentioned 11 genes is shown in Table 1, and their role in DDR pathways is summarized in Figure 1. 6. DDR and Cancer Treatment in Pancreatic Adenocarcinomas 6.1. DDR Pathogenic Variants in Pancreatic Cancer DDR gene alterations are correlated with increased overall survival (OS) (17.9 versus 9.6 months, p = 0.03) compared to patients without a DDR gene alteration [85]. Indeed, in a recent article, the median overall survival (mOS) in patients with ATM alterations was 40.2 months compared with 15.5 months in the control population (hazard ratio (HR) = 0.14, 95% confidence interval (CI) = 0.04 to 0.47, 2-sided p = 0.001). These findings suggest that pathogenic ATM alterations may be prognostic for improved outcomes in patients with pancreatic cancer [86]. Interestingly, DDR mutations seem to correlate with a significantly longer OS in patients treated with 5-Fluorouracile, irinotecan, and oxaliplatin (FOLFIRINOX) compared to patients without mutations in DDR genes [87]. The success of novel target therapies with PARP1-i in pancreatic cancer has prompted researchers to explore to a greater extent the role of DDR in pancreatic cancer genesis and progression, in order to broaden the set of patients who could benefit from those therapies and also identify potential targets for the development of other targeted therapies focused on DDR deficiency. In this review, we report the different mutations in DDR pathways currently investigated in pancreatic cancer patients as promising targetable alterations. 6.2. Clinical Trials Results in Pancreatic Cancer Ongoing clinical trials in pancreatic cancer are reported in Table 2. The POLO study is a phase III clinical trial conducted on BRCA1/2 mutated metastatic pancreatic cancer patients treated with olaparib vs. placebo as a maintenance therapy after a platinum-based chemotherapy. In this study, 3315 patients were screened, 154 underwent a 3:2 ratio randomization, and 92 received olaparib. The progression free survival (PFS) was longer in the olaparib group (7.4 vs. 3.8 months; HR = 0.53) [88]. Results from the POLO trial led to olaparib being approved by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) in pancreatic cancer with germline BRCA1/2 PV, as maintenance after a platinum-based chemotherapy. Updated results in 2021 showed that mOS was similar between olaparib and placebo, although this is probably due to 29% of the crossover of patients from placebo to PARP inhibitor upon progression. More importantly, the OS rate at 36 months was 33.9% for olaparib and 17.8% for placebo, which are impressive results considering the poor prognosis of pancreatic cancer patients [89]. Another PARP-i that has been investigated in pancreatic cancer patients harboring DDR gene mutations is Veliparib. In a phase I trial, Veliparib, in association with gemcitabine and cisplatin as first-line treatment in both BRCA1/2 germline mutated and wild-type patients, showed a good safety profile, although the clinical response was reported exclusively in the mutated group [90]. On the other hand, a phase II trial involving BRCA1/2 or PALB2 mutated pancreatic cancer patients did not demonstrate a benefit in response rate adding veliparib to Cisplatin + Gemcitabine treatment [91]. However, this trial established cisplatin + gemcitabine as a possible new standard treatment in patients harboring BRCA1/2 or PALB2 mutation [91]. Moreover, a phase II trial tested Veliparib in previously treated BRCA1/2 mutated pancreatic cancer patients. The trial did not show an objective response rate (ORR), although 25% of patients were stable for 4 months [92] An ongoing clinical phase II clinical trial (NCT02890355) is testing 5-fluorouracile + irinotecan (FOLFIRI) + veliparib as second-line treatment in pancreatic cancer patients with or without BRCA1/2 mutation. Preliminary results showed no difference in OS between the FOLFIRI group and FOLFIRI + veliparib [93]. In a phase I/II trial, veliparib in association with 5-fluorouracile and oxaliplatin showed promising results in terms of ORR in patients harboring a DDR mutation with a good safety profile [94]. Moreover, veliparib in combination with gemcitabine and radiotherapy demonstrated high tolerability and better mOSin in patients with a DDR pathway alteration compared to patients without DDR mutations [95]. Talazoparib is a new promising PARP-i. In vitro experiments demonstrated that talazoparib selectively targets tumor cells with BRCA1/2 or PTEN mutations with 20- to more than 200-fold greater efficacy than the old generation of PARP-i [96]. A phase I clinical trial of talazoparib in different BRCA1/2-mutated tumors showed a good safety profile and promising antitumor activity [97]. Phase II clinical trials in solid tumors are currently ongoing (see Table 2). The phase II RUCAPANC trial enrolled pancreatic cancer patients with a BRCA1/2 PV (either germline or somatic) to receive rucaparib. The disease control rate was 31.6% (6 out of 19 patients); hence, the insufficient response rate prompted the closure of the study [98]. Recently, Pishivain et al. published data on 1028 pancreatic cancer patients, 189 of whom harbored an actionable mutation. Of these, 46 (24%) received a molecularly matched therapy [99]. The most common pathway mutated was the DDR (94 of 189 patients). In a subgroup analysis on patients harboring a DDR mutation, 27 received a matched therapy and 67 received an unmatched one. In the subgroup treated with target therapy (PARP-i or ATR inhibitor), the mOS was significantly longer. Similarly, the mOS in patients with an actionable non-DDR mutation was longer in the group treated with the matched therapy [99]. This is a key study to understand the clinical role of using matched therapy in mutated pancreatic cancer patients. According to this study, harboring an actionable alteration and receiving a molecularly matched therapy can predict treatment response and improve mOS compared to receiving a non-matched therapy. An interesting aspect is the use of HRDness inducers, which can create artificial vulnerabilities allowing the use of PARP-i in patients without BRCA mutation, thus improving the number of patients who can benefit from this therapy [100]. Several ongoing trials are exploring the use of PARP inhibitors in PDAC patients, both as a monotherapy and in combination with other treatments [101,102]. 6.3. Beyond BRCA Two phase II ongoing trials are evaluating olaparib in patients with a negative BRCA germline mutation, a tumor with a BRCAness phenotype, and a family history of BRCA-related cancers, as a second or further line of therapy. Both studies (NCT02677038, NCT02511223) have shown encouraging results, although caution must be taken due to the small sample size (21 and 11 patients); hence, further studies are required [103]. The ataxia–telangiectasia mutated (ATM) gene plays an important role in the DDR. Preclinical data in ATM-deficient mouse model showed efficacy of PARP-i and ATM-inhibitors (ATM-i) [104]. Different ATM-i have been developed in recent years: KU55933 and KU60019 have shown to be potent radiosensitizers, while AZ31 improved the efficacy of irinotecan therapy [105]. The aToM study is a phase I trial evaluating the safety and efficacy of the ATM-i AZD0156 at increasing doses alone or in combination with other anti-cancer treatments (olaparib or FOLFIRI schedule) in patients with advanced cancers, including pancreatic cancer. Preclinical data demonstrate that the combination of ATM-i with PARP-i enhances the activity of the latter improving DNA DSB and then cell apoptosis [106]. In two phase 2 nonrandomized clinical trials, olaparib was well tolerated and showed limited antitumor activity in patients with advanced, platinum-sensitive pancreatic cancer with alterations in DDR genes, including ATM [107]. Preclinical data showed the high activity of ATR-inhibitors (ATR-i) in tumors with a somatic mutation of the ATM pathway, since the ATM-deficient cells rely on the ATR pathway for survival [108,109]. Currently, there are no clinical data available on ATR-i, but several clinical trials are ongoing (see Table 2). As reported in the previous chapters, checkpoint kinase 1 and 2 (CHK1/CHK2) are activated by ATR and ATM in response to DNA damage or stress [110]. A preclinical study on a CHK1 inhibitor in association with chemotherapy (gemcitabine) and radiotherapy demonstrated a synergic role in killing pancreatic tumor cells [111]. Furthermore, a CHK2 inhibitor has been tested in association with gemcitabine, demonstrating an increased apoptosis of pancreatic tumor cells [111]. No clinical data are available to date, yet CHK inhibitors are being investigated in some clinical trials (see Table 2). PALB2 has a key role in orchestrating DNA repair, and it is strongly linked to the BRCA1/2, ATM, and ATR pathways [112]. Currently, some trials are evaluating the prognostic role of PALB2 in PARPi-treated patients with mutations in this gene (see Table 2). Nowadays, pancreatic cancer remains a tumor with a poor prognosis and the search for actionable mutations, in order to improve the outcome, is a very hot topic in both preclinical and clinical research. However, although encouraging results are available in the preclinical setting, clinical data are needed to confirm and validate them. In the past decade, several treatment strategies have been approved for mCRPC patients and, recently, a better understanding of the underlying biology of prostate cancer allowed researchers to identify and investigate novel therapeutic agents. The DDR pathways are one of the main actionable molecular alterations of mCRPC, and the investigation of PARP-i has opened a new prospective in the advanced setting of prostate cancer [113]. 7. DDR and Cancer Treatment in Prostate Adenocarcinomas Prostate cancer is strongly driven by androgen receptors (ARs) at the beginning of the tumor natural history (“castration sensitive”), while the “castration-resistant” phase of prostate cancer is characterized by tumor heterogeneity caused by the onset of genomic and transcriptomic alterations [113]. HR genes (mainly BRCA1, BRCA2, and ATM), are present in up to 20% of mCRPC patients, at either germline or somatic levels [114]. Testing DDR gene mutations in prostate cancer is clinically relevant, due to their prognostic and predictive values [115]. In prostate cancer patients, harboring a DDR gene mutation, especially a BRCA2 mutation, is associated with a worse prognosis and a higher Gleason score and stage at diagnosis, as well as with an increased risk of developing distant metastases [116,117]. Moreover, DDR mutations were shown to be positive predictive markers of sensitivity to the platinum-based chemotherapy regimen and PARP-i response in different tumors, including breast, ovarian, and prostate cancers [118]. Due to the promising results of the use of PARP-i in ovarian or breast cancer patients harboring BRCA1/2 mutations, several studies have investigated the efficacy of PARP-i in DDR-mutated mCRPC patients, leading to FDA approval for some of these molecules (Table 3) [119,120]. In this review, we report the main clinical trials on the use of PARP-i in mCRPC. 7.1. Clinical Trials Results in Prostate Cancer Several phase 2/3 trials have investigated the efficacy and safety of PARP-i in mCRPC patients (Table 3). The PROFOUND phase 3 trial assessed olaparib compared with AR-directed therapy (enzalutamide or abiraterone acetate) in mCRPC patients with multiple loss-of-function DDR alterations who progressed to the new hormonal agents [121]. Cohort A included 245 patients with at least one mutation in BRCA1, BRCA2, or ATM, while cohort B included 142 patients who had a mutation in any of other 12 DDR genes. The somatic mutation status was evaluated with a tissue gene panel analysis. The primary endpoint was radiological PFS (rPFS) in cohort A and secondary endpoints included PFS in cohort B and ORR and OS in cohort A. The analysis revealed that in cohort A olaparib significantly improved rPFS (7.4 vs. 3.6 months, hazard ratio of 0.34; 95% CI: 0.25–0.47, p < 0.0001), the OS of the interim analysis (18.5 vs. 15.1 months, hazard ratio of 0.64; 95% CI: 0.43–0.97, p = 0.02), and the objective response rate (ORR) (33% vs. 2%) compared to the hormonal therapy arm. Moreover, olaparib also improved rPFS, OS, and ORR in the overall population (cohorts A and B). This study confirmed the survival improvement and the clinical benefits of olaparib in mCRPC patients harboring DDR alterations opening the path for a new promising class. On the basis of these results, in May 2020, olaparib was approved by the FDA for germline or somatic HR gene-mutated mCRPC patients who progressed to enzalutamide or abiraterone. The results of the final OS analysis have been subsequently reported [122], confirming the significantly longer OS in patients treated with olaparib in cohort A (19.1 vs. 14.7 months, hazard ratio of 0.69; 95% CI: 0.50–0.97; p = 0.02) than in cohort B (14.1 vs. 11.5 months) and overall population (17.3 vs. 14.0 months), despite the substantial crossover from the control therapy to olaparib (66%). Moreover, a sensitivity analysis adjusted for crossover to olaparib showed hazard ratios for death of 0.42 in cohort A, 0.83 in cohort B, and 0.55 in the overall population. The phase 2 single-arm TRITON2 trial investigated rucaparib 600 mg twice daily in patients with mCRPC with germline or somatic HR gene alterations, detected on blood and/or tumor biopsy. Patients had to be progressed after one or two lines of next-generation AR-directed therapy and one taxane-based chemotherapy. The primary endpoint was ORR and PSA response rate (decrease ≥ 50% from baseline) and secondary endpoints included rPFS and OS. The study demonstrated the efficacy of rucaparib in 115 mCRPC patients with a BRCA alteration, reporting confirmed ORRs per independent radiology review (IRR) and investigator assessment (IA) of 43.5% and 50.8%, respectively, and a PSA response rate of 54.8% [123]. No difference in ORR was seen in patients with a germline versus somatic BRCA alteration and in patients with a BRCA1 versus BRCA2 mutation, while a higher PSA response rate was observed BRCA2 mutation. (59.8% vs. 15.4%). Promising survival results were also reported: the median rPFS was 9.0 months per IRR assessment and 8.5 months per investigator assessment. Additionally, although OS data were not mature at the time of the analysis, the 12-month OS reported was 73.0%. A subgroup analysis revealed that non-BRCA DDR gene alterations, including ATM, CDK12, or CHEK2 mutations, were associated with limited radiographic/PSA responses to rucaparib, while promising responses were observed with mutations in genes that directly interact with the BRCA complex (e.g., PALB2, BRIP1, FANCA, and RAD51B) [124]. On the basis of this study, in May 2020, rucaparib was approved by the FDA for germline or somatic BRCA-mutated mCRPC patients treated with AR-directed therapy and a taxane-based chemotherapy. In addition to olaparib and rucaparib, other PARP-i have shown promising efficacy results and could be the next FDA-approved PARP-i. The phase 2 TALAPRO-1 evaluated the efficacy and safety of talazoparib in 104 mCRCP patients with DRR gene defects and measurable disease who received one or two taxane-based chemotherapy regimens for metastatic disease and AR-directed therapy (enzalutamide or/and abiraterone), for metastatic castration-resistant prostate cancers [125]. The study showed that talazoparib was associated with an ORR (primary endpoint) of 30% in the overall population, with a greater antitumor activity in BRCA1/2-mutated patients than in those with PALB2 and ATM mutations (46% vs. 25% vs. 12%, respectively). Additionally, the rPFS was higher in BRCA1/2-mutated patients compared to the other DDR alterations (overall population: 5.6 months; BRCA1/2-mutated patients: 11.2 months; PALB2-mutated patients: 5.6 months; ATM-mutated patients: 3.5 months). The composite response rate (CRR) (ORR and/or ≥50% PSA decline and/or circulating tumor cell conversion) was 51% in the overall population and was similar in BRCA1/2-mutated and PALPB2-mutated patients, but lower in ATM-mutated patients (72%, 75%, and 24%, respectively). These results showed that talazoparib has encouraging antitumor activity in heavily pretreated mCRPC patients with DDR–HRR gene alterations, especially those with BRCA1/2 mutations. The phase 2 GALAHAD trial investigated the efficacy and safety of niraparib (300 mg daily) in mCRPC patients with DDR defects who progressed on AR-directed therapy and taxane-based chemotherapy [126]. The primary endpoint was ORR and secondary endpoints included PSA response, CRR, rPFS, and OS. In the final study analysis, 289 patients were included in the overall efficacy analysis population showing an ORR in the measurable BRCA cohort (n = 76) (primary endpoint) of 34.2%, a median rPFS of 8.08 months, and a mOS of 13.01 months. These results in the BRCA1/2-mutated patients were better than those observed in the measurable non-BRCA cohort (ORR 10.6%, median rPFS 3.71 months and mOS 9.63 months). Additionally, this study concluded that niraparib has encouraging antitumor activity in heavily pretreated mCRPC patients with DDR–HRR gene alterations, especially those with BRCA1/2 mutations. At the ESMO Congress 2021, the biomarker analysis of cohort A of the phase 1b/2 KEYNOTE-365 trial on the combination of pembrolizumab + olaparib in molecularly unselected, docetaxel-pretreated mCRPC patients was presented [127]. The primary endpoints (PSA response and ORR) were higher in BRCA-mutated patients compared with patients without a BRCA mutation (50% vs. 14% and 33% vs. 6%, respectively) and in patients with an HR mutation compared with those without an HR mutation (22% vs. 13% and 8% vs. 3%, respectively). According to these results, the combination of pembrolizumab + olaparib have shown promising activity results, regardless of HR mutation status, even though higher response rates were observed in mutated patients. Recently, two randomized, double-blind phase 3 studies on PARP-i (the PROpel trial and the MAGNITUDE trial) reported the first analyses at the ASCO Genitourinary Cancers Symposium of February 2022 [128,129] The PROpel trial investigated the combination of abiraterone acetate plus olaparib versus abiraterone acetate + placebo as a first-line therapy of mCRPC patients [129]. The treatment with abiraterone acetate + olaparib significantly prolonged rPFS (primary endpoint) irrespective of the HRR status (24.8 vs. 16.6 months; hazard ratio of 0.66, 95% CI: 0.54–0.81; p < 0.0001). OS is currently immature, but a trend in OS favoring abiraterone acetate plus olaparib was observed (HR 0.86). The secondary endpoints of ORR (58.4% vs. 48.1%), time to first subsequent treatment (hazard ratio of 0.74), and time to second PFS (hazard ratio of 0.69) were supportive of activity and long-term benefits. The MAGNITUDE trial analyzed the combination of abiraterone acetate plus niraparib versus abiraterone acetate plus placebo as a first-line therapy in mCRPC patients with and without HR [128]. Niraparib + abiraterone acetate did not show any benefit in terms of rPFS or biochemical PFS, and for this reason the accrual in this cohort of patients was interrupted. The combination of niraparib + abiraterone acetate significantly improved rPFS (primary endpoint) in the BRCA1/2 subgroup (hazard ratio of 0.53, 16.6 vs. 10.9 months) and in all HR+ patients (hazard ratio of 0.73, 16.5 vs. 13.7 months). The first interim analysis of OS is immature, but a trend in OS favoring abiraterone acetate + niraparib was observed (hazard ratio of 0.77). The advantage was also observed in terms of ORR, time to subsequent chemotherapy, and time to symptomatic and biochemical progression, in both BRCA1/2-mutated and HR+ patients. 7.2. Ongoing Clinical Trials PARP-i represents a novel treatment option for mCRPC patients harboring HR mutations who progressed to chemotherapy and/or next generation AR-targeted therapy. However, primary resistance to PARP-i may be present upfront and acquired resistance to them may occur, mainly related to the restoration of the HR mechanism. For this reason, ongoing clinical trials are currently investigating PARP-i in different treatment settings and in combination with different types of oncological therapies, including AR-direct therapy, immunotherapy, and tyrosine kinase inhibitor (TKI) [114,130] (Table 4). Moreover, some clinical trials included as inclusion criteria the presence or absence of specific HR mutations. 7.2.1. PARP-i in Different Treatment Settings PARP-i are currently investigated in the early stages of prostate cancer as, e.g., neoadjuvant treatment for locally advanced prostate cancer (BrUOG 337 trial—NCT03432897) or high-risk localized prostate cancer (NCT04030559) and in the metastatic hormone sensitive prostate cancer (mHSPC) (TRYUMPH trial—NCT03413995). 7.2.2. PARP-i plus AR-Direct Therapy Preclinical data have shown synergy between olaparib and drugs targeting the AR-pathway [131,132]. The exact mechanisms are still not completely known, but it seems that novel hormonal agents can induce an HR phenotype in prostate cancer cells, the signaling of AR pathway may be involved in DNA repair, and the PARP1 gene may be activated in AR transcriptional activity [133]. Therefore, a phase 2 randomized trial was conducted by Clarke et al. to assess the efficacy and tolerability of olaparib in combination with abiraterone compared with placebo plus abiraterone in mCRPC patients previously received docetaxel, irrespective of their HR mutation status [134]. Olaparib in combination with abiraterone provided an additional clinical efficacy benefit compared with abiraterone alone, even though with an increase in serious adverse events. Upon on the same rationale, other ongoing phase 3 randomized trials are comparing PARP-i versus placebo in addition to new hormonal agents (e.g., TALAPRO-2 study—NCT03395197 [135]). 7.2.3. PARP-i plus Immunotherapy PARP-i have shown to have an immunomodulatory effect modulating the tumor immune microenvironment by a wide range of molecular and cellular mechanisms, such as increasing genomic instability, activating immune pathways and increasing PD-L1 expression on cancer cells, which might promote responsiveness to immune checkpoint inhibitors (ICIs) [136]. According to this biological rationale, several studies are investigating the combination of PARP-i and ICIs [137]. The phase I/II study NCT02484404 reported that the combination of olaparib and durvalumab demonstrates efficacy in terms of PSA response (reduction ≥50%) in mCRPC patients who have received prior enzalutamide and/or abiraterone. Patients with DDR mutations exhibited greater survival (in terms of PFS) benefit compared with those without known alterations [138]. Another phase II study is also assessing the efficacy of the combination olaparib plus durvalumab in castration-sensitive biochemically recurrent non-metastatic prostate cancer harboring at least one DDR deleterious mutation (NCT03810105). The phase 1–2 KEYNOTE-365 study (NCT02861573) investigated the association of pembrolizumab plus olaparib showing antitumor activity in docetaxel-pretreated and molecularly unselected mCRPC patients who previously received less than two second-generation hormone treatments [139]. A phase 3 study (KEYLYNK-010, NCT03834519) is currently ongoing assessing the efficacy and safety of this combination in molecularly unselected mCRPC patients who progressed to taxane chemotherapy and at least one novel hormonal therapy. Other phase 2 are currently investigating the combination of different PARP-i with ICIs in mCRPC patients (e.g., rucaparib plus nivolumab in the CheckMate 9 KD—NCT03338790). 7.2.4. PARP-i Associated with TKI Tumor cell growth is influenced by several factors, including the mechanisms of cell repair, which are contrasted by PARP-i, and the signaling of growth factors, which are contrasted by TKI [130]. The double targeting of these patterns may help to treat patients with CRPC. TKIs include inhibitors of angiogenesis (e.g., cediranib in NCT02893917) or of the AKT pathway (e.g., ipatasertib in NCT03840200]. 7.2.5. PARP-i Associated with Other Treatments Other combination strategies with PARP-i include therapies that induce DNA damage/replication stress enhancing the activity of PARP-i [130]. Other agents target DDR (ATR-i AZD6738 in NCT03787680), radionuclides (Radium-223 dichloride in NCT03076203 and NCT03317392; Lutetium 177 dotatate in NCT03874884), radiotherapy (NCT04037254), and bipolar androgen therapy (alternating between castration and supraphysiologic testosterone in NCT03516812). 7.3. Beyond BRCA in Prostate Cancer As the main clinical trials’ results on PARP-i in mCRPC were obtained in patients with BRCA1/2 mutations and data for other DDR genes come from subgroup analyses, the efficacy of PARP-i remains unclear in patients harboring non-BRCA DDR mutations. The “BRCAness” status is defined as a HR alteration not due to BRCA1/2 mutations, but to other DDR genes, such as PALB2, ATM, ATR, CDK12, CHEK1, FANC, and RAD51/54 [140]. 7.3.1. PARP-i The phase 3 PROfound trial reported that olaparib was less effective in terms of ORR and PFS in the cohort B of patients harboring non-BRCA DDR alterations compared to cohort A of BRCA-mutated patients [141]. These data were also observed in two phase II trials on olaparib (TOPARP-B) and rucaparib (TRITON2) [120,124]. The phase II TOPARP-B study assessed the association between olaparib and non-BRCA DDR mutations with the aim to extend the validation of olaparib in DDR-mutated mCRPC [120]. In this study, the antitumor activity of olaparib was higher in BRCA1/2 patients, but it was also observed in other DDR gene mutations, especially in the PALB2 and ATM subgroups [120]. The ad hoc analysis of the TRITON2 trial on patients with a non-BRCA DDR gene alteration, especially ATM, CDK2, or CHECK2 mutations, showed a limited ORR and PSA response rate. On the other hand, responses were observed in patients with PALB2, FANCA, BRIP1, and RAD5B alterations [124]. These results suggest that PARP-i might have a role as monotherapy or in combination with other therapies also in BRCAness mCRPC patients, although with a lower magnitude of benefit compared to BRCA-mutated patients. Further, ad hoc studies are needed to assess the therapeutic role of PARP-i in BRCAness mCRPC patients. 7.3.2. Other DDR Gene Inhibitors ATM and ATR genes are involved in complex DDR pathways [114]. Some evidence has suggested that targeting ATM/ATR mutated cancer with both PARP-i and ATR-i/ATM-i may be more efficacious than using PARP-i alone [142]. Several ATR-i and ATM-i are currently being investigated in early clinical trials as monotherapy or in association with PARP-i, as a double blockade of the DDR pathway, immunotherapy, and hormonotherapy chemotherapy [114]. Other promising preclinical data were obtained with compounds targeting other DDR alterations, including CHK1, WEE1, CDK12, and DNA-PKcs inhibitors, which were tested for activity in preclinical prostate cancer models and are currently under investigation in phase 1/2 trials in different solid tumors, including prostate cancer patients, or specifically in prostate cancer patients [114]. 8. Conclusions Pancreatic and prostate cancer were the first cancers after breast and ovarian cancer for which the efficacy of PARP inhibitors was evaluated in the presence of BRCA1/2 mutations. Since then, the presence and actionability of DDR deficiency in these tumors has been investigated. However, due to differences in cohort selection and DNA sequencing approaches, the burden of PVs in non-BRCA1/2 DDR genes shared by pancreatic and prostate cancers is not completely defined. The picture that emerges from the examined literature shows that the addition of other DDR genes to BRCA1/2 markedly increases the burden of actionable variants, even when looking only at point mutations. We conclude that the inclusion of DDR genes other than BRCA1/2 shared by both cancers considerably increases the detection rate of potentially actionable variants, which are triplicated in pancreatic and almost doubled in prostate cancer. For prostate cancer, this is particularly relevant at the somatic level, where DDR mutations are almost doubled compared to those found in germline samples, and a germline testing-based approach would miss a large amount of DDR-deficient tumors. Considering the growing applications of DDR-targeting agents in cancer therapy, and the importance of timely genetic testing for patient access to treatment, multi-gene panels for pancreatic cancer and prostate cancer that include these genes should be routinely used in the clinical setting for both cancers. Overall, DDR alterations are suitable targets for drug development and to improve the outcome in both pancreatic and prostate cancer patients. Importantly, this will increase the detection of germline pathogenic variants, thereby patient referral to genetic counseling. Acknowledgments S.E.R. and G.F. would like to thank the Italian Ministry of Health (Ricerca Corrente 2018–2021 grants) that financially supports their current research focused on the identification of prognostic and predictive markers for patients with genitourinary tumors. P.G. would like to thank AR3 onlus and patients’ families support for pancreatic cancer research studies. Author Contributions Conceptualization, methodology and writing—original draft preparation: B.D., A.P., S.E.R. and P.G.; writing—review and editing and supervision: F.C., R.B., M.L.I., W.B., G.F., S.S. and P.G. All authors have made substantial contributions to this review. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by grants from Lega Italiana per la Lotta contro i Tumori (LILT) 5 × 1000 IG 2019 to P.G., Italian Ministry of Health (Ospedale Policlinico San Martino Ricerca Corrente and 5 × 1000 funds 2018–2021) to P.G., Associazione Ricerca Tumori Rari ed Ereditari (AR3) onlus to P.G., and Italian Ministry of Health (Ricerca Corrente 2018–2021) grants to S.E.R. and G.F. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest for this work. Figure 1 Overview of DNA damage response pathways with genes altered in both pancreatic and prostate cancer. HR = homologous recombination, NHEJ = nonhomologous end-joining, MMR = mismatch repair, SBM = single-base mismatch. ijms-23-04709-t001_Table 1 Table 1 Frequency of the pathogenic variants in DDR genes shared by pancreatic and prostate cancers. Pancreatic Cancer Prostate Cancer Germline [25,35,59,60] Somatic [25,36,37,63] Germline [48,61,62] Somatic [61,63,64] Gene Range Max Range Max Range Max Range 1 Max BRCA1/2 0.9–5 5 0.9–4.8 4.80 0.3–8.6 8.60 0.6–15.2 15.2 ATM 2–3.09 2.2–9 1.59–2.3 1.9–7.3 ATR 0.2 0.29 BRIP1 0.22–1 0.48–1 0.28–0.45 CHEK1-2 0.3–2.2 0.2–0.6 1.87–4.1 0.9–1.9 FANCA 1.04 0.6 3 MMR 0.39–1.2 0.8 0.14–1.7 3–6 NBN 0.2–0.59 0.29–2 PALB2 0.1–0.97 0.2–1.2 0.45–0.56 0.4–2 RAD50 0.3–0.36 0.5 1 RAD51 0.35 2 0.14–0.57 2,3 0.54 2,3 Non-BRCA1/2 11 13.7 12.07 21.74 TOTAL 16 18.5 20.67 36.94 Frequencies are reported as percentages. 1 mCRPC, 2 RAD51C, 3 RAD51D. ijms-23-04709-t002_Table 2 Table 2 Ongoing clinical trials on PARP-i in pancreatic cancer. PARP-i Clinical Trial Phase Patient Population Somatic Mutations/Germline PVs Treatment Arm(s) Olaparib NCT04548752 2 Pancreatic cancer BRCA1/2 Olaparib + pembrolizumab NCT04005690 1 Pancreatic cancer nd Olaprib + Cobimetinib NCT02498613 2 Advanced Solid Tumors nd Olaparib + cediranib NCT03162627 1 Solid Tumors nd Olaparib + selumetinib NCT03842228 1 Advanced Solid Tumors ARID1A, ATM, ATRX, BARD1, BRCA1, BRCA2, BRIP1, CDK12, CHEK1, CHEK2, FANCA, FANCL, MRE11A, MSH2, PALB2, PARP1, POLD1, PP2R2A, RAD51B, RAD51C, RAD51D, RAD54L, XRCC2, PTEN, PIK3CA Olaparib + durvalumab + Copalinsib NCT02511223 2 Pancreatic cancer BRCAness Olaparib alone NCT02677038 2 Pancreatic cancer Somatic BRCA mutation, Fanconi anemia genes, ATM or RAD51 mutations Olaparib alone NCT02576444 2 Solid tumors ATM, CHK2, MRN (MRE11/NBS1/RAD50), CDKN2A/B, APOBEC, IDH1/IDH2, TP52, KRAS, PTEN, PIK3CA, AKT, or ARID1A Olaparib + AZD1775 OR AZD5363 OR AZD6738 Rucaparib NCT03140670 2 Pancreatic cancer BRCA 1/2 or PALB2 Maintenance after platino-based chemo NCT03337087 1–2 Pancreatic, colorectal, gastroesophageal, or biliary cancer BRCA 1/2 or PALB2 Liposomal Irinotecan, Fluorouracil, Leucovorin Calcium, and Rucaparib NCT04171700 2 Solid Tumors BRCA1, BRCA2, PALB2, RAD51C, RAD51D, BARD1, BRIP1, FANCA, NBN, RAD51 or RAD51B. Rucaparib alone Veliparib NCT02890355 2 Pancreatic cancer nd FOLFIRI or mFOLFIRI + Veliparib as II Line NCT01585805 2 Pancreatic cancer BRCA1/2 or PALB2 Germline PV Gemcitabine + Cisplatin with or without veliparib or veliparib wlone NCT02723864 1 Solid tumors nd M6620 (ATR inhibitor) + veliparib + cisplatin Niraparib NCT03601923 2 Pancreatic cancer Germline PVs or somatic mutation of one of these: BRCA1/2, PALB2, CHECK2 or ATM Niraparib alone NCT04409002 2 Pancreatic cancer nd Niraparib + Dostarlimab + RT NCT03553004 2 Pancreatic cancer DDR family mutation Niraparib alone NCT04493060 2 Pancreatic cancer BRCA1/2 or PALB2 Niraparib + Dostarlimab NCT03404960 1–2 Pancreatic cancer nd Niraparib + Nivolumab or Ipilimumab after platinum-based chemotherapy NCT03207347 2 Solid tumors ARID1A, ATM, ATR, BACH1 (BRIP1), BAP1, BARD1, BLM, CHEK1, CHEK2, CDK2, CDK4, ERCC, FAM175A, FEN1, IDH1, IDH2, MRE11A, NBN (NBS1), PALB2, POLD1, PRKDC (DNA-PK) PTEN, RAD50, RAD51, RAD52, RAD54, RPA1, SLX4, WRN, or XRCC Niraparib NCT03209401 1 Solid tumors ARID1A, ATM, ATRX, MRE11A, NBN, PTEN, RAD50/51/51B, BARD1, BLM, BRCA1, BRCA2, BRIP1, FANCA/C/D2/E/F/G/L, PALB2, WRN, CHEK2, CHEK1, BAP1, FAM175A, SLX4, MLL2 or XRCC Niraparib + carboplatin Talazoparib NCT02286687 2 Solid tumors Somatic BRCA1 or BRCA2; germline BRCA, ATM, PALB2, Fanconi Anemia genes, ARID1A, MER11, RAD50, NBS1, ATR; amplification of EMSY Talazoparib NCT03565991 2 Solid tumors ATM or BRCA Avelumab and talazoparib Fluzoparib NCT04300114 3 Pancreatic cancer Germline BRCA/PALB2 Maintenance after platinum Prexasertib NCT02873975 2 Solid tumors MYC amplification, Rb loss, FBXW7 mutation, BRCA1, BRCA2, PALB2, RAD51C, RAD51D, ATR, ATM, CHK2, the Fanconi anemia pathway genes, CCNE1 amplification of 6-fold or greater, or other genomic or somatic mutation in a known HR gene Prexasertib (CHK inhibitor) NCT03057145 1 Solid tumors nd Prexasertib + olaparib BTT-114 NCT02950064 1 Pancreatic, breast, ovarian, or prostate cancer BRCA or other DNA repair mutations, such as ATM, CHEK2, PALB2, and RAD51D BTT-114, a novel platino product ABT-144 NCT01489865 1 Pancreatic cancer BRCA1/2 or PALB2 or FANC mutation or family history ABT-144 + mFOLFOX6 AZD0156 NCT02588105 1 Solid tumors nd (ATM/ATR inhibitor) Alone or in combination M6620 (VX-970) NCT02595931 1 Solid tumors nd M6620 + irinotecan AZD6738 NCT03682289 2 Renal, urothelial or pancreatic cancer ATM or ARID1A AZD6738 (ATR inhibitor) +/− olaparib NCT02223923 1 Solid tumor nd AZD6738 (ATR inhibitor) + radiotherapy NCT02630199 1 Solid tumors nd AZD6738 (ATR inhibitor) + paclitaxel NCT03669601 1 Solid tumor nd AZD6738 (ATR inhibitor) + Gemcitabine Ceralasertib NCT02264678 1–2 Solid tumors ATM and BRCA evaluation Ceralasertib +/− other drugs BAY1895344 NCT03188965 1 Solid tumors ATM or other DDR defects BAY1895344 (ATR inhibitor) NCT04514497 1 Solid tumors ATM and other DDR defects BAY1895344 (ATR inhibitor) + irinotecan ijms-23-04709-t003_Table 3 Table 3 Main clinical trials and results of PARP-i in mCRPC patients with a HR mutation. PARP-i Clinical Trial Phase and Study Type Patient Population Treatment Arm N pts Results Status in February 2022 Olaparib PROFOUND (NCT02987543) Phase 3, randomized Progression to ≥1 novel HT 1 Cohort A: BRCA1m, BRCA2m, ATMm. Cohort B: other HR. Olaparib vs. Enzalutamide or Abiraterone acetate + prednisone Cohort A: 245 Cohort B: 142 Cohort A: Olaparib > Hormonal therapy in PFS = 7.4 vs. 3.6 mo, HR 0.34; p < 0.0001 OS = 18.5 vs. 15.1 mo, HR 0.64; p = 0.02 ORR = 33% vs. 2% Cohort A + B: Olaparib > Hormonal therapy in PFS = 5.8 vs. 3.5 mo, HR 0.49; p < 0.0001 OS = 17.5 vs. 14.3 mo, HR 0.67 ORR = 22% vs. 4% FDA-approved in May 2020 Active, not recruiting KEYNOTE-365 (NCT02861573) 1b-2, single arm mCRPC (molecularly unselected, docetaxel-pretreated) Pembrolizumab + Olaparib (Cohort A) 102 BRCA+ vs. BRCA - PSA response: 50% vs. 14% ORR: 33% vs. 6% HR+ vs. HR- PSA response: 22% vs. 13% ORR: 8% vs. 3% Active, recruiting PROpel (NCT03732820) Phase 3, randomized mCRPC 1 L treatment after failure of ADT Olaparib + Abiraterone Acetate 796 rPFS: 24.8 vs. 16.6 months, HR 0.66, p < 0.0001 OS: HR 0.86 ORR: 58.4% vs. 48.1% Active, not recruiting Rucaparib TRITON2 (NCT02952534) Phase 2, single arm Progression to 1–2 novel HT 1 AND 1 taxane-based CT Rucaparib 115 BRCAm ORR IRR = 43.5% ORR IA = 50.8% PSA RR = 54.8% m-rPFS IRR = 9.0 mo m-rPFS IA = 8.5 mo 12-mo OS = 73.0% FDA-approved in May 2020. Completed Talazoparib TALAPRO-1 (NCT03148795) Phase 2, single arm Progression to ≥1 novel HT AND 1–2 CT regimens (≥1 taxane-based CT) Talazoparib 86 overall population 46 BRCA1/2m 4 PALB2m 18 ATMm ORR overall population = 28% ORR BRCA1/2m = 43.9% ORR PALB2m = 33.3% ORR ATMm = 11.8% m-rPFS BRCA1/2m = 9.3 mo m-rPFS PALB2m = 7.4 mo m-rPFS ATMm = 5.5 mo Active, not recruiting Niraparib GALAHAD (NCT02854436) Phase 2, single arm Progression to ≥1 novel HT 1 AND ≥1 taxane-based CT Niraparib 46 BRCA 1/2m 35 non-BRCAm BRCA1/2m vs. non-BRCAm ORR = 41% vs. 9% PSA RR = 50% vs. 3% m-rPFS = 8.2 vs. 5.3 mo mOS = 12.6 vs. 14 mo Active, not recruiting MAGNITUDE (NCT03748641) Phase 3, randomized mCRPC 1 L treatment after failure of ADT Niraparib + Abiraterone Acetate 423 HR patients BRCA1/2m vs. non-BRCAm rPFS: 16.6 vs. 10.9 mo, HR 0.53 ORR: 52% vs. 31% HR+ vs. HR- rPFS: 16.5 vs. 13.7 mo, HR 0.73 ORR: 60% vs. 28% Active, not recruiting HT: hormonal therapy; N: number; pts: patients; BRCA1m: BRCA1 mutation; BRCA2m: BRCA2 mutation; ATMm: ATM mutation; HR: homologous recombination DNA damage response and repair; CT: chemotherapy; PALB2m: PALB2 mutation; PFS: progression-free survival; mo: month; HR: hazard ratio; p: p value; OS: overall survival; ORR: overall response rate; IRR: independent radiology review; IA: investigator assessment; PSA RR: prostate-specific antigen response rate; m-rPFS: median radiological progression-free survival; 12-mo OS: overall survival at 12 months; FDA: Food and Drug Administration. 1 Novel hormonal therapy, e.g.; abiraterone acetate and/or enzalutamide. ijms-23-04709-t004_Table 4 Table 4 Active clinical trials on PARP-i in prostate cancer. PARP-i Clinical Trial Phase Patient Population Somatic Mutations/Germline PVs Treatment Arm(s) Olaparib NCT03434158 (IMANOL) 2 mCRPC BRCA1, BRCA2, ATM, FANC, CHEK2, MLH1, MSH2, MSH6, PMS2, PALB2, RAD51C, MRE11 Olaparib NCT03012321 2 mCRPC BRCA1, BRCA2, ATM Other DDR mutations Abiraterone alone OR Olaparib alone OR Abiraterone + Olaparib NCT03516812 2 CRPC MMR deficiency, HR deficiency Olaparib + Testosterone NCT03317392 1–2 mCRPC Not required Olaparib + Radium 233 NCT03834519 (KEYLYNK-010) 3 mCRPC Not available Pembrolizumab + Olaparib Vs Abiraterone OR Enzalutamide NCT03432897 2 Locally advanced Prostate cancer BRCA1, BRCA 2, ATM, CHEK1, CHEK2, FANCONI ANEMIA (FANCL), HDAC2, PALB2, BARD1, BRIP1, CDK12, PPP2R2A, RAD51B, RAD51C, RAD51D, or RAD54L Olaparib alone NCT03810105 2 Castration sensitive AND biochemically recurrent prostate cancer BRCA1, BRCA2, ATM, CHEK2, FANCA, RAD51C, RAD51D, PALB2, BRIP1, BARD1, or CDK12 Olaparib + Durvalumab Niraparib NCT02854436 2 mCRPC BRCA1, BRCA2, or other DDR alteration Niraparib alone NCT04037254 2 High-risk prostate cancer Not required Niraparib + ADT NCT04030559 2 High-risk localized prostate cancer DDR deficiency Neoadjuvant Niraparib Rucaparib NCT03442556 2 mCRPC DDR deficiency Rucaparib NCT02975934 (TRITON3) 3 mCRPC BRCA1, BRCA2 or ATM Rucaparib OR Docetaxel OR Abiraterone OR Enzalutamide NCT03413995 (TRIUMPH) 2 mHSPC BRCA1, BRCA2, ATM, CHEK2, NBN, RAD50, RAD51C, RAD51D, PALB2, MRE11, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM Rucaparib alone NCT03533946 2 Non-metastatic prostate cancer ATM, ATR, BARD1, BRCA1, BRCA2, BRIP1, CDK12, CHEK1, CHEK2, ERCC3, FAM175A, FANCA, FANCL, GEN1, HDAC2, MLH1, MRE11, NBN, PALB2, PPP2R2A, RAD51, RAD54L Rucaparib alone Talazoparib NCT03395197 (TALAPRO-2) 3 mCRPC DDR assessment required Talazoparib + Enzalutamide NCT03330405 2 mCRPC ATM, BRCA1 and BRCA2 Talazoparib + Avelumab NCT04332744 2 mHSPC Not required Talazoparib + Enzalutamide mCRPC: metastatic castration-resistant prostate cancer; mHSPC: metastatic hormone-sensitive prostate cancer; Prostate cancer: prostate cancer; MMR: mismatch repair; HR: homologous recombination; DDR: DNA damage repair; ADT: androgen-deprivation therapy. 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==== Front Animals (Basel) Animals (Basel) animals Animals : an Open Access Journal from MDPI 2076-2615 MDPI 10.3390/ani12091214 animals-12-01214 Article Impact of the COVID-19 Pandemic on Primate Research and Conservation Reuter Kim E. 12* https://orcid.org/0000-0002-6098-2056 Andriantsaralaza Seheno 1 https://orcid.org/0000-0002-4152-7947 Hansen Malene Friis 3456 LaFleur Marni 12 https://orcid.org/0000-0003-0744-1987 Jerusalinsky Leandro 37 Louis Edward E. Jr. 8 Ratzimbazafy Jonah 39 https://orcid.org/0000-0001-6848-9154 Williamson Elizabeth A. 310 https://orcid.org/0000-0002-8002-826X Mittermeier Russell A. 311 Giacoma Christina Academic Editor Bonadonna Giovanna Academic Editor 1 Lemur Love, San Diego, CA 92122, USA 2 College of Arts and Sciences, University of San Diego, San Diego, CA 92093, USA 3 IUCN SSC Primate Specialist Group Executive Committee, c/o Re:wild, Austin, TX 78767, USA 4 The Long-Tailed Macaque Project, 5000 Copenhagen, Denmark 5 Behavioural Ecology Group, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark 6 School of Social Sciences, Oxford Brookes University, Oxford OX3 0PB, UK 7 Centro Nacional de Pesquisa e Conservação de Primatas Brasileiros, Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio/CPB), Cabedelo 58010-480, Brazil 8 Center for Conservation and Research, Omaha’s Henry Doorly Zoo and Aquarium, Omaha, NE 68107, USA 9 Groupe D’étude et de Recherche Sur Les Primates (GERP), Antananarivo 101, Madagascar 10 Faculty of Natural Sciences, University of Stirling, Stirling FK9 4LA, UK 11 Re:wild, Austin, TX 78767, USA * Correspondence: kimeleanorreuter@gmail.com 08 5 2022 5 2022 12 9 121422 3 2022 06 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary The Coronavirus Disease 2019 (COVID-19) pandemic has made it harder to effectively protect and manage biodiversity, and this could make it more difficult for countries to show progress towards the Sustainable Development Goals (SDGs). Here, we surveyed experts in early 2022 from 30 countries to collect data on the impacts of COVID-19 on non-human primate research and conservation initiatives. Of the 93 experts that responded to our survey, we found that 39% had not been able to visit any of their field sites since March 2020 and only one out of ten had managed to achieve at least 76–100% of their planned primate-related work since March 2020. Six out of ten respondents (61%) felt that primate conservation efforts in protected areas were worse than before the onset of the COVID-19 pandemic and one-third (33%) felt hunting was happening more frequently than before. This study provides evidence of the impacts of COVID-19 on progress towards achieving SDG15 (Life on Land) and provides practical lessons learned for biodiversity conservation efforts moving forward. Abstract There is evidence to suggest that the Coronavirus Disease 2019 (COVID-19) pandemic may hamper our achievement of the Sustainable Development Goals (SDGs). Here, we use non-human primates as a case study to examine the impacts of COVID-19 on the ability to achieve biodiversity conservation and management sustainability targets. We collected data through a survey of members of the IUCN SSC Primate Specialist Group from January to March 2022. Of the 93 experts that responded to our survey, we found that 39% had not been able to visit any of their field sites since March 2020, 54% said they had less funding available for their primate-related work, and only one out of ten said they had managed to achieve at least 76–100% of their planned primate-related work since March 2020. Six out of ten respondents (61%) felt that primate conservation efforts in protected areas were worse than before the onset of the COVID-19 pandemic and one-third (33%) felt hunting was happening more frequently than before. This study provides evidence of the impacts of COVID-19 on progress towards achieving the SDGs, and provides practical lessons learned for biodiversity conservation efforts moving forward. primates sustainability conservation novel coronavirus SARS-CoV-2 COVID-19 Primate Program at Re:wild, Austin, TX, USAThis research was funded by the Primate Program at Re:wild, Austin, TX, USA. ==== Body pmc1. Introduction As one of the deadliest diseases to emerge in the 21st century, the Coronavirus Disease 2019 (COVID-19) pandemic continues to impact global economies on an unprecedented scale. With an initial impact described by the World Bank as causing the largest economic decline since World War II [1], the first year of the pandemic sparked large-scale societal shifts, such as the mass exodus of millions of urban laborers to rural parts of India [2], a 4.5% increase in sovereign debt levels across sub-Saharan Africa [3], and one-in-four employed people in the United Kingdom being furloughed (temporarily suspended from work duties, with the government paying their partial salaries) by their employers [4]. To counteract these impacts, governments invested billions to kickstart their economies; the World Bank alone invested over USD 157 billion across a 15-month period, which was 60% more than it invested in the 15-month period prior to the pandemic [5]. This economic recovery effort is described by many governments and stakeholders as an opportunity to move away from business-as-usual and to reinvigorate a drive towards sustainability and meeting the Sustainable Development Goals [6]. The reality, however, is that the COVID-19 recovery has been more ‘brown’ (i.e., unsustainable business-as-usual) than ‘green’ (i.e., environmentally sustainable) [6]. The COVID-19 pandemic has also impacted global biodiversity (see for example, [7,8]). Prior to the onset of COVID-19, the indirect economic drivers of environmental degradation were already well documented (e.g., [9]), and included loss of biodiversity, decreased functionality of ecosystems, and landscape degradation. Following the onset of the pandemic, and although global carbon dioxide emissions fell by 6.4% (2.3 billion tonnes) in 2020 (primarily due to restrictions on travel [10]), there have been reports of increases in forest loss [11], pollution from plastic medical waste in the ocean [12], and supply chain disruptions that impacted biodiversity in unexpected ways [13]. People have also changed the way they interact with the nature around them during the COVID-19 pandemic, including their increased use of urban parks and green spaces [14] and consumption of wildlife [15]. The combined effects of these changes in how humans interact with and use biodiversity, are not yet known. The COVID-19 pandemic has made it more difficult to protect and manage biodiversity, including in and around protected areas. Several studies have documented the impact of lockdowns and national and international travel restrictions on the ability to conduct routine monitoring activities (e.g., [16,17,18,19]). In some cases, the interruption of funding flows and regular tourism activities have negatively impacted the functioning of day-to-day protected area management [18,20]. In Madagascar, for example, the COVID-19 pandemic resulted in the reduction in salaries for staff and local personnel (including local park rangers) and a move to remote protected area management by teleworking and phone-based communications with rangers [19]. For local communities living around protected areas, the significant drop in income from eco-tourism meant that in some areas, communities increased their reliance on natural resources (obtaining them illegally from the protected areas [20]) where conservations organizations did not have the means to increase their support. In other cases, donors shifted their giving to provide support to locally based, eco-tourism guide associations to try and counteract some of these impacts and also to ensure their continuity into the future (R. Mittermeier, pers. obs.). The impact of the COVID-19 pandemic on other drivers of biodiversity loss is less clear. For example, in regard to the hunting of wild animals, some studies found that wildlife consumption declined in high demand countries (for example, China −28%, Thailand −41%, Vietnam −39%), with nearly half of people surveyed indicating that their decreased consumption was related to concerns about zoonotic disease transmission [21]. In other countries, however, there were reports of increased hunting and poaching including in Cambodia [22], Indonesia (M. Friis Hansen, unpubl. data), Madagascar [19], and Mexico [23]. Regarding the live trade of wildlife, studies are likewise mixed in their findings. One study found virtually no mention of COVID-19 in more than 20,000 online wildlife trade advertisements [24], although others hypothesized there could have been increased demand for live primates [25,26]. A systematic survey of online trade in two species of macaques saw a steep increase in macaques offered for sale on Facebook in Indonesia at the beginning of the pandemic, and this has continued ever since (M. Friis Hansen, unpubl. data). All this within a broader backdrop in which some parts of the world saw a large increase in pet ownership during COVID-19 [27] while, in others, a “pathological fear” developed of companion animals due to worries about disease transmission [28]. As a whole, the available evidence points to a picture in which biodiversity in many parts of the world has been and is still being negatively impacted by the COVID-19 pandemic, and where these negative impacts have become harder to manage. These negative impacts are often indirect and quite complex, and linked to human development issues such as food and water security, as well as governance and political systems [29]. Shifts in their magnitude depend, therefore, on the local and national context. It is not surprising that the United Nations has indicated that the achievement of the Sustainable Development Goals (SDGs) will be hampered by the impacts of COVID-19 [30], including SDG15 (Life on Land). The SDGs, also known as the Global Goals, were adopted by the UN General Assembly (which is comprised of the 193 member states of the United Nations) in 2015 to promote global sustainable economic development by 2030. The 17 goals expanded upon the 8 Millennium Development Goals and are novel in their cross-cutting and interdisciplinary nature. Though it will take all actors in society to work together to achieve the SDGs, primary responsibility sits with national government as the signatories of the agreement. Here, we use non-human primates (hereafter referred to as ‘primates’) as a case study to examine the impacts of COVID-19 on society’s ability to achieve its sustainability targets in relation to biodiversity conservation and management under SDG15, with particular reference to SDG indicators 15.5.1, which uses the International Union for Conservation of Nature (IUCN) Red List of Threatened Species to assess risk of biodiversity extinction, and 15.1.2 and 15.4.1, which are related to the area of land under formal protection (Table 1). Primates provide an interesting case study not only because they are a particularly well-studied group of animals that are often the target of on-the-ground conservation initiatives (e.g., [31]), but also 63% of all primates are today classified as threatened with extinction on the IUCN Red List [32]. Perhaps most importantly, primates are not only indirectly susceptible to the impacts of the COVID-19 pandemic but also directly susceptible to the SARS-CoV-2 variant of coronavirus that causes COVID-19 [34]. Several lemur species are considered high risk [32], which is concerning as some species are unable to survive in captivity, and lemurs (Lemuriformes) are the most threatened of the larger groups of mammals—106 of the 112 species and subspecies (95%) are now categorized as threatened on the Red List [35]. The platyrrhine primates (those native to Central and South America) show decreased susceptibility to SARS-CoV-2 [34] and to in vivo pathology [36]. All catarrhine primates (African and Asian monkeys and apes) are susceptible to SARS-CoV-2 [34,37], and in vivo experiments in some monkeys have demonstrated their infection (Macaca mulatta, M. fascicularis, Papio hamadryas, Chlorocebus sabaeus) [34]. The susceptibility and severity of pathology varies by primate species [36]. When the pandemic was first declared, the IUCN Species Survival Commission (SSC) issued a statement recommending emergency measures be implemented at all great ape tourism and research sites [38] and similar initiatives were launched by regional organizations including in Brazil (e.g., [39,40]). In 2021, the first case of COVID-19 in a great ape was diagnosed in captive western gorillas (Gorilla gorilla) at San Diego Zoo Safari Park in 2021 [41]; subsequent outbreaks in gorillas in several other zoos have been confirmed. Given the impact of COVID-19 directly and indirectly on primates, it is important to understand how the pandemic has impacted our ability to protect and manage biodiversity for their benefit. 2. Materials and Methods 2.1. Online Survey In January 2022, we sent an English-language online survey to members of the IUCN Species Survival Commission (SSC) Primate Specialist Group (PSG), a group of more than 700 experts across the world. Members of the PSG are considered authorities regarding primate conservation initiatives on-the-ground, including experts in both range and non-range countries. The voluntary, 20 min survey asked PSG members to give their opinion on the impact of the COVID-19 pandemic on their ability to do their primate-related work, as well as on protected areas in primate range countries, on primate hunting, and on live primate ownership. These topics were included in the survey because of their direct relevance to SDG15 indicators and targets (Table 1). PSG members were invited twice to complete the survey, with 93 having done so by mid-March 2022. The survey solicited this information through a series of closed and open-ended questions (File S1). Many questions used Likert Scale responses which are often used to measure attitudes and opinions [42]. 2.2. Ethical Research Considerations Research was deemed exempt by an ethics oversight committee (Institutional Review Board, University of San Diego, 2022). All survey participants were adults over the age of 18. Only current members of the Primate Specialist Group were recruited to participate in this survey. 2.3. Analysis Results are presented as mean values with standard deviations. We examined the difference in responses between respondents living in primate range and non-range countries using Fisher’s Exact Tests. Due to the voluntary nature of the survey, sample sizes varied but are clearly noted where relevant. 3. Results 3.1. About the Survey Respondents Ninety-three PSG experts responded to the survey. They collectively worked on 262 primate taxa (out of 713 taxa currently recognized by the Primate Specialist Group; 5 ± 5 taxa per respondent). These respondents were from 30 countries on all continents except Antarctica. Just over half (56%) of the surveyed respondents lived in primate range countries. Just under one-fifth of the respondents (18% of 93 respondents) were based in the United States of America. Respondents were affiliated with a range of institutions: 51% with academia, 39% with non-profit organizations (local and international) and social enterprises, 7% with governments, 5% with zoos, and 2% with field stations. Almost one out of ten (9%) surveyed experts had changed their institutional affiliation due to the COVID-19 pandemic. 3.2. Impact of the COVID-19 Pandemic on the Ability of Primate Experts to Work on Primate-Related Initiatives Nine out of ten (90%) respondents had to work remotely from home at any point between March 2020 and March 2022, and this did not differ between respondents in primate range and non-range countries (85% of 52 respondents in range countries vs. 98% of 41 respondents in non-range countries; Odds Ratio = 0.14; Fisher’s Exact Test, p = 0.0724, n = 93). Two-thirds of respondents (67%) said the institution they were affiliated with had closed partially or completely in that same time period due to the COVID-19 pandemic, with respondents in primate range countries 1.58 times more likely to report a partial or complete closure of their workplace (71% of 52 respondents) than respondents in non-range countries (61% of 41 respondents, Fisher’s Exact Ratio, p = 0.3768, n = 93 respondents). Respondents explained that they had to stop field activities several times because of confinement orders, though in two cases, it was not a respondent’s institution that closed, but the protected area that he/she was working in. These closures resulted in lost income for institutions. For example, one respondent wrote: “Our [non-governmental organization (NGO)] runs a program where international students pay bench fees to conduct their own research projects at our sites under the supervision of our professional scientists. This program was completely suspended for eight months due to border closures”. COVID-19 pandemic restrictions continue to impact respondents; 58% were working remotely or from home due to the COVID-19 pandemic at the time of taking the survey, and 26% said their institution was partially or completely closed at the time of taking the survey. Four out of ten respondents (39%) had not been able to visit their field sites since March 2020 due to the COVID-19 lockdown, and a further one out of ten (10%) had only been able to visit some of their field sites in that time. Respondents in non-range countries were 5.43 times more likely not to have been able to visit all of their field sites since March 2020 than respondents in range countries (29% of 41 non-range country respondents vs. 69% of 52 range country respondents, Fisher’s Exact Test, p = 0.0002). Four out of five (80%) respondents said that they, or the institution they were affiliated with, could put adaptive measures in place to mitigate or minimize the impact of the COVID-19 pandemic on their primate-related work. Respondents in range countries were 2.67 times as likely to say that adaptive measures were possible (87% of 52 respondents) compared to respondents in non-range countries (71% of 41 respondents, Fisher’s Exact Test, p = 0.0734). Respondents noted different actions that had been/could be taken including: being flexible about when they undertake field work, engaging local conservationists, and a range of standard COVID-19 reduction measures (e.g., home working, use of face coverings, support staff and their families to get vaccinated). Since March 2020, 54% of respondents said the amount of funding they had available for their primate-related work was now lower or much lower, and this appeared to affect both range and non-range respondents equally (56% and 51% of range and non-range respondents, respectively; Fisher’s Exact Test, p = 0.6810). Only 36% said they had the same amount of funding available to them, and 9% said they had higher or much higher amounts of funding available. It was not just amounts of funding that had changed, but also the reliability of that funding to flow; half (50%) of respondents said the amount of funding for their primate-related work had been stopped or interrupted by the COVID-19 pandemic, with 50% of both range and non-range respondents having experienced such changes in funding reliability (Fisher’s Exact Test, p = 1.00). In some cases, the change in funding streams was indirectly due to COVID-19. For example, two respondents said they applied for less funding because of the pandemic. In another case, one respondent noted that sources of private sector funding (e.g., Corporate Social Responsibility funding) had been diverted away from their work and towards COVID-19 efforts. Respondents surveyed had not been able to achieve as much of their primate-related work as they had hoped, since the onset of the COVID-19 pandemic. Only one out of ten respondents (11%) said they had managed to achieve 76–100% of the primate-related work since March 2020, that they had planned prior to the COVID-19 outbreak (Figure 1). Looking forward, four out of ten respondents (41%) expect to be able to complete 76–100% of the primate-related work they previously planned. All but two respondents (91 out of 93 people) responded when asked to describe how the COVID-19 pandemic had affected their ability to conduct primate-related work. Many described the difficulties mentioned above (remote working, reduced funding, difficulty travelling to field sites, delays in progressing their work), with one respondent describing being in 400 days of lockdown and several describing their country/field sites as being virtually inaccessible for well over 1.5 years. In other cases, the closure of national parks meant that primatologists could go about their everyday lives, but not undertake their primate-related work. One respondent simply wrote, “No research. No tourism. [It’s] devastating. For two years no students [and] no researchers worked at our research station”. Aside from the physical and emotional impact of COVID-19 on themselves and their staff, they described:Additional administrative workloads from the pandemic taking away from their ability to do substantive primate-related work (three respondents) and working on a reduced salary (two respondents); Complete/permanent closures of programs (two respondents) or pausing programs to safeguard local communities (two respondents) or primates (two respondents) against increased disease risk; Increased financial costs due to COVID-19 testing and purchase of personal protective equipment (PPE) (two respondents) and increased time needed to quarantine prior to entry to field sites (two respondents); Research permits expiring and taking unusually long periods of time to be renewed due to the impacts of COVID-19 on governments (one respondent) or inability to export/import samples for months/years following the onset of the pandemic (one respondent); Two respondents said that the urgency for COVID-19 vaccinations or test processing has impacted on their work (e.g., laboratories being re-purposed away from offering a range of analytical services to focusing on COVID-related analyses); Breakdown of technical equipment in the field that could not be repaired due to lack of accessibility as a result of COVID-19 travel restrictions (one respondent); Long-term data collection disrupted, with one respondent writing, “we have 35 years of continuous primate follows but in 2020 we only have a few months of data”; Primates becoming unhabituated to respondents (one respondent); The risk of following habituated primates in the wild being too high due to disease transmission (one respondent); Delays for both range-country and non-range country students in obtaining their university degrees due to lack of ability to do field research (two respondents) and student field courses being cancelled (four respondents); Fewer discussions and exchanges with local leaders adjacent to/near project areas (one respondent). There were a few positive changes that respondents mentioned. For example:When the pandemic started in March 2020, one respondent’s students were safer staying at the field site than returning to crowded cities; Two respondents were able to expand their consulting services due to the wider acceptance of digital working; Four respondents described their in-country colleagues and staff as taking on a greater leadership role in projects, or local communities strengthening their participation in projects; One respondent described their organization proactively using the time to rebuild ageing tourism infrastructure; Several noted that they were able to publish more articles than usual, working with their existing datasets. 3.3. Impact of the COVID-19 Pandemic on Protected Areas Of the respondents surveyed, 80% (n = 75 out of 93 respondents) did primate-related work that involved working in/around protected areas. These respondents generally felt that the services provided by protected areas were worse than before the COVID-19 pandemic (Figure 2). Six out of ten respondents (61% of 70 respondents) felt that primate conservation in the protected area(s) where they worked was ‘somewhat worse’ or ‘much worse’. In relation to the protected areas where respondents did primate-related work:Two-thirds (65% of 69 respondents) felt that visitor services or tourism facilities at protected areas were ‘somewhat worse’ or ‘much worse’; Two-thirds (66% of 68 respondents) felt that conservation activities, such as patrolling, anti-poaching, monitoring, research, control of invasive species, and habitat restoration are ‘somewhat worse’ or ‘much worse’; Eight out of ten (78% of 69 respondents) felt that public engagement, outreach and the provision of services to local communities in and around the protected areas were ‘somewhat worse’ or ‘much worse’; Four out of ten (44% of 67 respondents) felt that protected area staffing levels were ‘somewhat worse’ or ‘much worse’; Four out of ten (42% of 67 respondents) felt that working conditions, workloads, safety or well-being of protected area staff were ‘somewhat worse’ or ‘much worse’; Over half (61% of 67 respondents) felt that the financing of protected areas was ‘somewhat worse’ or ‘much worse’. Respondents were asked what measures were introduced in protected areas in response to the COVID-19 pandemic, that will be continued after the pandemic is over. Half of the respondents (52% of 63 respondents) said they were not sure, or that there were no measures in place after the pandemic. The other half, however, listed a range of protective measures including: (1) new or improved health protocols to reduce disease transmission from humans to primates (27% of respondents) including the use of face masks, restricted visitor numbers, and minimum distancing with primates; and (2) more or different types of patrolling (10%) including the increased use of local communities in patrolling. Individual respondents also said that they thought there would be an increase in the use of technology to do remote protected area monitoring and an increase in other remote work. Respondents were asked what lessons for protected areas can be learned from the COVID-19 pandemic, and how protected area management should be changed in the post-COVID-19 era. One-quarter (28% of 57 respondents) mentioned a need to adjust protected area funding models and one-quarter (25% of 57 respondents) mentioned the need to improve governance and operations of protected areas. Several respondents mentioned, for example, the need to diversify funding sources (across state and non-state actors), and described the need to move away from a reliance on funding from tourism. One respondent even wrote, “we did not rely on tourism before [the pandemic] and I think that has been essential in being able to continue our project”. In regard to governance and operations, respondents noted the importance of having consolidated systems, decentralized staffing (e.g., use of staff in proximity to the protected area; establishment of local community groups to continue monitoring and management), improved protocols (for management, patrol, risk management, and monitoring), long-term/multi-year and sustainable financial and governance plans, improved facilities (technology, infrastructure, and programming), and adaptive management. Improved tourism management (through, for example, reduced numbers or introduction of virtual tourism) was mentioned by four respondents (7%), while five respondents (9%) mentioned the need to think about alternative livelihoods for local communities or consider how local communities were engaging with the protected area(s). Six respondents (11%) mentioned the need to continue to implement health protocols that protect primates. 3.4. Impact of the COVID-19 Pandemic on Primate Hunting Four out of ten respondents (39% of 93 respondents) did not know whether primate hunting had changed since the onset of the COVID-19 pandemic in March 2020. This included more than half of respondents (51% of 41 respondents) in non-range countries but less than one-third of respondents (29% of 52 respondents) in non-range countries (odds ratio: 0.39; Fisher’s Exact Test, p = 0.0336). Of the 52 respondents who had a view on whether or not hunting practices had changed following the onset of COVID-19, 56% felt that hunting rates had not changed, 33% felt hunting was happening ‘more frequently’ or ‘much more frequently’, and 12% felt that hunting was happening ‘less frequently’ or ‘much less frequently’ (Figure 3). One-third of respondents (32% of 87 respondents) did not know whether authorities had changed how effectively they enforced laws in regard to primate hunting in the sites/countries where they conducted their primate-related work. Of the 59 respondents who did have a view on the situation, two-thirds (64%) felt that law enforcement effort was the same as before, with only 5% saying it was ‘somewhat better’ or ‘much better’ and 27% saying it was ‘somewhat worse’ or ‘much worse’ (Figure 3). In regard to the coverage of hunting on social media, half of respondents did not know (47% of 87 respondents) whether or not hunted/dead primates were appearing more or less frequently on social media since the onset of the COVID-19 pandemic in March 2020. Of the 46 respondents that had a view, two-thirds (65%) said the situation was the same as before, 20% said it was happening ‘less frequently’ or ‘much less frequently’, and 15% said it was happening ‘more frequently’ (Figure 3). Respondents were asked why they believed COVID-19 had changed or not changed the hunting of primates at the sites where they work. In their view, hunting had increased because of lower levels of oversight including fewer patrols, less active research, and fewer tourists visiting (13 respondents). One respondent wrote bluntly, “[Our] personnel followed COVID restrictions. Poachers did not”. Hunting was also thought to have increased because of food security issues (including increase in food prices) and due to lack of alternative income often because of a lack of tourism or because of disruption in food commodity trade networks (11 respondents). In India and Cambodia, hunting was noted to have increased at the start of the pandemic when people temporarily moved from urban areas back to rural areas. In cases where respondents saw no change in hunting rates, they said it was either because hunting simply continued as normal (5 respondents), or because there were extenuating circumstances as to why hunting was not common in the first place including, for example, religion (1 respondent), the small size of the primates (1 respondent), and the presence of criminal groups operating in the region (1 respondent). Respondents were asked if they knew of any primates that had been killed specifically due to COVID-19, for example due to fear that primates were carriers of COVID-19. No respondents reported having heard about any primate deaths directly due to COVID, though in one case, a respondent wrote, “we noticed that there were A[louatta] pigra monkeys with coughs and sneezes at the same time as the peaks of contagion in the communities…[but] the death of primates has not increased”. 3.5. Impact of the COVID-19 Pandemic on Live Primate Ownership Most respondents did not know whether the COVID-19 pandemic had changed the frequency with which primates were being kept as pets (49% of 90 respondents). Of the 46 respondents with a view, 61% thought the situation was the same as before, 24% thought it was happening ‘less frequently’ or ‘much less frequently’, and 15% thought it was happening ‘more frequently’ (Figure 4). One respondent wrote that in Indonesia, “during the early months of the pandemic, the interest of keeping pets/wild animals increased, creating [an] additional market for wildlife and encouragement for poaching”. In another case, a respondent provided anecdotal information that chimpanzee (Pan troglodytes) orphans had been confiscated in greater numbers since the pandemic started. The majority of respondents did not know if the COVID-19 pandemic had changed the wellbeing of primates kept as pets within habitat range countries (67% of 89 respondents; though 69% of the 29 respondents who had a view said that pet primate wellbeing ‘stayed the same’). Four out of ten respondents did not know whether authorities had changed their effectiveness in enforcing the laws with regard to pet primate ownership in the sites/countries where they conducted their primate-related work (38% of 89 respondents). Of the 55 respondents that had a view, 64% said the situation was the same as before (though several commented that enforcement had already been so poor before the pandemic, so perhaps it could not get worse than it already was), 24% said it was ‘somewhat worse’ or ‘much worse’, and 13% said it was ‘somewhat better’ or ‘much better’ (Figure 4). In regard to the coverage of pet primates on social media, respondents often did not know (55% of 89 respondents) whether or not pet primates were appearing more or less frequently on social media since the onset of the COVID-19 pandemic in March 2020. Of the 40 respondents that had a view, 65% said the situation was the same as before, 18% said it was happening ‘more frequently’ or ‘much more frequently’, and 15% said it was ‘less frequently’ (Figure 4). One out of ten respondents were aware of a pet primate being released into the wild, sold, killed, or given away as a gift due to COVID-19 (10% of 89 respondents were aware of such an incident). 4. Discussion 4.1. Impact of the COVID-19 Pandemic on Primate Experts’ Ability to Work on Primate-Related Initiatives As with many other industries and professions, the COVID-19 pandemic impacted the respondents’ ability to progress in their primate-related work. We found that respondents in both primate range and non-range countries experienced professional difficulties due to COVID-19, and both reported, for example, similar difficulties accessing funding. Both sets of respondents also reported drastically reduced productivity as COVID-19 interrupted travel and research agendas. For example, to prevent primates from contracting COVID-19 from humans, many primate-viewing destinations (national parks and other protected areas) were temporarily closed to visitors (e.g., some parks in Gabon, Nigeria, and the Republic of Congo; parks managed by Madagascar National Parks; all protected areas in Indonesia) and researchers. In some cases, respondents reported voluntarily pausing their work so as not to potentially expose primates to COVID-19. If our survey results—which found that only one out of ten respondents had achieved most of their planned primate-related work since March 2020—are representative of wider progress on biodiversity research and conservation initiatives, it does not bode well for wider progress under SDG15. The slowdown of research and conservation initiatives, coupled with an inability to conduct fieldwork, has surely had an economic impact on primate habitat countries, many of which are heavily dependent on tourism (including from researchers) for revenue. It is interesting, though not surprising, that respondents in non-range countries experienced different types of difficulties than those in range countries and, perhaps consequently, the information they could provide differed. For example, respondents in range countries were more than five times more likely than respondents in non-range countries to have visited all of their field sites since March 2020, and almost three times more likely to say that adaptive measures were possible to ensure their primate-related work continued. Likewise, respondents in non-range countries were twice as likely to say, for example, that they did not know whether primate hunting in their study sites had changed since the onset of the COVID-19 pandemic. One leading primate respondent based in Europe whose career exceeds 45 years—and after having selected the response “I do not know” in almost every question of the survey—closed out their survey response simply by writing, “it is amazing how little we actually know”. Here, as elsewhere in this paper, it is important to acknowledge that the anecdotal observations captured within our survey may or may not reflect the overall trends in threats facing primates (e.g., trends in primate hunting or pet ownership) though many of the observations reported in the survey are concerning (see below). It is important to note that these differences between range and non-range primate respondents will impact conservation efforts differently in different parts of the world. For example, Neotropical primates tend to be studied proportionately more by range-country primatologists than African primates are, and hence Neotropical primate research and conservation efforts were perhaps more able to adjust to the COVID-19 pandemic than other geographies where most primatologists are from non-range countries. This may also be why our survey was responded to by respondents from just one African country (Madagascar), as compared to the other geographies, where we had responses from seven Asian countries and ten South American countries. A lesson learned here is to ensure the sustainability of research sites long term, including having exit plans in place [43], and to more proactively address broader social and ethical issues that arise in the course of tropical research and conservation agendas [44]. These exit plans should protect the local communities and primate population if events such as the COVID-19 pandemic occur. If it is not possible to have exit plans or to commit long-term to a site, researchers may need to reconsider initiating research [45]. A positive outcome, as described by respondents, is the increased collaboration between range and non-range country respondents, and with local communities. This increased collaboration and increased inclusion of range country respondents and local communities is a very positive trend and bodes well for the future of primate conservation and research, representing a possible permanent shift in the primatological community which has been noted by others [46,47]. 4.2. Impact of the COVID-19 Pandemic on Protected Areas The importance of effective protected areas to the delivery of SDG15 is evidenced in their inclusion in SDG15 indicators (15.1.2; 15.4.1; Table 1). It is important, therefore, that six out of ten respondents in our study felt that primate conservation in protected areas was worse than before the onset of the COVID-19 pandemic. When asked about six different aspects of protected area governance and management, more than half of respondents felt that the protected areas were worse off in four out of the six areas (including visitor services, patrolling and anti-poaching activities, provision of services to local communities, and in terms of the financing of the protected area). These results are concerning, not least because primates are charismatic megafauna that can generate significant resources for conservation and serve as flagship species for governmental and stakeholder aspirations and initiatives (e.g., [48]). In other cases, some primates are now entirely restricted to protected areas (e.g., mountain gorilla subspecies Gorilla beringei beringei) and protecting the integrity of these habitats is crucial. If the information collected in our survey reflects reality ‘on-the-ground’, it will take much more resource and significant effort to recover from the damage incurred over the last two years. Funding constraints were unfortunately described by numerous respondents, many of them proposing contradictory solutions: where government funding was lacking, they proposed that this needed to be secured, and where government funding was the sole funding source, they proposed that funding streams needed to be diversified. All this in the context that, even before COVID-19, protected areas in less-developed countries were experiencing higher anthropogenic pressure [49]—often because local communities’ livelihoods were based on subsistence living practices—and so were already in need of additional resources and support. This points to a need for primate conservation projects, both within and outside protected areas, to move towards diversified portfolios of funding to buoy these initiatives against the deleterious effects of sudden drops in tourism-related income. Helpfully, of the respondents in our survey who had an opinion on how protected area management could be improved, there was a clear consensus on the importance of continuing and strengthening health protocols and of diversifying and improving patrols and monitoring. This is important as it relates not just to the COVID-19 pandemic but to other communicable diseases such as the Avian influenza. Prior to the COVID-19 pandemic, disease prevention measures had been elaborated for primate tourism and research (e.g., [50,51]). Still, appreciation for the importance of these measures was low even in the primatology community. Prior to the pandemic, for example, disease prevention measures were not routinely promoted at lemur-watching sites in Madagascar despite evidence of human-lemur disease transmission (e.g., [52,53]). This meant that popular lemur-watching sites were over-crowded and minimum distancing was not observed, with lemurs in some sites continually disturbed by human visitors (J. Ratzimbazafy, pers. obs.). There is an opportunity now to ‘reset’ primate tourism in Madagascar and address these issues, so as to make it more sustainable. Likewise in Brazil, it was only after COVID-19 that research permits included precautionary recommendations for researchers to limit disease transmission. In Central Africa, the wearing of face masks by great ape tourists in Virunga National Park (Democratic Republic of the Congo) has been required for more than a decade, but adoption of the IUCN best practice guidelines has now improved at many sites. Post-COVID, additional measures have been put in place (handwashing stations constructed at tourist reception points, skin temperature of tourists measured, proof of vaccination and/or negative COVID test result required), and the wearing of face masks by great ape tourists and researchers has become obligatory in Rwanda and Uganda. Respondents also provided a good range of tangible governance and management improvements to institute, with many emphasizing the need to develop interdisciplinary programs to support local communities. Research has also shown that community-managed forests experience less deforestation than protected ones [54] further indicating a need for a shift in primate conservation. 4.3. Impact of the COVID-19 Pandemic on Primate Hunting Our survey showed that—where respondents felt that COVID-19 had changed hunting rates—they were more than two times more likely to say that hunting had increased rather than decreased due to COVID-19. While the anecdotal observations of experts in our study may not reflect overall changes in hunting patterns, the diversity in responses reflects, however, that wildlife trade markets and wildlife commodity chains are structured very differently in different countries (e.g., see [55] in Madagascar), and also that primate meat is eaten not just for food security reasons but also as a result of cultural preference. This meant that we sometimes received seemingly contradictory information from respondents. For example, in one case, the closure of food markets increased food insecurity which increased primate hunting (as people hunted primates in order to feed themselves). In another case, however, the closure of food markets and transit routes reduced primate hunting because the commodity chain had been disrupted and this then reduced demand from middlemen in the wild meat trade. One-quarter of respondents felt that the authorities were enforcing hunting laws less than before the pandemic. Extraction of primates from the wild can include hunting of primates for meat or extraction of live animals for pet ownership, entertainment, and research. While it is still difficult to conclude how the ongoing pandemic has changed the extraction of primates from the wild, reports from Southeast Asia and Colombia confirm an increase in hunting of primates, especially macaques, for research, both nationally and internationally (M. F. Hansen and A. Maldonado, pers. obs.). In Bangladesh, a local pharmaceutical company turned to wild rhesus macaques (Macaca mulatta) for preclinical COVID-19 vaccine testing, which lead to a local outcry. The demand for non-human primates for preclinical testing has undoubtedly increased during the COVID-19 pandemic, and further threatens wild primate populations [56]. 4.4. Impact of the COVID-19 Pandemic on Primate Pet Ownership Our survey showed that—where respondents felt that COVID-19 had changed primate pet ownership rates—they were more likely to say that pet ownership had decreased than increased. It was also clear that on this topic, respondents were far more likely not to know the answers to our questions, than when asked similar questions about primate hunting or protected area governance. Prior to the COVID-19 pandemic, it was already the case that in some regions, the magnitude and scope of hunting and capture of primates was not accurately reflected in the peer-reviewed literature because of the difficulty in researching oft-illegal extraction and trade (e.g., African lorises, [57]; lemurs, [58]). In some countries, the trade and ownership of pet primates is so hidden that not even neighbors of primate owners are aware that there is a pet primate in their vicinity (e.g., lemurs; [59]). In the context of COVID-19, which further restricted people’s movements within their national borders, it is perhaps not surprising that knowledge of this subject area is low among the respondents to our survey. Given the extensive closures of international borders, our assumption had been that during the first two years of the pandemic, any increases in the trade of live pet/captive primates would have been seen domestically (i.e., increases contained within primate range countries). Counterintuitively, however, the most significant example of increased live extraction of primates was reported for the “the use of wild primates (capture and transportation across international borders) to test medicines by labs against COVID” and was described as, “increasing and…being excused by the argument that new medicines need to be developed to fight COVID”. This anecdote is supported by data which show that from 2019 to 2020, international primate trade of the long-tailed macaque (Macaca fascicularis) increased 225% from 61,000 individuals traded to 151,000 individuals [60] due to the demand for primate research subjects for COVID-19 pre-clinical research and toxicology testing [25,26]. Many of these primates are suspected to have been wild-caught. Concurrently, both price and demand for M. fascicularis as a trade commodity have skyrocketed during the COVID-19 pandemic relative to the already regular and heavy pre-pandemic capture and trade [25,26]. The price per long-tailed macaque quadrupled from 2019 [26] to 2022. Should we see this trend in other primate species, the trade of live animals could represent a significant new threat and would directly impact on countries’ abilities to make progress towards SDG 15.5.1 (Table 1). 5. Conclusions This study provides further evidence that the impacts of COVID-19 have likely jeopardized progress towards SDG15. First, our study suggests that primates are likely to be facing increased threats due to the impacts of COVID-19. Respondents in our study felt that protected areas with primate populations were broadly doing worse following the onset of the pandemic, and many reported increases in primate hunting and the primate pet trade. In other cases, respondents to our survey listed a range of ways in which primates been impacted indirectly by COVID-19, including: (1) through habitat loss following increased agricultural production to address COVID-19-related food security issues; (2) where COVID-19 has been used as a pretext to weaken environmental protection (e.g., in Brazil); or (3) where COVID-19 simply distracted funders, governments, and other stakeholders away from environmental topics and towards health issues. All of these pieces of evidence, combined with what we already knew to be a difficult primate conservation and management landscape, do not paint a positive picture with regard to the SDG15.5′s aim to protect and prevent the extinction of species (Table 1). Addressing the impacts of COVID-19 on primate conservation and management initiatives will require more funding, although this begs the question from where this additional funding will come. Zoos, which fund a lot of primate work around the world, have seen their budgets drastically reduced due to lack of visitors during COVID-related closures. One respondent wrote that their zoo, “closed for a total of 242 days in 2020/2021 [and this] led to reduced funding of our primate projects. The same happened to zoos globally, so that primarily zoo-funded projects suffered considerably financially”. For zoos, it is not just the number of days they are open for visitors to consider, but also how “extended periods of visitor absence and changes in human behavior have affected and potentially continue to affect animal behavior”. There are similarly complex questions surrounding funding from other sources such as governments, the private sector, and high-net-worth individuals and private foundations. Although colleagues may have partially offset budget reductions through cost savings achieved by remote participation in workshops and conferences, it is not clear that this is good long-term budget management strategy. There are opportunities, however, to help engage the public in primate conservation. In many areas of the world, the COVID-19 pandemic helped reconnect people to nature and their natural surroundings, or represents an opportunity to enhance biodiversity conservation [14,16]. In one touching example, a respondent shared how an NGO in a primate range country had created a small remembrance forest where, “friends and relatives can plant native-tree seedlings to honor the memory of loved ones lost to COVID-19, moving many to tears”. The person concluded by noting that, “people who planted native trees there will value that forest forever”. Lastly, the COVID-19 crisis has been an opportunity to reassess the management and research strategy approaches for biodiversity conservation, particularly in low-income regions [16]. An inclusive approach is especially important when we consider that the wider primate conservation landscape typically goes beyond protected areas, and into areas where humans and primates must necessarily coexist. Acknowledgments Thank you to members of the IUCN SSC Primate Specialist Group for information that formed the basis of this paper. Thanks also to A. Rylands, T. Gillespie, and three anonymous reviewers for contributions that greatly improved the quality of the manuscript. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani12091214/s1, File S1. Survey questions used to collect data for this study. Click here for additional data file. Author Contributions Conceptualization, K.E.R., S.A., M.L., L.J. and R.A.M.; methodology, K.E.R., S.A., M.L., L.J. and R.A.M.; formal analysis, K.E.R.; resources, R.A.M.; data curation, K.E.R.; writing—original draft preparation, K.E.R., S.A., M.F.H., M.L., E.E.L.J., L.J., J.R., E.A.W. and R.A.M.; writing—review and editing, K.E.R., S.A., M.F.H., M.L., E.E.L.J., L.J., J.R., E.A.W. and R.A.M.; supervision, R.A.M. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Research was deemed exempt by an ethics oversight committee (Institutional Review Board, University of San Diego, 10 January 2022, Protocol Number: IRB-2022-209). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The disaggregated dataset used in this study is not publicly available due to the duty of confidentiality to survey participants, and given that their responses could make them publicly identifiable. Survey participants, as part of their informed consent statement, consented to the following confidentiality: “The results of this research project may be made public and information quoted in professional journals and meetings, but information from this study will only be reported as a group, and not individually”. Conflicts of Interest One of the authors (RAM) of this study is an employee and one provides occasional consulting services (KER) to a non-profit organization that supported this study. This organization, however, did not have a role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 1 Self-reported work productivity by respondents in the past (from March 2020 to March 2022) and into the future (into the next two years). Respondents were asked to estimate how much they had managed to achieve (from four categories) relative to what they would have achieved had the COVID-19 pandemic not occurred. Figure 2 Opinions given by respondents on the how the COVID-19 pandemic had affected different aspects of protected area functioning and governance. Figure 3 Opinions given by respondents on the how the COVID-19 pandemic had affected different aspects of primate hunting in sites and countries where they worked. Figure 4 Opinions given by respondents on the how the COVID-19 pandemic had affected different aspects of pet primate ownership in sites and countries where they worked. animals-12-01214-t001_Table 1 Table 1 Targets and Indicators under Sustainable Development Goal 15 (Life on Land) [33]. Indicators of particular relevance to this article are presented in bold font. Goal 15. Protect, Restore and Promote Sustainable Use of Terrestrial Ecosystems, Sustainably Manage Forests, Combat Desertification, and Halt and Reverse Land Degradation and Halt Biodiversity Loss Target Description Indicator 15.1 By 2020, ensure the conservation, restoration and sustainable use of terrestrial and in-land freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands, in line with obligations under international agreements. 15.1.1 Forest area as a proportion of total land area. 15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type. 15.2 By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globally. 15.2.1 Progress towards sustainable forest management. 15.3 15.3 By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation neutral world. 15.3.1 Proportion of land that is degraded over total land area. 15.4 15.4 By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development. 15.4.1 Coverage by protected areas of important sites for mountain biodiversity. 15.4.2 Mountain Green Cover Index. 15.5 15.5 Take urgent and significant action to reduce the degradation of natural habitats, halt the loss of biodiversity and, by 2020, protect and prevent the extinction of threatened species. 15.5.1 Red List Index. 15.6 15.6 Promote fair and equitable sharing of the benefits arising from the utilization of genetic resources and promote appropriate access to such resources, as internationally agreed. 15.6.1 Number of countries that have adopted legislative, administrative and policy frameworks to ensure fair and equitable sharing of benefits. 15.7 15.7 Take urgent action to end poaching and trafficking of protected species of flora and fauna and address both demand and supply of illegal wildlife products. 15.7.1 Proportion of traded wildlife that was poached or illicitly trafficked. 15.8 By 2020, introduce measures to prevent the introduction and significantly reduce the impact of invasive alien species on land and water ecosystems and control or eradicate the priority species. 15.8.1 Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of invasive alien species. 15.9 15.9 By 2020, integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts. 15.9.1 (a) Number of countries that have established national targets in accordance with or similar to Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011–2020 in their national biodiversity strategy and action plans and the progress reported towards these targets; and (b) integration of biodiversity into national accounting and reporting systems, defined as implementation of the System of Environmental-Economic Accounting. 15a 15.a Mobilize and significantly increase financial resources from all sources to conserve and sustainably use biodiversity and ecosystems. 15.a.1 (a) Official development assistance on conservation and sustainable use of biodiversity; and (b) revenue generated and finance mobilized from biodiversity-relevant economic instruments. 15b 15.b Mobilize significant resources from all sources and at all levels to finance sustainable forest management and provide adequate incentives to developing countries to advance such management, including for conservation and reforestation. 15.b.1 (a) Official development assistance on conservation and sustainable use of biodiversity; and (b) revenue generated and finance mobilized from biodiversity-relevant economic instruments. 15c Enhance global support for efforts to combat poaching and trafficking of protected species, including by increasing the capacity of local communities to pursue sustainable livelihood opportunities. 15.c.1 Proportion of traded wildlife that was poached or illicitly trafficked. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093197 materials-15-03197 Article Effects of Tung Oil Composite Regenerating Agent on Rheological Properties and Microstructures of Reclaimed Asphalt Binder Wang Qimin 1 Ye Qunshan 23* Luo Junhui 1 Xie Cheng 1 Liu Haobin 1 Liu Jianhua 3 Qin Mengnan 3 Li Yuanyuan Academic Editor 1 Guangxi Beitou Transportation Maintenance Technology Group Co., Ltd., Nanning 530201, China; wangqm1615@163.com (Q.W.); jhluo85@hotmail.com (J.L.); xiecheng1357@163.com (C.X.); liuhaobin9203@163.com (H.L.) 2 Key Laboratory of Road Structure and Material of Ministry of Transport (Changsha), Changsha University of Science and Technology, Changsha 410114, China 3 Department of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China; 005848@csust.edu.cn (J.L.); szct@stu.csust.edu.cn (M.Q.) * Correspondence: yequnshan@csust.edu.cn; Tel.: +86-136-8732-6030 28 4 2022 5 2022 15 9 319731 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The single light oil regenerating agent has certain limitations on the performance recovery of aged asphalt. In this study, tung oil, dioctyl phthalate (DOP), C9 petroleum resin, and organic montmorillonite (OMMT) were used to prepare the composite regenerating agent, and its optimal mix proportion was determined by the orthogonal experimental design. The rheological properties and anti-aging performance of reclaimed asphalt were studied by the dynamic shear rheometer (DSR) and bending beam rheometer (BBR); and the Fourier transform infrared (FTIR) spectrometer, gel permeation chromatography (GPC), and scanning electron microscope (SEM) were adopted to explore its microstructure, morphology, and mechanism of action. The results show that with the addition of tung oil composite regenerating agent, the rheological properties of aged asphalt can be effectively recovered, even better than that of base asphalt. By using the complex modulus aging index (CMAI) and phase angle aging index (PMAI) it is found that the anti-aging performance of reclaimed asphalt is better than that of base asphalt. With the optimal content of the tung oil composite regenerating agent, the contents of characteristic functional groups and macromolecular asphaltenes in the aged asphalt can be reduced, indicating that the composite regenerating agent is beneficial to the dispersion and dissolution of polar substances in the aged asphalt. After aging, a large number of wrinkles appear on the surface of the asphalt. However, the addition of the tung oil composite regenerating agent can make the asphalt surface smooth, which indicates that the tung oil composite regenerating agent can restore the microstructure and morphology of aged asphalt to a certain extent. tung oil composite regenerating agent aged asphalt reclaimed asphalt rheological properties microstructure and morphology Ministry of Transport of People’s Republic of China 2020-MS1-005 the Education Department of Hunan ProvinceNo. 20A012 the Department of Science of Changsha City kq2014107 the Key Laboratory of Road Structure and Material of Ministry of Transport (Changsha University of Science and Technology) kfj140301 This research was funded by Ministry of Transport of People’s Republic of China (No.2020-MS1-005), the Education Department of Hunan Province (No. 20A012), the Department of Science of Changsha City (No. kq2014107), and the Key Laboratory of Road Structure and Material of Ministry of Transport (Changsha University of Science and Technology, No. kfj140301). ==== Body pmc1. Introduction With the rapid development of road construction in China, asphalt pavement is widely used due to its excellent road performance. However, under the comprehensive action of various factors, such as a complex traffic environment and driving load, the aging phenomena of asphalt materials inevitably appear, which weakens the adhesion, aging resistance, low-temperature crack resistance, and other properties of asphalt pavement, eventually producing a large amount reclaimed asphalt pavement (RAP) [1,2]. By rationally treating a large number of old materials, the shortage of raw asphalts can be alleviated and an economical and environmentally friendly society with green transportation can be realized. Therefore, much attention has been paid to the recycling technology of reclaimed asphalt mixtures. The recycling technology of asphalt is that a certain proportion of a regenerating agent is added to the reclaimed asphalt and effectively achieves its regeneration, thereby prolonging the service life of asphalt pavement [3,4,5]. The demand for regenerating agents is increasing day by day. However, it is difficult for the traditional single light oil to be used as the regenerating agent to achieve the ideal regeneration effect. In addition, the regeneration cost is growing higher and higher, which restricts the recycling technology. Therefore, it is inevitable to develop a green, environmentally friendly, and economical composite regenerating agent to replace the traditional regenerating agent and single light oil. At present, regenerants with mineral oil as the main component are widely used. However, its shortcomings such as strong volatility and non-renewable restrict the development of regeneration technology [6]. The reclaimed light oil is usually used as a regenerating agent because of its good performance, low cost, and easily available raw materials. For example, reclaimed vegetable oil, organic oil, aromatic extract, distilled tall oil, and bio-oil can be used as regenerating agents to restore the performance of aged asphalt [7,8,9]. The reclaimed cooking oil (WCO) is conducive to the physical properties, rheological properties, and other pavement behaviors of asphalt binders [10,11,12]. Eleyedath et al. [13] believed that the light components contained in such regenerating agents had a small molecular weight and low viscosity, which could quickly restore their performance after mixing with the aged asphalt; however, the difference in molecular weight between these lightweight components and other molecules was too large, which led to poor compatibility, and the rapid loss of small and medium molecules caused the poor durability of asphalt pavement. The use of bio-oil can improve the low-temperature cracking resistance of aged asphalt, and its microstructure is similar to that of base asphalt [14,15,16]. The residual soybean oil selected as a regenerating agent increases the permeability and reduces the viscosity, which is unfavorable for the high-temperature rutting resistance [17,18]. Moreover, the reclaimed engine oil chosen as a regenerating agent improves the viscoelasticity and flexibility of the asphalt binder, and its anti-stripping performance is comparable to that of base asphalt [19,20]. Zhang et al. [21] used reclaimed wood-derived bio-oil to balance the chemical components of aged asphalt. Tung oil with the main chemical component of fatty acid triglycerides can be used as a natural light oil to supplement and balance the missing components of aged asphalt [22]. From the above-mentioned information, most of the current research focuses on a single oil regenerating agent. However, a single light oil or other aromatic compounds used as a regenerating agent can only improve the fluidity of the asphalt but finds it difficult to restore or improve the overall performance of the reclaimed asphalt. In this paper, tung oil, dioctyl phthalate (DOP), C9 petroleum resin, and organic montmorillonite (OMMT) were compounded to prepare a composite regenerating agent of asphalt. The optimal content of each raw material of the composite regenerating agent was determined by the orthogonal design test, which effectively restored the rheological properties and anti-aging performance of aged asphalt, and combined with the microstructure test, its regeneration mechanism was analyzed as well. 2. Raw Materials and Test Methods 2.1. Raw Materials The tung oil from a tung oil factory in Mianyang, Sichuan was used as the base oil; DOP from a chemical plant in Yixing, Wuxi was adopted as the plasticizer; C9 petroleum resin from a chemical plant in Dongguan, Guangdong was chosen as the tackifier resin; and OMMT with the advantages of barrier properties, aging resistance, and flame retardancy was selected from a mineral products processing plant in Hebei. The main technical indexes of each raw material are shown in Table 1. 2.2. Asphalt Preparation 2.2.1. Aged Asphalt The PG64-22 petroleum asphalt provided by Hunan Baoli International was used as the base asphalt, and the aged asphalt was prepared by the laboratory simulation accelerated aging test method. The specific operation is that the base asphalt is aged by the rolling thin film oven test (RTFOT) for 85 min, and then placed in a pressurized aging vessel (PAV) to accelerate the aging for 20 h. The technical indexes of base asphalt and aged asphalt are shown in Table 2. 2.2.2. Reclaimed Asphalt According to the previous research [22,23], for long-term-aged asphalt with RTFOT aging for 85 min and PAV aging for 20 h, the appropriate content of tung oil is in the range of 2–8%. We must consider that different sources of asphalts have different performances to some extent. The tung oil composite regenerating agent was blended with the aged asphalt (4%, 6%, 8%, 10%, and 12%) to prepare the reclaimed asphalt. The aged asphalt was kept at 135 ± 5 °C with the tung oil composite regenerating agent added. It was sheared for 20 min (3000 r/min) by the high-speed shear, and then continuously stirred for 10 min (500 r/min). The reclaimed asphalt is named according to the content of the regenerating agent, such as R-4% reclaimed asphalt, that is, the content of the tung oil composite regenerating agent in the reclaimed asphalt is 4%. 2.3. Test Methods 2.3.1. Rheological Property Test The DSR test can measure the complex modulus (G*) and the phase angle (δ) of the asphalt, and the rutting resistance of asphalt pavement can be characterized by the rutting factor G*/sinδ. In this study, high-temperature rheological properties were evaluated by temperature sweep and frequency sweep tests. A rotor with a diameter of 25 mm was selected for base asphalt and reclaimed asphalt, and its setting interval is 1 mm. However, an 8 mm rotor with an interval of 2 mm was chosen for the aged asphalt. The temperature range of the temperature sweep test is 42~72 °C. The temperatures of the frequency sweep are 28 °C, 40 °C, 52 °C, 64 °C, and 76 °C, and its frequency is in the range of 0.1–10 Hz at each temperature. The master curves of the complex modulus and phase angle were constructed by the time-temperature equivalence principle. Based on the time–temperature equivalence principle, the effect of the temperature and loading frequency on the asphalt material was converted into a reduced frequency by using the displacement factor. The displacement factor can be obtained by the WLF empirical equation:(1) LogαT=−C1T−TrefC2+T−Tref where αT is the displacement factor at T; TR is the reference temperature; and C1 and C2 are empirical constants. In addition, the low temperature cracking resistance of asphalt can be characterized by the BBR test. The BBR test can measure the creep stiffness (S) and creep rate (m), and the test temperature includes −12 °C, −18 °C, and −24 °C. 2.3.2. Anti-Aging Performance Test The aging resistance of reclaimed asphalt was analyzed by rheological properties after PAV aging and UV aging. The aging resistance of reclaimed asphalt was analyzed according to the effects of rheological indicators (CMAI, PMAI) on the rheological properties of aged asphalt. The calculation of Formulas (2) and (3) is shown below:(2) CMAI=G*G0* (3) PMAI=δδ0 where G* is the complex modulus of asphalt after aging; G0* is the complex modulus of asphalt before aging; δ is the phase angle of asphalt after aging; and δ0 is the phase angle of asphalt before aging. 2.3.3. Micro Performance Test Scanning electron microscopy (SEM) was used to compare the difference between the microstructure and morphology of reclaimed asphalt with the optimum content and base asphalt. The asphalt samples were sprayed with gold prior to SEM. Fourier transform infrared (FTIR) spectroscopy was adopted to compare the differences in the composition of characteristic functional groups between the reclaimed asphalt with the optimum content and the base asphalt. FTIR spectroscopy studied the physical and chemical changes during the aging and regeneration processes of asphalt, and the information on chemical bonds or functional groups was obtained through the absorption peaks with FTIR spectroscopy. The changes in asphalt functional groups after adding the tung oil composite regenerating agent were analyzed. The FTIR test wavelength range is 500–4000 cm−1, and the number of scans is 32. Gel permeation chromatography (GPC) analyzed the molecular weight distribution changes of asphalt during the aging and regeneration processes. The molecular weight distribution of asphalt measured by GPC is closely related to the macroscopic properties of asphalt [24]. In this study, the mobile phase is tetrahydrofuran (THF), the concentration of the asphalt sample is 2 mg/mL, and the flow rate is 10 mL/min. 3. Orthogonal Test Design and Analysis We used orthogonal tables to analyze multi-factor and multi-level experiments [25]. Under the condition that the orthogonal test can ensure the level of each test factor, the same number of tests can simplify the test groups and improve the test efficiency. The components of the tung oil composite regenerating agent mainly composed of tung oil, DOP, C9 petroleum resin, and OMMT were taken as the main factors, and the factor levels of the orthogonal test design are shown in Table 3. 4. Test Results and Analysis 4.1. Orthogonal Test Results of Tung Oil Composite Regenerating Agent In order to select the primary and secondary effects of each factor on each index, a 25 °C penetration and softening point, a 15 °C ductility, and 135 °C viscosity of reclaimed asphalt were used as evaluation indexes to discuss, analyze, and determine the optimal level of each factor. The orthogonal test results and preferred combinations are shown in Table 4 and Table 5, respectively. It can be seen from Table 4 and Table 5 that the performance of aged asphalt can be restored by selecting 70% or 75% of tung oil; however, when the content of tung oil increases from 70% to 75%, the change trend of the penetration is relatively small, while the change trend of the ductility, viscosity, and softening are relatively large. Therefore, the optimal content of tung oil is 75%. With the increase in DOP content, the indexes of the softening point and viscosity first decrease and then increase, while the indexes of the penetration and ductility first increase and then decrease, indicating that the DOP starts to have adverse effects after improving the aging asphalt maximally. As a result, the optimal content of DOP is 15%. To meet the principle whereby the softening point is the minimum while the penetration and ductility are the maximum, the optimal contents of the C9 petroleum resin and OMMT are 6% and 9%, respectively. According to the comprehensive analysis of the orthogonal test, the best combination of the tung oil composite regenerating agent is A1B2C3D3, namely tung oil: DOP: C9 petroleum resin: OMMT = 25:5:2:3. 4.2. Effects of Composite Regenerating Agent on the Rheological Properties of Reclaimed Asphalt 4.2.1. Complex Modulus Figure 1 and Figure 2 show the effect of the tung oil composite regenerating agent on the complex modulus and phase angle of aged asphalt. The range of the test temperature is 42–72 °C, and its increase rate is 2 °C/min; the loading frequency ω is 10 rad/s, and the strain control is 12%. It can be seen from Figure 1 that the complex modulus G* of all asphalt samples decreases gradually with the increase in temperature. For instance, at the initial test temperature, the G* of aged asphalt is nearly 6 times higher than that of base asphalt, indicating that the aging makes the asphalt harder. The addition of the tung oil composite regenerating agent can reduce the complex modulus of reclaimed asphalt, because the tung oil can dissolve macromolecular substances and supplement the light components of asphalt, softening the asphalt and reducing the complex modulus. As the content of the tung oil composite regenerating agent increases, the G* of each reclaimed asphalt gradually decreases, which has a negative influence on the deformation resistance of the asphalt. However, the appropriate content of the tung oil composite regenerating agent can restore the fluidity of the aged asphalt. The G* of the R-8% reclaimed asphalt is close to or even higher than that of base asphalt, partly because the C9 petroleum resin in the composite regenerating agent is favorable to high temperatures. YAN [22] et al. used tung oil as a regenerating agent to restore the high-temperature rheological properties of aged asphalt only to the level of base asphalt. However, the composite regenerating agent of tung oil in this paper caused the high-temperature performance of R-8% asphalt to be better than that of matrix asphalt. Therefore, the tung oil composite regenerating agent can restore and improve the deformation resistance of aged asphalt. 4.2.2. Phase Angle In Figure 2, it is shown that after the asphalt is aged, the phase angle δ decreases and the deformation resistance increases. With the addition of the tung oil composite regenerating agent, the phase angle δ of reclaimed asphalt gradually decreases and is still smaller than that of base asphalt, indicating the elastic recovery ability of reclaimed asphalt is better than that of base asphalt. 4.2.3. Rutting Factor The test results of the rutting factor of reclaimed asphalt are shown in Figure 3. As the temperature increases, the G*/sinδ of all asphalts gradually decreases. Moreover, as the content of the tung oil composite regenerating agent increases, it also declines, indicating that the addition of the regenerating agent and the increase in temperature reduce the deformation resistance of the asphalt. The G*/sinδ of aged asphalt is the largest, indicating its rutting resistance is the best. As the content of the tung oil composite regenerating agent increases, the G*/sinδ of reclaimed asphalt gradually decreases and is close to that of base asphalt. The addition of too much of the tung oil composite regenerating agent can lead to poorer rutting resistance of reclaimed asphalt. Therefore, the proper content of the regenerating agent ensures that the reclaimed asphalt has sufficient rutting resistance. The G*/sinδ of R-8% reclaimed asphalt is very close to or even better than that of base asphalt. Thus, the content of the tung oil composite regenerating agent should not exceed 8%. 4.2.4. Master Curve The temperature range of the frequency sweep test is 28–76 °C (the temperature interval is 12 °C), and its sweep frequency is 0.1–10 Hz. According to the WLF equation [26], the master curve of the complex modulus and phase angle constructed at the reference temperature of 20 °C is shown in Figure 4 and Figure 5. As shown in Figure 4, compared with the base asphalt, the aged asphalt shows a higher complex modulus, which is beneficial to the rutting resistance of RAP at a low frequency and high temperature; G* has a great linear relationship with the frequency. As the content of the tung oil composite regenerating agent increases, the G* of the asphalt shifts close to that of base asphalt and increases with the increase in frequency, which means that the asphalt has the advantage of road deformation resistance at a high-frequency state and a low temperature. As the loading frequency decreases and the temperature increases, the G* of reclaimed asphalt decreases and the G* of R-8% reclaimed asphalt is the closest to that of base asphalt. It can be seen from Figure 5 that the aged asphalt has the smallest δ due to the loss of light components, which increases the proportion of elastic components in the asphalt; the addition of the tung oil composite regenerating agent can increase the δ of aged asphalt. As the content of the tung oil composite regenerating agent increases, the δ of the asphalt gradually increases and is close to that of base asphalt, indicating that the tung oil composite regenerating agent can increase the proportion of viscous components in the aged asphalt and improve the viscoelastic properties of aged asphalt; Moreover, when the content of the tung oil composite regenerating agent is 8%, the δ of R-8% asphalt is smaller than that of base asphalt, indicating that the elastic recovery performance of R-8% asphalt is better than that of base asphalt. The black diagram used to evaluate the viscoelastic properties of asphalt is a diagram of rheological data for asphalt materials in the form of complex modulus and phase angle. Figure 6 shows a black diagram of base asphalt, aged asphalt, and reclaimed asphalt. The curve of aged asphalt is incoherent in the black diagram. However, because the tung oil composite regenerating agent can improve the molecular conformation of aged asphalt to a certain extent, the curve of reclaimed asphalt is smooth, which is basically a coherent curve. The phase angle of reclaimed asphalt is smaller than that of the base asphalt, indicating that the elastic response of reclaimed asphalt is stronger. 4.2.5. Creep Stiffness and Creep Rate The results of creep stiffness S and creep rate m of different asphalt samples were shown in Figure 7 and Figure 8. The test temperatures are −12 °C, −18 °C, and −24 °C. There is no test result for R-10% and R-12% reclaimed asphalts due to their excessive deformation at −12 °C. It can be seen from Figure 7 and Figure 8 that as the content of the tung oil composite regenerating agent increases, the S and m of reclaimed asphalt gradually decreases and increases, respectively, indicating that with the addition of the tung oil composite regenerating agent, the low-temperature flexibility and cracking resistance of reclaimed asphalt are gradually improved. YAN [22] restored the low-temperature performance of aged asphalt by using tung oil as a regenerating agent. At −18 °C, the S of the regenerated asphalt with 8% tung oil is 170 MPa, and its m is 0.38, while the content of the tung oil composite regenerating agent in this paper is 8%, the S of R-8% asphalt is 135 MPa, and its m is 0.375, indicating that the tung oil composite regenerating agent has better recovery ability compared to the low-temperature performance of aged asphalt. This is because the plasticizer in the tung oil composite regenerating agent can improve the flexibility, low-temperature ductility, and crack resistance of the asphalt. Compared with those of base asphalt, the S and m of R-8% reclaimed asphalt decrease by 60% and increase by 15.1% at −12 °C, respectively; at −18 °C, the S and m decrease by 57.7% and increase by 21.4%, respectively; and at −24 °C, the S and m decrease by 41.1% and increase by 23.2%, indicating that the tung oil composite regenerating agent can not only restore the S and m of aged asphalt to the level of base asphalt, but also improve the low-temperature crack resistance of reclaimed asphalt with the optimal content, which is better than that of base asphalt. 4.3. Anti-Aging Performance of Reclaimed Asphalt with Composite Regenerating Agent 4.3.1. Thermo-Oxidative Aging Resistance Figure 9 shows the change trends of CMAI and PMAI of base asphalt and different contents of reclaimed asphalt after the PAV aging. The CMAI of asphalt first increases and then decreases with the increase in temperature. The motion state of asphalt molecules is closely related to the ambient temperature. According to its deformation characteristics under the action of external force, the asphalt can be divided into a glass state, high elastic state, and viscous flow state. Liquids with lower molecular weights usually tend to show lower viscous flow temperatures [27]. Prior to asphalt aging, more light components and fewer heavy components are generated in the asphalt. Under the action of external force, the relaxation time of asphalt molecules is shorter so that the asphalt is more prone to viscous flow, and its viscous flow temperature is relatively low; however, after thermal-oxidative aging, light components decrease and heavy components increase in the asphalt, which leads to an increase in the average relative molecular mass of the asphalt and increases the relaxation time of asphalt molecules; that is, the viscous flow temperature becomes higher. During the temperature scanning process, as the temperature increases, the viscous flow of original asphalt appears earlier than that of aging asphalt at the stage of 42~54 °C so that the complex modulus of original asphalt decays much faster than that of aged asphalt and improves the CMAI of the asphalt. It can be seen from Figure 9a that the CMAI of reclaimed asphalt after PAV aging is smaller than that of base asphalt, indicating that the OMMT in the tung oil composite regenerating agent can effectively block the penetration and propagation of gaseous substances in the asphalt, such as water molecules and oxygen, and delay the aging process of asphalt under thermal-oxidative action. Therefore, the tung oil composite regenerating agent can effectively improve the thermal-oxidative aging resistance of aged asphalt. As the content of the tung oil composite regenerating agent increases, the CMAI of reclaimed asphalt first decreases and then increases, of which the CMAI of R-8% reclaimed asphalt is the smallest, indicating that the reclaimed asphalt has the best thermal-oxidative aging resistance. When the content of the tung oil composite regenerating agent exceeds 8%, the CMAI of reclaimed asphalt gradually increases. Thus, when the content of the tung oil composite regenerating agent is 8%, the reclaimed asphalt can be guaranteed to have better thermal-oxidative aging resistance. It can be seen from Figure 9b that the phase angle of the asphalt decreases after PAV aging, and more viscous components in the asphalt are transformed into elastic components; when the content of the tung oil composite regenerating agent is 4%, the PMAI of reclaimed asphalt is close to that of base asphalt; and when its content exceeds 4%, the PMAI of reclaimed asphalt is larger than that of base asphalt, indicating that the tung oil composite regenerating agent can restore the thermo-oxidative aging resistance of aged asphalt and effectively improve the thermo-oxidative aging resistance of reclaimed asphalt. 4.3.2. UV Aging Resistance In Figure 10, the change trends of CMAI and PMAI of base asphalt and reclaimed asphalt with different contents after UV aging are shown. It can be seen from Figure 10a that the CMAI of base asphalt ranges from 2.2 to 2.7 and gradually decreases with the increase in temperature; and the CMAI of reclaimed asphalt is lower than that of base asphalt, indicating that the tung oil composite regenerating agent can reflect and absorb the UV light and decrease the damage of UV light to the reclaimed asphalt. When the content of the tung oil composite regenerating agent increases to 8%, the CMAI of reclaimed asphalt is the smallest, and thereby its UV aging resistance is the best. As shown in Figure 10b, the PMAI of base asphalt is 0.92~0.98 and increases linearly with the increase in temperature, while the PMAI of R-8%, R-10%, and R-12% reclaimed asphalts is larger than that of base asphalt, indicating that the tung oil composite regenerating agent can improve the UV aging performance of reclaimed asphalt. However, when the content of the tung oil composite regenerating agent exceeds 8%, the PMAI of reclaimed asphalt tends to decrease. As a result, when the content of the tung oil composite regenerating agent is 8%, it can be ensured that the reclaimed asphalt has better UV aging resistance. 4.4. Microstructure and Mechanism Analysis of Reclaimed Asphalt with Composite Regenerating Agent 4.4.1. Morphology A Zeiss sigma 300 SEM was used to collect the surficial micro-morphologies of asphalt samples. The microstructures and morphologies of base asphalt, aged asphalt, and reclaimed asphalt are shown in Figure 11. It can be seen from Figure 11 that the overall surface of base asphalt is in a flat and smooth state, which is basically a homogeneous structure, and a large number of wrinkles appear on the surface of aged asphalt. This is because the light components decrease, the molecular polarity increases, the molecular movement ability is weakened, and the asphalt fluidity deteriorates after aging. With the addition of the tung oil composite regenerating agent, the surface of R-8% reclaimed asphalt tends to be flat and smooth, which is similar to that of base asphalt. In addition, the uneven striped “honeycomb structure” can be seen from the graphs of both aged asphalt and reclaimed asphalt, and the area of a single honeycomb structure of aged asphalt is larger than that of reclaimed asphalt, which may be due to the aggregation of macromolecular asphaltenes of the asphalt [20]. After the tung oil composite regenerating agent is added, the light components increase. Furthermore, the area of the honeycomb structure of R-8% reclaimed asphalt decreases, and its surface tends to be smooth, indicating that the tung oil composite regenerating agent can roughly restore the microstructure and morphology of aged asphalt. 4.4.2. Composition of Regenerating Agent and Reclaimed Asphalt The functional groups of the asphalt samples were determined by the Nicolet iS50 FTIR spectrometer. The wavelength range of the test is 500–4000 cm−1, and the number of scans is 32. The infrared spectra are shown in Figure 12 and Figure 13. The infrared spectrum of the tung oil composite regenerating agent is shown in Figure 12. Through the analysis of characteristic peaks in the infrared spectrum of the tung oil composite regenerating agent, it is found that the absorption peak near 2958 cm−1 is a CH3 antisymmetric and symmetric stretching vibration, while the absorption peak in the interval of 2922 cm−1~2854 cm−1 is a -CH2 antisymmetric and symmetric stretching vibration, indicating that the tung oil composite regenerating agent contains non-polar methyl and methylene functional groups. The absorption peak near 3009 cm−1 is the C-H stretching vibration. The tung oil composite regenerating agent has absorption peaks of C=C stretching vibration of three aromatics near 1595 cm−1, 1456 cm−1, and 1378 cm−1, and C-H bending vibration of the benzene ring in the interval of 900 cm−1~650 cm−1, indicating that the main components of the tung oil composite regenerating agent are light components rich in aromatic hydrocarbons. There is a C=O stretching vibration absorption peak of saturated fatty acid ester near 1740 cm−1 and a C-O stretching vibration absorption peak near 1265 cm−1, 1156 cm−1, and 1072 cm−1. Moreover, there is a C-H in-plane bending and stretching characteristic peak in the interval of 900 cm−1~600 cm−1, indicating that the tung oil composite regenerating agent is rich in aromatic compounds and has good compatibility with the asphalt. Figure 13 shows the infrared spectra of base asphalt, aged asphalt, and reclaimed asphalt. It can be found from Figure 13 that the positions of characteristic peaks of all asphalts are almost the same. Compared with the base asphalt, the aging asphalt has an absorption peak caused by the carbonyl C=O at 1700 cm−1, and the characteristic peak of sulfoxide group S=O at 1030 cm−1 increases, which is caused by the oxidation reaction during the aging process of the asphalt. For the reclaimed asphalt, a new characteristic peak appears near 1742 cm−1 after the addition of the tung oil composite regenerating agent, which is caused by the C=O stretching vibration of saturated fatty acid ester. In addition, no other characteristic peaks appear in the reclaimed asphalt. Its characteristic peak is almost the same as that of base asphalt, indicating that the tung oil composite regenerating agent has no chemical reaction with the asphalt, rather only physical blending. 4.4.3. Molecular Weight and Distribution of Different Asphalts The molecular weight and distribution of asphalt were analyzed by Waters 1515 GPC, and tetrahydrofuran (THF) was used as the mobile phase. The concentration and flow rate of the asphalt sample are 2 mg/mL and 10 mL/min, respectively. In Figure 14, the abscissa of the GPC curve is the molecular weight, and its ordinate is the differential distribution of molecular weight. The GPC is usually divided into 13 blocks, of which blocks 1–5 are macromolecules (LMS), blocks 6–9 are medium molecules (MMS), and blocks 10–13 are small molecules [28]. According to the distribution of molecular weight in Figure 14, the integral areas of LMS, MMS, and SMS of base asphalt, aged asphalt, and reclaimed asphalt were calculated, and the content of each molecule of LMS, MMS, and SMS was obtained, as shown in Figure 15. It can be seen from the figure that compared with those of base asphalt, the LMS content of aged asphalt increases by 12.5%, while the MMS and SMS contents decrease by 5.8% and 6.7%, respectively. This is because, in the process of thermo-oxidative aging, there is a polymerization reaction between aromatic and colloidal components of small molecular weight and asphaltenes of a large molecular weight produced, thereby enhancing the intermolecular force of the asphalt and weakening its molecular movement ability. Compared with those of aged asphalt, the contents of LMS and MMS of R-8% reclaimed asphalt decrease by 3.2% and 1.4%, respectively, and the content of its SMS increases by 4.6%. It is shown that the tung oil composite regenerating agent contains a certain number of medium and small molecules, which can fully supplement small and medium molecules in the components of aging asphalt and dissolve a small part of the macromolecules, thus solving the agglomeration problem of macromolecules. In Figure 15, it is shown that after the PAV and UV aging, the LMS and MMS of R-8% reclaimed asphalt have different increasing trends, while its SMS has decreasing trends. Furthermore, the molecular weight of PAV-aged asphalt is larger than that of UV-aged asphalt, indicating that during the UV aging process, the R-8% reclaimed asphalt without thermo-oxidative aging loses few medium and small molecules. According to the change trend of the molecular weight of LMS, MMS, and SMS in Figure 15, the change range of molecular weight of base asphalt and R-8% reclaimed asphalt after the PAV aging was calculated, as shown in Table 6. It can be seen from Table 6 that after PAV aging, the LMS content of base asphalt increases by 12.5%, while its MMS and SMS contents decrease by 5.8% and 6.7%, respectively. Furthermore, the LMS content of R-8% reclaimed asphalt increases by 4.2%, while its MMS and SMS contents decrease by 2% and 2.2%, respectively. It is obvious that the change range of the molecular weight of R-8% reclaimed asphalt is small, indicating that the aging performance of R-8% reclaimed asphalt decays slowly after PAV aging, which is beneficial to the aging resistance of reclaimed asphalt. Moreover, it can be found from Figure 15 that after PAV aging, the SMS content of R-8% reclaimed asphalt is more than that of base asphalt, indicating that the tung oil composite regenerating agent can inhibit the loss of small molecules in the reclaimed asphalt. 5. Conclusions - The optimal mix proportion of the tung oil composite regenerating agent was determined by the orthogonal design test method; that is, tung oil: DOP: C9 petroleum resin: OMMT = 25:5:2:3. - As the content of the tung oil composite regenerating agent increases, the rutting factor and creep stiffness gradually decrease and the creep rate increases, indicating that the tung oil composite regenerating agent can restore the rheological properties of aged asphalt, which is even better than that of base asphalt. The CMAI of reclaimed asphalt is smaller than that of base asphalt, while the PMAI of reclaimed asphalt is larger than that of base asphalt. The anti-aging ability of reclaimed asphalt is significantly improved, and the optimal content of the tung oil composite regenerating agent is 8%. - As the content of macromolecules increases, the fluidity of aged asphalt becomes poor and a wrinkled texture and large honeycomb structure appear on its surface. The addition of the tung oil composite regenerating agent can restore the morphological features of aged asphalt to a certain extent, which makes its surface tend to be flat and smooth, and the size of te honeycomb structure is reduced. - The FTIR diagram shows that the tung oil composite regenerating agent is mainly composed of light components rich in aromatic hydrocarbons, and the characteristic peaks of reclaimed asphalt are basically consistent with those of base asphalt, indicating that the tung oil composite regenerating agent is beneficial to the dispersion and dissolution of polar substances in the aged asphalt. - The GPC results of reclaimed asphalt show that the tung oil composite regenerating agent can reduce the content of macromolecule in the aged asphalt, and the change range of molecular weight of reclaimed asphalt after aging is smaller than that of base asphalt, indicating that the aging of reclaimed asphalt decays slowly, which is favorable for the aging resistance of reclaimed asphalt. Author Contributions Conceptualization, Q.W. and Q.Y.; methodology, Q.W. and Q.Y.; validation, J.L. (Junhui Luo), J.L. (Jianhua Liu) and C.X.; formal analysis, H.L. and M.Q.; investigation, Q.Y. and M.Q.; resources, J.L. (Junhui Luo), J.L. (Jianhua Liu) and C.X.; data curation, H.L., J.L. (Junhui Luo) and J.L. (Jianhua Liu); writing—original draft preparation, Q.Y., J.L. (Junhui Luo) and J.L. (Jianhua Liu); writing—review and editing, Q.Y. and M.Q.; visualization, Q.W. and Q.Y.; supervision, Q.W. and Q.Y.; project administration, J.L. (Junhui Luo), J.L. (Jianhua Liu) and C.X. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Test results of complex modulus (G*) of aged asphalt after the regeneration. Figure 2 Test results of phase angle (δ) of aged asphalt after the regeneration. Figure 3 Test results of rutting factor (G*/sinδ). Figure 4 Master curve of complex modulus of reclaimed asphalt. Figure 5 Master curve of phase angle of reclaimed asphalt. Figure 6 Black diagram of reclaimed asphalt. Figure 7 S of different reclaimed asphalt samples. Figure 8 m of different reclaimed asphalt samples. Figure 9 Thermo-oxidative aging resistance of reclaimed asphalt after PAV aging. (a) CMAI. (b) PMAI. Figure 10 UV aging resistance of reclaimed asphalt after the UV aging. (a) CMAI. (b) PMAI. Figure 11 Micromorphologies of different asphalts under SEM (5000 times). (a) Base asphalt. (b) Aged asphalt. (c) R-8%. Figure 12 Infrared spectrum of tung oil composite regenerating agent. Figure 13 FTIR images of different asphalt samples. Figure 14 Molecular weight distribution of different asphalts. Figure 15 Molecular weight proportion of different asphalts. materials-15-03197-t001_Table 1 Table 1 Technical indexes of tung oil. Raw Materials Appearance Density/g·cm−1 Flash Point/°C Tung oil Yellow liquid 0.943 236 DOP Colorless oily liquid 0.985 225 C9 petroleum resin Yellow particle 0.995 260 OMMT White powder 1.03 - materials-15-03197-t002_Table 2 Table 2 Technical indexes of substrate asphalt and aged asphalt. Technical Indexes Base Asphalt Aged Asphalt Test Methods Penetration (25 °C)/0.1 mm 68.7 21.1 ASTM D5 Ductility (15 °C)/cm 142.0 5.6 ASTM D113 Softening point (ring and ball method)/°C 48.0 65.6 ASTM D36 Viscosity (135 °C)/mPa∙s 485 936 ASTM D4402 materials-15-03197-t003_Table 3 Table 3 Factor levels of orthogonal test design. Levels Tung Oil (A)/% DOP (B)/% C9 Petroleum Resin (C)/% OMMT (D)/% Level 1 75 10 14 1 Level 2 70 15 10 5 Level 3 65 20 6 9 materials-15-03197-t004_Table 4 Table 4 Orthogonal test results. No. Tung Oil (A)/% DOP(B)/% C9 Petroleum Resin (C)/% OMMT(D)/% Softening Point/°C Penetration/0.1 mm Ductility/cm Viscosity/mPa∙s 1 75 10 14 1 48.4 93.5 143.6 480.0 2 75 15 10 5 47.0 100.1 138.2 455.0 3 75 20 6 9 45.1 130.0 142.6 404.0 4 70 10 10 9 46.6 145.2 125.4 447.0 5 70 15 6 1 46.7 103.2 134.3 426.0 6 70 20 14 5 48.4 82.5 121.0 477.5 7 65 10 6 5 46.5 99.6 117.4 451.0 8 65 15 14 9 47.2 98.9 119.3 483.0 9 65 20 10 1 48.2 86.0 97.6 505.0 materials-15-03197-t005_Table 5 Table 5 Preferred combinations of orthogonal tests. Indexes Preferred Combinations Softening point/°C A1B2C3D3 Penetration/(0.1mm) A2B2C3D3 Ductility/cm A1B2C3D3 Viscosity/(mPa∙s) A1B2C3D3 materials-15-03197-t006_Table 6 Table 6 Change range of molecular weight of asphalt after PAV aging. Types of Asphalt LMS (%) MMS (%) SMS (%) Base asphalt 12.5 −5.8 −6.7 R-8% reclaimed asphalt 4.2 −2 −2.2 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Zhang H. Liu H. Zhang Z. Study on the mechanism of the repeated asphalt ageing and recycling based on the macro-performance Road Mater. Pavement Des. 2016 17 920 932 10.1080/14680629.2015.1120683 2. Zhao K. Wang Y. Influences of aging conditions on the rheological properties of asphalt binders Int. J. Pavement Eng. 2020 21 653 665 10.1080/10298436.2018.1502438 3. Zhou C.L. Zheng C.C. Cheng D.X. Evaluation of Environmental Pollution from Asphalt Recycling Technology Adv. Mater. Res. 2014 898 482 485 10.4028/www.scientific.net/AMR.898.482 4. Xiao F. Yao S. Wang J. Li X. Amirkhanian S. A literature review on cold recycling technology of asphalt pavement Constr. Build. Mater. 2018 180 579 604 10.1016/j.conbuildmat.2018.06.006 5. Cong P. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095664 ijerph-19-05664 Article Potential Biomarkers and Drugs for Nanoparticle-Induced Cytotoxicity in the Retina: Based on Regulation of Inflammatory and Apoptotic Genes Xie Dongli 1† Hu Jianchen 1† Wu Tong 2 Cao Kangli 3 Luo Xiaogang 1* Tchounwou Paul B. Academic Editor 1 College of Textile and Clothing Engineering, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China; xdl202111@163.com (D.X.); hujianchen@suda.edu.cn (J.H.) 2 Shanghai Jing Rui Yang Industrial Co., Ltd., 3188 Xiupu Road, Pudong New Area, Shanghai 200122, China; wutong@lead-all.cn 3 Shanghai Institute of Spacecraft Equipment, 251 Huaning Road, Shanghai 200240, China; connieckl@126.com * Correspondence: xgluo@suda.edu.cn; Tel.: +86-0512-67162531 † These authors contributed equally to this work. 06 5 2022 5 2022 19 9 566427 2 2022 05 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The eye is a superficial organ directly exposed to the surrounding environment. Thus, the toxicity of nanoparticle (NP) pollutants to the eye may be potentially severer relative to inner organs and needs to be monitored. However, the cytotoxic mechanisms of NPs on the eyes remain rarely reported. This study was to screen crucial genes associated with NPs-induced retinal injuries. The gene expression profiles in the retina induced by NPs [GSE49371: Au20, Au100, Si20, Si100; GSE49048: presumptive therapeutic concentration (PTC) TiO2, 10PTC TiO2] and commonly used retinal cell injury models (optic nerve injury procedure: GSE55228, GSE120257 and GSE131486; hypoxia exposure: GSE173233, GSE151610, GSE135844; H2O2 exposure: GSE122270) were obtained from the Gene Expression Omnibus database. A total of 381 differentially expressed genes (including 372 mRNAs and 9 lncRNAs) were shared between NP exposure and the optic nerve injury model when they were compared with their corresponding controls. Function enrichment analysis of these overlapped genes showed that Tlr2, Crhbp, Ccl2, Cxcl10, Fas, Irf8, Socs3, Stat3, Gbp6, Casp1 and Syk were involved in inflammatory- and apoptotic-related processes. Protein-protein interaction network analysis revealed eight of them (Tlr2, Ccl2, Cxcl10, Irf8, Socs3, Stat3, Casp1 and Syk) were hub genes. Moreover, Socs3 could interact with upstream Stat3 and downstream Fas/Casp1/Ccl2/Cxcl10; Irf8 could interact with upstream Tlr2, Syk and downstream Cxcl10. Competing endogenous RNAs network analysis identified Socs3, Irf8, Gdf6 and Crhbp could be regulated by lncRNAs and miRNAs (9330175E14Rik-mmu-miR-762-Socs3, 6430562O15Rik-mmu-miR-207-Irf8, Gm9866-mmu-miR-669b-5p-Gdf6, 4933406C10Rik-mmu-miR-9-5p-Crhbp). CMap–CTD database analyses indicated the expression levels of Tlr2, Ccl2, Cxcl10, Fas, Irf8, Socs3, Stat3, Gbp6, Casp1 and Syk could be reversed by folic acid. Crhbp and Gdf6 were also verified to be downregulated, while Tlr2, Ccl2, Irf8, Socs3 and Stat3 were upregulated in hypoxia/H2O2-induced retinal injury models. Hereby, our findings suggest that Crhbp, Irf8, Socs3 and Gdf6 as well as their upstream mRNAs, lncRNAs and miRNAs may be potential monitoring biomarkers and therapeutic targets for NP-induced retinal injuries. Folic acid supplementation may be a preventive and therapeutic approach. nanoparticles retinal injury inflammation apoptosis long non-coding RNAs competing endogenous RNAs National Key Research and Development Program of China2017YFA0204600 Project funded by China Postdoctoral Science Foundation2017M621322, 2018T110324 Financial support from National Key Research and Development Program of China (2017YFA0204600) and Project funded by China Postdoctoral Science Foundation (2017M621322, 2018T110324) is greatly acknowledged. ==== Body pmc1. Introduction Nanoparticles (NPs), which are defined as particles with a size between 1 and 100 nm in at least one dimension, have been widely utilized in consumer products and medical applications due to their unique properties. The long-term exposure (especially for manufacturing workers or scientists) increases the possibility of NPs entering the human body through various routes (i.e., inhalation, skin absorption or ingestion) and then inducing the potential toxicity on human organs and tissues [1]. As a superficial organ, the eyes (ocular surface) are usually directly in contact with NPs dispersed in the air of the surrounding environment [2]. Thus, compared to inner organs (lung, respiratory tract, liver, kidney and brain), ocular injuries induced by toxic NPs may be severer and should be timely monitored to prevent their occurrence and progression to blindness. Recently, there have been some toxicity studies to explore the influence of NP exposure on retinal cells as well as potential molecular mechanisms. Soderstjerna et al. used the in vitro tissue culture model of the mouse retina to observe the toxicity between 20 and 80 nm silver (Ag) and gold (Au) NPs. The results showed that compared with the control, the number of apoptotic cells in the outer nuclear layer and ganglion cell layer of mice retina was significantly increased after exposure to Ag and Au NPs (20 or 80 nm) [3]. Kalishwaralal et al. demonstrated that treatment of retinal endothelial cells with different concentrations of Ag NPs was able to block cell proliferation and migration but induce apoptosis according to the evidence of enhanced caspase-3 activity and formation of DNA fragmentation [4]. An inflammatory response with increased expressions of interleukin (IL)-6, IL-8 and tumor necrosis factor (TNF)-α was shown to be triggered after short-term exposure to graphene oxide in the eyes [5]. The study by Quan et al. revealed that Ag NPs induced apoptosis in human retinal pigment epithelium ARPE-19 cells by activating the endoplasmic reticulum stress response. Treatment with an endoplasmic reticulum stress inhibitor (4-phenylbutyric acid) significantly reduced the expression levels of caspase-3 cleavage and attenuated apoptosis in ARPE-19 cells [6]. Zinc oxide [7,8,9,10], copper NPs [11], mesoporous silica NPs [12], cerium oxide NPs [13] and PEGylated graphene oxide [14] were reported to induce apoptosis of retinal cells by elevating the levels of reactive oxygen species. The use of reactive oxygen species scavengers (reduced glutathione and N-acetylcysteine) partially restored the cell viability and alleviated retinal developmental defects [11,14]. Titanium dioxide (TiO2) NPs were proved to impair the inner blood-retinal barrier and retinal electrophysiology through rapid activation of ADAM17 metalloproteinase to induce ADAM17-mediated claudin-5 degradation. The use of ADAM17 chemical inhibitors (GM6001 and TAPI-2) preserved the expression levels of tight junction protein (claudin-5) and protected the integrity of the blood-retinal barrier [15]. These findings indicate that investigation of the molecular toxicological mechanisms may be beneficial for screening potential biomarkers to monitor NP-exposed individuals in order to prevent the development of retinal injury and prove potential preventive and therapeutic approaches. However, NP-related molecular toxicological mechanisms in the retina remain rarely reported. In 2013, Jo et al. used the high-throughput microarray technique to analyze the gene expression profile of retina tissues collected from mice exposed to Au, Si and TiO2 NPs [16]. Although they did not detect the increased apoptotic cells in retinal tissues [16,17], we found the expression levels of several apoptosis- and inflammation-related genes were significantly changed in our preliminary analysis of this study, indicating these genes may represent potential monitoring biomarkers and therapeutic targets. Early intervention to reverse the expression levels of these genes may prevent the progression to apoptotic phenotypes. In this study, we aimed to use the raw expression profile data of Au-, Si- and TiO2-NPs exposure provided by Jo et al. [16] and to deeply mine crucial genes involved in the toxicity of NPs by using a series of bioinformatics analysis methods. As described above, oxidative stress damage is the common toxic effect of various NPs [7,8,9,10,11,12,13]. Thus, we hypothesize that our identified crucial genes may be altered commonly in various types of NP-induced retinal injuries and may represent underlying biomarkers for monitoring all NP-induced retinal injuries. To obtain the genes that are definitely associated with retinal injuries and apoptosis, we also collected the microarray data of commonly used retinal cell injury models (including traumatic optic nerve injury animal model, hypoxia- or H2O2-exposed animal or cell models) [18,19,20,21] and integrated with the expression profile established by Jo et al. [16]. 2. Materials and Methods 2.1. Dataset Collection The gene expression profiles in the retina exposed to NPs were obtained by searching the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/, accessed on 1 July 2021) using the keywords (“nanoparticle”) AND (“retina” or “retinal”). Following this step, only two datasets were retrieved, including GSE49371 and GSE49048 [16]. The GSE49371 dataset investigated the gene expression in the retina after phosphate buffer saline (PBS, regarded as the negative control, n = 12) or NPs (gold with diameters of 20 nm, Au20: n = 3; gold with diameters of 100 nm, Au100: n = 3; silicate with diameters of 20 nm, Si20: n = 3; silicate with diameters of 100 nm, Si100: n = 3) were intravitreally injected into the right vitreous cavity of 5-week-old male C57BL/6 mice for 7 days. The GSE49048 dataset [16] analyzed the gene expression in the retina after PBS (regarded as the negative control, n = 12) or ~25 nm TiO2 NPs (presumptive therapeutic concentration, PTC: 130.47 ng/mL, n = 3; 10 times PTC: 1.30 μg/mL, n = 3) were intravitreally injected into the right eye of 8-week-old male C57BL/6 mice for 7 days [16]. The PTC was determined by the cellular viability analysis in HRMECs and SNUOT-Rb1 cells and by histological analysis in mice [16]. Both GSE49371 and GSE49048 datasets were run on the platform of Agilent-026655 Whole Mouse Genome Microarray 4x44K v2 (GPL11202, Probe Name version). Additionally, to identify whether the genes induced by NPs were associated with the retinal injury, optic nerve injury model datasets were also collected from the GEO database under accession numbers GSE55228 [22], GSE120257 [23] and GSE131486 [24]. GSE55228 [22] and GSE131486 [24] datasets explored the retinal gene expression profiles of 12-week-old mice after an optic nerve crush (ONC, n = 3) or sham procedure (n = 3) treatment by deep sequencing with Illumina Hiseq2000 platform (GPL13112). Retinal tissues were collected two days after ONC procedure in these two datasets [22,24]. The GSE120257 dataset analyzed the retinal transcriptome in 6-week-old mice that underwent (n = 3) or did not receive ONC (n = 4) treatment by high-throughput sequencing with Illumina HiSeq 3000 platform (GPL21493) [23]. Mice were sacrificed and retinas were extracted on the fourth day post-injury [23]. Moreover, hypoxia- or H2O2-exposed retinal injury model data were obtained to validate the expression levels of crucial genes in the retina, including GSE173233 (normoxia, n = 6; normobaric hypoxia, n = 24; hypobaric hypoxia, n = 12) [25], GSE151610 (normoxia, n = 3; hypoxia, n = 3), GSE135844 (normoxia, n = 3; hypoxia, n = 3) [26] and GSE122270 (normoxia, n = 3; hypoxia, n = 3). In the GSE173233 dataset [25], mice in normobaric hypoxia were exposed to 7% or 14% O2; mice in hypobaric hypoxia were exposed to the environmental air pressure of approximately 64.65 kPa, which decreased the ambient partial pressure of oxygen from 21.2 kPa (sea level) to 13.7 kPa. The GSE151610 dataset analyzed mRNA profiles of ARPE-19 cells under normoxic conditions and conditions of hypoxic stress with 0.5% O2. In the GSE135844 dataset [26], 7-day-old mice were placed in a 75% oxygen atmosphere or normoxic conditions. In the GSE122270 dataset, ARPE19 cells were treated with or without 10 mU/mL glucose oxidase for 3 h. The platforms of GSE173233, GSE151610, GSE135844 and GSE122270 datasets were Illumina NovaSeq 6000 (GPL24247), Illumina HiSeq 3000 (GPL21290), Affymetrix Mouse Genome 430A 2.0 Array (GPL8321) and Affymetrix Human Gene Expression Array (GPL15207), respectively. 2.2. Differential Expression Analysis GSE49371 and GSE49048 datasets were two-channel microarray experiments and only the averages of normalized ratios calculated by dividing the average of normalized signal channel intensity by the average of normalized control channel intensity were provided. The differentially expressed RNAs induced by Au20, Au100, Si20, Si100 and TiO2 NPs were identified by calculating the p-value with a t-test and setting the average normalized ratios of three samples as the fold change (FC). The normalized expression matrix data of GSE55228, GSE120257 and GSE131486 datasets were downloaded from the GEO database. The differentially expressed RNAs between ONC and control groups in these three datasets were screened using DESeq2 (http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html, accessed on 3 July 2021) package in R [27]. The differentially expressed RNAs in all datasets were selected based on the threshold of |log2FC| > 0.5 and p-value < 0.05. The distribution of differentially expressed genes was manifested by a volcano plot for GSE49371 and GSE49048 datasets, while a heatmap was generated for GSE55228, GSE120257 and GSE131486 datasets with the ‘pheatmap’ package (v1.0.8; https://cran.r-project.org/web/packages/pheatmap, accessed on 3 July 2021). Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/, accessed on 5 July 2021) was applied to illustrate the overlapped genes between genes induced by NPs and ONC models. The expression differences of crucial genes in the retina were validated between hypoxia/H2O2 models and controls by a direct t-test with the SPSS software or online GEO2R provided by NCBI. Data management system BioMart (http://asia.ensembl.org/biomart/martview/59b47575be5aaf1f82c976009a472b38, accessed on 5 July 2021) was used to re-annotate differentially expressed RNAs to distinguish differentially expressed long non-coding RNAs (DE-lncRNAs) and protein-coding messenger RNAs (DE-mRNAs) from other RNA types. 2.3. Function Enrichment Analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate the potential functions of DE-mRNAs using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool (v6.8; http://david.abcc.ncifcrf.gov, accessed on 8 July 2021) [28]. p-value < 0.05 was set as the cut-off value. 2.4. Construction of a Protein–Protein Interaction (PPI) Network To screen crucial DE-mRNAs changed by NPs, a PPI network was constructed based on the protein interaction pairs (combined score ≥ 0.4) collected from the STRING (Search Tool for the Retrieval of Interacting Genes; v10.0; http://stringdb.org/, accessed on 13 July 2021) database [29]. Then, the topological characteristics of each protein in the PPI network were computed using the CytoNCA plugin in Cytoscape software (http://apps.cytoscape.org/apps/cytonca, accessed on 13 July 2021) [30,31], including the degree centrality (DC, measure of the number of interactive neighbors of a protein), eigenvector centrality (EC, measure of the component of the principal eigenvector of adjacency matrix), local average connectivity (LAC, measure of the local connectivity of its neighbors), betweenness centrality (BC, measure of the number of shortest paths going through a protein) and closeness centrality (CC, measure of the average distance from a protein to all other proteins). The proteins that had higher levels of these topological parameters were considered to be important for diseases. The proteins ranked in the top 60 according to the values calculated for each centrality measure were selected as hub genes. Furthermore, function-related modules were also extracted from the PPI network using the Molecular Complex Detection (MCODE; v1.4.2, http://apps.cytoscape.org/apps/mcode, accessed on 13 July 2021) plugin of Cytoscape software with the following setting parameters: MCODE score > 4, degree cut-off = 2, node score cut-off = 0.2, k-core = 2 and max depth = 100 [32]. The hub genes included in modules were further confirmed to be crucial. 2.5. Construction of a Competing Endogenous RNAs (ceRNAs) Regulatory Network LncRNAs were reported to function as ceRNAs to competitively bind with microRNAs (miRNAs) and then influence the negative regulatory roles of miRNAs on the expressions of targeted mRNAs [33]. To screen crucial DE-lncRNAs and reveal their functions, a ceRNA network was constructed based on the interaction pairs of lncRNAs-miRNAs and miRNAs-mRNAs. DIANA-LncBase (v2.0; http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2/index-predicted, accessed on 16 July 2021) [34] and starbase (v3.0; http://starbase.sysu.edu.cn, accessed on 16 July 2021) databases were used to predict the interacted miRNAs for DE-lncRNAs. miRwalk database (v2.0; http://www.zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2, accessed on 16 July 2021) that contained 12 prediction programs [35] was used to predict the target mRNAs regulated by DE-lncRNA-interacted miRNAs. Only the miRNA-target gene interaction pairs that were predicted by more than 8 prediction programs were selected. Additionally, target mRNAs were subsequently overlapped with DE-mRNAs. The expression direction of DE-lncRNAs and DE-mRNAs that interacted with miRNAs should be consistent (that is, both upregulated or downregulated). The ceRNA network was constructed and visualized using Cytoscape (v3.6.1; www.cytoscape.org/, accessed on 16 July 2021). 2.6. Small Molecule Drug Analysis To predict potential drugs for prevention and treatment of NP-induced retinal injury, Connectivity Map (CMap, https://portals.broadinstitute.org/cmap/, accessed on 18 July 2021) and Comparative Toxicogenomics Database (CTD, http://ctdbase.org, accessed on 18 July 2021) analyses were performed. The upregulated and downregulated DE-mRNAs were uploaded as the gene signature to query the CMap database, after which a series of small molecule drugs with enrichment scores ranging from −1 to 1 could be obtained. The small molecule drugs with negative connectivity scores and p-value < 0.05 were considered to reverse the expression direction of the query gene signature and may be therapeutic drugs. The small molecule drugs identified by CMap were then used as the keywords to search the CTD to collect the chemical-gene interaction pairs. The drugs that specifically targeted the DE-mRNAs were believed to be particularly important. The structures of the candidate drugs were obtained from the DrugBank database (https://www.drugbank.ca/, accessed on 18 July 2021). The gene-drug interaction networks were visualized using Cytoscape software. 3. Results 3.1. Differential Expression Analysis In the GSE49048 dataset, a total of 1346 (794 upregulated; 552 downregulated) (Figure 1A) and 1852 (1266 upregulated; 586 downregulated) (Figure 1B) RNAs were considered to be differentially expressed in the retina of mice undergoing PTC and 10PTC TiO2 NP exposure, respectively. In the GSE49371 dataset, a total of 365 (263 upregulated; 102 downregulated) (Figure 1C), 503 (416 upregulated; 87 downregulated) (Figure 1D), 514 (391 upregulated; 123 downregulated) (Figure 1E) and 392 (297 upregulated; 95 downregulated) (Figure 1F) differentially expressed RNAs were, respectively, found between Au20-, Au100-, Si20-, Si100- and PBS-exposed retina. Compared with the control group, the GSE55228 dataset analysis identified 303 upregulated and 587 downregulated RNAs in the retina of ONC model mice (Figure 1G); a total of 520 upregulated and 212 downregulated RNAs were screened in the GSE120257 dataset between the ONC and control groups (Figure 1H); for the GSE131486 dataset, 322 RNAs were shown to be upregulated and 199 were downregulated in the ONC model retina in comparison with controls (Figure 1I). The ONC procedure is the most commonly used retinal damage model [21]. To obtain crucial genes involved in NP-induced retinal injury, we selected the shared differentially expressed RNAs between GSE49048-GSE49371 and GSE55228-GSE120257-GSE131486 datasets. As a result, Venn diagram showed that there were 16, 62 and 30 commonly upregulated genes (Figure 2A); 26, 18 and 13 commonly downregulated genes (Figure 2B) between PTC TiO2 NP exposure in the GSE49048 dataset with ONC models of GSE55228, GSE120257, GSE131486 datasets, respectively; a total of 58, 246 and 113 commonly upregulated genes (Figure 2C); 36, 32 and 23 commonly downregulated genes (Figure 2D) were found between 10PTC TiO2 NP exposure in the GSE49048 dataset with ONC models of GSE55228, GSE120257, GSE131486 datasets, respectively; there were four and two commonly upregulated genes (Figure 2E); each one commonly downregulated gene (Figure 2F) between Au20 exposure in the GSE49371 dataset with ONC models of GSE55228 and GSE131486 datasets, respectively; a total of seven and one commonly upregulated genes (Figure 2G) were found between Au100 exposure in the GSE49371 dataset with ONC models of GSE55228 and GSE120257 datasets, respectively; there were eight, three and four commonly upregulated genes (Figure 2I) between Si20 exposure in the GSE49371 dataset with ONC models of GSE55228, GSE120257, GSE131486 datasets, respectively; only two commonly downregulated genes (Figure 2J) were identified between Si20 exposure in the GSE49371 dataset with ONC models of the GSE55228 dataset; each one upregulated gene (Figure 2K) was shown to be shared between Si100 exposure in the GSE49371 dataset with ONC models of GSE55228, GSE120257, GSE131486 datasets, respectively. No overlapped downregulated genes were identified when Au100 (Figure 2H) and Si100 (Figure 2L) exposure were compared with ONC models. Ultimately, 389 shared RNAs were obtained, including 372 mRNAs, 9 lncRNAs (6430562O15Rik, 9330175E14Rik, Gm833, Gm10030, 0610009B14Rik, Gm9866, Dbpht2, 4933406C10Rik, D030047H15Rik) and 8 other RNA types. These 372 DE-mRNAs and 9 DE-lncRNAs were used for the following analysis (Table 1). 3.2. Function Enrichment Analysis These 372 DE-mRNAs were uploaded into the DAVID database to predict their functions. The results showed that 170 significant GO biological process terms and 40 significant KEGG pathways were enriched. As shown in Figure 3, these genes were mainly involved in inflammation-related processes, such as GO:0045087~innate immune response (IFIH1, TLR2, SYK), GO:0006954~inflammatory response (CRHBP, CCL2, CXCL10, FAS, TLR2), GO:0006955~immune response (CXCL10, CCL2, FAS, IRF8, TLR2), GO:0071346~cellular response to interferon-gamma (CCL2), GO:0006935~chemotaxis (CXCL10, CCL2), mmu04668:TNF signaling pathway (CXCL10, SOCS3, CCL2, FAS), mmu04060:Cytokine-cytokine receptor interaction (CX3CR1, CXCL9, CXCL10, CCL2, FAS) and mmu04062:Chemokine signaling pathway (STAT3, CXCL10, CCL2). Furthermore, apoptosis-related processes were also included, such as GO:0043066~negative regulation of apoptotic process (STAT3, SOCS3), GO:0043065~positive regulation of apoptotic process (CASP1, FAS), GO:0006915~apoptotic process (CASP1, FAS, GDF6), GO:0042981~regulation of apoptotic process (CASP1, FAS, GDF6), GO:0043524~negative regulation of neuron apoptotic process (CCL2) (Table 2) and mmu04210:Apoptosis (FAS) (Table 3). Importantly, some genes enriched in inflammatory and apoptotic processes were shared, suggesting NP-induced retinal injuries may be associated with inflammation-mediated apoptosis of retinal neurons. 3.3. PPI Network Totally, 300 of the 372 DE-mRNAs were shown to interact with each other to form 2603 PPI pairs (e.g., Socs3-Stat3/Casp1/Fas/Ccl2/Cxcl10, Irf8-Tlr2/Syk/Cxcl10) that were used to construct the PPI network. After calculation of 5 topological features for each protein in the PPI network, 116 genes were ranked in the top 60 genes (20%), indicating they were potential hub genes (Table 4). Among them, inflammation- or apoptosis-related Tlr2, Irf8, Ifih1 and Cxcl10 were listed in the top 60 genes of all topological features (DC, BC, CC, DC and EC); Stat3, Ccl2 and Casp1 were listed in the top 60 genes based on BC, CC, DC and EC ranking; Syk was considered as a hub gene according to CC, DC and EC ranking; Socs3 was believed as a hub gene according to BC and CC ranking. Furthermore, five significant modules were extracted from the PPI network (Table 5; Figure 4). The above inflammation- or apoptosis-related hub genes were also contained in these five modules (Ifih1, Cxcl10 in module 1; Irf8 in module 2; Stat3, Tlr2, Casp1, Ccl2 in module 3; Syk in module 5), further explaining their importance for NP-induced retinal injuries. 3.4. Construction of a ceRNA Network The DIANA-LncBase and starbase databases predicted that 7 lncRNAs (0610009B14Rik, 4933406C10Rik, D030047H15Rik, 6430562O15Rik, Gm9866, 9330175E14Rik, Gm833) could interact with 278 miRNAs. The miRwalk2.0 database predicted 62 of these 278 miRNAs could interact with 105 target genes according to more than 8 prediction programs. After screening the DE-lncRNAs and DE-mRNAs with the consistent expression directions, regulatory relationship pairs among 5 lncRNAs, 49 miRNAs and 69 mRNAs were ultimately obtained, which was used to construct the ceRNA network (Figure 5). Among them, Socs3, Irf8 and Ifih1 were overlapped with hub genes identified in the PPI network, forming the following interaction axes: 9330175E14Rik-mmu-miR-762-Socs3, 6430562O15Rik-mmu-miR-207-Irf8 and 9330175E14Rik-mmu-miR-670-5p-Ifih1. These findings suggested that these ceRNA axes were important for NP-induced retinal injuries. The Gm9866-mmu-miR-669b-5p-Gdf6 and 4933406C10Rik-mmu-miR-9-5p-Crhbp ceRNA axes were also crucial because Gdf6 and Crhbp were enriched in apoptotic or inflammatory processes, respectively. Moreover, Gdf6 and Crhbp have limited overlapped genes between Au or Si NP exposure and ONC models. 3.5. Identification of Small Molecule Drugs A total of 20 small molecules were found to have a negative enrichment score and a p-value < 0.05 (Table 6), indicating they may be candidate drugs for the prevention and treatment of NP-induced retinal injuries. Among them, folic acid could target 69 hub DE-mRNAs (including Casp1, Ccl2, Cxcl10, Fas, Gbp6, Ifih1, Irf8, Socs3, Stat3, Tlr2, Syk) (Figure 6). Thus, folic acid tablets or folic acid-enriched foods should be properly supplemented for NP-exposed individuals to prevent the development of retinal injuries. 3.6. Validation of Crucial Genes in Hypoxia/H2O2-Induced Retinal Injury Models The expression levels of crucial genes (including Casp1, Ccl2, Cxcl10, Fas, Gbp6, Ifih1, Irf8, Socs3, Stat3, Tlr2, Syk and Crhbp) obtained after the above-integrated analyses were also validated in hypoxia/H2O2-induced retinal injury models. In line with NP exposure and ONC, Gbp6 and Crhbp were further confirmed to be lowly expressed (Figure 7A,D), while Socs3, Tlr2, Irf8, Ccl2 and Stat3 were highly expressed in hypoxia/H2O2-induced retinal injury models compared with controls (Figure 7B–D). 4. Discussion Although there are anatomical barriers on the eyeballs, only the airborne large-sized particles could be blocked from the ocular surface by blinking and tear film, while small-sized particulate matter NPs penetrate the barriers of the ocular surface and reach the posterior segments of the eyes [2,36,37]. Once entering the eyes, NPs may subsequently induce cellular toxicity in the lens, retina, optic nerve, and macula by stimulating inflammatory cell infiltration and cell apoptosis [2,36,37], ultimately leading to the development of ocular diseases. Furthermore, NPs are commonly utilized as drug delivery agents, which gives them the chance to be directly injected into the retina or reach the retina by impairing and crossing the blood-retinal barrier after systemic administration [38]. Hereby, the toxic mechanisms of NPs for retinal injuries should be given high attention. In the present study, we integrated the expression profile data of NP exposure and ONC models and identified that NP exposure triggered expression changes in 12 inflammation- or apoptosis-related genes (Ifih1, Tlr2, Crhbp, Ccl2, Cxcl10, Fas, Irf8, Socs3, Stat3, Gbp6, Casp1, Syk) in the retina. Among them, 9 (Ifih1, Cxcl10, Irf8, Stat3, Tlr2, Casp1, Ccl2, Syk, Socs3) were revealed to be hub genes according to PPI network and module analyses; 5 (Socs3, Irf8, Ifih1, Gdf6, Crhbp) were contained in the ceRNA network; 11 (Casp1, Ccl2, Cxcl10, Fas, Gbp6, Ifih1, Irf8, Socs3, Stat3, Tlr2, Syk) could be reversed by folic acid; 7 (Gbp6, Crhbp, Socs3, Tlr2, Irf8, Ccl2, Stat3) were validated to be differentially expressed in hypoxia/H2O2-induced retinal injury models. Two (Socs3 and Irf8) were the overlapped genes of all these procedures. Thus, we considered Socs3 and Irf8 to be particularly important monitoring biomarkers and therapeutic targets associated with NP-induced retinal injuries. There have been several studies to show that genetic deletion of suppressor of cytokine signaling 3 (SOCS3) enhances robust and sustained axon regeneration in the optic nerve of mice after ONC, while overexpression of SOCS3 results in almost complete regeneration failure of retinal ganglion cells [39,40,41,42]. In vitro studies demonstrated that the administration of SOCS3 markedly promoted the apoptosis of retinal pigment epithelial cells by increasing the expression levels of inflammatory mediators (IL-6 and TNF-α) [43]. Knockdown of SOCS3 significantly increased anti-apoptotic proteins (Akt, Bcl-xL), while decreased pro-apoptotic proteins (cytochrome C, Bax, caspase 3) in retinal endothelial cells [44]. The SOCS3 upregulated in degenerative retinal tissues resulted from the long-term activation of the signal transducer and activator of the transcription 3 (STAT3) pathway [45]. Furthermore, long-term exposure to TiO2 NPs was reported to lead to the infiltration of inflammatory cells and hepatocyte apoptosis or necrosis by upregulating the expression levels of STAT3 [46]. Thus, the hypothesis that NP exposure promoted the apoptosis of retinal cells by activating the STAT3-SOCS3-inflammation pathway may be believable. In line with these studies, we also observed that SOCS3 could interact with STAT3 as well as pro-apoptotic genes FAS (Fas cell surface death receptor) [47], CASP1 (caspase 1) [48], pro-inflammatory CCL2 (C-C motif chemokine ligand 2, also known as Mcp-1) [49] and CXCL10 (C-X-C motif chemokine ligand 10) [50] in the retina, confirmed by previous studies. Similar to the expected results, all of these genes were found to be upregulated in retinal tissues after TiO2 NP exposure and the ONC procedure in our study. SOCS3, STAT3 and CCL2 were also validated to be highly expressed in hypoxia/H2O2-induced retinal injury models. A previous study observed that loss of interferon regulatory factor 8 (IRF8) in retinal microglial cells and neurons protected the mice from the development of uveitis (with fewer numbers of inflammatory cells in the vitreous and less retinal infolding) [51]. Retinal degeneration (showing increased retinal thickness) was detected to be alleviated when the expression levels of IRF8 were inhibited by immunomodulatory agents [52,53]. Mechanistic investigation showed that the amelioration roles of retina-specific Irf8-deficiency for retinal diseases were ascribed to enhance the production of anti-inflammatory cytokines (IL-10, IL-27 and IL-35) and reduce the expression levels of pro-inflammatory IL-17 in the retina [51], while IRF8 was suggested as a downstream target gene of spleen tyrosine kinase (SYK) [54] and toll-like receptor (TLR)-4 was an adaptor protein of SYK [55] in the pathogenesis of ocular injuries. Cui et al. [56] and Hong et al. [57] proved that TiO2 NP exposure induced inflammatory histopathological changes and apoptosis of hepatocytes or spermatogenic/Sertoli cells by significantly increasing the mRNA and protein expression levels of TLR2, TLR3 and TLR4, respectively. Ag NP-mediated apoptosis in chondrocytes and periodontal ligaments was implied to be reduced after treatment with TLR2 siRNAs and antibodies [58]. Zinc oxide NPs were indicated to have significant adjuvant effects to induce inflammatory responses via activation of SYK and TLR signaling pathways [59]. In agreement with these studies, we found that IRF8 could interact with SYK, TLR2 and CXC10. All of these genes were upregulated in retinal tissues after TiO2 NP exposure and the ONC procedure in our study. IRF8 and TLR2 were also validated to be highly expressed in hypoxia/H2O2-induced retinal injury models. Therefore, NP exposure may stimulate visual impairments by activation of the following pathway: TLRs→SYK→IRF8→CXCL10→inflammation→apoptosis→retinal injury. Existing evidence supports that depletion of growth differentiation factor 6 (GDF6, also known as BMP13) in embryos results in a reduction in the eye size, a loss of mature neurons and an increase in cell death [60]. Overexpression of CRHBP (corticotropin-releasing hormone-binding protein) was shown to promote apoptosis in renal cell carcinoma via activating the nuclear factor (NF)-κB signaling pathway [61]. Similar to these studies, we also identified that GDF6 was downregulated while CRHBP was upregulated to participate in inflammation-mediated apoptosis in retinal tissues after TiO2, Au, Si NP exposure, hypoxia/H2O2 and ONC procedures. Although there had been studies to investigate the molecular mechanisms for NP exposure by the high-throughput technology, all of them focused on the protein-coding mRNAs [10,14] and lncRNAs remained underexplored. In the present study, we identified 9 crucial lncRNAs (6430562O15Rik, 9330175E14Rik, Gm833, Gm10030, 0610009B14Rik, Gm9866, Dbpht2, 4933406C10Rik, D030047H15Rik) associated with NP exposure. Most of them were not reported previously, except for Gm9866, which was demonstrated to be highly expressed in cardiac hypertrophy model mice [62]. This study indicated that Gm9866 may be downregulated if cell apoptosis predominated, which seemed to be consistent with our results of TiO2 NP exposure. To imply the possible functions of these lncRNAs, we constructed a ceRNA network. Consequently, 9330175E14Rik-mmu-miR-762-Socs3, 6430562O15Rik-mmu-miR-207-Irf8, Gm9866-mmu-miR-669b-5p-Gdf6, and 4933406C10Rik-mmu-miR-9-5p-Crhbp ceRNA axes were obtained. Thus, upregulated 9330175E14Rik, 6430562O15Rik, 4933406C10Rik and downregulated Gm9866 may exert pro-inflammatory and pro-apoptotic roles by leading to the upregulation of Socs3, Irf8, Crhbp, and downregulation of Gdf6, respectively. Although miRNAs in ceRNAs were not differentially expressed by NP exposure, their roles in other diseases may indirectly indicate their functions. For example, Gao et al. found that treatment with miR-762 inhibitors significantly inhibited the proliferation of retinal progenitor cells [63]. Tao et al. reported that miR-207 mimics protected against autophagic cell death after ischemic stroke and attenuated neurological deficit scores and infarct volumes [64]. Chi et al. observed that enforced expression of miR-9 increased cell viability, inhibited cell apoptosis and inactivated endoplasmic reticulum stress in oxygen-glucose deprivation neurons [65]. Overexpression of miR-669b promoted the secretion of pro-inflammatory TNF-α in CD4+ T cells [66]. Accordingly, we speculated that miR-762, miR-207 and miR-9 were downregulated, while miR-669b was upregulated in retinal cells with NP exposure to promote inflammation and cell apoptosis, which conformed to our ceRNA theory according to the expression trend of miRNA-interacted lncRNAs and mRNAs. Additionally, we identified that folic acid may be an underlying drug to treat NP-induced retinal injuries by reversing the above inflammatory and apoptotic genes. Our results were consistent with previous studies that demonstrated the protective roles of folic acid in retinal injuries induced by other factors and its influence on the expression levels of some genes in other inflammatory diseases. For example, Muralidharan et al. found that 2% ethanol exposure was shown to increase retinal cell death in zebrafish, while supplementation of 75 µM folic acid rescued retinal photoreceptor and ganglion cell differentiation defects [67]. Iskandar et al. observed that intraperitoneal treatment of adult rats with folic acid (80 µg/kg) for two weeks significantly improved the regrowth of retinal ganglion cells and enhanced neurological recovery from a spinal cord contusion injury [68]. Ma et al. verified that folic acid supplementation (40 μg/mL) repressed a hypoxia-induced inflammatory response via downregulating the activity of STAT3 and decreasing the expression levels of NF-κB p65 protein in human promyelomonocytic cells [69]. The study by Cui et al. showed that folic acid (2.1 mg/kg diet) reduced the atherosclerotic lesion size of mice by inhibiting the expression levels of CCL2 [70]. Some limitations should be acknowledged. First, this study only used the public transcriptome data of retinal tissues after Au, Si, and TiO2 exposure in mice to identify the molecular toxicological mechanisms of NPs. To confirm whether they represent underlying biomarkers for monitoring all NP-induced retinal injuries, the expression levels of our identified genes still need to be detected in animal or cell models by exposing them to different sizes, concentrations, chemical modifications and other characteristics of various NPs and collecting different tissue (e.g., cornea, conjunctiva, lens, sclera, choroid and retina) and cell types (e.g., epithelial, endothelial, ganglion cells) of eyes [2,5]. Second, our identified crucial genes were predicted to be involved in NP-induced retinal injury by regulation of inflammation and apoptosis. Thus, overexpression or silencing wet experiments should be scheduled in vitro and in vivo and then validated for their influence on cell proliferation (CCK8 assay), apoptosis (annexin V/PI staining, TUNEL), the release of inflammatory cytokines (enzyme-linked immunosorbent assay) and visual functions (symptom observation, fluorescein staining, tear secretion, electroretinogram and iris angiography) [5,12,71,72]. Third, the PPI and ceRNA regulatory mechanisms among genes need to be verified by immunoprecipitation or a dual-luciferase reporter assay. Fourth, folic acid was suggested to be a potential drug for preventing and treating NP-induced eye injury. Which dose may be most beneficial needs to be first determined by in vitro and in vivo experiments and then converted to a dose for humans and recommended for occupational exposure to NPs. 5. Conclusions In summary, our findings suggest that Socs3, Irf8, Crhbp and Gdf6 as well as the upstream mRNAs (Stat3, Tlr2-Syk), lncRNAs (9330175E14Rik, 6430562O15Rik, 4933406C10Rik and Gm9866) and miRNAs (miR-762, miR-207, miR-9 and miR-669b) that regulate these genes may be potential monitoring biomarkers and therapeutic targets associated with retinal injuries induced by NPs. They may participate in NP-induced visual impairments by activating inflammatory and apoptotic pathways. Folic acid nutrient supplementation may be a preventive and therapeutic approach for NP-induced retinal injuries. Author Contributions D.X.: Conceptualization, methodology, data curation, formal analysis, writing—original draft. J.H.: methodology, investigation, software, formal analysis. T.W.: investigation, visualization. K.C.: investigation, funding acquisition. X.L.: conceptualization, supervision, funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement All data were downloaded from the GEO database (GSE49371, GSE49048, GSE55228, GSE120257, GSE131486, GSE173233, GSE151610, GSE135844, GSE122270; http://www.ncbi.nlm.nih.gov/geo/). Conflicts of Interest The authors declare that they have no competing interests. Tong Wu is an employee of Shanghai Jing Rui Yang Industrial Co., Ltd. Moreover, no hidden conflict of interest exists with this company. Abbreviations NPs nanoparticles Ag silver Au gold Si silicate IL interleukin TNF tumor necrosis factor TiO2 titanium dioxide NCBI National Center for Biotechnology Information GEO Gene Expression Omnibus PBS phosphate buffer saline PTC presumptive therapeutic concentration ONC optic nerve crush FC fold change DE-lncRNAs differentially expressed long non-coding RNAs DE-mRNAs differentially expressed messenger RNAs GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes DAVID Database for Annotation, Visualization and Integrated Discovery PPI Protein–protein interaction DC degree centrality EC eigenvector centrality LAC local average connectivity BC betweenness centrality CC closeness centrality MCODE Molecular Complex Detection ceRNAs competing endogenous RNAs miRNAs microRNAs CMap Connectivity Map CTD Comparative Toxicogenomics Database SOCS3 suppressor of cytokine signaling 3 STAT3 signal transducer and activator of transcription 3 FAS Fas cell surface death receptor CASP1 caspase 1 CCL2 C-C motif chemokine ligand 2 CXCL10 C-X-C motif chemokine ligand 10 IRF8 interferon regulatory factor 8 SYK spleen tyrosine kinase TLR toll-like receptor GDF6 growth differentiation factor 6 CRHBP corticotropin-releasing hormone-binding protein NF-κB nuclear factor κB Figure 1 Identification of differentially expressed RNAs. (A) a volcano plot to show the differentially expressed RNAs of the GSE49048 dataset with PTC TiO2 NP exposure; (B) a volcano plot to show the differentially expressed RNAs of the GSE49048 dataset with 10PTC TiO2 NP exposure; (C) a volcano plot to show the differentially expressed RNAs of the GSE49371 dataset with Au20 NP exposure; (D) a volcano plot to show the differentially expressed RNAs of the GSE49371 dataset with Au100 NP exposure; (E) a volcano plot to show the differentially expressed RNAs of the GSE49371 dataset with Si20 NP exposure; (F) a volcano plot to show the differentially expressed RNAs of the GSE49371 dataset with Au100 NP exposure; (G) a heat map to show the differentially expressed RNAs of the GSE55228 dataset undergoing ONC procedures; (H), a heat map to show the differentially expressed RNAs of the GSE120257 dataset undergoing ONC procedures; (I) a heat map to show the differentially expressed RNAs of the GSE131486 dataset undergoing ONC procedures. Blue dots in the volcano plots are downregulated genes; red dots in the volcano plots are upregulated genes; blue in the heatmap indicates high expressed genes; red in the heatmap indicates low expressed genes; FC, fold change; ONC, optic nerve crush; PTC: presumptive therapeutic concentration; NPs: nanoparticles; Ag: silver; Au: gold; Si: silicate. Figure 2 Venn diagrams to show the overlapped genes between NP exposure and ONC treatment. (A) overlapped upregulated genes between PTC TiO2 NPs of GSE49048 and GSE55228-GSE120257-GSE131486; (B) downregulated upregulated genes between PTC TiO2 NPs of GSE49048 and GSE55228-GSE120257-GSE131486; (C) overlapped upregulated genes between 10PTC TiO2 NPs of GSE49048 and GSE55228-GSE120257-GSE131486; (D) overlapped downregulated genes between 10PTC TiO2 NPs of GSE49048 and GSE55228-GSE120257-GSE131486; (E) overlapped upregulated genes between Au20 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (F) overlapped downregulated genes between Au20 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (G) overlapped upregulated genes between Au100 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (H) overlapped downregulated genes between Au100 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (I) overlapped upregulated genes between Si20 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (J) overlapped downregulated genes between Si20 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (K) overlapped upregulated genes between Si100 NPs of GSE49371 and GSE55228-GSE120257-GSE131486; (L) overlapped downregulated genes between Si100 NPs of GSE49371 and GSE55228-GSE120257-GSE131486. PTC: presumptive therapeutic concentration; NPs: nanoparticles; Ag: silver; Au: gold; Si: silicate. Figure 3 Function enrichment for the overlapped differentially expressed mRNAs between GSE49048-GSE49371 and GSE55228-GSE120257-GSE131486 datasets. (A) GO term; (B) KEGG pathways. Only top 20 are shown. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes. Figure 4 Modules extracted from the protein–protein interaction network. (A) module 1; (B) module 2; (C) module 3; (D) module 4; (E) module 5. Red, upregulated genes; green, downregulated genes. Figure 5 A ceRNA network among differentially expressed mRNAs, lncRNAs and miRNAs. Circle indicates the mRNAs; hexagon indicates the lncRNAs; triangle indicates the miRNAs; Red, upregulated genes; green, downregulated genes; blue, not differentially expressed. ceRNA, competing endogenous RNAs. Figure 6 The target relationships between folic acid and genes. (A) the structure of folic acid; (B) the regulatory network between folic acid and genes. Red, upregulated genes. Figure 7 Validation of the expression levels of crucial genes in hypoxia/H2O2-induced retinal injury models. (A) GSE173233 dataset; (B) GSE151610 dataset; (C) GSE135844 dataset; (D) GSE122270 dataset. h, hour. ijerph-19-05664-t001_Table 1 Table 1 Crucial genes changed by nanoparticles to induce the retinal injury. Treatment Gene Symbol GSE49048 GSE55228 GSE120257 GSE131486 Log2FC p-Value Log2FC p-Value Log2FC p-Value Log2FC p-Value TiO2 vs. control (PTC) Gm9866 −0.50 3.94 × 102 −0.57 2.74 × 102 Cx3cr1 1.44 3.97 × 102 0.85 6.09 × 1013 0.62 1.47 × 102 Stat3 1.00 2.98 × 102 1.04 4.73 × 1051 0.63 2.83 × 102 Aif1 1.35 4.44 × 102 0.78 1.90 × 106 Cxcl9 0.65 3.49 × 102 0.91 4.91 × 1011 TiO2 vs. control (100 PTC) 6430562O15Rik 0.54 1.32 × 103 0.77 1.04 × 107 9330175E14Rik 1.24 3.92 × 102 0.62 3.30 × 106 Tlr2 1.08 1.60 × 103 2.38 1.55 × 103 1.14 3.06 × 1012 1.54 4.20 × 102 C1qa 1.44 1.59 × 103 0.64 5.26 × 103 1.73 5.75 × 1051 Cx3cr1 1.16 4.06 × 103 0.85 6.09 × 1013 0.62 1.47 × 102 Irf1 0.66 2.83 × 102 1.25 5.84 × 1030 0.57 1.01 × 102 Irf8 1.39 6.97 × 104 1.05 3.01 × 1013 1.07 4.07 × 103 Cxcl10 2.43 8.73 × 103 1.39 4.11 × 1018 1.90 1.31 × 102 Socs3 1.36 1.3 × 104 1.61 3.81 × 1038 1.08 2.43 × 102 Ccl2 1.60 3.24 × 103 5.63 7.48 × 103 Stat3 1.03 9.11 × 104 1.04 4.73 × 1051 0.63 2.83 × 102 Ifih1 0.91 1.10× 102 0.73 7.64 × 104 Aif1 1.17 8.88 × 103 0.78 1.90 × 106 Fas 1.00 9.77 × 103 0.70 1.63 × 105 Casp1 1.19 3.2 × 104 0.56 7.35 × 105 Cxcl9 0.61 1.37 × 103 0.91 4.91 × 1011 Pou4f1 −0.62 1.00× 102 −0.70 2.27 × 107 −1.50 3.19 × 1055 −1.48 1.78 × 1010 Au20 vs. control 4933406C10Rik 0.73 3.32× 102 0.96 9.35 × 103 Crhbp 1.22 4.29× 102 0.61 2.44 × 102 Gdf6 −0.98 1.20 × 103 −1.14 4.97 × 102 Au100 vs. control 4933406C10Rik 0.74 385× 102 0.96 9.35 × 103 Crhbp 2.10 3.40× 102 0.61 2.44 × 102 D030047H15Rik 0.56 5.06 × 103 0.87 2.08 × 102 Si20 vs. control 4933406C10Rik 0.61 1.60× 102 0.96 9.35 × 103 Crhbp 1.74 2.73× 102 0.61 2.44 × 102 FC: fold change; PTC: presumptive therapeutic concentration; TiO2: titanium dioxide; Au20: gold with diameters of 20 nm; Au100: gold with diameters of 100 nm; Si20: silicate with diameters of 20 nm; Si100: silicate with diameters of 100 nm. ijerph-19-05664-t002_Table 2 Table 2 GO term enrichment results. Term p-Value Genes GO:0002376~immune system process 3.04 × 1027 IFITM3, H2-T23, SPON2, CD84, CSF1, H2-K1, AHR, IFI30, IFIT1, IFIT3, IFIH1, MAP3K8, LGALS9, JAK3, B2M, OASL2, HERC6, RSAD2, SYK, DDX58, TAP1, FCGR1, NAIP2, HCK, IRF1, SERPING1, H2-D1, TLR13, TLR2, TRIM56, C1QB, C1QA, UNC93B1, H2-Q7, NLRC5, ZC3HAV1, INPP5D, C1RA, LY86, EIF2AK2, IRGM1, PSMB8, PSMB9, BST2, BCL6, AXL, MYO1G, C1QC GO:0045087~innate immune response 7.54 × 1022 IFITM3, C1QB, SPON2, C1QA, CD84, CSF1, UNC93B1, NLRC5, OAS1A, TREM2, ZC3HAV1, IFIT1, IFIT3, IFIH1, C4B, CLEC7A, JAK3, TRIM21, B2M, HERC6, OASL2, C1RA, FCER1G, RSAD2, SYK, DDX58, LY86, CYBB, IRGM1, EIF2AK2, CYBA, BST2, FCGR1, NAIP2, HCK, TYROBP, AXL, IRF1, SERPING1, TLR13, TLR2, C1QC, TRIM56 GO:0006954~inflammatory response 5.15 × 1014 PTGFR, CCL12, CALCA, CXCL9, NCF1, CSF1, CXCL1, AIF1, PTGS1, C4B, CRHBP, CLEC7A, STAB1, SPP1, CCL3, C3AR1, CCL2, SLC11A1, LY86, CYBB, CYBA, TNFRSF1B, TNFRSF1A, P2RX7, CXCL10, NAIP2, HCK, BCL6, TNIP2, AXL, FAS, TLR13, TLR2 GO:0051607~defense response to virus 5.61 × 1014 APOBEC1, IFITM3, SPON2, CXCL9, RSAD2, UNC93B1, DDX58, NLRC5, EIF2AK2, OAS1A, ZC3HAV1, DDX60, IFIT1, IFIT3, IFIH1, BST2, CXCL10, IRF1, ITGAX, PMAIP1, TRIM56, OASL2 GO:0009615~response to virus 2.52 × 1012 IFITM3, RSAD2, DDX58, EIF2AK2, OAS1A, ZC3HAV1, DDX60, IFIT1, IFIT3, IFIH1, BST2, CXCL10, BCL3, TLR13, OASL2 GO:0035458~cellular response to interferon-beta 2.65 × 109 GBP6, IFI204, IRF1, IFI203, IGTP, IRGM2, IRGM1, IFIT1, GBP2, IFIT3, GBP3 GO:0032496~response to lipopolysaccharide 4.44 × 109 SPON2, PTGFR, CEBPB, CXCL9, GCH1, SLC11A1, EIF2AK2, CXCL1, TNFRSF1B, LITAF, TNFRSF1A, P2RX7, CXCL10, CASP8, PENK, CASP1, ACP5, FAS, LGALS9, TLR2 GO:0050830~defense response to Gram-positive bacterium 1.53 × 108 GBP6, H2-T23, GBP7, NCF1, GBP9, LYZ2, P2RX7, HCK, ACP5, GBP2, B2M, MYO1F, TLR2, GBP3 GO:0042832~defense response to protozoan 5.00 × 108 GBP6, GBP7, GBP9, SLC11A1, BCL3, IRGM2, IRF8, GBP2, GBP3 GO:0071222~cellular response to lipopolysaccharide 6.62 × 108 CX3CR1, GBP6, SPON2, CEBPB, ARG1, SERPINE1, TNFRSF1B, LITAF, ICAM1, CXCL10, SBNO2, PLSCR2, FCGR4, TNIP2, AXL, CCL2, IRF8, GBP2, B2M GO:0006955~immune response 1.68 × 107 CCL12, CXCL9, H2-K1, LIF, CXCL1, TNFRSF1B, CTSS, VAV1, TNFRSF1A, CXCL10, BLNK, CCL3, CCL2, FAS, IRF8, LCP2, FCGR2B, B2M, H2-D1, TLR2, OASL2 GO:0044406~adhesion of symbiont to host 2.44 × 107 GBP6, GBP7, GBP9, GBP2, ICAM1, GBP3 GO:0071346~cellular response to interferon-gamma 4.42 × 107 GBP6, CCL12, GBP7, GBP9, H2-Q7, CCL3, CCL2, GBP2, AIF1, GBP4, GBP3 GO:0030593~neutrophil chemotaxis 5.09 × 107 FCGR3, CCL12, EDN2, FCER1G, SYK, SPP1, CCL3, CCL2, CXCL1, NCKAP1L, VAV1 GO:0042742~defense response to bacterium 5.22 × 107 SPON2, CEBPB, FCER1G, SYK, NCF1, ANXA3, LYZ2, SLC11A1, H2-M3, H2-K1, IRGM2, TNFRSF1A, FCGR1, NAIP2, BCL3, STAB1, IRF8 GO:0042590~antigen processing and presentation of exogenous peptide antigen via MHC class I 5.57 × 107 FCGR1, FCGR3, FCER1G, H2-K1, IFI30 GO:0032760~positive regulation of tumor necrosis factor production 1.22 × 106 SASH3, H2-T23, SPON2, FCER1G, CCL3, CCL2, CYBA, LGALS9, TNFRSF1A, TLR2 GO:0009617~response to bacterium 2.91 × 106 FCGR1, P2RX7, NCF1, SLC11A1, CASP1, IRF8, FCGR2B, TLR2 GO:0002474~antigen processing and presentation of peptide antigen via MHC class I 3.56 × 106 H2-T23, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, B2M, H2-D1 GO:0006935~chemotaxis 1.16 × 105 CX3CR1, CXCL10, CXCL9, CCL12, RAC2, C3AR1, CCL3, CCL2, NCKAP1L, LGALS9, DOCK2, CYR61 GO:0002479~antigen processing and presentation of exogenous peptide antigen via MHC class I, TAP-dependent 1.52 × 105 H2-T23, H2-Q7, H2-K1, B2M, PSMB8, H2-D1, PSMB9 GO:0019221~cytokine-mediated signaling pathway 1.69 × 105 CX3CR1, CCL12, EDN2, STAT3, CSF2RB, TNFRSF1A, CSF2RB2, SOCS3, CCL2, PTPN6, IL6ST, JAK3, CD44 GO:0010628~positive regulation of gene expression 1.75 × 105 PTGFR, CSF1, DDX58, SLC11A1, SERPINE1, STAT3, PLAUR, LIF, SOX11, IKZF1, POU4F1, FGF2, TNFRSF1A, P2RX7, CCL3, CTSH, VIM, LGALS9, ATF3, CD44, NKX3-1, TLR2 GO:0001916~positive regulation of T cell mediated cytotoxicity 1.93 × 105 P2RX7, H2-T23, H2-K1, H2-M3, B2M, H2-D1 GO:0050766~positive regulation of phagocytosis 2.60 × 105 FCGR1, FCGR3, FCER1G, PROS1, SLC11A1, CYBA, DOCK2, FCGR2B GO:0009636~response to toxic substance 2.89 × 105 CDKN1A, PENK, CCL3, EIF2AK2, FAS, NEFL, AHR, NUPR1, SLC7A11, TLR2 GO:0007155~cell adhesion 3.58 × 105 CX3CR1, LGALS3BP, SPON2, CD84, VWF, CD93, TNFAIP6, TNFRSF12A, FBLIM1, MCAM, PCDH8, CYR61, ICAM1, MFAP4, GPNMB, CHL1, PDPN, STAB1, SPP1, ITGAX, CD9, CTNNAL1, CD33, CD44 GO:0006909~phagocytosis 4.43 × 105 HCK, ANXA3, AXL, SLC11A1, PLD4, IRF8, VAV1, MYO1G GO:0045576~mast cell activation 7.08 × 105 FCGR3, FCER1G, LCP2, CD48, FCGR2B GO:0048246~macrophage chemotaxis 9.77 × 105 CX3CR1, CCL12, EDN2, CCL3, CCL2 GO:0034341~response to interferon-gamma 9.84 × 105 IFITM3, BST2, GCH1, SLC11A1, IRGM2, TRIM21 GO:0030335~positive regulation of cell migration 1.04 × 104 TNFAIP6, CSF1, SEMA3C, MCAM, AIF1, CYR61, CXCL10, GPNMB, PDPN, C3AR1, CCL3, CTSH, ROR2, MYO1F GO:0050729~positive regulation of inflammatory response 1.54 × 104 CCL12, SERPINE1, CCL3, CCL2, CTSS, TNFRSF1A, TLR2, TGM2 GO:0045730~respiratory burst 2.05 × 104 NCF1, SLC11A1, CYBB, CYBA GO:0043029~T cell homeostasis 2.36 × 104 P2RX7, PMAIP1, FAS, NCKAP1L, AHR, JAK3 GO:0042771~intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator 2.36 × 104 CDKN1A, IFI204, BCL3, SHISA5, PMAIP1, NUPR1 GO:0045071~negative regulation of viral genome replication 2.75 × 104 IFITM3, BST2, RSAD2, EIF2AK2, ZC3HAV1 GO:0008285~negative regulation of cell proliferation 2.92 × 104 IFITM3, CDKN1A, IGFBP3, STAT3, LIF, EIF2AK2, DHCR24, FGF2, IFIT3, MYO16, RUNX1, BCL6, IRF1, INPP5D, CD9, ROR2, NKX3-1, SKAP2, TLR2 GO:0043615~astrocyte cell migration 3.24 × 104 CCL12, HEXB, CCL3, CCL2 GO:0007568~aging 3.72 × 104 CALCA, ARG1, STAT3, PENK, SERPING1, CCL2, APOD, TIMP1, TNFRSF1B, LITAF, FGF2, CTSC GO:0071407~cellular response to organic cyclic compound 4.58 × 104 P2RX7, MSR1, CEBPB, CCL12, CASP8, STAT3, CCL3, CYBA GO:0071347~cellular response to interleukin-1 6.78 × 104 CEBPB, CCL12, IRF1, SERPINE1, CCL3, CCL2, ICAM1, NKX3-1 GO:0030168~platelet activation 7.08 × 104 ENTPD1, VWF, SYK, AXL, ADRA2C, VAV1 GO:0045944~positive regulation of transcription from RNA polymerase II promoter 8.08 × 104 CSRNP1, CEBPB, CEBPD, HEXB, NLRC5, IKZF1, FGF2, CYR61, SBNO2, CCL3, JAK3, ZBTB7C, NKX3-1, WWTR1, EGR2, DDX58, SLC11A1, STAT3, ARID5A, LIF, SOX11, POU4F1, POU4F2, RUNX1, TNFRSF1A, HOXB9, TNIP2, IRF1, BCL3, CAPRIN2, FOSB, ATF3, TLR2, CREB5 GO:0043066~negative regulation of apoptotic process 8.49 × 104 PTGFR, CDKN1A, STAT3, PLAUR, EIF2AK2, DHCR24, AIF1, POU4F1, CYR61, IFIT3, TNFRSF1A, BTC, HCK, SOCS3, BCL6, AXL, BCL3, SPP1, CTSH, FAS, NCKAP1L, TIMP1, CD44 GO:0008217~regulation of blood pressure 9.63 × 104 CALCA, EDN2, GCH1, C3AR1, CYBA, AHR, PTGS1 GO:0071356~cellular response to tumor necrosis factor 9.78 × 104 CRHBP, CCL12, CALCA, IRF1, CCL3, CCL2, CYBA, ICAM1, NKX3-1 GO:0035457~cellular response to interferon-alpha 1.20 × 103 IFI204, AXL, IFIT1, IFIT3 GO:0043065~positive regulation of apoptotic process 1.45 × 103 HRK, TNFRSF12A, GADD45B, IGFBP3, EIF2AK2, POU4F1, CYR61, P2RX7, CASP8, BCL6, INPP5D, CASP1, PMAIP1, FAS, NUPR1, TGM2 GO:0097191~extrinsic apoptotic signaling pathway 6.69 × 103 P2RX7, CASP8, TNFRSF12A, FAS, TNFRSF1B GO:0006915~apoptotic process 9.46 × 103 HRK, CSRNP1, TNFRSF12A, NCF1, GADD45B, SHISA5, GDF6, LITAF, TNFRSF1A, RNF144B, NAIP2, CASP8, TNIP2, IRF1, INPP5D, CASP1, PMAIP1, FAS, MAP3K8, XAF1 GO:0042127~regulation of cell proliferation 9.50 × 103 APOBEC1, CXCL10, HCK, CXCL9, BCL6, SERPINE1, TCF7, FAS, TNFRSF1B, TNFRSF1A, PTGS1 GO:0042981~regulation of apoptotic process 1.16 × 102 HRK, CASP8, BCL3, CASP1, PMAIP1, EIF2AK2, FAS, TNFRSF1B, GDF6, TNFRSF1A GO:0048678~response to axon injury 2.07 × 102 ARG1, APOD, FGF2, AIF1 GO:0043524~negative regulation of neuron apoptotic process 2.89 × 102 CEBPB, CCL12, CHL1, AXL, NEFL, CCL2, POU4F1, NES GO: Gene Ontology. Only results with false discovery rate < 0.05 and apoptosis-related processes are listed. ijerph-19-05664-t003_Table 3 Table 3 KEGG pathway enrichment results. Term p-Value Genes mmu04380:Osteoclast differentiation 3.44 × 109 SYK, NCF1, CSF1, CYBB, CYBA, TREM2, TNFRSF1A, FCGR1, SOCS3, FCGR3, TYROBP, FCGR4, BLNK, ACP5, FOSB, LCP2, FCGR2B, IRF9 mmu04145:Phagosome 1.08 × 108 H2-T23, MSR1, C1RA, H2-BL, NCF1, H2-Q6, H2-Q7, H2-M3, H2-K1, TAP1, CYBA, CTSS, FCGR1, FCGR3, CLEC7A, TUBB3, FCGR4, FCGR2B, H2-D1, TLR2 mmu04668:TNF signaling pathway 2.29 × 108 CEBPB, CCL12, CSF1, LIF, CXCL1, TNFRSF1B, TNFRSF1A, ICAM1, CXCL10, SOCS3, CASP8, BCL3, CCL2, FAS, MAP3K8, CREB5 mmu05168:Herpes simplex infection 5.17 × 108 H2-T23, CCL12, H2-BL, DDX58, H2-Q6, H2-Q7, H2-M3, H2-K1, EIF2AK2, OAS1A, TAP1, IFIT1, TNFRSF1A, IFIH1, SOCS3, CASP8, CCL2, FAS, IRF9, H2-D1, TLR2 mmu05169:Epstein-Barr virus infection 7.42 × 108 H2-T23, ENTPD1, CDKN1A, H2-BL, SYK, DDX58, H2-Q6, H2-Q7, H2-M3, STAT3, H2-K1, EIF2AK2, ICAM1, VIM, JAK3, H2-D1, CD44 mmu04610:Complement and coagulation cascades 1.31 × 107 C1QB, C1QA, C1RA, VWF, PROS1, SERPINE1, PLAUR, PLAT, C4B, C3AR1, SERPING1, A2M, C1QC mmu05150:Staphylococcus aureus infection 1.50 × 107 FCGR1, C1QB, C4B, C1QA, FCGR3, C1RA, FCGR4, C3AR1, FCGR2B, ICAM1, C1QC mmu04612:Antigen processing and presentation 1.65 × 105 H2-T23, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, TAP1, IFI30, B2M, CTSS, H2-D1 mmu05203:Viral carcinogenesis 2.12 × 105 H2-T23, EGR2, CDKN1A, H2-BL, SYK, H2-Q6, H2-Q7, H2-M3, STAT3, H2-K1, EIF2AK2, CASP8, PMAIP1, IL6ST, JAK3, IRF9, H2-D1, CREB5 mmu05142:Chagas disease (American trypanosomiasis) 2.23 × 105 C1QB, GNA14, C1QA, CCL12, CASP8, SERPINE1, CCL3, FAS, CCL2, TNFRSF1A, C1QC, TLR2 mmu05164:Influenza A 3.64 × 105 CCL12, RSAD2, DDX58, MX2, EIF2AK2, OAS1A, TNFRSF1A, ICAM1, IFIH1, CXCL10, SOCS3, CASP1, CCL2, FAS, IRF9 mmu04060:Cytokine-cytokine receptor interaction 3.86 × 105 CX3CR1, IL15RA, CCL12, CXCL9, TNFRSF12A, CSF1, LIF, CXCL1, CSF2RB, OSMR, TNFRSF1B, TNFRSF1A, CSF2RB2, CXCL10, CCL3, CCL2, FAS, IL6ST mmu05416:Viral myocarditis 7.61 × 105 H2-T23, CASP8, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, RAC2, H2-D1, ICAM1 mmu04666:Fc gamma R-mediated phagocytosis 1.23 × 104 FCGR1, HCK, NCF1, SYK, MYO10, INPP5D, RAC2, DOCK2, FCGR2B, VAV1 mmu04650:Natural killer cell mediated cytotoxicity 1.41 × 104 TYROBP, FCER1G, SYK, FCGR4, RAC2, FAS, PTPN6, LCP2, CD48, VAV1, ICAM1 mmu04062:Chemokine signaling pathway 1.60 × 104 CX3CR1, CCL12, CXCL9, NCF1, STAT3, CXCL1, VAV1, CXCL10, HCK, CCL3, RAC2, GNB3, CCL2, DOCK2, JAK3 mmu05332:Graft-versus-host disease 1.69 × 104 H2-T23, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, FAS, H2-D1 mmu05330:Allograft rejection 2.72 × 104 H2-T23, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, FAS, H2-D1 mmu05133:Pertussis 2.77 × 104 C1QB, C4B, C1QA, C1RA, IRF1, CASP1, SERPING1, IRF8, C1QC mmu04940:Type I diabetes mellitus 5.14 × 104 H2-T23, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, FAS, H2-D1 mmu05152:Tuberculosis 7.21 × 104 CEBPB, FCER1G, SYK, CTSS, TNFRSF1A, FCGR1, FCGR3, CASP8, CLEC7A, FCGR4, ITGAX, FCGR2B, TLR2 mmu05144:Malaria 7.51 × 104 CCL12, CCL2, HBB-B1, HBA-A2, HBA-A1, ICAM1, TLR2 mmu04115:p53 signaling pathway 8.25 × 104 CDKN1A, CASP8, GADD45B, IGFBP3, SERPINE1, SHISA5, PMAIP1, FAS mmu04662:B cell receptor signaling pathway 1.07 × 103 CD72, SYK, INPP5D, BLNK, RAC2, PTPN6, FCGR2B, VAV1 mmu05162:Measles 1.13 × 103 IFIH1, DDX58, MX2, STAT3, EIF2AK2, FAS, OAS1A, FCGR2B, JAK3, IRF9, TLR2 mmu05320:Autoimmune thyroid disease 1.17 × 103 H2-T23, H2-BL, H2-Q6, H2-Q7, H2-K1, H2-M3, FAS, H2-D1 mmu04630:Jak-STAT signaling pathway 1.83 × 103 CSF2RB2, IL15RA, SOCS3, STAT3, LIF, PTPN6, CSF2RB, IL6ST, OSMR, JAK3, IRF9 mmu05140:Leishmaniasis 3.39 × 103 FCGR1, FCGR3, NCF1, FCGR4, CYBA, PTPN6, TLR2 mmu05160:Hepatitis C 4.04 × 103 SOCS3, CDKN1A, DDX58, IRF1, STAT3, EIF2AK2, OAS1A, IFIT1, IRF9, TNFRSF1A mmu04142:Lysosome 6.97 × 103 HEXB, SLC11A1, LAPTM5, ACP5, CTSH, CD68, LITAF, CTSS, CTSC mmu05143:African trypanosomiasis 8.14 × 103 FAS, HBB-B1, HBA-A2, HBA-A1, ICAM1 mmu04621:NOD-like receptor signaling pathway 9.72 × 103 NAIP2, CCL12, CASP8, CASP1, CCL2, CXCL1 mmu05166:HTLV-I infection 1.10 × 102 H2-T23, IL15RA, EGR2, CDKN1A, H2-BL, H2-Q6, H2-Q7, H2-M3, H2-K1, TNFRSF1A, ICAM1, JAK3, ATF3, H2-D1 mmu05323:Rheumatoid arthritis 1.13 × 102 CCL12, CSF1, CCL3, CCL2, ACP5, ICAM1, TLR2 mmu05161:Hepatitis B 1.92 × 102 IFIH1, EGR2, CDKN1A, CASP8, DDX58, STAT3, FAS, CREB5, TLR2 mmu04664:Fc epsilon RI signaling pathway 1.98 × 102 FCER1G, SYK, INPP5D, RAC2, LCP2, VAV1 mmu04620:Toll-like receptor signaling pathway 2.87 × 102 CXCL10, CXCL9, CASP8, SPP1, CCL3, MAP3K8, TLR2 mmu04611:Platelet activation 3.12 × 102 FCER1G, VWF, SYK, COL5A2, TBXAS1, PRKG2, LCP2, PTGS1 mmu05134:Legionellosis 4.17 × 102 NAIP2, CASP8, CASP1, CXCL1, TLR2 mmu04210:Apoptosis 4.88 × 102 CSF2RB2, CASP8, FAS, CSF2RB, TNFRSF1A KEGG: Kyoto Encyclopedia of Genes and Genomes. ijerph-19-05664-t004_Table 4 Table 4 Hub genes identified by topological characteristics. Genes DC Genes EC Genes LAC Genes BC Genes CC Cxcl10 77 Tlr2 0.16 Ifit3 28.38 Stat3 6332.91 Cxcl10 0.044 Tlr2 73 Tyrobp 0.16 Ifit1 28.13 Cd44 5426.13 Stat3 0.044 Tyrobp 70 Cxcl10 0.16 Irf9 27.79 Cxcl10 4681.39 Tlr2 0.043 Itgax 66 Itgax 0.16 Rtp4 27.67 Tlr2 4583.62 Ccl2 0.043 Stat3 64 Aif1 0.15 Oasl2 27.58 Aif1 4057.10 Cd44 0.043 Aif1 62 Cd68 0.15 Irgm1 27.47 Vwf 3852.56 Aif1 0.043 Cd68 60 Irf8 0.14 Irgm2 27.46 Atf3 3484.15 Itgax 0.043 Ccl2 60 Fcgr2b 0.14 Gbp2 27.11 Vim 3158.54 Cd68 0.043 Ctss 58 Ctss 0.14 Parp14 26.92 Ccl2 3102.06 Cxcl9 0.043 Cd44 57 Cxcl9 0.13 Gbp3 26.69 Fgf2 2981.26 Tyrobp 0.043 Fcgr2b 55 Fcgr1 0.13 Rnf213 26.37 Entpd1 2352.07 Icam1 0.043 Cxcl9 55 Nckap1l 0.13 Fcer1g 26.27 Igfbp3 2348.79 Irf8 0.043 C1qa 55 C1qa 0.13 Rsad2 26.15 Antxr2 2238 C1qa 0.043 Irf8 54 Ccl2 0.13 Tyrobp 26 Tnnt2 2238 Cxcl1 0.043 Nckap1l 54 Casp1 0.12 Parp9 25.94 C1qa 2156.60 Csf1 0.043 Irf1 54 Cybb 0.12 Herc6 25.89 Itgax 2130.30 Fcgr2b 0.043 Ifih1 54 Fcer1g 0.12 Ddx60 25.83 Socs3 2039.41 Fgf2 0.043 Ddx58 54 Cd53 0.12 Nckap1l 25.48 Timp1 1996.1 Casp1 0.043 Mx1 50 Fcgr4 0.12 Pld4 25.44 Cxcl1 1920.96 Timp1 0.043 C1qb 48 Lyz2 0.12 Cd53 25.32 Cxcl9 1830.66 Cybb 0.043 Casp1 47 Stat3 0.12 Trim30a 25.30 C1qb 1772.68 Lyz2 0.043 Syk 47 Irf1 0.11 Gbp6 25.24 Tyrobp 1763.02 Ctss 0.043 Icam1 47 Ifih1 0.11 Ifih1 25.15 Spp1 1704.59 Fcgr1 0.043 Ptpn6 47 Ly86 0.11 Mx1 25.08 Bst1 1680 Irf1 0.043 Fcgr1 46 H2-K1 0.11 Cxcl10 24.80 Irf1 1619.14 H2-K1 0.043 Lyz2 46 Ddx58 0.11 Igtp 24.77 Irf8 1564.14 Ccl3 0.043 Ly86 46 Pld4 0.11 Irf1 24.59 Cd68 1519.08 Serpine1 0.043 H2-K1 46 Mx1 0.11 Ctss 24.24 C4b 1491.62 Ddx58 0.043 Cybb 45 Fcgr3 0.11 Gbp7 24.23 Arg1 1475.19 Psmb8 0.043 Fcgr4 45 Syk 0.11 Fcgr1 24.23 Mx1 1474.78 Syk 0.043 Ifit1 45 Cd48 0.11 Cd68 23.6 Cyr61 1448.63 Socs3 0.043 Fcer1g 44 C1qb 0.11 Fcgr2b 23.56 Ddx58 1429.51 Spp1 0.043 Cd53 44 Vav1 0.101 Ly86 23.52 Lyz2 1427.96 Mx1 0.043 Psmb8 44 Cd44 0.101 Samd9l 23.36 Gnb3 1422.19 B2m 0.043 Pld4 43 Slc11a1 0.101 Itgax 23.33 Csf1 1404.39 Gfap 0.043 Fcgr3 43 Icam1 0.101 Ddx58 23.22 Myo1g 1373.28 Arg1 0.043 Lcp2 43 Hck 0.101 Aif1 22.90 Ctsc 1337.92 Fcgr4 0.043 Trim30a 43 Ptpn6 0.10 Trim21 22.85 Lcp2 1322.43 Vwf 0.043 Cxcl1 43 Ifit1 0.10 Hck 22.70 Icam1 1297.18 Vav1 0.043 Oasl2 43 Psmb8 0.10 Fcgr3 22.60 Serpine1 1273.40 C1qb 0.043 Cd48 42 Lcp2 0.10 Tlr2 22.52 Gbp7 1255.33 Ptpn6 0.043 Vav1 42 Trim30a 0.10 Laptm5 22.4 Ctss 1219.90 Cd48 0.043 Ifit3 42 Rac2 0.09 Fcgr4 22 Cdkn1a 1208.70 C3ar1 0.043 Rtp4 42 Csf1 0.09 Rac2 21.68 B2m 1202.51 Nckap1l 0.043 Csf1 41 Ccl3 0.09 Cd48 21.67 Hbb-bs 1200.55 Cx3cr1 0.043 Rsad2 41 C3ar1 0.09 Cybb 21.56 Pou4f1 1195.79 Ifih1 0.043 Psmb9 40 Cxcl1 0.09 Bst2 21.44 Hexb 1156.12 Psmb9 0.043 C1qc 40 Laptm5 0.09 Cxcl9 21.13 Pola2 1154.03 Cd53 0.043 C3ar1 39 Psmb9 0.09 Eif2ak2 20.93 Lrrc2 1126 Lcp2 0.043 Igtp 39 C1qc 0.09 Irf8 20.85 Prkg2 1126 Ly86 0.043 Irf9 39 Trem2 0.09 Lyz2 20.70 Ahr 1124 Fcgr3 0.043 Parp14 39 Irgm1 0.09 Psmb8 20.5 Map3k8 1124 Clec7a 0.043 Irgm1 38 Igtp 0.09 Slc11a1 20.27 Irx4 1124 Fas 0.043 Gbp2 38 Oasl2 0.09 C1qa 20.22 Nes 1109.62 C1qc 0.043 Rnf213 38 Gbp2 0.08 Psmb9 20.15 Fcgr2b 1042.78 Atf3 0.043 B2m 38 Ifit3 0.08 Cd52 20.08 Nckap1l 1029.29 Trem2 0.043 Slc11a1 37 Rtp4 0.08 Myo1f 19.48 Casp1 1026.18 Slc11a1 0.043 Hck 37 Irf9 0.08 Vav1 19.43 C3ar1 979.36 Cyba 0.043 Rac2 37 Rsad2 0.08 Casp1 19.23 Psmb8 891.35 Lcp1 0.043 Irgm2 37 Cyba 0.08 Ptpn6 19.23 Ifih1 883.81 Hck 0.043 DC: degree centrality; EC: eigenvector centrality; LAC: local average connectivity; BC: betweenness centrality; CC: closeness centrality. ijerph-19-05664-t005_Table 5 Table 5 Modules screened from the PPI network. Cluster Score Nodes Edges Node IDs 1 27.357 29 383 Herc6, Irf1, Irf9, Ifih1, Oasl2, Gbp7, Parp9, Gbp2, Ddx60, Rsad2, Psmb9, Bst2, Gbp3, Irgm2, Rnf213, Ifit1, Trim21, Samd9l, Gbp6, Trim30a, Irgm1, Eif2ak2, Ddx58, Cxcl10, Rtp4, Igtp, Parp14, Psmb8, Ifit3 2 20.968 32 325 Ptpn6, Ly86, Ifi203, Lyz2, Ctss, Cybb, Inpp5d, Tyrobp, Irf8, Laptm5, Ifi204, Csf2rb2, C1qa, Cxcl9, Cd68, Fcgr3, Nckap1l, Hck, Lcp2, Slc11a1, Gbp4, Fcer1g, Cd52, Cd48, Fcgr1, Rac2, Sash3, Pld4, Aif1, Myo1g, Cd53, Fcgr4 3 9.692 27 126 Stat3, H2-Q6, Tlr2, Cd93, Casp1, Icam1, Myo1f, Arg1, Timp1, Ifitm3, Fcgr2b, Cyba, Ccl2, Unc93b1, Itgax, Csf1, C1qc, H2-Q7, Ccl3, C1qb, Tnfrsf1a, C3ar1, Vav1, H2-D1, Arhgap30, H2-T23, H2-M3 4 4.8 6 12 Gfap, Pvalb, Calb2, Pou4f1, Tubb3, Lif 5 4.4 16 33 Cx3cr1, Fas, H2-K1, Syk, Fgf2, Serpine1, Cd44, Ccl12, Ikzf1, Lcp1, Wdfy4, B2m, Cxcl1, Cyth4, Spp1, Trem2 PPI: protein–protein network. ijerph-19-05664-t006_Table 6 Table 6 Potential drugs that reversed the expression of genes induced by nanoparticles. CMap Name Mean Enrichment p-Value 5182598 −0.66 −0.97 2.03 × 103 Tocainide −0.58 −0.92 6.00 × 105 NU-1025 −0.38 −0.88 2.88 × 102 Harpagoside −0.37 −0.88 5.20 × 104 Cloxacillin −0.24 −0.83 1.55 × 103 Prestwick-1103 −0.26 −0.80 3.18 × 103 Benzathine benzylpenicillin −0.27 −0.79 4.08 × 103 Folic acid −0.25 −0.78 4.44 × 103 Prestwick-967 −0.50 −0.77 5.61 × 103 Timolol −0.25 −0.77 5.89 × 103 Prestwick-675 −0.26 −0.77 6.15 × 103 Indoprofen −0.21 −0.75 7.70 × 103 Atractyloside −0.33 −0.71 4.45 × 103 Arcaine −0.41 −0.69 2.01 × 102 Retrorsine −0.38 −0.69 2.14 × 102 Prestwick-689 −0.38 −0.68 2.32 × 102 Fursultiamine −0.51 −0.67 2.72 × 102 Isometheptene −0.23 −0.65 3.44 × 102 Diphenylpyraline −0.29 −0.56 2.94 × 102 Vincamine −0.20 −0.54 3.79 × 102 CMap: Connectivity Map. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Mohajerani A. Burnett L. Smith J.V. Kurmus H. Milas J. Arulrajah A. 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==== Front J Clin Med J Clin Med jcm Journal of Clinical Medicine 2077-0383 MDPI 10.3390/jcm11092462 jcm-11-02462 Review Effects of Antioxidants on Pain Perception in Patients with Fibromyalgia—A Systematic Review https://orcid.org/0000-0002-5324-1014 Fernández-Araque Ana 1* https://orcid.org/0000-0002-9260-7672 Verde Zoraida 1 Torres-Ortega Clara 23 https://orcid.org/0000-0002-4353-055X Sainz-Gil Maria 4 https://orcid.org/0000-0003-4739-9760 Velasco-Gonzalez Veronica 1 https://orcid.org/0000-0002-7298-9060 González-Bernal Jerónimo Javier 5 https://orcid.org/0000-0002-6554-4602 Mielgo-Ayuso Juan 5 Hollmann Markus W. Academic Editor Pino Richard Mario Academic Editor 1 Research Group Pharmacogenetics, Cancer Genetics, Genetic Polymorphisms and Pharmacoepidemiology, Department of Nursing, Faculty of Health Sciences, University of Valladolid, Campus of Soria, 42003 Soria, Spain; zoraida.verde@uva.es (Z.V.); veronica.velasco.gonzalez@uva.es (V.V.-G.) 2 Department of Nursing, Faculty of Health Sciences, University of Valladolid, 42005 Soria, Spain; clarajaen@yahoo.es 3 Emergency Service of the Hospital Santa Bárbara, Soria Healthcare Management, 42005 Soria, Spain 4 Recognized Research Group “Pharmacogenetics, Cancer Genetics, Genetic Polymorphisms and Pharmacoepidemiology”, Department of Cell Biology, Histology and Pharmacology, Faculty of Medicine, University of Valladolid, Center for Drug Safety Studies, 47005 Valladolid, Spain; maria.sainz@uva.es 5 Department of Health Sciences, Faculty of Health Sciences, University of Burgos, 09001 Burgos, Spain; jejavier@ubu.es (J.J.G.-B.); jfmielgo@ubu.es (J.M.-A.) * Correspondence: anamaria.fernandez@uva.es 27 4 2022 5 2022 11 9 246213 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In recent years, antioxidant supplements have become popular to counteract the effects of oxidative stress in fibromyalgia and one of its most distressing symptoms, pain. The aim of this systematic review was to summarize the effects of antioxidant supplementation on pain levels perceived by patients diagnosed with fibromyalgia. The words used respected the medical search terms related to our objective including antioxidants, fibromyalgia, pain, and supplementation. Seventeen relevant articles were identified within Medline (PubMed), Scopus, Web of Science (WOS), the Cochrane Database of Systematic Review, and the Cochrane Central Register of Controlled Trials. This review found that antioxidant supplementation is efficient in reducing pain in nine of the studies reviewed. Studies with a duration of supplementation of at least 6 weeks showed a benefit on pain perception in 80% of the patients included in these studies. The benefits shown by vitamins and coenzyme Q10 are remarkable. Further research is needed to identify the effects of other types of antioxidants, such as extra virgin olive oil and turmeric. More homogeneous interventions in terms of antioxidant doses administered and duration would allow the effects on pain to be addressed more comprehensively. antioxidants fibromyalgia pain supplementation systematic review This research received no external funding. ==== Body pmc1. Introduction Fibromyalgia (FM) is a syndrome characterized by chronic widespread pain [1]. Pain is the predominant symptom; allodynia and hyperalgesia are also frequent [2]. These patients also present severe fatigue, impaired cognition, and sleep disturbance, among others [3,4]. In the 1990s, the American College of Rheumatology (ACR) approved criteria for diagnosing fibromyalgia. These criteria, in addition to chronic pain, established eighteen other areas of tenderness including chronic widespread and skeletal pain. Their duration had to be longer than 3 months and they would be positive if during the examination the tender points were positive with pressures of 4 kg/cm2 [5]. It was then that this disease shifted from a musculoskeletal to a neurobiological focus [6,7]. The diagnostic criteria for this pathology established in 2010 focused on the widespread pain index (WPI) and a symptom severity score scale [SS-Score] [8]. According to published evidence, this method allowed 88.1% of cases diagnosed by the 1990 ACR criteria to be correctly classified. This makes it easier and more comprehensive to determine the diagnosis based on patient information [7]. Unfortunately, conventional medical therapies targeting this pathology produce limited benefits. Review studies suggest that the combination of pharmacological and alternative therapies (including heat and light treatments, the use of bioactive plant molecules, electro stimulators, and body exercises) can improve quality of life and decrease pain and other symptoms of fibromyalgia [9,10,11]. Recent preclinical studies are currently investigating a beneficial impact on the resolution of this disease through different approaches. Among the most current is the use of a new compound called Hydrox® (HD), which contains 40–50% hydroxytyrosol. The results show that this supplement could activate the WNT/catenin signaling route after reserpine-induced FM [12]. On the other hand, an important factor related to pain and FM is oxidative stress through the production of reactive oxygen species (ROS). These are formed by oxidation at low levels in the body′s cells and tissues and their concentration is controlled by the action of a defense system made up of enzymes and non-enzymatic species [8]. However, if oxidation levels increase due to disposal complications or other circumstances, it leads to significant oxidative stress that can cause metabolic and biological macromolecule alterations [13]. Such stress can lead to peripheral and central sensitization and affect nociception. This interferes with the musculature through a decrease in nociceptors locally, resulting in a decreased pain threshold with pain as a characteristic symptom [14]. In addition, the increase in oxidative stress in these patients is increasing and is related to the severity of symptoms. Therefore, the relationship between these symptoms and an imbalance between oxidation products and antioxidant defenses has been established [15]. Although oxidative stress is thought to play an important role in the pathogenesis of FM, further studies are needed [16]. One of the ways to counteract the excess of free radicals is to resort to certain nutrients, such as antioxidants. The higher the level of antioxidants in our body, the more protected we are against oxidative damage. In people with FM, a decrease in antioxidant levels can increase pain [17]. Therefore, antioxidants such as vitamins, coenzyme Q10, virgin olive oil, and alpha-lipoic acid (ALA) are of interest due to their association with the characteristic symptoms of FM, one of the main symptoms being pain [18,19,20,21,22]. Other mineral supplements, such as magnesium or iron, could be used as a co-treatment in this disease, helping to counteract the level of pain and improve quality of life [23]. This is because FM patients with reduced magnesium levels are related to low-grade swelling, muscle weakness, and paresthesia, all of which are common symptoms of FM [24]. In the case of iron, depletion leads to reduced production of biogenic amines [25]. The beneficial effects of vitamin D, as an antioxidant, on pain and its possible association with FM have already been highlighted in a previous review [26], although we note that there is no consensus on the association between vitamin D and FM specifically. However, a correlation between low vitamin D status and non-specific musculoskeletal pain has been demonstrated [27]. Given the wide range of antioxidant supplements used to treat pain caused by FM, as well as the great heterogeneity in the duration of these treatments, the following systematic review is proposed to determine the possible beneficial effects of antioxidant supplementation on pain levels perceived by patients diagnosed with FM. We also aim to determine the best duration of treatment to reduce pain in FM patients. 2. Materials and Methods 2.1. Review Procedure For this review, we used the guidelines set out in the protocol of preferred reporting items for systematic reviews and meta-analyses (PRISMA) [28]. To select studies, the PICOS question model [29] was used as follows: P (population), “people with FMS”; I (intervention), “administration or supplementation of antioxidant substances”; C (comparison), “some conditions without supplementation”; O (outcomes), “changes in perceived pain level across different pain questionnaires in fibromyalgia”. The types of pain outcomes were to be assessed by validated pain scales in FMS patients. The most frequent being Pain Catastrophizing Scale (PCS), Visual Analogue Scale (VAS), Chronic Pain Grading Scale (CPGS), Short Form McGill Pain Questionnaire (SF-MPQ), Fibromyalgia Impact Questionnaire (FIQ), Brief Pain Inventory (BPI), Present Pain Intensity (PPI), and Pain Pressure Threshold (PPT) [30,31,32,33,34,35]. A structured search of the following databases was performed: Medline (PubMed), Scopus, Web of Science (WOS), Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. The controlled medical vocabulary used was that set out in MeSH and keywords related to FMS, antioxidants, and pain. They were as follows: (“fibromyalgia” OR “fibromyalgia syndrome”) AND (“antioxidants” OR “supplements”) AND (“pain”). A snowball strategy was used to find articles related to the topic under review. Titles and abstracts of identified studies were cross-checked to discard duplicates and unrelated studies. After selection of those related to the review we proceeded to read them in full. The search for published studies was conducted independently by two authors and disagreements were resolved by assessment by a third author. 2.2. Process Followed for the Selection of Articles Aspects related to age and race/ethnicity were not filtered for selection. Inclusion criteria focused on: (i) showing an adequate design in the use of antioxidant supplements in humans; (ii) the use of antioxidants even if they are of different types due to the difficulty of finding different studies with the same type of antioxidant; (iii) randomized controlled study with or without placebo; and (iv) specific information related to the pain variable and the validated scales for fibromyalgia mentioned above. Exclusion criteria included (i) studies with no human outcomes and not related to pain; (ii) animal studies; (iii) studies with other types of interventions or diets mixed with nutritional supplements other than antioxidants (if the study contains many types of antioxidants as a mixture, it is uncertain which one acts as a beneficial effect or not); (iv) studies published in languages other than English or Spanish; (v) studies using vitamin D for its previous scientific evidence of benefit for pain; and (vi) editorials, review studies, and letters to the editor. 2.3. Data Extraction Once the criteria established for the selection of each study had been applied, we proceeded to extract the data corresponding to the relevant aspects of our study, which are shown under the heading of the tables in this review. For this purpose, a spreadsheet was used by two researchers and then jointly agreed with a third researcher when there were discrepancies. 2.4. Methodological Quality The review of the methodology of the selected studies was carried out using the McMaster critical review form for quantitative studies [36]. Studies that did not meet the methodological quality requirements were excluded. This review was carried out by the same researchers as above and discrepancies were also resolved in the same way with the participation of the third researcher. 3. Results 3.1. Selection of Studies The review initially extracted 277 records, of which 150 were eliminated due to duplication, leaving a total of 127 articles. Likewise, after eliminating studies which did not meet the inclusion criteria (n = 49), 78 records were selected. Subsequently, 38 studies were eliminated because 7 were conducted in animals, 9 were non-Spanish or non-English, 6 were published as letters to the editor or literature reviews, and 16 were not related to food or supplements with antioxidant characteristics. Of the 40 full-text articles that remained for eligibility, 23 studies were excluded due to an inadequate overall study protocol (n = 6) and lack of an appropriate control design for supplementation (n = 17). The remaining 17 studies were included in this review (Figure 1). 3.2. Results of the Quality Assessment Then we made the quality assessment of each selected article. The score of the evaluated articles ranged from 12 to 16 points. Two studies were assessed as “excellent,” twelve as “very good,” and three as “good.” The results with the scores for each study are presented below (Table 1). 3.3. Descriptive Information of the Selected Articles Included in the Systematic Review This systematic review included 17 experimental studies. The characteristics of each study necessary to obtain relevant data in relation to our objective are displayed in Table 2. Table 3 shows the synthesis of the studies included in the systematic review with the intervention and pain measurement instruments. Nine studies reported benefits in reducing pain scores [37,39,40,43,44,45,47,50,51]. In the remaining eight studies there was no evidence of benefit after the intervention [22,38,41,42,46,48,49,52]. Table 3 shows that all studies that have used coenzyme Q10 as an antioxidant at a dose of at least 300 mg/day and for between 10 [39] and 24 weeks [43] have shown beneficial effects on pain reduction. We can also note that these studies also agree that they all have in common the use of the VAS scale in the measurement of pain [39,43,47]. Two studies show benefits [37,44] using vitamins with a minimum daily dose of 200 mg, with a duration of at least 6 weeks of supplementation and agreeing on the use of the VAS scale to measure pain perception after supplementation. The rest of the studies with beneficial effects have used minerals and algae with high antioxidant power [40,45,51], with all but one [40] coinciding in the use of the VAS scale as a measure of pain perception after supplementation and in having supplemented for more than 6 weeks. 4. Discussion This review summarizes the possible relationship between FM and antioxidant supplements and their relationship to possible effects on pain reduction. Pain is one of the conditions that can influence and alter oxidative stress. For example, a disequilibrium between pro-oxidants and antioxidants in people with fibromyalgia and persistent pain suggests that there may be an influence on nociceptive processing. [52,53]. A total of 17 studies analyzed specifically assessed the pain symptom using one of the eight scales used for pain in the different trials. Perception of pain as scored on the scales improved significantly after consumption of some antioxidants, as discussed in the previous section. The most commonly used scales for measuring pain perception were VAS (76.47%) and FIQ (47.05%). The study of bibliography has shown that the implication of antioxidants supplements is not without controversy, although clinical trials with CoQ10 and alpha-lipoic acid (ALA) show promising results [23,54], according to this review. In the present discussion, we present each of the antioxidants reviewed in the trials under study and discuss their benefits and possible controversies and evidence. Alpha-Lipoic Acid (ALA) and Acetyl-L-Carnitine (LAC) There is strong evidence that ALA and LAC are effective for peripheral neuropathy, especially in diabetics [22,55]. They not only cut down pain but also improve numbness and tingling. There is evidence to suggest possible benefits in reducing the frequency and severity of migraines and pain associated with fibromyalgia. However, this evidence needs further support [56]. In the study by Gilron, et al. (2021) [22] there is no evidence of a significant effect to demonstrate this, so we will have to wait for further studies to definitively prove this or not. One aspect observed in this review related to this type of antioxidant is the need for more RCTs that can confirm sufficient benefits in practical application and be extrapolated to a larger number of patients worldwide. However, in this review we have shown that ALA is beneficial in reducing pain perception in FM. Compared with placebo, VAS scores improved significantly after ALA supplementation (p < 0.05) [49], although we could not find specific scores. Coenzyme Q10 Ubiquinone plays a key role in oxidative phosphorylation. Coenzyme Q10 has been shown to beneficially stimulate the AMPK gene, which may be responsible for the inflammation, low antioxidant levels, and low mitochondrial production that characterize the pathophysiology of fibromyalgia [57]. This review shows that there are studies demonstrating its benefits for pain [38,42,46]. The pathophysiology of FM may be influenced by oxidative stress by detecting reduced levels of coenzyme Q10 in blood mononuclear cells derived from FM patients [58]. The study by Miyamae, et al. (2013) [59] showed that CoQ10 levels are reduced in these patients, but that supplementation can restore levels and reduce fibromyalgia symptoms, including pain. However, this study has not been included in our review because it did not meet the inclusion criteria, as it was conducted in children. This research supports the results discussed in this review. Importantly, supplementation with CoQ10 and pregabalin provides additional benefit in relieving pain sensation in patients with FM [38]. In addition, the study by Pierro, et al. (2016) [42] also confirms the beneficial effects of CoQ10 in counteracting pain in women affected by fibromyalgia. It shows that, compared with a control group, CoQ10 administration significantly improved most pain-related outcomes by 24–37%. However, it does not have sufficient statistical evidence due to the limitation pointed out by the authors themselves on the limited number of participants. Overall, Q10 supplementation in the three studies [38,42,46] included in this review did not differ greatly in terms of the amount of CoQ10 300–400 mg/day administered. However, in terms of administration time, one of them [42] doubled the administration time compared with the other two. In addition to the need for a larger number of participants, we also highlight the need for a longer CoQ10 administration time for comparison. Vitamins Randomized placebo-controlled trials can show the therapeutic role of vitamin C, acerola root, and freeze-dried royal jelly [36], and vitamin E, vitamin C and Nigella Sativa [43]. Both studies showed benefits in decreasing pain perception after supplementation; however, on different measurement scales. Pain perception showed a decrease in pain perception with the VAS-Pain scale in the study by Iqbal, et al. (p < 0.05) [44] and with the FIQ scale in the study by Bermarki, et al. [36]. This study compared both the efficacy and safety of a supplement called FibromyalgineR (Fib) (vitamin C, acerola root, and freeze-dried royal jelly) with that of another food supplement (FS) (acting as a placebo) and with a control arm that received no supplement. The Fib vitamin supplement resulted in a significant improvement (p < 0.001) relative to the other two study groups on the FIQ scale only. It is important to note that the FIQ scale only measures pain intensity on item five, and the VAS scale is specific to pain only; we cannot claim a significant reduction by this supplement in overall pain rating, although it does improve pain intensity. However, another study has also been reported that showed a decrease in VAS pain in FM patients treated with an improved diet and vitamin supplementation in an open-label, non-randomized controlled study [60]. These results confirm existing evidence that supplementation with antioxidants such as vitamins C and E to therapy may be helpful in treating FM symptoms. Ginger, which is a potent antioxidant, may also act on fatigue and pain by decreasing oxidative stress [61]. In addition, vitamins such as vitamin C have analgesic effects on pain as demonstrated by clinical trials. [62]. Other Types of Antioxidants Other types of antioxidants used in different trials analyzed showed neutral effects on the reduction of pain perception in FM patients. These antioxidants were turmeric supplementation [37], EVOO [40], caffeine [41], creatine monohydrate [45], soy protein with soy isoflavone [47], cherry juice [48], and malic acid with magnesium hydroxide [51]. However, we note that in the study by Bagis, et al. [44], combining magnesium citrate with amitriptyline did show beneficial effects in reducing pain perception using the same VAS pain scale. Magnesium citrate supplementation in combination with amitriptyline was effective in reducing pain, intensity, and other fibromyalgia-related parameters. However, uncombined magnesium citrate only had an effect on pain points. [44]. These are important data for future studies. Among these antioxidants, we focus specifically on two due to their extensive use in food and in different studies. These are extra virgin olive oil (EVOO) and turmeric. The effects of olive oil intake on cardiovascular disease [63] and rheumatoid arthritis [64] have been beneficial. Both coincide in being diseases with an association with oxidative stress. However, few studies are available that measure the effect of this type of antioxidant in women with FM in relation to pain [40]. The antioxidant activity of EVOO is responsible for the protection of DNA, proteins, and lipids against ROS, and it is in FM that several studies found elevated levels of ROS [23,40]. The clinical trial reviewed in this study investigated the effect of 50 mL/day EVOO compared to refined olive oil in 23 female subjects with FM. Comparing extra virgin olive oil with refined olive oil after 21 days of intervention, one study showed that protein charring and lipid peroxidation were significantly improved. However, there was no improvement in the pain variable [40]. Therefore, although its efficacy on pain is promising, more studies with this type of antioxidant are needed. The other antioxidant widely used in everyday life as a spice is turmeric. Turmeric is a spice that has antioxidant, anti-inflammatory, antiviral, and antifungal properties [65]. The effects of a turmeric-based supplement in women with fibromyalgia have not shown a beneficial effect on scales of perceived degree of chronic pain in FM patients [37]. Finally, we conclude that more scientific evidence is needed to show whether turmeric could actually improve chronic pain in FM. Several human studies have found some evidence for the anti-inflammatory activity of curcumin [65]. However, no statistically significant benefit in reducing perceived pain in FM patients has been demonstrated, although it may be recommended for its general anti-inflammatory benefits [66,67]. 4.1. Study Limitation There are several limitations to note about this systematic review. The data from the studies could not be pooled due to methodological diversity, and there is heterogeneity among the studies, mainly with the time of intervention and the type of antioxidants. We suggest increasing the time and sample with antioxidants that require more scientific evidence to affirm their beneficial effect on pain perception. The scales used to measure pain perception levels were different in four studies, the rest all used at least the VAS scale, which is the most commonly used. In terms of the limitations of the studies analyzed, we highlight the following. Firstly, the sample size is rather small in the published studies. Secondly, the methodology used to measure the results is very heterogeneous and does not take into account the confounding phenomenon. Nevertheless, a decrease in fibromyalgia pain was observed in studies using coenzyme Q10, acetyl-l-carnitine, combination of vitamin C, E, and Nigella sativa seeds, vitamin C, acerola ginger root, freeze-dried royal jelly, ferric carboxymaltose, and a combination of amitriptyline + magnesium citrate and Chlorella green algae. 4.2. Recommendations for Further Research Further research is needed with larger sample sizes and using homogeneous measurement scales that can have more scientific rigor. In addition, oxidative stress levels should be measured and inflammatory biomarkers should be analyzed with antioxidants to understand their influence on FM pathophysiology. Research combining substances based on antioxidant and anti-inflammatory mechanisms is necessary; however, we believe that we first need more separate research to understand further combined modality research to understand how it may or may not influence FM. In the future these studies could establish an active and targeted treatment for pain in these patients. 5. Conclusions This review encompassed the literature and showed that the role of antioxidant supplements in FM could improve pain perception in patients, as measured by scales for this purpose. Nine studies showed a significant improvement in pain perceived by FM patients that used the VAS scale and/or the FIQ scale. Specifically, supplementation with Fibromyalgine® (Fib) (vitamin C, acerola ginger root, and freeze-dried royal jelly), coenzyme Q10 alone in combination with Pregabalin, ferric carboxymaltose, vitamin C, E, and Nigella sativa, magnesium + amitriptyline, LAC, and Sun Chlorella™ green algae appear to be effective in reducing FM pain perception. In addition, supplementation time could be associated with improved quality of life in these patients. Suggesting that supplementation time could have a functional impact on the efficacy of antioxidants on pain. These results should be interpreted with caution due to the limitations mentioned above. These limitations have not allowed us to make solid comparisons due to the heterogeneity of the antioxidants used and the short supplementation times carried out. Finally, we believe that there is a lack of studies that analyze the duration of the beneficial effect of antioxidants on pain. Author Contributions A.F.-A., researcher, expert in the field of pain, participated in data extraction and in the search for protocols of methodological quality; Z.V., researcher, expert in genetics, statistical data analysis, and expert in biochemistry; C.T.-O., expert in the field of pain and care in neurodegenerative diseases, reviewed the extracted studies together with the first author; M.S.-G., researcher specializing in pharmacology and pharmacogenetics, participated in the supervision of the paper and in the development of the methodology; V.V.-G., specialist in pain research, participated in the search and selection of articles according to established criteria; J.M.-A., expert researcher in nutrition, participated in the preparation of the manuscript and data analysis in tables; J.J.G.-B., first reviewer, involved in the supervision of the methodology and evaluation of the methodological quality criteria of the studies applying the test used, and supervision of the writing of the manuscript. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and the vice-dean of research of the Faculty of Health Sciences of the University of Valladolid was notified. The coding of the ethical review was waived as it was not applicable due to the type of study, being a review. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Flowchart for item selection. jcm-11-02462-t001_Table 1 Table 1 Quality assessment of the studies included in the systematic review by McMaster critical review form for quantitative studies [36]. Author/s Items according to Critical Review By McMaster T1 % MQ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Gilron I, et al., 2021 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 14 87.5 VG Barmaki M, et al., 2019 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 12 75 G San Mauro I, et al., 2019 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 13 81.25 VG Sawaddiruk P, et al., 2019 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 14 87.5 VG Boomershine, et al., 2018 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 15 93.75 E Rus A, et al., 2017 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 13 81.25 VG Umeda M, et al., 2016 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 14 87.5 VG Di Pierro F, et al., 2016 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 13 81.25 VG Iqbaq R, et al., 2015 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 14 87.5 VG Bagis, et al., 2013 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 14 87.5 VG Alves, et al., 2013 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 13 81.25 VG Cordero, et al., 2012 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 13 81.25 VG Wahner-Roedler, et al., 2011 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 14 87.5 VG Elliot D, et al., 2010 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 15 93.75 E Rossini, et al., 2007 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 13 81.25 VG Merchant, et al., 2001 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 12 75 G Russel, et al., 1995 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 1 12 75 G T2 + A1:S14 19 19 19 19 11 15 9 11 19 19 19 11 19 17 19 10 Abbreviations: (1) Criterion was met; (0) Criterion was not met; (T1) Total items fulfilled by study; (T2) Number of studies that fulfilled the item; (%) Percentage of methodological quality assessment; (MQ) Methodological quality; (A) acceptable 9–10 points; (G) good 11–12 points; (VG) very good 13–14 points; (E) excellent ≥ 15 points. jcm-11-02462-t002_Table 2 Table 2 Synthesis of the studies included in the systematic review with type of study, participants, and groups. Author/s, Year of Study Country Participants, Sex Age (±) Intervention Placebo Study Duration Gilron I, et al., 2021 [22] Canada n = 22 women, 5 men 47 ± 6.72 27 - 10 weeks *G Barmaki M, et al., 2019 [37] France n = 100 all women 49 ± 7.12 I1 = 36; I2 =33 31 24 weeks San Mauro I, et al., 2019 [38] Spain n = 13 all women 51.46 ± 8.04 6 7 4 weeks Sawaddiruk P, et al., 2019 [39] Thailand n = 2 men, 9 women 46 ± 11 5 6 10 weeks Boomershine, et al., 2018 [40] US n = 80 women, 1 man 42,5 ± 10.9 41 40 6 weeks Rus A, et al., 2017 [41] Spain n = 23 all women 50.88 ± 6.5 11 12 3 weeks Umeda M, et al., 2016 [42] US n = 23 all women 43.57 ± 18.49 12 11 3 sessions Di Pierro F, et al., 2016 [43] Italy n = 22 all women 53 ± 9.1 12 10 24 weeks Iqbaq R, et al., 2015 [44] Pakistan n = 50 all woman 37.87 ± 1.68 50 16 8 weeks Bagis, et al., 2013 [45] Turkey n = 80 all women 41.4 ± 10.5 60 20 ** 8 weeks Alves, et al., 2013 [46] Brazil n = 28 all women 48.85 ± 9.25 15 13 16 weeks Cordero, et al., 2012 [47] Spain n = 35 all women 45.75 ± 4.5 20 15 ** 12 weeks Wahner-Roedler, et al., 2011 [48] US n = 50 all woman 47.7 ± 4.25 25 25 6 weeks Elliot D, et al., 2010 [49] US n = 14 all women 51 ± 2.0 14 - 4 weeks *E Rossini, et al., 2007 [50] Italy n = 89 all women 46.8 ± 5.05 47 42 10 weeks Merchant, et al., 2001 [51] US n = 43 all women 47.1 ± 9 22 21 12 weeks Russel, et al., 1995 [52] US n = 21 women, 3 men 49 12 12 4 weeks Notes: * Crossover study (*G Each period lasted 5 weeks, with a 4-week treatment period and a 1-week washout period; *E Each period lasted 2 weeks, with a 10-day treatment a 4-day washout period) ** Healthy controls had no signs or symptoms of FM. jcm-11-02462-t003_Table 3 Table 3 Synthesis of the studies included in the systematic review with types of supplementation and doses and instruments for measuring pain. Study Intervention Group (IG) Control Pain Scale Results Effect for Pain Gilron I, et al., 2021 [22] IG = ALA * 1663 mg/day for 5 weeks and placebo during the second 5 weeks. IGP = the first 5 weeks were treated with placebo and ALA for the next 5 weeks. Placebo FIQ, BPI, VAS For women, the perception of pain for all scales with respect to the placebo group was for ALA of (p = 0.13) and for men (p = 0.01). Neutral effect for women and beneficial for men. Barmaki M, et al., 2019 [37] G1 = Fibromyalgine® (Fib) (vitamin C, acerola ginger root, freeze-dried royal jelly), 2 capsules/day; G2 = food supplement (FS), 2 capsules/day; G3 = control arm not receiving any supplementation. NoST FIQ, VAS The supplementation with Fibromyalgine® showed an improvement in pain intensity on the FIQ scale (p < 0.001). Positive benefit. San Mauro I, et al., 2019 [38] Turmeric supplement 500 mg/day, gluten-free diet and low in histamine. NoST CPGS, PCS PCS (p = 0.190), GPGS (p = 0.671). Neutral benefit. Sawaddiruk P, et al., 2019 [39] G1 = CoQ10 supplementation 300 mg/day+ pregabalin (150 mg/day); G2 = placebo + pregabalin (150 mg/day) for 40 days. At day 40, patients who received CoQ10 therapy were switched to placebo, and vice versa. Placebo VAS, PPT Decrease in VAS and increase in PPT significantly increased in pregabalin-treated FM patients with CoQ10, compared to those treated with pregabalin and placebo alone. Positive benefit. Boomershine, et al., 2018 [40] Ferric carboxymaltose 15 mg/kg (up to 750 mg). Placebo FIQ, BPI Greater improvements from baseline to day 42 were observed for ferric carboxymaltose vs. placebo in FIQ total score and BPI total score. Positive benefit. Rus A, et al., 2017 [41] Extra virgin olive oil (EVOO) 50 mL/day. Control group = Refined olive oil (ROO) 50 mL/day. Control FIQ, VAS In the EVOO group, a decrease in FIQ (p < 0.011) was observed, but not in pain (p < 0.279) compared to the ROO consumption group. Neutral benefit. Umeda M, et al., 2016 [42] Gum with 100 mg of caffeine. Placebo SF-MPQ, PPI, VAS Pain results improved in the experimental group measured with SF-MPQ (p = 0.006). Pain measured with VAS (p = 0.396)/PPI (p = 0. 87). Neutral benefit. Di Pierro F, et al., 2016 [43] 200 mg × 2/day CoQ10 formula. Control VAS, FIQ Statistical significance is only evidenced p <0.005, for the pain scale (VAS), in the rest the results were not significant. Positive benefit. Iqbaq, et al., 2015 [44] Vitamin C (200 mg daily), E (200 mg daily) and Nigella sativa seeds (13 mg 4–5 times daily). Control VAS VAS (p < 0.05). Positive benefit. Bagis, et al., 2013 [45] IG1 (n = 20) Mg citrate 300 mg/day; IG2 (n = 20) amitriptyline 10 mg/day; GI3 (n = 20) Mg citrate 300 mg/day + amitriptyline 10 mg/day. NA VAS, FIQ Positive effects on all pain parameters with the combination of amitriptyline + magnesium citrate proved to be effective on all pain parameters (p < 0.001). Positive effects combination of amitriptyline + magnesium citrate. Alves, et al., 2013 [46] 20 gm of creatine monohydrate for 5 days divided into 4 equal doses, followed by 5 gm/day as a single dosage throughout the trial. Placebo MPQ, FIQ FIQ and MPQ (p > 0.005). Neutral benefit. Cordero, et al., 2012 [47] 300 mg/day of CoQ10. Control FIQ, VAS There was a decrease in the FIQ score (p < 0.001) and VAS (p < 0.001) in the experimental group compared with the control group. Positive benefit. Wahner-Roedler, et al., 2011 [48] Soy protein (20 g), soy isoflavone (160 mg) (1 serving daily). Placebo FIQ No significant differences between groups. Neutral benefit. Elliot D, et al., 2010 [49] GE = received tart cherry juice, 2 bottles/day, morning and evening. Placebo VAS There were no significant differences for either group in terms of pain (p > 0.005). Neutral benefit. Rossini, et al., 2007 [50] 1000 mg acetyl L-carnitine (LAC) or placebo. Placebo VAS VAS (p < 0.02). Positive benefit. Merchant, et al., 2001 [51] Sun Chlorella™ green algae and Wakasa Gold Chlorella™ (500 g and 100 mL/day, respectively) Placebo VAS VAS (p < 0.05). Positive benefit. Russel, et al., 1995 [52] 200 mg malic acid + 50 mg magnesium, from 3 capsules up to 6 per day. Placebo VAS Pain with this supplement was not significantly different from placebo (P5 0.7). Neutral benefit. Notes: * Alpha-Lipoic Acid Capsules (ALA); Fibromyalgia Impact Questionnaire (FIQ), Brief Pain Inventory (BPI), Visual Analogue Scale (VAS), Chronic Pain Grade Scale (CPGS), Pain Catastrophizing Scale (PCS), Pain Pressure Threshold (PPT), Short-form McGill Pain Questionnaire (SF-MPQ), Present Pain Intensity (PPI). Supplementary treatment (NoST); NA (not applicable); Positive = significant improvement in pain perception; Neutral = no significant improvement in pain perception. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095713 ijerph-19-05713 Article Concentration, Health Risk, and Hydrological Forcing of Heavy Metals in Surface Water Following Water-Sediment Regulation of the Xiaolangdi Dam in the Yellow River https://orcid.org/0000-0001-5280-9264 Zhao Qinghe Ding Shengyan * Geng Zihan Lu Xunling Hong Zhendong Liu Yi Yu Jinhai Meneguzzo Francesco Academic Editor Zabini Federica Academic Editor Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions of the Ministry of Education, College of Geography and Environmental Science, Henan University, Kaifeng 475004, China; zhaoqinghe@henu.edu.cn (Q.Z.); 104753190167@henu.edu.cn (Z.G.); luxunling@henu.edu.cn (X.L.); zdhong@henu.edu.cn (Z.H.); liuyi@henu.edu.cn (Y.L.); yjh666@henu.edu.cn (J.Y.) * Correspondence: syding@henu.edu.cn 07 5 2022 5 2022 19 9 571302 4 2022 05 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Water and sediment regulation aimed at aquatic ecosystems and preserving reservoir capacity to minimize the negative consequences of dams can fundamentally change the distribution of heavy metals (HMs) in the reservoir and downstream reaches. However, the effects of water and sediment regulation on variation in HMs are still poorly understood. In this study, the variations in concentration, contamination, human health risk, potential sources, and influencing factors of the metalloid As and HMs (Cr, Hg, Ni, Pb, and Zn) in surface water in the reservoir and the downstream reach of the Xiaolangdi Dam (XLD) following the operation of the water-sediment regulation scheme (WSRS) were determined. These results indicate that HM concentrations in the two post-WSRS seasons were much lower than the water quality standards, but were significantly increased over time due to the trapping effects of the XLD (p < 0.05, except for Zn). However, As concentration in the reservoir was significantly lower than that observed in downstream reaches, likely due to anthropogenic input from agricultural activities. Meanwhile, HM concentrations varied with distance to the dam, which displayed a distinct accumulation closer to the dam in the post-WSRS II season. The contamination of HMs, the carcinogenic risk of exposure to As, and the noncarcinogenic risks associated with exposure to Hg, Ni, Pb, and Zn via the direct ingestion pathway of drinking water were all within acceptable levels following the WSRS, but increased over time. The carcinogenic risk of Cr in the post-WSRS II season was at an unacceptably high level, particularly at sites near the dam. Hydrological characteristics (water level and flow rate) were the dominant factors in determining the distribution of HMs. These results can provide new insight for a better understanding of the variations in HMs following the water and sediment regulation practices, and guide future management in regulating the trapping effects of dams. water-sediment regulation trapping effect hydrological forcing heavy metals health risk National Natural Science Foundation of ChinaU1804119 41771202 41971229 Natural Science Foundation of Henan Province202300410050 Program for Science and Technology Innovation Talents in Universities of Henan Province22HASTIT013 2019GGJS030 Science and Technology Project of Henan Province212102310224 This research was funded by the National Natural Science Foundation of China (grant number: U1804119, 41771202, 41971229), the Natural Science Foundation of Henan Province (grant number: 202300410050), the Program for Science and Technology Innovation Talents in Universities of Henan Province (grant number: 22HASTIT013, the Young Backbone Teachers Foundation of Henan Province (grant number: 2019GGJS030), and the Science and Technology Project of Henan Province (212102310224). ==== Body pmc1. Introduction The presence of dams on rivers not only alters hydrological processes but also impedes the flow of essential materials, including water, nutrients, metallic elements, and sediments, leading to enhanced material transformation and accumulation via retention, sedimentation, adsorption, and primary productivity in dammed reservoirs [1,2]. Changes in these essential materials can lead to marked environmental consequences. For example, the reduced intensity and frequency of extreme runoff events could affect the downstream hydraulics and physical habitat required by various aquatic organisms [3,4], while the accumulated metallic elements in reservoirs may create permanent environmental pressure on aquatic organisms [5]. Meanwhile, deposited sediments upstream of the dam could significantly decrease reservoir storage capacity [6], while reduced sediments downstream of the dam could lead to channel incision, bank collapse, and loss of morphology and habitat diversity and connectivity [7,8]. In addition to these environmental consequences, the trapping effects of dams also have the potential to threaten the safety of the dams themselves as well as those living downstream, which may even trigger serious regional tensions [9]. Considering the environmental and social consequences related to the trapping effects of dams, combined with the increasing impacts of climate change, there is growing interest in finding strategies for sustainable management of dammed rivers [2,10]. A variety of solutions have therefore been developed to mitigate the material accumulation upstream of the dam and the material loss downstream of the dam. For example, the mechanisms of energy availability (light and temperature), nutrient input, and the presence of metal-oxide minerals have been implemented to govern the extent of nutrient elimination induced by dams [1,2]. Strategies such as flood flushing, sediment bypassing, and artificial replenishment have been applied to mitigate sediments reduction downstream of the dam [8]. Sluicing operations have been implemented by the Water Framework Directive of the European Community to reestablish sediment continuity and preserve the water storage function of reservoirs [11]. All of these practices focus on regulating reservoir and downstream conditions by manipulating dam operations and have proven to be effective practices in mitigating the trapping effects of dams [12,13]. However, despite the notable effectiveness of these practices in regulating water and sediments, there is a current lack of understanding of how contaminants vary during and after these practices, particularly regarding contaminant dynamics and water quality in reservoirs and the downstream reaches. During such practices, large quantities of sediments and both dissolved and particulate contaminants would be discharged to the downstream reaches in a short period of time (about one week, depending on the size and silting situation of the reservoir), thus causing a potentially high level of contamination and a decrease in water quality for downstream aquatic environments [11,12]. Evidence suggests that sediments act as a sink for organic and inorganic contaminants due to their high sorption capacity [5,13]. Therefore, the resuspension and redeposition of sediments could inevitably lead to speciation, transport, and bioavailability of contaminants in the downstream reaches [14,15,16], particularly for metallic elements, which are prone to being released from sediment matrices into the water column and becoming bioavailable when facing changes in fluvial environments [11,17,18]. In this regard, sediments in turn act as a source of metallic elements. These metallic elements, particularly the heavy metals (HMs), in aquatic ecosystems are of major concern due to their abundant, persistent bioaccumulation, and toxicity [19,20]. Evidence has suggested that HMs can a pose permanent risk to aquatic biota and humans even at a low concentration [5,21,22]. Therefore, effective river management requires promoting the mechanistic understanding of how these water and sediment regulation practices affect the biogeochemical and physical transformation and accumulation of essential materials [2], particularly the HMs in water. However, the distribution and accumulation of HMs in surface water during and after these water and sediment regulation practices are not well understood. The lower Yellow River in China is famous for its substantial sedimentation, as 4 × 108 kg per year of suspended sediments derived from the Loess Plateau have been deposited on the riverbed over the last century [23]. To address the issues of channel siltation and water shortages, as well as to control floods in the lower Yellow River, the Xiaolangdi Dam (XLD), located at the exit of the last canyon in the middle reaches of the Yellow River, began construction in September 1991 and started operation in October 1999. To reduce the sediment retention and maintain the capacity of the XLD Reservoir, the water-sediment regulation scheme (WSRS) has been carried out by the Yellow River Conservancy Commission (YRCC) of China since 2002 [24]. Generally, the WSRS lasts for 10–20 days; however, almost half of the total annual sediments accompanied by large amounts of terrestrial materials including nutrients and metallic elements are discharged into the sea during this short period [23,24,25,26]. More than 57% of the annual quantity of particulate HMs was discharged into the ocean during the 2009 WSRS [20], while approximately 30%, 42–54%, and 49–60% of the total annual HM flux were transported to the ocean during the WSRS in 2013 [27], 2015 [25], and 2018 [21], respectively. The dramatic changes in the transportation of HMs have likely driven the growing interest in the distribution and accumulation of HMs following the WSRS. While previously mentioned studies have explored the impacts of the WSRS on the flux of HMs, they were based on HM data observed at the Lijin gauge station, which is located about 740 km downstream from the XLD. However, few studies have investigated the distribution and accumulating process of HMs in surface water following the WSRS. To better understand this process, the spatial distribution of HMs (considering the analogous toxicity of the metalloid As to heavy metals, the term HMs in the present study includes As for convenience of description) from the XLD reservoir to the downstream reach, temporal accumulation of HMs in different seasons after the WSRS, and the factors influencing the spatial distribution and temporal accumulation of HMs in water require further investigation. Therefore, the main objectives of this study are: (1) to analyze spatial–temporal variations in the concentrations of As Cr, Hg, Ni, Pb, and Zn in surface water of the XLD Reservoir and downstream reach following the 2018 WSRS; (2) to assess the contamination degree and health risk level of HMs in surface water; (3) to determine the potential sources of HMs in surface water of different seasons based on multiple statistical analyses; and (4) to reveal the factors influencing the concentrations of HMs in water. The investigation of these objectives has potential implications for future management in regulating the trapping effects of dams. 2. Materials and Methods 2.1. Study Area This work was conducted in the XLD Reservoir and its downstream reach from the outlet to Jihetan hydrological station (Figure 1). The Xiaolangdi Dam (XLD) is the second-largest dam in China. It is the last large dam on the mainstream of the Yellow River with a controlled drainage area of 6.94 × 105 km2 and a reservoir storage capacity of 126.5 × 108 m3. It controls 91.5% of the total water discharge and 98% of the total sediment discharge of the Yellow River [23]. The XLD primarily serves to reduce siltation, control floods, alleviate water shortages, and generate power for the middle and lower reaches of the Yellow River [20,28]. The XLD Reservoir area is characterized by a temperate continental monsoon climate, with an average annual temperature of 12.4–14.3 °C, average annual precipitation of 616 mm, and average annual humidity of 62%. The downstream reach of the XLD Reservoir passes through the Huang-Huai-Hai Plain with channel gradient ranging from 0.1 to 0.5 m km−1 [23,29]. Meanwhile, the downstream reach of the XLD Reservoir is famously referred to as a “suspended river” with its riverbed 7–13 m higher than the surrounding landscape and a large area of riparian zones bounded by levees [24,30]. Since the implementation of the WSRS in 2002, drastic alterations in the hydrodynamic conditions of the reservoir area and the downstream reach have occurred [31], which has fundamentally altered the resuspension and redeposition of HMs in water, as well as the interaction and exchange between the riverbed and the water column. 2.2. Sample Collection and Analysis The 2018 WSRS was implemented in July, from the 3rd to the 30th. Water samples were collected from 11 transverse sections in the reservoir and downstream reach in October 2018 in the post-WSRS I season and in April 2019 in the post-WSRS II season (Figure 1 and Table S1). In each season, surface water at a depth of 0.5 m was sampled from 11 transverse sections located in the reservoir area (S1–S5) and the downstream reach (S6–S11) as shown in Figure 1 and Table S1. Each transverse section comprised three sampling sites, specifically, a right, middle, and left site. At each site, three parallel surface water samples were collected by a hydrophore, acidified with HNO3 (pH < 2), and stored in polyethylene bottles. The hydrophore and bottles were precleaned with HCl solution (pH = 2) and were then rinsed five times using water to be sampled. All water samples were placed in a car-carried refrigerator and were transported to the Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions at Henan University. Samples were then stored at 4 °C for later analysis. At each site, pH, dissolved oxygen (DO), and electrical conductivity (EC) of surface water were measured using an SX736 multi-probe. In the laboratory, the concentrations of Cr, Ni, Pb, and Zn were measured via the inductively coupled plasma mass spectroscopy (ICP-MS, Thermo Fisher, Waltham, MA, USA). The concentrations of As and Hg in water samples were measured via atomic fluorescence spectrophotometry (DB51/T836–2008). The precision and accuracy of HM concentrations were assessed using blanks, duplicate samples, and certified standard reference materials for water samples (GBW08607), which were analyzed with groups of 20 samples. The relative standard deviation of HM concentrations among triplicate samples was ±5%. The average recovery of HMs spiked with the standards were in the range of 90% and 110%. These results were acceptable and consistent with certified values, which were expressed as the mean concentration of triplicate samples. The water level and flow rate data at the inlet (Sanmenxia), reservoir (Xiaolangdi Reservoir), outlet (Xiaolangdi), and downstream reaches (Huayuankou, and Jiahetan) gauge stations during the two sampling periods were obtained from the YRCC (http://www.yrcc.gov.cn/ (accessed on 1 September 2021)). 2.3. Contamination Evaluation for Heavy Metals Prior to evaluating the contamination level of HMs, we briefly reviewed the traditionally used geochemical indexing approaches, such as the contamination degree (Cd) developed by Backman et al. [32], heavy metal evaluation index (HEI) by Edet and Offiong [33], heavy metal contamination index (HCI) by Rajkumar et al. [34], heavy metal pollution index (HPI) by Mohan et al. [35], and modified heavy metal pollution index (m-HPI) by Chaturvedi et al. [36]. We found that Cd assumes that the analytical value lower than the upper permissible limit should not pose any environmental hazard; the HEI is easy to calculate but lacks the prescribed scale for assessment purposes; the HPI is widely used but the prescribed scale of 100 is too large to make assessments when the concentration of a particular HM is markedly high; the HCI is similar to HEI, but it classifies the critical value with 5 water classes between 0 to 100, based on mean deviation and percent deviation at each sampling site; and the m-HPI, along with its various modifications, express the pollution status of HMs for any water sample as a paired positive index (PI) and negative index (NI) based on the highest desirable and maximum permissible concentrations, which aims to address the various shortcomings of the existing indexing systems [37]. However, there is currently a lack of further classification when the observed HM concentration is lower than the maximum permissible concentrations, with the logic that the water samples should be classified as excellent when PI equals zero [36]. In addition to the above limitations, the evaluation results of these indices are not always consistent as shown in previous studies [34,36,38]. Therefore, it may become quite confusing when multiple indices are used to assess contamination levels of HMs in water samples. Therefore, we ultimately selected the HCI proposed by [34] to analyze the spatial–temporal variations in the contamination level of HMs following the WSRS. However, we also assessed the HEI described by Edet and Offiong [33] along with two modifications of m-HPI offered by Chaturvedi et al. [36,37] and Sahoo and Swain [39], as shown in the Supplementary Table S2. The calculation of HCI followed that of Rajkumar et al. [34] can be found below. (1) HCI=∑i=1nMIi (2) MIi=Wi×qi (3) qi=Ci/Si×100 (4) Wi=Awi/∑i=1nAwi where MIi is the sub-index of the ith HM, Wi is the unit weight of the ith HM, qi is the rating based on concentrations of the ith HM, Ci is the concentrations of the ith HM, Si is the permissible limit of the ith HM, Awi is the assigned weight of the ith HM, and n is the number of HMs. The permissible limit and relative weights of individual HMs for HCI can be found in Supplementary Table S3. The scales for contamination levels of HMs in this study referred to the three classes proposed by Edet and Offiong [33] to better characterize moderate levels HMs contamination when all values are less than the critical value of 100 [40,41,42]: low (HCI values < 15), medium (HCI values within 15–30), and high (HCI values > 30). 2.4. Human Health Risk Assessment In this study, the human health risk assessment guide proposed by the US Environmental Protection Agency (EPA), which has been described elsewhere [22,40,41,42,43,44,45], was used to assess human health risk exposure to HMs. Generally, human exposure to heavy metals occurs through several pathways, including direct ingestion, dermal adsorption, and inhalation [46,47,48,49,50,51]. However, ingestion is known to be the most common pathway for HMs in water [40,41,42,43,44]. Meanwhile, human health risks regarding exposure to a specific HM can be classified as either carcinogenic or noncarcinogenic risks [40,44]. For this study, the non-carcinogenic and carcinogenic risk for specific HMs through the direct ingestion route of drinking water was determined for both adults and children [22]. The noncarcinogenic health risk of individual HMs (Hg, Ni, Pb, and Zn) was calculated with Equation (5):(5) Rinc=[ADD/(RfDi×70)]×10−6 where Rinc refers to the noncarcinogenic health risk of the ith HM, RfDi refers to the reference dose of the ith HM offered by the USEPA [52], 70 is the average human lifespan (years), and ADD is the average daily dose (mg kg−1 d−1) calculated using Equation (6):(6) ADD=C×IR×EF×EDBW×AT where C refers to the concentration of the target HM; IR refers to the intake rate (kg d−1 or L d−1), which is 2.2 for adults and 1.0 for children up to 7 years old; EF represents the exposure frequency (365 d per year); ED is the exposure duration (years) and is equal to 30 years; BW is the body weight (kg), which is 64.3 for adult and 22.9 for children of Henan Province; AT is the average exposure time (days); and AT is the average exposure time (days). The carcinogenic health risk of individual HM (As and Cr) was calculated with Equation (7):(7) Ric=ADD×SFi/70 where Ric refers to the carcinogenic health risk of the ith HM and SFi refers to cancer slope factor in units of mg kg−1 day−1 offered by the USEPA [52]. ADD for the carcinogenic health risk is the same as that of the noncarcinogenic health risk; however, AT is 30 years for carcinogens. Following the USEPA guidance, risk values lower than 1 × 10−6 are not considered to pose significant carcinogenic health effects, values higher than 1 × 10−4 signify a high cancer risk to humans, and values falling within the range of 1 × 10−6 to 1 × 10−4 are generally considered acceptable [42,45]. 2.5. Statistical Analysis To determine how HM concentrations varied between the two seasons following the WSRS and between the XLD Reservoir and its downstream reach, a two-way analysis of variance (ANOVA) with a statistical significance level of 5% (p < 0.05) was performed using SPSS v.22.0 software (IBM, Chicago, IL, USA). To analyze the potential sources of HMs in surface water in different seasons, the positive matrix factorization (PMF) and hierarchical cluster analysis (HCA) were performed using EPA PMF 5.0 program and OriginPro2021 software (version 9.8.5, OriginLab Corporation, Northampton, MA, USA), respectively. To determine the correlation between HM concentrations and hydrological and physicochemical characteristics, redundancy analysis (RDA) was performed using OriginPro2021 software. Prior to RDA, the mean concentration of each individual HM at S1 and S2 was paired with the hydrological and physicochemical characteristics observed at the inlet station (Sanmenxia). In the same way, S3–S5 were paired with the reservoir station (Xiaolangdi Reservoir), S6 and S7 with the outlet station (Xiaolangdi), S8 and S9 with the downstream station at Huayuankou (HYK), and S10 and S11 with the downstream station at Jiahetan (JHT). 3. Results and Discussion 3.1. Spatial–Temporal Variation in Concentration of Heavy Metals in Surface Water Table 1 displays the estimated descriptive statistics for HM concentrations in surface water in the reservoir and the downstream reach of the Xiaolangdi Dam in the post-WSRS I (former) and post-WSRS II (latter) seasons. Generally, average concentrations of As, Cr, Hg, Ni, Pb, and Zn were lower than the criteria for drinking water proposed by the Ministry of Health of China [46] and the World Health Organization (WHO) [47], with values ranging from 0.36 to 5.51, 0.87 to 4.40, 0.02 to 0.09, 1.81 to 9.43, 1.10 to 5.38, and 25.61 to 40.81 μg/L, respectively. This result suggests that HMs may not pose a major challenge to the water quality of the XLD Reservoir and its downstream reach. The highest level of As and Zn was observed in the downstream reach in the latter season, while relatively low concentrations of As and Zn were observed in the reservoir during the same season. Temporally, concentrations of As in the former season were significantly lower (p < 0.05) than that in the latter season. Spatially, the concentration of As in the downstream reach was significantly higher (p < 0.05) than that in the reservoir. In contrast, Zn did not exhibit any significant difference temporally or spatially, though the temporal and spatial differences were obvious. The higher concentrations of Hg, Ni, and Pb were found in the reservoir, while the low values were measured in the downstream reach. However, there was no significant difference between the reservoir and the downstream reach, except for Ni, which displayed significant accumulation in the reservoir in comparison with the downstream reach. A higher concentration of Cr was found in the downstream reach in the former season, while it was measured in the reservoir in the later season. Additionally, HM concentrations in surface water varied with distance to the dam (Figure 2). Specifically, in the reservoir area, all HMs in the former season did not show distinct accumulation near the dam, except for Cr, which showed a distinct accumulation closer to the dam in the latter season. In the downstream reach, the concentration of As, Cr, and Zn generally increased, Cu decreased, and Pb and Ni exhibited no apparent change with distance to the dam in the former season; the concentration of As and Zn generally increased, Cr, Cu, and Ni decreased, and then Pb decreased first and then increased with distance to the dam in the latter season. The results of this research are in agreement with the previous reports stating that concentrations of the selected HMs were lower than their corresponding limits for drinking water in both the mid-WSRS season [12] and the post-WSRS seasons [48]. However, inconsistent results were observed regarding the spatial difference between the reservoir and the downstream reach. Previous studies reported no significant difference regarding HM concentrations (As, Cr, Pb, Cu, Ni, and Zn) between the reservoir and the downstream reach [12], while significant differences were observed for As and Ni in the present study. This inconsistency may ascribe to the regulating effects of the WSRS and the trapping effects of the XLD. During the WSRS, the artificially released sediment from the Xiaolangdi Reservoir may adsorb HMs in the surface water, which could reduce the concentrations and regional differences in dissolved HMs along the downstream reach [5,17,20,25,49]. In contrast, during the post-WSRS seasons, the trapping effects of the XLD could enlarge these regional differences between the reservoir and the downstream reach, which has been suggested by several previous studies [14,22,41]. This may also explain the accumulation of HMs near the dam in the reservoir area [5]. Additionally, besides Zn, concentrations of the other five HMs in the latter season were significantly higher than those in the former season, regardless of whether they were in the reservoir area or the downstream reach. This is likely attributed to the unsteady flow conditions at the initial stage after the WSRS, which disequilibrates the adsorption–desorption processes between the particulate and dissolved HMs, resulting in more dissolved HMs being adsorbed on the surface of suspended sediments [1,2]. Consequently, HM concentrations were relatively low in the post-WSRS I season due to the sorption effects of suspended sediments, which is consistent with the result observed during the WSRS within the study area [12]. In contrast, the steady-state flow condition after completion of storing water in the reservoir was favorable for HMs to reach the adsorption–desorption equilibrium state in the latter season, as it is characterized by slow flow rate and significant sedimentation and desorption of suspended sediments [3,4,22]. Therefore, the suspended and deposited sediments desorbed more dissolved HMs into the water, which lead to higher HM concentrations in the later season [12,13]. Meanwhile, the trapping effect of the dam is also known to cause the accumulation of HMs over time [5,8]. Furthermore, As and Cr concentrations, which showed quadratic variations in the reservoir in the later season, should be attributed to the releasing effect of the Sanmenxia Reservoir and the trapping effect of the XLD Reservoir, which led to high values of As and Cr occurring downstream near the Sanmenxia Dam and in the XLD reservoir before the dam. Pb concentrations in the downstream also showed quadratic variations in the later season, which should be attributed to the exogenous input from agricultural practices further downstream the XLD [31]. Additionally, the HM concentrations obtained in this study were comparable to those of the other water bodies in China, for example, the Three Gorges Reservoir [14,22], Taihu Lake [50], and Wen-Rui Tang River [42]. However, HM concentrations in the present study were lower than those in Mangla Lake, Rawal Lake, and Simly Lake in Pakistan [43] as well as the average values of HMs in surface water of India, South Africa, Iran, and the USA as reviewed by Kumar et al. [51]. 3.2. Assessing the Contamination and Human Health Risk of Heavy Metals in Surface Water The calculated heavy metal contamination index (HCI) values in the reservoir area ranged from 3.76 to 5.26 during the post-WSRS I season (former) and from 14.59 to 31.27 during the post-WSRS II (latter) season. In the downstream reach, the HCI value ranged from 2.73 to 9.71 during the former season and from 15.99 to 26.78 during the latter season (Figure 3), confirming that all HCI values were less than the critical value of 100 for drinking water [33]. However, the contamination of HMs in surface water following the WSRS increased over time. Meanwhile, the contamination of HMs increased slightly along the flow direction. To better characterize the moderate levels of HMs contamination, the HCI values below 100 were classified into three classes [42]. In the former season, all HCI values were lower than 15, suggesting a low HM contamination level at the former stage following the WSRS. In contrast, a medium HMs contamination level was measured at the latter stage following the WSRS. Among the HMs, As and Pb contributed the most to HCI, accounting for 43.05% and 42.18% in the former season and 29.98% and 48.27% in the latter season, respectively. HM contaminations were most severe around monitoring site S1 (31.27) in the latter season. This observation may be mainly attributed to the release of accumulated HMs trapped by the Sanmenxia Dam, as S1 is located downstream of the dam outlet. It is well documented that As and Pb contamination has been a global environmental issue due to their toxicity and persistence [41,47]. Therefore, contamination by As and Pb should be of great concern in the study area. In general, water in the XLD Reservoir and the downstream reach were not distinctly contaminated by HMs, though the HCI values were observed to increase over time. These findings highlight the efficiency of the WSRS in mitigating the trapping effects of the XLD, which greatly alleviates the material siltation in the reservoir area and the material loss in the downstream reach [1,2,8,10,11]. Indeed, in comparison with measurements taken prior to the operation of the WSRS in 1999–2001 (311 million tons), the annual sediment retention for 2002–2013 (262 million tons) was much lower following the operation of the WSRS [49]. Figure 4 presents the carcinogenic and non-carcinogenic risks of HMs via the direct ingestion pathway of drinking water. In the reservoir area, the carcinogenic risk from exposure to As in surface water for adults and children in the former season was 0.14–0.47 × 10−5 and 0.18–0.60 × 10−5, respectively, reaching 1.48–5.09 × 10−5 and 1.88–6.49 × 10−5 in the latter season (Figure 4a). The carcinogenic risk associated with exposure to As for adults and children in the downstream reach was 0.16–1.66 × 10−5 and 0.21–2.30 × 10−5 in the former season, respectively, reaching 2.72–5.39 × 10−5 and 3.48–6.88 × 10−5 in the latter season. According to the criteria established by the USEPA [52], the values of carcinogenic risk for As were all within the acceptable range of 1 × 10−6 to 1 × 10−4, suggesting that there may be inconspicuous adverse effects on the health of residents through the ingestion pathway [42,45]. However, the carcinogenic risk of As via the direct ingestion pathway of drinking water increased over time (Figure 4b). The carcinogenic risk of Cr intake through the ingestion pathway for adults and children in the former season also were at acceptable levels, with mean values of 1.75 × 10−5 and 2.23 × 10−5 in the reservoir area, and mean values of 4.47 × 10−5 and 5.70 × 10−5 in the downstream reach, respectively. However, they were at unacceptably high levels in the latter season, as the carcinogenic risk of Cr ranged from 0.67 × 10−4 to 1.32 × 10−4 for adults (1/5 of sites exceeded the critical level) and 0.85 × 10−4 to 1.68 × 10−4 for children (2/5 of sites exceeded the critical level) in the reservoir area, and then ranged from 0.28 × 10−4 to 1.24 × 10−4 for adults (2/5 of sites exceeded the critical level) and 0.36 × 10−4 to 1.59 × 10−4 for children (4/5 of sites exceeded the critical level) in the downstream reach. Meanwhile, the highest values of carcinogenic risk for Cr were found in the site surrounding the dam in both the reservoir (S5) and the downstream reach (S6). The above findings imply that residents living near the dam are exposed to adverse carcinogenic effects from Cr through the ingestion pathway of drinking water [40,44], particularly children. In addition, although the highest carcinogenic risks of Cr were observed near the dam and then decreased away from the dam in the reservoir area and as the river flows down, the health risk caused by Cr may still pose a significant issue as residents living in the area surrounding the reservoir and the downstream reach may be exposed to Cr through other pathways, including direct and indirect [41,45]. Assessing the carcinogenic risk of As and Cr based on the direct ingestion pathway of drinking water alone, likely underestimates the real health risk to residents via exposure. Like the carcinogenic risk assessment, the noncarcinogenic risks to adults and children were calculated. The noncarcinogenic risks posed by the four HMs were relatively low in this study, suggesting that no apparent noncarcinogenic health risks were observed study area following the WSRS [40]. Specifically, noncarcinogenic risks of Hg (Figure 4c), Pb (Figure 4d), Ni (Figure 4e), and Zn (Figure 4f) ranged from 0 to 2.01 × 10−11, 0.21 to 2.47 × 10−10, 0.28 to 2.54 × 10−10, and 1.84 to 10.18 × 10−10 for adults, and 0 to 2.56 × 10−11, 0.36 to 3.24 × 10−10, 0.26 to 3.15 × 10−10, and 2.34 to 12.99 × 10−10 for children, respectively. In general, except for Zn, the noncarcinogenic risks were greater in the reservoir area, in the latter season, and most notably for children. These results may be explained by the trapping effects of the XLD, which causes the accumulation of these HMs in the reservoir area over time (Table 1), as well as by the fact that children are more susceptible to the health risk of HMs than adults [22]. 3.3. Statistical Analyses for Heavy Metals in Surface Water To better understand the potential sources and related behaviors of HMs in surface water in the seasons following the WSRS, positive matrix factorization (PMF) and hierarchical cluster analysis (HCA) were performed. As shown in Figure 5, the PMF results indicated that, in the former season (Figure 5a), factor 1 contributed 59% of the total HMs in surface water and was mainly dominated by Cr (100%), As (73.2%), and Zn (58.9%); and factor 2 had a degree of interpretation of 27% and was weighted heavily on Hg (99.9%), Ni (61.3%), and Pb (51.3%). In the post-WSRS II season (Figure 5b), factor 1 contributed 50% of the total HMs in surface water and was mainly dominated by Zn (100%), As (82.4%), and Hg (50.4%); and factor 2 had a degree of interpretation of 2% and was weighted heavily on Ni (64.2%), Pb (64.4%), Cr (60.4%), and Hg (49.6%). Comparably, the HCA results were basically in agreement with those of PMF, where all the selected HMs were grouped into two significant categories. In the former season (Figure 5c), the first group included Cr and As, while the second contained Hg, Ni, Pb, and Zn. In the latter season (Figure 5d), the first group included As and Zn, and the second was composed of Hg, Ni, Pb, and Cr. Combining the results of PMF, HCA, and the spatial and temporal variations in HM concentrations together during the former season, it can be inferred that Cr, As, and Zn likely came from common sources, while Hg, Ni, and Pb originated from another common source. By contrast, during the latter season, As and Zn likely originate from common sources, while Hg, Ni, Pb, and Cr originated from another common source. According to previous studies, the sources of HMs in surface water can be classified as natural and anthropogenic sources [53,54]. In this study, concentrations of Cr, As, and Zn in the downstream reach were higher than those in the reservoir area during the former season (Table 1), indicating that these three HMs may likely be anthropogenic inputs. Previous reports suggest that Cr, As, and Zn mainly originate from industrial activities such as mineral mining and machinery manufacturing [22], while there are other researchers who reported that As and Zn mainly were associated with the abuse of phosphate fertilizer and pesticides [55]. In the downstream reach, the river channels are characterized by the “perched” riverbed surrounded by intensive agricultural activities with almost no industrial activities [21,25]. Therefore, we deduce that Cr, As, and Zn in the former season may be greatly attributed to agricultural activities. However, the higher value of these three HMs may also be explained by the desorption from the deposited sediments during the WSRS [21]. Concentrations of Hg, Ni, and Pb in the reservoir area were higher than those observed in the downstream reach (Table 1), indicating that the above HMs could be attributed to an accumulation from natural origins [22]. The XLD traps fine-grained loess sediments that have eroded from the Chinese Loess Plateau, which, consequentially, causes the accompanied contaminants to accumulate in the reservoir in both the particulate and dissolved forms [12,49]. Therefore, Hg, Ni, and Pb, in the first stage after the WSRS, were likely derived from the natural weathering source upstream from the reservoir. In the latter stage following the WSRS, As and Zn should also be attributed to common sources of agricultural activities. However, Hg, Ni, Pb, and Cr, which showed significant accumulation in the reservoir during the later stage in comparison with those during the first stage and those in the downstream reach, are likely derived from both natural and anthropogenic sources. This is evidenced by the increased concentration of these four HMs in the downstream reach (Table 1). On one hand, the continuous accumulation of HMs in the reservoir originating from natural sources undoubtedly could lead to increased HM concentrations. On the other hand, the XLD controls approximately 90% of the Yellow River basin, where the anabatic point and non-point discharge in the upper and middle reaches may likely contribute to the increased concentration of these four HMs as well [20]. Thus, the trapping effects of the XLD could serve as anthropogenic and natural sources, leading to the increased concentration of these four HMs [11]. However, it is difficult to accurately determine the contribution rate between natural and anthropogenic sources of these HMs, both in this study and in previous studies [21,22,25,56]. To establish the relationship between sampling sites according to their similarities, HCA was performed on HM concentrations. As shown in the left vertical dendrogram of each heat map, each of the 11 sites was classified into two major groups according to HM concentrations. In the former season (Figure 5c), group 1 was comprised of sites S1–S5, while group 2 comprised of sites S6–S11. In the latter season (Figure 5d), group 1 was comprised of sites S1–S7, and then group 2 comprised of sites S8–S11. The sites in each group displayed similar features and indicated potentially analogous contributing sources [43]. Based on these observations, the distribution of HMs at S1–S5 in the reservoir area should likely have common geochemical behaviors [57], as well as those at S6–S11 in the downstream reach. This can be determined by the variations in hydrological characteristics between the reservoir and the downstream ranch [45]. However, in the latter season, the HM concentrations at sites S6 and S7 downstream of the dam exhibited common geochemical behaviors with S1–S5, which is likely a result of the release of water from the reservoir. Therefore, further analysis regarding the relationship between hydrological characteristics and concentrations of HMs is needed (Section 3.4). 3.4. Relationship between Hydrological Characteristics and Concentration of Heavy Metals in Surface Water Hydrological characteristics play a key role in determining the geochemical behaviors of HMs in aquatic environments [45,57]. In this study, based on the redundancy analysis (RDA), the relationships between hydrological and physicochemical characteristics and HM concentrations in surface water were determined. The hydrological characteristics considered here include water level (WL) and flow rate (FL), while the physicochemical characteristics include pH, dissolved oxygen (DO), and electrical conductivity (EC). The RDA results shown in Figure 6 indicated that the first two explanatory variables explained 98.34% of the total variation between HMs and sample sites. Regarding the sample sites, HM concentrations at the reservoir (S3–S6) and the outlet (S6–S7) stations in the former season were correlated with changes in WL, while those at the inlet of the reservoir (S1–S2) and the downstream stations (HYK and JHT: S8–S11) in the former season were correlated with changes in flow rate. In the latter season, changes in HM concentrations at the inlet (S1–S2), reservoir (S3–S5), and outlet (S6–S7) stations were all correlated with changes in physicochemical variables (pH, DO, and EC). Regarding individual HMs, Zn concentration was positively correlated with FR and was negatively correlated with WL; As concentration was positively correlated with pH, DO, and EC; Ni, Pb, and Zn concentrations were positively correlated with WL, pH, DO, and EC and negatively correlated with FR. In comparison to HMs listed above, the concentration of Hg was less influenced by hydrological characteristics in comparison. In general, the RDA results indicate that HM concentrations in the former season were mainly influenced by WL and FR, while in the latter season, HM concentrations were mainly influenced by pH, DO, and EC. These results may be attributed to the changes in hydrological characteristics over the different stages following the WSRS [45,57]. During the initial stage following the WSRS, the XLD Reservoir was at the impoundment stage, with a large amount of suspended material. Therefore, the relatively high and unstable fluctuations in water level and flow rate could influence the equilibrium state between the deposition and suspension processes of sediment, simultaneously affecting the adsorption–desorption equilibrium and, ultimately, the transformation of HMs in water [22]. Meanwhile, unstable hydrological characteristics can lead to abnormal hydrodynamics, including the turbulence, flow velocity, and hydraulic residence time during the rising limb of the hydrograph [2,58], which can affect the distribution and migration of HMs in both the reservoir and the downstream reach. In addition to the hydrodynamic conditions, hydrological characteristics play a vital role in causing variations in physicochemical conditions (e.g., pH, DO, and EC) of surface water, as extensively reported in the literature [5,50]. Therefore, as long as the hydrological characteristics remain unstable, variations (i.e., disequilibrium state) in HM concentrations may persist [11]. In contrast, during the latter season, the hydrological characteristics were characterized by a stable water level and flow rate, while variations in surface water HM concentrations were closely related to physicochemical conditions (e.g., pH, DO, and EC) that are determined by certain hydrological and hydraulic characteristics [5,50]. These factors likely explain the low explanatory ability (7.06%) of the second axis, which was dominated by the physicochemical conditions (pH, DO, and EC), and may also inform the high explanatory ability (91.28%) of the first axis, which was dominated by the hydrological characteristics for the RDA results in this study. However, the hydraulic characteristics at each station were not measured and the measured physicochemical characteristics were insufficient in this study. For this reason, further investigations on the effects of hydrological, hydraulic, and physicochemical parameters on HM concentrations in surface water following the WSRS are needed in future work. 4. Conclusions HM concentrations in the surface water of the reservoir and downstream reach of the XLD in the two seasons following the WSRS were well below the drinking water standards in China and those offered by the WHO. However, likely due to the trapping effects of the XLD, concentrations of As, Cr, Hg, Ni, and Pb in both the reservoir and the downstream reach in the post-WSRS II season were significantly higher (p < 0.05) than those in the post-WSRS I season. Spatially, concentrations of As in the reservoir were significantly lower (p < 0.05) than those in the downstream reach, while concentrations of Ni showed the opposite trend. Meanwhile, HM concentrations increased with decreased distance to the dam in the post-WSRS II season. The contamination of HMs, carcinogenic risks via exposure to As, and noncarcinogenic risks via exposure to Hg, Ni, Pb, and Zn via the direct ingestion pathway of drinking water were all within the acceptable range following the WSRS. However, contamination and health risks of the above HMs increased over time and decreased along flow direction (except for noncarcinogenic risks associated with Zn). Likely due to the trapping effects of the XLD, the carcinogenic risk of Cr intake through the ingestion pathway for adults and children in the post-WSRS II season was at an unacceptably high level, particularly at sites near the dam (S5 and S6). In the post-WSRS I season, Cr, As, and Zn likely originated from agricultural activities, while Hg, Ni, and Pb should be greatly attributed to natural weathering sources upstream from the reservoir. In the post-WSRS II season, As and Zn also likely originated from common agricultural activities, while Hg, Ni, Pb, and Cr were most likely derived from both natural and anthropogenic sources. HM concentrations were mainly affected by hydrological characteristics (WL and FR) in the post-WSRS I season, while HM concentrations were mainly affected by physicochemical parameters (pH, DO, and EC) in the post-WSRS II season. In conclusion, further investigation regarding the effects of hydrological, hydraulic, and physicochemical parameters on HM concentrations in surface water following the WSRS are needed. Acknowledgments We would like to thank Shannon Elliot for his assistance with the English language and grammatical editing. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19095713/s1, Table S1: Sampling sites characteristics in the Xiaolangdi Reservoir and the downstream reach in the Yellow River; Table S2: Values of water pollution indices for individual sampling locations in the Xiaolangdi Reservoir and its downstream reach in the middle and lower reaches of the Yellow River, China. Table S3: Standard permissible limit, assigned weights and calculated unit weights of individual heavy metals for the heavy metal contamination index (HCI). Click here for additional data file. Author Contributions Conceptualization, Q.Z. and S.D.; methodology, Q.Z. and Z.H.; software, Z.G. and Z.H.; validation, S.D.; formal analysis, Q.Z.; investigation, Q.Z., Z.G., X.L., Z.H., J.Y. and Y.L.; resources, S.D.; data curation, S.D.; writing—original draft preparation, Q.Z.; writing—review and editing, Q.Z. and S.D.; visualization, Q.Z., J.Y. and Y.L.; supervision, Q.Z. and S.D.; project administration, Q.Z., X.L. and S.D.; funding acquisition, Q.Z., X.L. and S.D. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Location of the study area and sampling sites in the Xiaolangdi Reservoir (S1–S5) and the downstream reach (S6–S11) in the middle and lower reaches of the Yellow River. Figure 2 Concentrations of As (a), Cr (b), Cu (c), Ni (d), Pb (e), and Zn (f) in relation to distance above and below the Xiaolangdi Dam in the post-WSRS I and post-WSRS II seasons. Figure 3 Contamination of heavy metals in surface water of the Xioalangdi Reservoir and the downstream reach following the WSRS. Figure 4 Carcinogenic risk of As (a) and Cr (b) and Non-carcinogenic risk of Hg (c), Pb (d), Ni (e), and Zn (f) in surface water of the Xiaolangdi Reservoir and the downstream reach in the post-WSRS I and II seasons. The dashed line shows the unacceptable limit for the carcinogenic risks. The non-carcinogenic risks were all far below the unacceptable limit. The diamond marker stands for outliers. Figure 5 Potential sources of HMs in surface water in the XLD Reservoir and its downstream reach following the WSRS in the post-WSRS I (a,c) and II (b,d) seasons, based on PMF (a,b) and HCA (c,d). Figure 6 Biplot from redundancy analysis (RDA) showing the relationship between hydrological characteristics and heavy metal concentrations in surface water in the Xiaolangdi Reservoir and its downstream reach. Inlet, Reservoir, Outlet, HYK, and JHT represent sample sites located near the inlet (S1 and S2), reservoir (S3, S4, and S5), outlet (S6 and S7), Huayuankou (S8 and S9), and Jihetan stations (S10 and S11), respectively. I and II represent the post-WSRS I and post-WSRS II seasons, respectively. The environmental variables include water level (WL), flow rate (FR), pH, dissolved oxygen (DO), and electrical conductivity (EC). ijerph-19-05713-t001_Table 1 Table 1 Heavy metal concentrations in surface water in the reservoir and the downstream reach of the Xiaolangdi Dam in the Yellow River. The effects of season, region, and their interaction were conducted based on two-way analysis of variance with a statistical significance level of p < 0.05 (highlighted in bold). Season Region Statistic As (μg/L) Cr (μg/L) Hg (μg/L) Ni (μg/L) Pb (μg/L) Zn (μg/L) Post-WSRS I Reservoir (n = 5) Mean 0.36 0.87 0.06 3.88 1.16 34.75 SD 0.18 0.69 0.05 0.73 0.33 10.83 CV 0.49 0.79 0.77 0.19 0.29 0.31 Downstream (n = 6) Mean 1.31 2.23 0.02 1.81 1.10 39.06 SD 1.09 0.96 0.02 0.49 0.42 14.73 CV 0.84 0.43 0.91 0.27 0.38 0.38 Post-WSRS II Reservoir (n = 5) Mean 3.60 4.40 0.09 9.43 5.38 25.61 SD 1.99 1.30 0.04 0.88 1.19 17.81 CV 0.55 0.30 0.41 0.09 0.22 0.70 Downstream (n = 6) Mean 5.51 4.07 0.08 5.38 3.83 40.81 SD 1.35 1.82 0.02 2.53 1.91 20.07 CV 0.25 0.45 0.30 0.47 0.50 0.49 Season F 43.883 23.676 8.282 53.315 46.862 0.277 Sig. 0.000 0.000 0.010 0.000 0.000 0.605 Partial η2 0.709 0.568 0.315 0.748 0.722 0.015 Region F 6.469 0.872 3.549 23.993 2.532 1.931 Sig. 0.020 0.363 0.076 0.000 0.129 0.182 Partial η2 0.264 0.046 0.165 0.571 0.123 0.097 Season × Region F 0.754 2.349 0.062 2.937 2.177 0.602 Sig. 0.397 0.143 0.362 0.130 0.157 0.448 Partial η2 0.040 0.115 0.046 0.123 0.108 0.032 “Bold” represents a statistical significance level of p < 0.05. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094882 ijms-23-04882 Article Neurogenesis of Subventricular Zone Progenitors in the Premature Cortex of Ferrets Facilitated by Neonatal Valproic Acid Exposure https://orcid.org/0000-0001-8402-1041 Sawada Kazuhiko Pisani Antonio Academic Editor Department of Nutrition, Faculty of Medical and Health Sciences, Tsukuba International University, Tsuchiura 300-0051, Ibaraki, Japan; k-sawada@tius.ac.jp; Tel.: +81-29-883-6032 28 4 2022 5 2022 23 9 488225 3 2022 20 4 2022 © 2022 by the author. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The present study evaluated the neurogenesis of neonatal valproic acid (VPA) exposure on subventricular zone progenitors of the developing cerebral cortex in ferrets. VPA was injected at a dose of 200 µg/g of body weight into ferret infants on postnatal days 6 and 7. Two different thymidine analogues, 5-ethynyl-2′-deoxyuridine (EdU) and 5-bromo-2′-deoxyuridine (BrdU), were injected with a 48 h interval to label proliferating cells before and after VPA exposure. Two hours after BrdU injection, BrdU single- and EdU/BrdU double-labeled cells, but not EdU single-labeled cells, were significantly denser in both the inner and outer subventricular zones of VPA-exposed infants than in control infants. Notably, more than 97% of BrdU single- and EdU/BrdU double-labeled cells were immunopositive for Pax6, a stable marker for basal radial glia (bRG), in both groups. In contrast, the percentage of cells positively immunostained for Cux1, a postmitotic marker for upper-layer cortical neurons, in both EdU single- and BrdU single-labeled cells, was significantly higher in VPA-exposed infants than in control infants. These findings suggest that neonatal VPA exposure facilitates bRG proliferation, including self-renewal, followed by their differentiation into upper layer cortical neurons in the premature cortex of ferrets. valproic acid outer subventricular zone basal radial glia Pax6 ferret JSPS KAKENHI15K08144 This study was supported by JSPS KAKENHI (grant number: 15K08144). ==== Body pmc1. Introduction Valproic acid (VPA), an antiepileptic/anticonvulsant drug, is also known to be an inhibitor of histone deacetylases 1 and 2 [1]. When various mammalian species [2,3,4,5,6,7,8], including ferrets [9], are exposed to VPA during prenatal and early postnatal development, it may cause social behaviors associated with autism spectrum disorder (ASD). Altered brain morphology due to developmental VPA exposure has been reported mainly in the cerebral cortex, and includes cortical thickening [10,11] or thinning [4], an increased number of cortical neurons [7] and altered gyrification [12,13,14,15]. Our recent study revealed altered sulcal infolding with an increased neuron density and cortical thickening in the sulcal floors following neonatal VPA exposure in a gyrencephalic animal model, the ferret [16]. Some human ASD patients reportedly exhibit similar cortical characteristics [17,18,19]. VPA mediates neurogenesis by regulating the proliferation, maintenance, and differentiation of neuronal stem/progenitor cells, although the effects of VPA vary depending on the exposure time points and neuronal stem/progenitor cell types. For example, VPA mediates the upregulation of Wnt/β-catenin pathways [20] and the proliferation of dentate gyrus (DG) progenitors in the developing hippocampus [21,22], and promotes the adult neurogenesis of DG progenitors by inhibiting HDAC activity [23]. In contrast, VPA alters the cell-cycle exit of subventricular zone (SVZ) progenitors via the inhibition of HDAC activity during the early stage of cortical neurogenesis in mice [11]. In our recent study, VPA exposure during the late stage of cortical neurogenesis increased the density of cortical neurons derived from basal radial glia (bRG) in ferrets [16]. The bRG, also called the outer RG or transient RG, are self-renewable neuronal stem cells that appear transiently in the inner and outer subventricular zones (iSVZ and oSVZ) during cortical neurogenesis in humans [24,25] and other mammalian species [26,27,28,29], including ferrets [30]. They are distinct from intermediate progenitor cells which are classically known cortical progenitors [24,25]. Likewise, bRG emerges in the developing cortex of mice, but is less abundant than in primates and carnivores [26,27]. Therefore, ferrets are more suitable than rodents for investigating the effect of VPA with bRG as a target. Therefore, this study aimed to determine the intermediate influence of VPA exposure on the proliferation, maintenance, and/or differentiation of bRG in the developing cerebral cortex, which may be involved in gyrification anomalies previously reported in ferrets [16]. Two thymidine analogues, 5-ethynyl-2′-deoxyuridine (EdU) and 5-bromo-2′-deoxyuridine (BrdU), were administered within a 48 h interval to label proliferating cells before and after VPA exposure. This administration schedule can also label self-renewing bRG in ferrets [30]. 2. Results 2.1. Immunofluorescence Staining for Various Markers with EdU and BrdU Labeling EdU-labeled cells, which proliferated 24 h before the first VPA injection (at postnatal day (PD) 5), were distributed abundantly in the iSVZ and diffusely in the oSVZ on PD 7 (Figure 1a). Although a great majority of these cells were single-labeled with EdU, some were also labeled with BrdU, indicating that these cells experienced a second round of cell division 48 h after EdU injection in both VPA-exposed and control groups (Figure 1). In contrast, BrdU single-labeled cells were distributed diffusely in the iSVZ through the oSVZ (Figure 1a). BrdU labeling was observed in proliferating cells immediately following the second VPA injection in the VPA-exposed group. Positivity of immunostaining for various markers was examined in EdU single-, BrdU single-, and EdU/BrdU double-labeled cells. Some of these cells were immunopositive for Sox2 (a marker for neuronal stem cells [28,29,31]) (Figure 1); Pax6 (a stable marker for bRG across mammals [28,29,31]) (Figure 2); Olig2 (expressed in progenitors with glial progeny [29,31]) (Figure 3); Cux1 (a marker for postmitotic upper cortical layer neurons [32]) (Figure 4a); and Ctip2 (a marker for postmitotic upper cortical layer neurons [33]) (Figure 4b). 2.2. Densities of EdU Single-, BrdU Single- and EdU/BrdU Double-Labeled Cells The present study was designed to label proliferating cells with EdU 24 h prior to VPA injection (PD 5) and with BrdU immediately following two consecutive days of VPA injection (PD 7). The 48 h interval between EdU and BrdU administration would lead to the labeling of self-renewing bRG in the ferret premature cortex because it covers the minimum time for the S-shape of either the first or second round of cell divisions [30]. No statistical difference in the density of EdU single-labeled cells between VPA-exposed and control ferret infants was observed for either the iSVZ or oSVZ, indicating no alteration in cell proliferation (Figure 5). In contrast, a significant main effect at the group level was detected in the density of BrdU single-labeled cells by two-way repeated-measures ANOVA (F(1,4) = 150.928; p < 0.001). Scheffe’s test indicated significantly denser BrdU single-labeled cells in both the iSVZ (p < 0.001) and oSVZ (p < 0.001) in VPA-exposed infants than in control infants (Figure 5). Notably, although a small population of proliferating cells on PD 5 experienced self-renewal after a 48 h interval (EdU/BrdU double-labeled cells), they were significantly denser in VPA-exposed infants than in control infants and were detected either in the iSVZ (p < 0.01) or oSVZ (p < 0.01) (Scheffe’s test), following a significant main effect on the group by two-way repeated-measures ANOVA (F(1,4) = 20.196; p < 0.05) (Figure 5). 2.3. Incidence of Immunostaining for Various Markers in EdU Single-, BrdU Single- and EdU/BrdU Double-Labeled Cells The incidence of cells immunostained for various markers was estimated in EdU single-, BrdU single-, and EdU/BrdU double-labeled cells in the SVZ, and the results are shown in Table 1. Incidence was estimated independently for each marker antigen. Therefore, the proportion at which the expression of each antigen overlapped with one another is unclear. In both VPA-exposed and control infants, EdU single-labeled cells were immunopositive for Sox2, Cux1, and Ctip2 in a small population of less than 10% of cells for each marker. Significantly greater incidences of Sox2 (p < 0.01) and Cux1 (p < 0.01) expression in EdU single-labeled cells were observed in VPA-exposed infants than in control infants (Table 1). Pax6 immunostaining was observed in EdU single-labeled cells at a relatively larger proportion compared with other markers. Pax6 immunostaining was observed in 32.0% of EdU single-labeled cells in control infants, which was significantly reduced to 24.5% in VPA-exposed infants (p < 0.05) (Table 1). In contrast, a significant difference in the incidence of Olig2 immunostaining was observed: 14.3% of EdU single-labeled cells in control infants and 6.9% in VPA-exposed infants (p < 0.001) (Table 1). More than 80% of BrdU single-labeled cells were immunopositive for the neuronal stem/progenitor cell markers Sox2 and Pax6 in both groups (Table 1). A significantly greater incidence of immunostaining for Pax6, but not Sox2, was detected in the VPA-exposed infants than in the control infants (p < 0.05) (Table 1). Cux1 immunostaining was observed in 24.4% of BrdU single-labeled cells in control infants, and a significant increase (p < 0.05) in 34.8% of such cells in VPA-exposed infants (Table 1). There were no differences in the incidence of Olig2 and Ctip2 expression between the two groups (Table 1). Among EdU/BrdU double-labeled cells, the incidence of Pax6 immunostaining was significantly higher in VPA-exposed infants (98.5%) than in control infants (82.0%) (p < 0.05) (Table 1). However, the incidence of Sox2 immunostaining was significantly lower in VPA-exposed infants (86.1%) than in control infants (100%) (p < 0.05) (Table 1). 3. Discussion VPA is known to mediate neurogenesis by regulating the proliferation, maintenance, and/or differentiation of neuronal stem/progenitor cells, although VPA exhibits diverse effects depending on the type of neuronal stem/progenitor cells and the developmental stages of the brain [11,20,21,22,23,34,35]. In the current study, more than 90% of BrdU single-labeled cells in the SVZ were immunopositive for Pax6, which is a stable marker for bRG across mammals [28,29,31], and their density increased immediately following VPA exposure in PD 7 ferrets. Such VPA-related changes may not be accompanied by a massive loss of SVZ progenitors, because VPA prevented apoptosis [36] and increased the anti-apoptotic factors (such as Bcl-2) of neuronal progenitors [37,38]. The present study further examined the effect of VPA on SVZ progenitors that had already experienced the S-phase before VPA exposure (on PD 5) using EdU labeling. A large population of proliferating SVZ progenitors on PD 5 was EdU single-labeled, and the density of these cells was not altered by VPA exposure. On the other hand, a small population of proliferating SVZ progenitors on PD 5 was EdU/BrdU double-labeled, and 82.0% of them were Pax6 immunopositive in control infants. Notably, VPA exposure increased EdU/BrdU double-labeled cell density and the incidence of Pax6 immunostaining (98.5%) on PD 7. The 48 h interval between EdU and BrdU administrations applied in the present study covered the minimum times for the S-shape of either the first or second round of bRG cell divisions, allowing self-renewing bRG to be labeled in the ferret premature cortex [30]. Thus, VPA may primarily facilitate SVZ progenitor proliferation, including the self-renewal of bRG, in the ferret premature cortex during the late stage of cortical neurogenesis. VPA exposure to mice throughout gestation primarily increased the number of Cux1-immunopositive upper cortical-layer neurons and enhanced the non-specific expression of G1-phase regulatory proteins, namely a differentiation-inducing protein p27Kip1, and the differentiation-inhibitory proteins cdk2, cdk4, and cyclinD1 [11]. Juliandi et al. (2012) [34] also reported that Cux1-immunopositive upper layer cortical neurons were induced in vitro from mouse embryonic stem cells by VPA. In the current study, the percentages of cells immunopositive for various markers in SVZ progenitors were significantly altered in ferret infants, although such changes were small. Notably, the incidence of Cux1 immunostaining increased in SVZ progenitors with EdU single and BrdU single labeling immediately after the second VPA injection. These results suggest that VPA exposure at the late stage of cortical neurogenesis also facilitates the differentiation of SVZ progenitors, including bRG, into upper layer cortical neurons in the premature cortex of ferrets. This VPA effect may be mediated by the non-specific enhanced expression of G1-phase regulatory proteins, as observed in mice [11]. Furthermore, the effect of VPA exposure on SVZ progenitors differed before and after S-phase. The expression ratios of various markers in EdU single-labeled cells revealed that SVZ progenitors exposed to VPA after undergoing the S-phase prevented the differentiation into Olig2-positive glial cells and sustained Sox2-positive stem cell characteristics (rather than Pax6-positive bRG characteristics). Neonatal exposure to VPA, which covers the late stage of cortical neurogenesis, induces ASD-like social behavioral deficits in ferrets [9]. Our recent study revealed a gyrification abnormality with increased neuron density and cortical thickening in the sulcal floors following neonatal VPA exposure in ferrets [16]. In the current investigation, bRG proliferation (including self-renewal), followed by Cux1 immunostaining, was facilitated immediately following VPA exposure in the premature cortex of ferrets. The findings, therefore, suggest that the neurogenesis of upper-layer cortical neurons from bRG is substantial as VPA effects cause gyrification abnormalities seen in VPA-exposed ferrets. 4. Materials and Methods 4.1. Animals Six male ferrets were purchased on PD 5 from Japan SLC (Hamamatsu, Japan). They were reared with lactating ferret dams (3 pups/mother) in stainless-steel cages (80 cm × 50 cm × 35 cm) kept at 21.5 °C ± 2.5 °C under 12 h artificial illumination in the Facility of Animal Breeding, Nakaizu Laboratory, Japan SLC. All dams were fed a pellet diet (High Density Ferret Diet 5L14, PMI Feeds, Inc., St. Louis, MO, USA) and provided tap water ad libitum. All ferret infants were injected intraperitoneally with EdU at 30 µg/g body weight (Sigma-Aldrich, St. Louis, MO, USA) on PD 5 and BrdU at 30 µg/g body weight (Sigma-Aldrich) on PD 7. Three infants were administered VPA intraperitoneally at 200 µg/g body weight on PD 6 and 7, corresponding to the late stage of cortical neurogenesis [31]. The schedule for VPA administration was designed in accordance with our recent study [16]. The second injection of VPA was administered at the same time as the BrdU injection. The remaining three infants that did not receive VPA were used as controls. Two hours after BrdU injection, all infants were perfused with 4% paraformaldehyde (PFA) in PBS under deep anesthesia with ~2% isoflurane gas. 4.2. Immunofluorescence Procedures The cerebral hemispheres were separated on the left and right sides of the longitudinal cerebral fissure, immersed in 30% sucrose in PBS, and embedded in an optimal cutting-temperature compound. Coronal sections were made at 100 μm thickness using a Retratome (REM-700, Yamato Koki Industrial Co., Ltd., Asaka, Japan) with a refrigeration unit (Electro Freeze MC-802A, Yamato Koki Industrial). The brain tissue of infant ferrets is very soft due to unmyelination and a high water content. To maintain the integrity and morphology of the premature cortex, it was necessary to make cryosections of 100 μm thickness. All sections were collected in vials containing a 4% PFA solution. Five serial coronal sections taken at the level of the anterior commissure underwent immunofluorescence staining and EdU detection. These sections included large expansions of both the iSVZ and oSVZ, and the cortical region examined corresponded to the primary somatosensory cortex [39]. All procedures were performed on floating sections, according to a previous report [30]. Sections were heated in Antigen Retrieval Reagent UNIVERSAL (R&D system, Minneapolis, MN, USA) for 30 min in a 90 °C water bath and then cooled at 4 °C for 30 min. Two hours following a preincubation with PBS containing 0.1% Triton-X 100 (Triton-PBS) at room temperature at 37 °C, EdU was detected on the sections by a reaction with a Click-iT reaction cocktail containing Alexa Fluor 488 (Click-iT EdU Alexa Fluor 488 Imaging Kit, Thermo Fisher Scientific, Waltham, MA, USA) at 37 °C for 2 h. Then, sections were incubated with a mixture of a rat BrdU monoclonal antibody (1:1000; ab6326, Abcam, Cambridge, UK), a goat Sox2 polyclonal antibody (1:500; AF2018, R&D Systems, Minneapolis, MN, USA), mouse Pax6 monoclonal antibody (1:500, ab78545, Abcam, Cambridge, UK), mouse Cux1 monoclonal antibody (1:500, SC-514008, Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA), rat Ctip2 monoclonal antibody (1:500, ab18465, Abcam), or rabbit Olig2 polyclonal antibody (1:500; IBL, Gunma, Japan) dissolved in Triton-PBS containing 10% normal horse serum (Vector Labs. Inc. Burlingame, CA, USA) at 4 °C overnight. Highly specific immunostaining was reported in the ferret brain tissue using these primary antibodies [30,40,41]. The sections were subsequently incubated at 37 °C for 2 h with appropriate secondary antibodies, which included Alexa 350 goat anti-rat IgG (1:500; A-21093; Thermo Fisher Scientific, Waltham, MA, USA), Alexa 350 donkey anti-sheep IgG (1:500; A21097, Abcam), Alexa 647 chicken anti-rat IgG (1:500; A-21472, Thermo Fisher Scientific), biotinylated horse anti-mouse IgG (1:200; BA-2001, Vector Labs), and biotinylated donkey anti-rabbit IgG (1:200; A16027, Thermo Fisher Scientific). When using biotinylated secondary antibodies, sections were further incubated with Alexa 555-conjugated streptavidin (1:500, S21381, Thermo Fisher Scientific) at 37 °C for 1 h. 4.3. Evaluating the Density of Immunostained and/or Thymidine Analogue-Labeled Cells Serial digital sectioning images were acquired at a 10 μm depth (section plane thickness = 1 μm; number of sections = 10) from the most superficial plane, where EdU and BrdU labeling with immunostaining for various markers were obtained. Images were captured with a 20x objective using an Axio Imager M2 ApoTome.2 microscope with a 20× objective equipped with an AxioCam MRm camera (Zeiss, Gottingen, Germany) with Zen 2.3 blue edition software (Zeiss). A set of sectional images, 4 μm apart in the Z-direction (the third and seventh from the superficial slices of the acquired images), were selected as the lookup and reference images, respectively. The disector method using systematic random sampling was used to estimate the density of immunostained and EdU- and/or BrdU-labeled cells according to a previous report [30]. In sections immunostained for each marker, frames with 12 square boxes (box size = 40 × 40 μm) were used from one section (the left or right hemispheres) to systematically select the region of interest (ROI) randomly superimposed on the iSVZ and oSVZ of both the lookup and reference images at the same positions perpendicular to the ventricular surface. Thymidine analog-labeled or immunostained cells were counted within the ROIs using the “forbidden line” rule [42]. Their densities were calculated using the following formula: [Cell density = Qn−/(a × b × t)] (Qn− = total number of thymidine analogue-labeled and/or immunostained cells appearing within ROIs in the lookup images, but not in the reference images; a = 24, total number of ROIs in the lookup images from two sections (the left and right hemispheres) per animal; b = 40 × 40 μm, areas of counting box; and t = 4 μm, distance between the lookup and reference images). The percentages of immunostained cells and EdU- and/or BrdU-labeled cells were estimated by summing the cells counted within all ROIs from all animals of each group. 4.4. Statistical Analysis Measurements from the left and right hemispheres were combined and the number of animals (n) was set to “3” in each group. The density of immunostained cells and EdU- and/or BrdU-labeled cells were statistically analyzed by two-way repeated-measures ANOVA with the region (iSVZ and oSVZ) and group (VPA-exposed and control groups) as factors. For post hoc testing, Scheffe’s test was applied to detect significant differences in the group and/or region × group interactions using two-way repeated-measures ANOVA and simple main effects. The percentage of cells immunolabeled for the markers among thymidine analogue-labeled cells was statistically assessed using the χ2 test. The total number of EdU single-, BrdU single-, and EdU/BrdU double-labeled cells was defined as “n” for the χ2 test. 5. Conclusions VPA mediates neurogenesis [11,20,21,22,23,34,35]. It alters the cell-cycle exit of SVZ progenitors (largely the intermediate progenitor cells) during the early stage of cortical neurogenesis in mice [11]. Similar results were obtained in this ferret model, but findings on the stage of neurogenesis and the SVZ progenitor type altered by VPA were distinct. Notable findings of this study are that VPA exposure facilitated the proliferation of SVZ progenitors, mainly Pax6-positive bRG, and their differentiation into Cux1-positive upper layer cortical neurons. The bRG are a major source of a massive expansion of the cerebral cortex, particularly in gyrencephalic mammals [43], and bRG-derived neurons were placed densely in multimodal-associated cortical regions [30]. Thus, a ferret model can offer new insights into the pathogenesis of neurodevelopmental disorders affecting cortical neurogenesis caused by epigenetic factors such as VPA, which may not be observed in a mouse model. Institutional Review Board Statement All procedures were carried out in line with the National Institutes of Health’s (NIH) Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of Tsukuba International University (Approval no. 25-1). All procedures attempted to minimize the number and suffering of animals used. Conflicts of Interest The author declares no conflict of interest. Figure 1 Sox2 immunofluorescence with EdU and BrdU labeling in the SVZ of the premature cortex of VPA-exposed and control ferrets at PD 7. (a) Low-magnification images of the oSVZ through iSVZ. (b) High-magnification images of the oSVZ and iSVZ. Open arrowheads, Sox2-positive progenitors with EdU/BrdU double labeling. Figure 2 Pax6 immunofluorescence with EdU and BrdU labeling in the SVZ of the premature cortex of VPA-exposed and control ferrets at PD 7. (a) Low-magnification images of the oSVZ through iSVZ. (b) High-magnification images of the oSVZ and iSVZ. Open arrowheads, Pax6-positive progenitors with EdU/BrdU double labeling. Figure 3 High-magnification images of Olig2 immunofluorescence with EdU and BrdU labeling in the SVZ of the premature cortex of VPA-exposed and control ferrets at PD 7. Open arrowheads, Olig2-positive progenitors with EdU/BrdU double labeling; closed arrowheads, Olig2-positive progenitors with EdU single labeling. Figure 4 Immunofluorescence for postmitotic markers with EdU and BrdU labeling in the SVZ of the premature cortex of VPA-exposed and control ferrets at PD 7. (a) High-magnification images of Cux1 immunofluorescence with EdU and BrdU labeling in the oSVZ and iSVZ. Open arrowheads, Cux1-positive immature neurons with EdU/BrdU double labeling. (b) High-magnification images of Ctip2 immunofluorescence with EdU and BrdU labeling in the oSVZ and iSVZ. Open arrowheads, Ctip2-positive immature neurons with EdU/BrdU double labeling. Figure 5 Densities of EdU single-, BrdU single-, and EdU/BrdU double-labeled cells in the iSVZ and oSVZ of the premature cortex of postnatal day (PD) 7 ferrets. Data are shown as mean ± standard error of the mean (SEM). Significance is indicated using Scheffe’s test at * p < 0.01, ** p < 0.001; number of ferrets = 3 each. ijms-23-04882-t001_Table 1 Table 1 Incidence of immunostained cells for various markers in EdU single-, BrdU single- and EdU/BrdU double-labeled cells in the subventricular zone of the premature cortex. VPA Control     EdU+ cells % of Sox2+ 7.8% (25/320) ** 2.5% (8/315) % of Pax6+ 24.5% (91/372) * 32.0% (88/275) % of Olig2+ 6.9% (22/320) *** 14.3% (45/315) % of Cux1+ 5.8% (25/430) ** 1.8% (7/369) % of Ctip2+ 4.7% (20/430) 2.3% (9/396)     BrdU+ cells % of Sox2+ 83.7% (154/184) 88.8% (87/98) % of Pax6+ 97.6% (201/206) * 92.5% (99/107) % of Olig2+ 26.1% (48/184) 34.7% (34/98) % of Cux1+ 34.8% (106/305) * 24.4% (41/168) % of Ctip2+ 41.6% (127/205) 37.5% (63/168)     EdU+/BrdU+ cells % of Sox2+ 86.1% (31/36) * 100% (26/26) % of Pax6+ 98.5% (66/67) * 82.0% (41/50) % of Olig2+ 41.7% (15/36) 26.9% (7/26) % of Cux1+ 31.8% (21/66) 16.7% (5/30) % of Ctip2+ 30.3% (20/66) 23.3% (7/30) Percentages were calculated by summing each labeled cell counted within all ROIs from the inner and outer subventricular zones in the premature cortex from 3 ferrets. The numbers of labeled cells used to calculate the percentages are shown in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001 vs. control (χ2 test). EdU+, EdU single-labeled; BrdU+, BrdU single-labeled; EdU+/BrdU+, EdU/BrdU double-labeled; Sox2+, Sox2 immunostaining; Pax6+, Pax6 immunostaining; Olig2+, Olig2 immunostaining; Cux1+, Cux1 immunostaining; Ctip2+, Ctip2 immunostaining. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Phiel C.J. Zhang F. Huang E.Y. Guenther M.G. Lazar M.A. Klein P.S. Histone deacetylase is a direct target of valproic acid, a potent anticonvulsant, mood stabilizer, and teratogen J. Biol. Chem. 2001 276 36734 36741 10.1074/jbc.M101287200 11473107 2. Miyazaki K. Narita N. Narita M. Maternal administration of thalidomide or valproic acid causes abnormal serotonergic neurons in the offspring: Implication for pathogenesis of autism Int. J. Dev. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094904 ijms-23-04904 Article Role of Presenilin-1 in Aggressive Human Melanoma Sidor Julia 1 Gillette Megan 1 Dezi Lindsay Ann 2 https://orcid.org/0000-0003-2644-2168 Untiveros Gustavo 2 Strizzi Luigi 2* Bauer Johann Academic Editor Koller Ulrich Academic Editor 1 College of Osteopathic Medicine, Midwestern University, Downers Grove, IL 60515, USA; jsidor12@midwestern.edu (J.S.); mgillette53@midwestern.edu (M.G.) 2 Department of Pathology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA; dezilindsay@gmail.com (L.A.D.); guntiv@midwestern.edu (G.U.) * Correspondence: lstriz@midwestern.edu 28 4 2022 5 2022 23 9 490407 4 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Presenilin-1 (PS-1), a component of the gamma (γ)-secretase catalytic complex, has been implicated in Alzheimer’s disease (AD) and in tumorigenesis. Interestingly, AD risk is inversely related to melanoma, suggesting that AD-related factors, such as PS-1, may affect melanomagenesis. PS-1 has been shown to reduce Wnt activity by promoting degradation of beta-catenin (β-catenin), an important Wnt signaling partner. Since Wnt is known to enhance progression of different cancers, including melanoma, we hypothesized that PS-1 could affect Wnt-associated melanoma aggressiveness. Western blot results showed that aggressive melanoma cells expressed significantly lower levels of both PS-1 and phosphorylated-β-catenin (P-β-catenin) than nonaggressive melanoma cells. Immunohistochemistry of human melanoma samples showed significantly reduced staining for PS-1 in advanced stage melanoma compared with early stage melanoma. Furthermore, γ-secretase inhibitor (GSI) treatment of aggressive melanoma cells was followed by significant increases in PS-1 and P-β-catenin levels, suggesting impaired Wnt signaling activity as PS-1 expression increased. Finally, a significant reduction in cell migration was associated with the higher levels of PS-1 and P-β-catenin in the GSI-treated aggressive melanoma cells. We demonstrate for the first time that PS-1 levels can be used to assess melanoma aggressiveness and suggest that by enhancing PS-1 expression, Wnt-dependent melanoma progression may be reduced presenilin-1 melanoma biomarker Wnt signaling This research received no external funding. ==== Body pmc1. Introduction Melanoma develops from the malignant transformation of melanocytes, the neural crest-derived, pigment-producing cells of the skin [1,2]. Despite making up only 1% of all skin cancers, it accounts for almost the entirety of skin cancer-related deaths, with a dismal 5-year survival rate of only 27% for metastatic disease [1,2,3]. However, with early detection and excision of in situ lesions, the 5-year survival rate of melanoma significantly improves to 99% [1]. The progression of melanoma from in situ, radial growth phase (RGP) to locally infiltrating vertical growth phase (VGP) is multifactorial and influenced by aspects that include but are not limited to: environmental factors such as ultraviolet radiation; genetic mutations of tumor suppressor genes such as TP53; mutations that lead to constitutively active signaling of molecules such as those of the mitogen-activated protein kinase (MEK) pathway; and epigenetic disturbances of gene transcription, such as DNA methylation [4,5,6]. Undoubtedly, this complexity creates a challenge in managing melanoma, as targeting one pathway invariably leads to activation or selection of cells with alternative signaling that continue to drive melanoma progression [5,7,8]. There has been continued interest in exploring how neurodegenerative diseases and associated cellular and microenvironmental factors can affect tumorigenesis. For instance, patients diagnosed with melanoma showed decreased risk for Alzheimer’s disease (AD), encouraging research focused on identifying factors involved in pathogenesis of AD that could also affect melanoma growth [9,10]. Although the exact cause of AD is still unknown, current theory suggests that dysfunctional processing of amyloid-β precursor protein (APP) results in the formation of β-amyloid peptide (Aβ) that accumulates and creates extracellular neuritic plaques, which are neurotoxic and believed to act as a catalyst for development of AD [11,12,13]. AD is classified into two main categories: late-onset, or sporadic, type (SAD) and early-onset, or familial, type (FAD) [14,15]. Both types share the same pathological features and pathogenic events that lead to dysfunctional APP processing [11,12]. While no specific gene has been linked to SAD, there are distinct genetic variants that cause FAD, such as mutation of PSEN-1 [11,14]. PSEN-1 encodes presenilin-1 (PS-1), a ubiquitously expressed multipass transmembrane protein involved in APP processing and other molecular functions [11,14]. PS-1 is commonly known for its role, in conjunction with presenilin-2 (PS-2), as the catalytic core of the gamma (γ)-secretase complex, an intramembrane aspartyl protease complex, the substrates of which include the amyloid-β precursor protein (APP) [12]. Mutations, whether they result in decreased levels of functional PS-1 or increased expression of dysfunctional PS-1, can lead to aberrant processing of APP that is involved in the pathogenesis of FAD [12]. Initially, γ-secretase inhibitors (GSI), such as DAPT (N-[N-(3,5-Difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester ), were used to target Aβ production for treatment of AD, but results were unsuccessful, in part because of rebound increases in PS-1 expression with continued aberrant processing of APP and possibly additional γ-secretase-independent PS-1 functions such as intracellular calcium signaling, autophagy degradation, and turnover of the important Wnt signaling cofactor β-catenin [11,13]. In fact, PS-1 negatively regulates Wnt signaling by promoting phosphorylation and subsequent degradation of β-catenin [11,16,17]. In this regard, PS-1 has been shown to bind to β-catenin and then recruit kinases, such as glycogen synthase kinase-3, into the PS-1/β-catenin complex, causing phosphorylation of β-catenin (P-β-catenin) at Ser33/37/Thr41 [18,19]. PS-1 can also induce phosphorylation of β-catenin at Ser45 [20]. Interestingly, Liu et al. demonstrated that phosphorylation of β-catenin at Ser45 then leads to phosphorylation at Ser33/37/Thr 41 [21]. Thus, detection of P-β-catenin Ser33/37/Thr41 implies that β-catenin is also phosphorylated at Ser45. Regardless of the different phosphorylated sites, it is important to note that P-β-catenin Ser33/37/The41 is the form that is ultimately recognized for ubiquitination and proteasomal degradation [22,23]. It is not clear, however, how quickly ubiquitination and proteasomal degradation occur, especially in cancer cells, before significant reductions in total cellular β-catenin levels can be detected. Wnt/β-catenin signaling is a highly conserved pathway that plays a critical role during development by controlling activities such as cell fate, organogenesis, and regulation of cell migration and polarity [24]. The Wnt/β-catenin signaling pathway is activated by Wnt ligands, which are secreted glycoproteins that bind to the Frizzled receptor. This triggers the accumulation and translocation of β-catenin to the nucleus, where it acts as a transcriptional coactivator of genes involved in cellular activities including differentiation, proliferation, and survival [24,25]. Different studies have reported increased Wnt/β-catenin signaling during melanoma progression [26,27]. Furthermore, it was shown that patient melanoma tissue samples with increased Wnt/β-catenin signaling were associated with reduced overall survival [28]. Thus, inhibiting Wnt/β-catenin signaling in melanoma can lead to therapeutic effects. However, targeting Wnt/β-catenin signaling in different cancers has been associated with unacceptable safety profiles as a result of off-target effects due to the ubiquitous expression of Wnt/β-catenin [29,30]. As mentioned above, Wnt function is dependent on β-catenin availability, and PS-1 can regulate Wnt activity by controlling β-catenin levels. Several studies have reported that decreased expression of PS-1 is associated with worse outcomes in certain cancers, such as breast, skin, and glioblastoma [17,31,32]. In skin, PS-1 deficiency led to an increase in β-catenin expression, which caused epidermal hyperplasia and tumor formation [31]. Similarly in glioblastoma, PS-1 showed an antiproliferative effect due to its ability to enhance β-catenin degradation by increasing P-β-catenin levels, which leads to repression of Wnt signaling [17]. It is reasonable to predict, therefore, that PS-1 may play a role as a potential tumor suppressor by regulating β-catenin availability for nuclear translocation and induction of Wnt signaling in cancer cells. Here, we investigated the role of PS-1 in melanoma. We showed that increased melanoma aggressiveness was associated with reduced PS-1 expression. Moreover, we demonstrated that increasing PS-1 expression in melanoma cells led to reduced Wnt/β-catenin signaling and migratory potential. These results indicated a novel role for PS-1 as a biomarker for melanoma aggressiveness and repressor of melanoma progression. 2. Results 2.1. PS-1 Expression Was Lower in Aggressive Melanoma Cells with Active Wnt Signaling We previously showed that nonaggressive melanoma cells exposed to normal skin cells acquired phenotypic and molecular characteristics of increased aggressiveness, which included enhanced cell migration and increased Wnt signaling [33]. Here, WB analysis was performed to ascertain whether PS-1, a known regulator of Wnt function, was implicated in Wnt signaling observed in nonaggressive melanoma cells exposed to normal skin cells. Our WB results (Figure 1A,B) showed a significant decrease (p < 0.05) in PS-1 expression in the nonaggressive melanoma cell lines WM1552C and UACC1273 (Supplementary Table S1) after exposure to normal human epidermal keratinocytes (keratoCC) and normal human dermal fibroblasts (fibroCC) compared with reference melanoma cells used as control. These results suggest that downregulation of PS-1 expression may be an early event in melanoma cells localized in the skin, and this could facilitate Wnt signaling during the progression of melanoma to more aggressive disease. To determine whether PS-1 was in fact decreased in aggressive melanoma, WB analysis for PS-1 expression was performed on lysates from the aggressive melanoma cell lines Sk-Mel28, A375, and C8161 (Supplementary Table S1). Western blot results showed that Sk-Mel28, A375, and C8161 expressed 52%, 29%, and 32%, respectively, of the PS-1 levels detected in cell lysates from the nonaggressive melanoma cell line WM1552C (Figure 1C). Since lower PS-1 expression could translate into increased Wnt activity, we also analyzed the lysates from the aggressive melanoma cells for P-β-catenin and found that Sk-Mel28, A375, and C8161 also expressed 54%, 71%, and 57%, respectively, of the P-β-catenin level detected in lysates from WM1552C (Figure 1D). These results suggest that the lower PS-1 levels detected in the aggressive melanoma cells than in the nonaggressive melanoma cells could explain the relatively higher Wnt signaling activity in the aggressive compared with the nonaggressive melanoma cells. 2.2. PS-1 Expression Was Significantly Lower in Advanced-Stage Than in Early-Stage Melanoma Our in vitro data showing decreased PS-1 expression in melanoma cells as they acquired more aggressive traits suggested that low PS-1 levels could represent a marker for melanoma aggressiveness in vivo. To investigate whether PS-1 expression in melanoma could have clinical significance, IHC staining was performed to assess PS-1 expression in tissue samples from human melanoma at various clinical stages and in normal skin (Supplementary Table S2), used as reference because it is known to ubiquitously express PS-1 [34] (Figure 2A). The mean IHC staining intensity results were grouped into normal skin, early-stage melanoma (Stages I and II), and advanced-stage melanoma (Stages III and IV) (Figure 2B). These data showed that the mean staining intensity for PS-1 was significantly lower in advanced-stage melanoma (0.77 +/− 0.17, N = 20) than in normal skin (2.1 +/− 0.16, N = 10) (p < 0.01) or early-stage melanoma (1.52 +/− 0.21, N = 35) (p < 0.02). There was no significant difference in mean staining intensity for PS-1 between early-stage melanoma and normal skin. These results suggest that low PS-1 expression could represent a biomarker for increased aggressiveness in melanoma. 2.3. Inhibition of γ-Secretase in Aggressive Melanoma Cells Led to Increased PS-1 Expression and Decreased Wnt Activity Gamma-secretase inhibition has been shown to increase PS-1 expression [13]. Therefore, to determine whether PS-1 expression could be increased in aggressive melanoma cells, we treated these cells with the GSI DAPT. Western blot analysis of lysates from C8161, A375, and Sk-Mel28 treated for 72 h with 15 μM or 30 μM DAPT showed a significant increase in PS-1 expression compared with vehicle-treated melanoma cells used as control (fold increase: C8161, 15 μM = 2.0 +/− 0.18, 30 μM = 1.8 +/− 0.26; A375, 15 μM = 1.9 +/− 0.02, 30 μM = 1.7 +/− 0.07; Sk-Mel28, 15 μM = 1.6 +/− 0.01, 30 μM = 2.6 +/− 0.2) (p < 0.05) (Figure 3A). Since PS-1 is known to downregulate Wnt/β-catenin signaling by increasing P-β-catenin levels [16,17,31], we analyzed lysates from the DAPT-treated cells for P-β-catenin expression. Results from WB analysis showed significant increases in P-β-catenin expression in C8161 (1.5 +/− 0.02-fold increase), A375 (1.9 +/− 0.05-fold increase), and Sk-Mel28 (1.7 +/− 0.1-fold increase) treated for 72 h with 15 μM DAPT, compared with vehicle-treated control (p < 0.05) (Figure 3B). These results demonstrated that increasing expression of PS-1 in aggressive melanoma cells with DAPT treatment could reduce Wnt signaling by enhancing P-β-catenin production. 2.4. Increased PS-1 Expression in DAPT-Treated Aggressive Melanoma Cells Was Associated with Reduced Cell Migration Wnt signaling has been shown to be involved in the migration and spread of aggressive melanoma [27]. Therefore, cell migration assays were performed to determine if the PS-1-associated decrease in Wnt activity in the DAPT-treated aggressive melanoma cells can lead to a functional effect. Our results showed that DAPT treatment of C8161, A375, and Sk-Mel28, in addition to the increased expressions of PS-1 and P-β-catenin shown above, also led to a significant reduction in cell migration compared with vehicle-treated control melanoma cells (C8161, −36% of control; A375, −46% of control; Sk-Mel28, −35% of control) (p < 0.05) (Figure 4A,B). Moreover, results from MTT cytotoxicity assay showed no significant difference in toxicity between the 15 μM DAPT treatment and control (Supplementary Figure S1). Thus, these findings suggest that the impaired migration of aggressive melanoma cells treated with the 15 μM DAPT was likely the result of a treatment-induced increase in PS-1, with a subsequent increase P-β-catenin levels leading to reduced Wnt signaling. 3. Discussion Presenilins are multipass transmembrane proteins that include the highly homologous PS-1 and PS-2, which play a role in γ-secretase activity and other cell functions [11,12,13,15]. The main difference between PS-1 and PS-2 is that PS-1 is mostly expressed at the cell membrane level while PS-2 is mostly found within the cell and is associated with endosomes and lysosomes. Presenilins were first identified in screening for mutations in patients with familial Alzheimer [12,35]. In fact, it was found that aberrant PS-1 activity was associated with incomplete degradation of amyloid β-peptide, a known contributing factor to the onset of AD [12,13,36]. The interesting finding of lower incidence of certain cancers in patients with AD [9,10], in addition to the PS-1 regulatory function of Wnt signaling, has prompted studies to investigate the role of PS-1 in cancer biology. In some studies, PS-1 was shown to enhance carcinogenesis [37], while other studies, such as in nonmelanoma skin cancer, PS-1 functioned as a tumor suppressor [31]. Thus, the precise role of PS-1 in cancer remains unclear. The main purpose of this study was to investigate the potential biologic role for PS-1 in melanoma. We recently showed that nonaggressive melanoma cells exposed to normal skin cells increased Wnt signaling and became more aggressive [33]. In this study, we also found that nonaggressive melanoma cells expressed lower PS-1 levels when exposed to normal skin cells compared with control. These results suggest that the increased Wnt signaling and aggressiveness we previously reported for these exposed nonaggressive melanoma cells could be the result of decreased PS-1 expression. In fact, screening cell lysates from several aggressive human melanoma cell lines revealed lower PS-1 expression and increased Wnt signaling, as demonstrated by the reduced P-β-catenin levels compared with nonaggressive melanoma cells. These findings were consistent with previous reports showing the association between loss of PS-1 and increased Wnt signaling and tumorigenesis [31]. To investigate whether PS-1 expression correlated with melanoma aggressiveness in vivo, we performed IHC on human melanoma tissue samples at different clinical stages and found that PS-1 expression was significantly lower in advanced-stage than in early-stage melanoma, demonstrating for the first time that PS-1 could be used as a marker of aggressive melanoma. Moreover, since AD has been associated with aberrant PS-1 function and/or expression [12], our finding that PS-1 levels were lower in patients with certain types of melanoma also provides a possible explanation as to why the incidence of AD is lower in patients with melanoma [9,10]. Given the role of Wnt during tumor progression, it remains an attractive target for anticancer therapy. Thus, preventing Wnt activity in early-stage melanoma could reduce the chances of metastatic spread. For this reason, we explored whether, by increasing PS-1 expression, we could reduce melanoma aggressiveness. DAPT is a GSI previously shown to increase PS-1 expression in treated cells [13]. We reported that aggressive melanoma cells treated for 72 h with 15 μM or 30 μM DAPT had increased PS-1 and P-β-catenin levels. Interestingly, although there was a trend towards lower total β-catenin levels in the treated melanoma cells, this was not significant (data not shown). This suggests that additional time may have been required for our treated aggressive melanoma cells to continue with ubiquitination and proteasomal degradation of the P-β-catenin, which would have resulted in further reduction in total β-catenin. Nevertheless, since only active, nonphosphorylated/nonubiquitinated β-catenin can translocate to the nucleus to induce Wnt-dependent gene transcription [38], the increased P-β-catenin Ser33/37/Thr41 expression observed in our DAPT-treated aggressive melanoma cells could, in itself, be sufficient to negatively affect Wnt-associated functions. In fact, treatment of aggressive melanoma cells with 15 μM DAPT significantly reduced cell migration, a well-known Wnt-regulated activity [16,26], compared with control. Studies investigating GSI to treat cancers, including melanoma have shown conflicting results. For example, while GSI treatment led to decreased self-renewal and stemness of melanoma cells, long-term treatment facilitated tumor growth, especially in patients with advanced metastatic disease [39]. One explanation as to the inefficacy of certain GSIs in cancer is the negative effects that GSIs have on host antitumor immune activity [40,41]. Another possible explanation of the conflicting data on GSI effects in cancer is the fact that different GSIs have varying pharmacologic and functional profiles depending on the concentrations used [42]. In fact, most clinical trials involve administering relatively high doses of GSI to treat metastatic disease. Some of these doses reach plasma levels that are several orders of magnitude greater than the concentrations used in our study to increase PS-1 expression [43,44]. Thus, treatment of early-stage melanoma with GSIs for enhancing PS-1 expression may require lower doses or shorter treatment regimens, thereby reducing the chances of undesired effects that are associated with longer-term or higher-dose treatment regimes. Our results showed that treatment of aggressive melanoma cells with just 15 μM DAPT could increase PS-1 levels and decrease Wnt signaling and cell migration, suggesting that metastatic potential in melanoma could be reduced by increasing PS-1 expression. Thus, future research aimed at developing treatment strategies to enhance PS-1 expression in aggressive melanoma may reduce melanoma progression. In summary, our results demonstrated that decreased PS-1 expression was associated with melanoma aggressiveness both in vitro and in human samples. We showed that in aggressive melanoma cells, Wnt signaling activity may increase because of low PS-1 expression. Moreover, we demonstrated that by enhancing PS-1 expression in aggressive melanoma cells, Wnt signaling activity was reduced, and a less migratory and more static phenotype was acquired. Ultimately, these results confirm the importance PS-1in regulating Wnt signaling and suggest a novel role for PS-1 as a biomarker for aggressiveness with therapeutic potential in melanoma. 4. Materials and Methods 4.1. Cell Cultures The following cell lines were used (Supplementary Table S1): poorly aggressive melanoma cell lines WM1552C (Rockland Immunochemicals, Limerick, PA, USA) and UACC1273 (a generous gift from Dr. Richard Seftor, University of West Virginia, WV, USA) and aggressive melanoma cell lines C8161 (Dr. Richard Seftor, University of West Virginia), A375 (CRL-1619, ATCC, Manassas, VA, USA), and Sk-Mel28 (HTB-72, ATCC, USA). All cells were maintained in RPMI1640 media (GenClone, San Diego, CA, USA) except for Sk-Mel28 (EMEM, ATCC) and supplemented with 5% FBS (Seradigm, Batavia, IL, USA). Epidermal melanocytes (PCS-200-013, ATCC), epidermal keratinocytes (PCS-200-010, ATCC), and dermal fibroblasts (PCS-201-012, ATCC, USA) were grown according to ATCC specifications. All cell lines were incubated at 37 °C and 5% CO2. 4.2. Serial Coculture Nonaggressive melanoma cells were exposed in sequence first to normal human epidermal keratinocytes and then to normal dermal fibroblasts as previously described [33]. Briefly, WM1552C or UACC1273 were seeded into transwells (Corning, Corning, NY, USA) containing inserts with porous membranes that allowed for molecular crosstalk between melanoma and normal cells without the cells coming into physical contact. These inserts were then placed in 6-well plates containing the normal epidermal keratinocytes or dermal fibroblasts. The nonaggressive melanoma cells were allowed to grow in sequence first for 24 h with the normal keratinocytes and then for 24 h with the normal dermal fibroblasts. After this coculture sequence, the melanoma cells were harvested for Western blot (WB) protein analysis. 4.3. Western Blotting WB analysis was performed as previously described [45]. Briefly, lysates from melanoma cells exposed to normal skin cells or from treated melanoma cells were obtained using standard RIPA buffer (Pierce, Waltham, MA, USA) containing protease and phosphatase inhibitors (Pierce). SDS–PAGE electrophoresis was used to separate 30 µg of protein per sample, which was then transferred to PVDF membranes (Millipore, Burlington, MA, USA). Afterwards, membranes were washed 3 times with TBST (tris-buffered saline with 0.1% Tween 20) and then blocked with 5% nonfat dry milk or 5% bovine serum albumin for 1 h at room temperature (RT). Membranes were then incubated with adequate dilutions of primary antibody in blocking buffer overnight at 4 °C. The listed antibodies/dilutions were used: goat anti-presenilin/1:400 (AF166, R&D Systems, Minneapolis, MN, USA); rabbit anti-P-β-catenin (Ser33/37/Thr41)/1:1000 (9561, Cell Signaling, Danvers, MA, USA); mouse anti-β-catenin/1:1000 (NBP1-54467, Novus Biologicals, Centennial, CO, USA); and rabbit anti-α-tubulin/1:5000 (2144, Cell Signaling). After incubation, membranes were washed and then incubated for 1 h with adequate dilutions of conjugated secondary antibodies: antimouse 1:5000 (NA931, GE Amersham, Marlborough, MA, USA); antirabbit 1:5000 (NA934, GE Amersham); and antigoat 1:5000 (HADF109, R&D Systems). After washing, membranes were incubated in ECL (GE Amersham) for 5 min and images captured using Bio-Rad Universal Chemidoc system. 4.4. Drug Treatment In a recent study reporting the effects of g-secretase inhibition in melanoma cells, the authors showed that treatment of cells with DAPT had negligible toxic effects at concentrations ranging from 5 to 60 μM [39]. Based on these results, for this study, it was decided to treat our aggressive melanoma cells with 15 μM and 30 μM DAPT to determine whether these concentrations could have effects on PS-1 expression. Thus, C8161, Sk-Mel28, and UACC1273 were treated were washed once in PBS and then treated with a final concentration of 15 μM or 30 μM DAPT (A8200, Apexbio, Houston, TX, USA) or vehicle control for 72 h. A standard 4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT) cell viability assay was performed as previously described [45] to determine whether the concentrations of DAPT used resulted in cellular toxicity. Lysates were also obtained from the treated cells to perform WB for the detection of PS-1 and P-β-catenin, as described above. 4.5. Immunohistochemistry Immunohistochemistry (IHC) staining was performed as previously described [45] to assess PS-1 expression in commercially available paraffin-embedded tissue microarray containing normal human skin and melanoma tissue samples at different clinical stages (Me1002b, USBiomax, Rockville, MD, USA). Briefly, after hydration, antigen retrieval, and blocking of endogenous peroxidases and nonspecific binding sites, slides were incubated with primary rabbit anti-PS-1 antibody (1:100, LS-C800364-100, LSBio, Seattle, WA, USA) for 1 h. Then, slides were washed and incubated with secondary antirabbit antibody (PK-4001, Vector Laboratories, Burlingame, CA, USA) for 30 min, washed and incubated with avidin–biotin complex reagent (PK-4001, Vector Laboratories, USA) for 30 min, and washed again and treated with DAB substrate (SK-4105, Vector Laboratories, Burlingame, CA, USA) for stain development. Slides were then washed with distilled water, counterstained with hematoxylin, washed, and dehydrated in a graded ethanol series followed by Histo-Clear. Slides were finally mounted with Aqua-Poly mounting media (Polysciences, Warrington, PA, USA) and observed under light microscopy. To determine appropriate sample size for our IHC study, we assumed 100,000 melanoma patients in the United States [46] and a confidence level of 95% for a difference in PS-1 expression of at least 1/3 (33%). These assumptions indicated that at least 9 samples per group should be analyzed. Since the PS-1 antibody used was specific for mouse and human PS-1, tissue sections from archival paraffin-embedded mouse brain (generous gift from Dr. Maria Traka, Midwestern University, Downers Grove, IL, USA) were used as positive control for IHC PS-1 expression. 4.6. Migration Assay Migration assay was performed as previously described [45]. Briefly, cells were seeded into 3.0 μm pore, 24-well transwell inserts (Falcon, Corning, NY, USA) at 150,000 cells/transwell insert and incubated overnight at 37 °C and 5% CO2. Transwell inserts were then cleansed of nonmigrated residue with moist cotton swabs and then further washed in PBS. Transwell inserts were then transferred to wells with crystal violet solution and incubated for 10 min at RT. To extract the stain, stained transwells were then washed with PBS and incubated in wells with 10% acetic acid solution for 10 min. Finally, stain from each sample was transferred to a 96-well plate for OD reading at 590 nm. C8161, A375, and Sk-Mel28 cells were treated with DAPT or vehicle for 72 h prior to the actual migration assay. To acquire images of cell migration, transwell inserts were washed as stated above, and the cells were fixed in methanol for 20 min before staining with crystal violet for 30 s. Next, transwell inserts were washed with PBS. This was followed by cutting of the growth surface (bottom), which was finally mounted on a slide with Cytoseal-60 (Thermo Scientific, Waltham, MA, USA) and observed under light microscopy. A standard 4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT) assay was performed as previously described [45] to determine whether the concentration of DAPT used for the migration assay could result in confounding cytotoxicity. 4.7. Statistical Analysis GraphPad statistical software was used to perform t-tests to compare the mean values (+/− standard error of the mean (SEM)) calculated from a minimum of triplicate results from at least two independent experiments between treated and untreated cells or between cocultured and reference control melanoma cells. GraphPad statistical software was also used to perform t-tests to compare the mean intensity IHC staining results (+/−standard deviation of the mean (SD)) among normal skin and early- and advanced-stage melanoma samples. Results showing p values of less than 0.05 (p < 0.05) was considered statistically significant. Acknowledgments The authors thank the Midwestern University (MWU) Research Core Facility for technological support. This study was supported by the OneHealth Student Research Award to J.S., the MWU Kenneth Suarez Student Research Award to M.G., and MWU research startup funds to L.S. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23094904/s1. Click here for additional data file. Author Contributions Conceptualization, L.S.; methodology, L.S., G.U., J.S., M.G. and L.A.D.; validation, G.U., J.S. and M.G.; formal analysis, L.S., G.U., J.S., M.G. and L.A.D.; investigation, L.S., G.U., J.S., M.G. and L.A.D.; resources, L.S.; data curation, L.S. and G.U.; writing—original draft preparation, L.S. and J.S.; writing—review and editing, L.S., J.S., G.U., M.G. and L.A.D. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Data is contained within the article or supplementary material. The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Western blot analysis of PS-1 and P-β-catenin in nonaggressive and aggressive melanoma cells. Western blot results with corresponding densitometric analysis of WB bands showed decreased PS-1 protein expression compared with control cells (reference) in two separate nonaggressive melanoma cell lines, WM1552C (A) and UACC1273 (B), after exposure to normal skin keratinocytes (keratoCC) and normal dermal fibroblasts (fibroCC) (* p < 0.05). (C) Western blot results with corresponding densitometric analysis of WB bands of cell lysates from aggressive melanoma cells (Sk-Mel28, A375, and C8161) showed lower PS-1 levels than results from nonaggressive melanoma cells (WM1552C). (D) Western blot results with corresponding densitometric analysis of WB bands also showed increased Wnt signaling, as demonstrated by the decreased levels of P-β-catenin (P-β-cat), in the aggressive versus nonaggressive melanoma cells analyzed. Figure 2 Immunohistochemistry for PS-1 in human melanoma tissue samples. (A) Representative IHC results showed poor staining for PS-1 in a late-stage melanoma compared with early-stage and normal skin tissue samples. Normal human skin and normal mouse brain tissue sections were used as positive control for PS-1 expression. IHC staining results obtained by omitting the primary antibody (no antibody control) or using an irrelevant isotype immunoglobulin (Ig control) represented negative controls. (B) Box plots show distribution of the IHC staining intensities for the cases of normal skin, early-stage melanoma (Stage I and II), and late-stage melanoma (Stage III and IV). These results showed that staining intensity for PS-1 was significantly lower in late-stage melanoma than in normal skin (* p < 0.01) and early-stage melanoma (** p < 0.02). Figure 3 Effects of GSI treatment on PS-1 and P-β-catenin levels in aggressive melanoma cells. (A) Western blot results and relative densitometric analysis of bands showed that PS-1 expression increased in the aggressive human melanoma cell lines C8161, A375, and Sk-Mel28 when treated for 72 h with 15 μM or 30 μM GSI (DAPT) compared with vehicle-treated cells (cntl). (B) Western blot results and relative densitometric analysis of bands also showed that treatment of C8161, A375, and Sk-Mel28 for 72 h with 15 μM DAPT significantly increased levels of P-β-catenin (P-β-cat) compared with vehicle-treated cells (cntl) (* p < 0.05). Figure 4 Cell migration assay in aggressive human melanoma cells treated with DAPT. (A) Microphotographic images from direct microscopic observation showed low staining intensity for reduced migration of C8161, A375, and Sk-Mel28 aggressive melanoma cells after 72 h treatment with 15 μM DAPT compared with vehicle-treated cells used as control (cntl). (B) Densitometric analysis of stain extracted from the migrated cells demonstrated the significant reduction in migration in the aggressive human melanoma cells lines after treatment for 72 h with 15 mM DAPT compared with control (* p < 0.05). Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Eddy K. Shah R. Chen S. Decoding Melanoma Development and Progression: Identification of Therapeutic Vulnerabilities Front. Oncol. 2020 10 626129 10.3389/fonc.2020.626129 33614507 2. Liu Y. Sheikh M.S. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092808 molecules-27-02808 Article One-Pot Synthesis of (E)-2-(3-Oxoindolin-2-ylidene)-2-arylacetonitriles https://orcid.org/0000-0002-7125-9066 Aksenov Nicolai A. 1* Aksenov Alexander V. 1 https://orcid.org/0000-0002-0911-4093 Kurenkov Igor A. 1 Kirillov Nikita K. 1 Aksenov Dmitrii A. 1 Arutiunov Nikolai A. 1 Aksenova Daria S. 1 https://orcid.org/0000-0002-1668-9311 Rubin Michael 12* Puglisi Alessandra Academic Editor 1 Department of Chemistry, North Caucasus Federal University, 1a Pushkin St., Stavropol 355017, Russia; aaksenov@ncfu.ru (A.V.A.); kurenkman@icloud.com (I.A.K.); lyncheron@gmail.com (N.K.K.); daksenov@ncfu.ru (D.A.A.); naarutiunov@ncfu.ru (N.A.A.); dasha.severilo@gmail.com (D.S.A.) 2 Department of Chemistry, University of Kansas, 1567 Irving Hill Road, Lawrence, KS 66045, USA * Correspondence: naksenov@ncfu.ru (N.A.A.); mrubin@ku.edu (M.R.) 28 4 2022 5 2022 27 9 280810 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). A highly efficient and expeditious one-pot approach towards 2-(3-oxoindolin-2-yl)acetonitriles was designed, which involves a base-assisted aldol reaction of ortho-nitroacetophenones, followed by hydrocyanation, triggering an unusual reductive cyclization reaction. nitroalkanes brønsted acid catalysis indoles rearrangements cascade transformations Russian Science Foundation21-73-10029 The study was supported by the Russian Science Foundation (grant № 21-73-10029, https://rscf.ru/project/21-73-10029/ accessed on 27 July 2021). ==== Body pmc1. Introduction The derivatives of 2-Alkylideneindolin-3-one play important role in modern medicinal chemistry. The bis-indole indirubin known as “Tyrian purple” dye has been used for many years as an active component of a traditional Chinese herbal medicine [1,2,3]. Lately, numerous synthetic analogs of this compound were reported to show potent and highly selective pharmacological inhibition of glycogen synthase kinases and cycline-dependent kinases [4,5,6]. Related compounds possessing a single 3-oxoindoline subunit (or two remotely positioned subunits) also were isolated from natural sources and are also known to exhibit a wide spectrum of important biological properties [7,8,9,10,11,12,13]. Normally, the preparation of these structures relies heavily on the chemistry of isatins, which greatly limits the diversity of accessible substitution patterns. Alternative methods have also been developed based on the aldol condensation of 3H-indol-3-ones with carbonyl compounds [14,15,16,17], the transition metal-catalyzed carbonylative cross-coupling of ortho-iodoanilines to alkynes [18,19,20,21], or the cascade cyclizations of anilines with α-ketoesters [7,22]. We recently reported an original, facile, and highly efficient method for the preparation of 2-(3-oxoindolin-2-ylidene) acetonitriles 2 from ortho-nitrochalcones 1, operating as a triggered Michael addition of the cyanide anion to the chalcone followed by a cascade cyclization mechanistically related to the Baeyer–Drewson reaction (Scheme 1) [23]. Herein, we report on the development of a more streamlined synthetic approach which allows for the combining of this transformation with the preparation of the chalcone precursor 1 from aldehydes 4 and ortho-nitroacetophenones 3 in a single one-pot operation. 2. Results and Discussion Since our newly developed method for preparation of 2-(3-oxoindolin-2-ylidene) acetonitriles 2 relies heavily on the availability of the corresponding chalchone precursors 1, we wondered about possibility of generating them in situ via an aldol condensation reaction. It was initially planned that this step could be carried out in the presence of catalytic amounts of alkali, which later would be neutralized with acetic acid to trigger the hydrocyanation step. However, much more attractive was the idea of employing potassium cyanide as a base to catalyze the aldol condensation step. This unusual approach to the aldol reaction was precedented in a single report by Migita et al. [24]. To test this idea, we subjected 2-nitroacetophenone (2a) to the reaction with p-anisaldehyde (4a) in the presence of KCN in methanol (2 mL). Water additive (130 μL) was used, as it was previously demonstrated it is crucial for successful cyclization [23]. The reaction mixture was refluxed for 1 h, then treated with acetic acid (150 μL) and refluxed for an additional 30 min to afford 3-oxoindoline 2aa in 64% yield (Table 1, entry 1). This reaction was accompanied by the formation of a large number of unidentified side products, as evidenced by TLC analysis. In attempt to improve this situation, we decided to carry out the reaction under conditions ensuring a faster kinetic rate of the desired transformation. First, cyanide loading was increased (with a proportional increase of water and acetic acid concentrations). The reaction proceeded faster, but the yield of the target product was not improved under these conditions (entry 2). Then, the concentration effect was evaluated, with the knowledge that this effect should be favorable for bimolecular reactions. Indeed, when the concentration of starting materials was increased twice, 2aa was formed cleanly in 95% yield (entry 3). Finally, it was found that decreasing the temperature had a detrimental effect on the reaction performance. The reaction proceeded very sluggishly, affording 2aa in 58% yield along with several unidentified side products (entry 4). Without water additive, this reaction affords lower yields (entry 5), and this is also the case when other acids are employed instead of acetic (entries 6, 7). With the optimized conditions in hand, we performed these transformations in a preparative scale (up to 2.00 mmol) and managed to obtain comparably high isolated yields of 2aa (85%) (Table 1, entry 3, Scheme 2). The reaction demonstrated good tolerance and compatibility with a variety of substituents, including alkoxyarenes, halogenated arenes, and cyclic acetals (Scheme 2). The formation of (E)-2-(3-oxoindolin-2-ylidene)acetonitrile moiety in this reaction was unambiguously confirmed by single crystal X-ray diffraction of compound 2al (CCDC #2157035, Figure 1). It was possible to engage heterocyclic aldehydes into the featured transformation. Thus, the reaction of ortho-nitroacetophenone (3a) with thiophene-2-carbaldehyde (4o) afforded thienyl-substituted compound 2ao in good yield (Scheme 3). On the other hand, the reaction involving benzo[b]thiophene-3-carbaldehyde (4p) led to the formation of rearranged polycyclic product 2ap (Scheme 3). Further investigation of this unusual transformation is currently under way in our laboratories. The mechanistic rationale for the featured transformation is shown in Scheme 4. It is believed that potassium cyanide can serve as a base to induce the enolization of ortho-nitroacetophenone 3 to provide enolate form 5, which can be engaged into an aldol reaction with aldehyde 4 affording chalchone 1 (Scheme 4). The subsequent conjugate addition of cyanide to chalchone would provide 4-oxo-4-arylbutanenitrile in enolate form 6. To demonstrate the possibility of carrying out this sequence of reactions, we tested the reactivity between acetophenone (11) and benzaldehyde (4b) in the presence of excess potassium cyanide under typical reaction conditions. Expectedly, 4-oxo-4-phenylbutanenitrile 12 was formed smoothly, albeit in marginal yield (Scheme 5). Once formed, enolate species 6 would experience intramolecular nucleophilic attack involving the ortho-nitro group to afford cyclic keto-azinate 7, which should exist in tautomeric equilibrium with azinic acid-enolate form 8 (Scheme 4). The elimination of the hydroxyl group from this species would lead to the formation of imine N-oxide intermediate 9, which should tautomerize into cyclic hydroxylamine 10. Subsequent reduction (most likely involving methanol as a reducing agent) [25] would afford final 2-(3-oxoindolin-2-yl)acetonitriles 2 (Scheme 4) [23]. It should be pointed out that carrying out a reaction between ortho-nitroacetophenone 3a and benzaldehyde 4b followed by quenching with phenacyl bromide (serving as a precursor of HBr, slowly released over an extended period) allowed for the isolation of compound 10ab in moderate yield (Scheme 6). The same compound was detected by mass spectroscopy in the reaction mixture carried out under standard conditions. It proved to be stable upon heating in DMSO at 100 °C for 2 hr. However, treatment of this sample with water or formic acid caused the slow consumption of 10ab (mass 285 [M + Na]) and accumulation of product 2ab (mass 269 [M + Na]). In the presence of methanol and acetic acid, this process proceeded notably faster. In preparative scale, this reaction afforded a nearly quantitative conversion into 2ab, identical to the sample obtained in one-pot fashion (Scheme 6). 3. Conclusions An improved protocol for the preparation of 2-(3-oxoindolin-2-yl)acetonitriles 2 via a cyanide-mediated cascade reaction of ortho-nitroacetophenones (3) with aromatic aldehydes (4) was developed. This transformation involves an initial aldol condensation followed by a Michael-type conjugated addition of cyanide anion to the intermediate chalcone, which triggers an unusual cyclization mechanistically related to the Baeyer–Drewson reaction. This methodology was employed to synthesize a small, focused library of target molecules. It should be mentioned that aliphatic aldehydes do not participate in aldol condensation under these reaction conditions and cannot be used as precursors for preparation of alkyl-substituted analogs. An investigation into the biological activity of these new €-2-(3-oxoindolin-2-ylidene)-2-arylacetonitriles is currently under way in our laboratories. 4. Experimental Part General NMR spectra (1H and 13C) were measured in solutions of CDCl3 or DMSO-d6 on a Bruker AVANCE-III HD instrument (at 400.40 or 100.61 MHz, respectively). HRMS spectra were measured in MeCN solutions on Bruker maXis impact (electrospray ionization, employing HCO2Na–HCO2H for calibration). See Supplementary Materials for NMR and HRMS spectral charts and X-Ray crystallography data. IR spectra was measured on an FT-IR spectrometer Shimadzu IRAffinity-1S equipped with an ATR sampling module. Reaction progress, purity of isolated compounds, and Rf values were assessed by TLC on Silufol UV-254 plates. Column chromatography was performed on silica gel (32–63 μm, 60 Å pore size). Melting points were measured on Stuart SMP30 apparatus. All reagents and solvents were purchased from commercial vendors and used as received. The reactions involving KCN are accompanied by the formation of highly toxic fumes of HCN. A well-ventilated fume hood must be used. Preparation of (E)-2-aryl-2-(3-oxoindolin-2-ylidene)acetonitriles (2aa–2cb) by reaction of benzaldehydes (4a–n) and 2′-nitroacetophenones (3a–c) (General procedure): A 5-mL round bottom flask equipped with magnetic stirring bar was charged with 2′-nitroacetophenone (1 mmol), benzaldehyde (1 mmol), KCN (2 mmol, 130 mg), H2O (130 mg), and MeOH (1 mL) and refluxed for 30 min (TLC control). After the consumption of the starting material, the reaction mixture was cooled to room temperature and AcOH (2.5 mmol, 150 mg, 143 µL) (exothermic reaction) was added and reflux was continued to another 30 min. Then, the reaction mixture was diluted with 100 mL of EtOAc and washed twice with 30 mL of concentrated NaHCO3 solution. The organic layer was concentrated and purified by column chromatography (gradient: EtOAc:Hex 1:4–1:1) or by recrystallization from EtOH. (E)-2-(4-methoxyphenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2aa): Red solid, mp (EtOH) 247.5–249.4 °C (Literature data: [23] mp 250.1–251.1 °C), Rf 0.23 (EtOAc/Hex, 1:2). Yield: 240 mg (0.87 mmol, 87%). 1H NMR (400 MHz, DMSO-d6) δ 10.42 (s, 1H), 7.61 (dt, J = 16.6, 7.9 Hz, 4H), 7.11 (dd, J = 15.3, 8.2 Hz, 3H), 7.01 (t, J = 7.5 Hz, 1H), 3.84 (s, 3H). (E)-2-(3-oxoindolin-2-ylidene)-2-phenylacetonitrile (2ab): Orange solid, mp 235.0–236.3 °C (Literature data: [23] mp 233.1–235.9 °C), Rf 0.32 (EtOAc/Hex, 1:2). Yield: 162 mg (0.66 mmol, 66%). 1H NMR (400 MHz, DMSO-d6) δ 10.52 (s, 1H), 7.72–7.52 (m, 6H), 7.48 (t, J = 7.4 Hz, 1H), 7.09 (d, J = 8.1 Hz, 1H), 7.03 (t, J = 7.5 Hz, 1H). (E)-2-(3-oxoindolin-2-ylidene)-2-(p-tolyl)acetonitrile (2ac): Orange solid, mp (MeOH) 241.2–242.6 °C (literature data: [23] mp 236– 240 °C), Rf 0.46 (EtOAc/Hex, 1:2). Yield: 187 mg (0.72 mmol, 72%). 1H NMR (400 MHz, DMSO-d6) δ 10.45 (s, 1H), 7.65 (d, J = 7.6 Hz, 1H), 7.56 (dd, J = 16.0, 7.5 Hz, 3H), 7.38 (d, J = 8.0 Hz, 2H), 7.09 (d, J = 8.1 Hz, 1H), 7.02 (t, J = 7.4 Hz, 1H), 2.38 (s, 3H). (E)-2-(4-ethylphenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2ad): Red solid, mp (EtOH) 214.4–215.1 °C (literature data: [23] mp 223.2–225.7 °C), Rf 0.56 (EtOAc/Hex, 1:2). Yield: 145 mg (0.53 mmol, 53%). 1H NMR (400 MHz, DMSO-d6) δ 10.47 (s, 1H), 7.65 (d, J = 9.8 Hz, 1H), 7.57 (d, J = 5.1 Hz, 3H), 7.42 (d, J = 8.3 Hz, 2H), 7.10 (d, J = 8.1 Hz, 1H), 7.03 (t, 1H), 2.67 (q, 2H), 1.22 (t, 3H). (E)-2-(4-isopropylphenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2ae): Red solid, mp (EtOH) 223.4–224.6 °C (literature data: [23] mp 223.0–226.6 °C), Rf 0.47 (EtOAc/Hex, 1:2). Yield: 127 mg (0.44 mmol, 44%). 1H NMR (400 MHz, DMSO-d6) δ 10.48 (s, 1H), 7.65 (d, J = 8.4 Hz, 1H), 7.62–7.53 (m, 3H), 7.45 (d, J = 8.3 Hz, 2H), 7.09 (d, J = 8.1 Hz, 1H), 7.02 (t, J = 7.5 Hz, 1H), 2.97 (hept, J = 7.0 Hz, 1H), 1.24 (d, J = 7.0 Hz, 6H). (E)-2-(4-(tert-butyl)phenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2af): Red solid, mp 241–243 °C, Rf 0.57 (EtOAc/Hex, 1:1). Yield: 269 mg (0.89 mmol, 89%). 1H NMR (400 MHz, DMSO-d6) δ 10.49 (s, 1H), 7.65 (d, J = 7.6 Hz, 1H), 7.59 (s, 5H), 7.10 (d, J = 8.0 Hz, 1H), 7.02 (t, J = 7.4 Hz, 1H), 1.33 (s, 9H). 13C NMR (101 MHz, DMSO-d6) δ 184.2, 152.5, 151.9, 142.2, 137.5, 129.3, 128.6 (2C), 126.3 (2C), 124.9, 121.4, 119.5, 118.0, 112.8, 89.1, 34.7, 31.0 (3C). IR, vmax/cm−1: 3328, 2966, 2206, 1709, 1586, 1470, 1329, 1278, 1222, 1099, 969. HRMS (ES TOF) calculated for (M + Na)+ C20H18N2NaO 325.1311, found 325.1309 (0.6 ppm). (E)-2-(3-oxoindolin-2-ylidene)-2-(o-tolyl)acetonitrile (2ag) Red crystals, mp (MeOH) 205.3–206.6 °C (literature data: [23] mp 201.8–203.5 °C), Rf 0.29 (EtOAc/Hex, 1:2). Yield: 140 mg (0.54 mmol, 54%). 1H NMR (400 MHz, DMSO-d6) δ 10.06 (s, 1H), 7.64 (d, J = 7.6 Hz, 1H), 7.55 (t, J = 7.6 Hz, 1H), 7.41 (dt, J = 7.6, 3.2 Hz, 3H), 7.35 (ddd, J = 7.9, 5.2, 2.7 Hz, 1H), 7.03–6.94 (m, 2H), 2.31 (s, 3H). (E)-2-(4-fluorophenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2ah): Orange solid, mp (EtOH) 279.3–281.4 °C (literature data: [23] mp 281.1–282.8 °C), Rf 0.54 (EtOAc/Hex, 1:2). Yield: 190 mg (0.72 mmol, 72%). 1H NMR (400 MHz, DMSO-d6) δ 10.50 (s, 1H), 7.74–7.63 (m, 3H), 7.58 (t, J = 7.7 Hz, 1H), 7.42 (t, J = 8.7 Hz, 2H), 7.08 (d, J = 8.1 Hz, 1H), 7.03 (t, J = 7.5 Hz, 1H). 19F NMR (376 MHz, DMSO-d6) δ -111.4. (E)-2-(4-chlorophenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2ai): Red solid, mp 286–288 °C (literature data: [23] mp 285.9–287.8 °C), Rf 0.22 (EtOAc/Hex, 1:2). Yield: 182 mg (0.65 mmol, 65%). 1H NMR (400 MHz, DMSO-d6) δ 10.55 (s, 1H), 7.68–7.62 (m, 5H), 7.61–7.56 (m, 1H), 7.08 (d, J = 8.0 Hz, 1H), 7.04 (t, J = 7.5 Hz, 1H). (E)-2-(4-bromophenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2aj): Red solid, mp (EtOH) 283.0–284.8 °C (literature data: [23] mp 278.8–282.5 °C), Rf 0.66 (EtOAc/Hex, 1:2). Yield: 187 mg (0.58 mmol, 58%). 1H NMR (400 MHz, DMSO-d6) δ 10.56 (s, 1H), 7.77 (d, J = 8.7 Hz, 2H), 7.65 (d, J = 7.2 Hz, 1H), 7.61–7.56 (m, 3H), 7.10–6.99 (m, 2H). (E)-2-(3-chlorophenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2ak): Orange solid, mp 270–271 °C (literature data: [23] mp 267–269 °C), Rf 0.57 (EtOAc/Hex, 1:2). Yield: 188 mg (0.67 mmol, 67%). 1H NMR (400 MHz, DMSO-d6) δ 10.63 (s, 1H), 7.66 (d, J = 7.4 Hz, 2H), 7.62–7.52 (m, 4H), 7.09 (d, J = 8.0 Hz, 1H), 7.04 (t, J = 7.5 Hz, 1H). (E)-2-(5-fluoro-2-methylphenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2al) Red crystals, mp (EtOH) 236.1–237.4 °C, Rf 0.22 (EtOAc/Hex, 1:2). Yield: 100 mg (0.36 mmol, 36%). 1H NMR (400 MHz, DMSO-d6) δ 10.17 (s, 1H), 7.65 (dd, J = 7.6, 1.2 Hz, 1H), 7.56 (ddd, J = 8.4, 7.3, 1.3 Hz, 1H), 7.44 (dd, J = 8.5, 5.9 Hz, 1H), 7.33–7.23 (m, 2H), 7.05–6.94 (m, 2H), 2.28 (s, 3H). 13C NMR (101 MHz, DMSO-d6) δ 184.1, 160.6 (d, J = 243.2 Hz), 152.5, 144.3, 137.8, 133.2 (d, J = 3.3 Hz), 132.7 (d, J = 8.1 Hz), 132.3 (d, J = 8.4 Hz), 125.1, 121.5, 119.6, 117.2, 116.9 (d, J = 22.2 Hz), 116.4 (d, J = 20.8 Hz), 112.4, 86.3, 18.5. 19F NMR (376 MHz, DMSO-d6) δ -116.2. IR, vmax/cm−1: 3372, 2206, 1713, 1608, 1482, 1463, 1333, 1224, 176, 1075, 1003, 858, 751. HRMS (ES TOF) calculated for (M + Na)+ C17H11FN2NaO 301.0745, found 301.0748 (0.8 ppm). (E)-2-(3-oxoindolin-2-ylidene)-2-(3,4,5-trimethoxyphenyl)acetonitrile (2am): Red solid, mp 216–218°C, Rf 0.29 (EtOAc/Hex, 1:1). Yield: 289 mg (0.86 mmol, 86%). 1H NMR (400 MHz, DMSO-d6) δ 10.49 (s, 1H), 7.65 (d, J = 7.6 Hz, 1H), 7.60–7.55 (m, 1H), 7.08 (d, J = 8.0 Hz, 1H), 7.02 (t, J = 7.4 Hz, 1H), 6.89 (s, 2H), 3.86 (s, 6H), 3.73 (s, 3H). 13C NMR (101 MHz, DMSO-d6) δ 184.1, 153.3 (2C), 152.5, 142.5, 138.1, 137.5, 127.4, 124.9, 121.4, 119.5, 117.9, 112.7, 106.4 (2C), 89.2, 60.1, 56.0 (2C). IR, vmax/cm−1: 3745, 3622, 3272, 2978, 2214, 1737, 1713, 1560, 1506, 1459, 1421, 1274, 1218, 1130, 993, 751. HRMS (ES TOF) calculated for (M + Na)+ C19H16N2NaO4 359.1002, found 359.1004 (−0.6 ppm). (E)-2-(benzo[d][1,3]dioxol-5-yl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2an): Red solid, mp (EtOH) 247.3–248.6 °C (literature data: [23] mp 247.9–248.7 °C), Rf 0.62 (EtOAc/Hex, 1:2). Yield: 200 mg (0.69 mmol, 69%). 1H NMR (400 MHz, DMSO-d6) δ 10.42 (s, 1H), 7.64 (d, J = 7.7 Hz, 1H), 7.60–7.51 (m, 1H), 7.18 (d, J = 1.7 Hz, 1H), 7.15–7.07 (m, 3H), 7.01 (t, J = 7.5 Hz, 1H), 6.14 (s, 2H). (Z)-2-(3-oxoindolin-2-ylidene)-2-(thiophen-2-yl)acetonitrile (2ao): Red solid, mp (EtOH) 229–230 °C, Rf 0.71 (EtOAc/Hex, 1:2). Yield: 159 mg (0.63 mmol, 63%). 1H NMR (400 MHz, DMSO-d6) δ 10.39 (s, 1H), 7.86 (d, J = 5.4 Hz, 1H), 7.65–7.52 (m, 3H), 7.29 (t, J = 4.7 Hz, 1H), 7.20 (d, J = 8.2 Hz, 1H), 7.02 (t, J = 7.5 Hz, 1H). 13C NMR (101 MHz, DMSO-d6) δ 184.0, 152.1, 139.9, 137.3, 135.2, 129.43, 129.40, 129.1, 124.8, 121.9, 119.7, 117.1, 113.3, 84.6. IR, vmax/cm−1 3320, 2214, 1705, 1685, 1624, 1584, 1552, 1528, 1480, 1466, 1421, 1391, 1363, 1299, 1254, 1198. HRMS (ES TOF) calculated for (M + Na)+ C14H8N2NaOS 275.0250, found 275.0250 (−0.1 ppm). 5b-Hydroxy-5b,10-dihydrobenzo [4’,5’]thieno [2’,3’:3,4]cyclopenta [1,2-b]indole-11-carbonitrile (2ap): Red solid, mp (EtOH) 218–219 °C, Rf 0.37 (EtOAc/Hex, 1:2). Yield: 139 mg (0.46 mmol, 46%). 1H NMR (400 MHz, DMSO-d6) δ 12.04 (s, 1H), 11.39 (s, 1H), 9.00–8.90 (m, 1H), 7.77 (d, J = 8.1 Hz, 1H), 7.70 (d, J = 8.4 Hz, 1H), 7.46–7.40 (m, 1H), 7.39–7.30 (m, 3H), 7.15 (t, J = 7.5 Hz, 1H).13C NMR (101 MHz, DMSO) δ 160.3, 159.5, 138.8, 137.4, 131.0, 130.7, 129.1, 128.4, 126.3, 126.2, 125.8, 125.4, 120.3, 117.7, 116.7, 116.0, 113.7, 113.4. IR, vmax/cm−1 3316, 3049, 2210, 1679, 1624, 1584, 1550, 1530, 1504, 1463, 1387, 1321. HRMS (ES TOF) calculated for (M + Na)+ C18H10N2NaO 325.0406, found 325.0404 (0.7 ppm). (E)-2-(5,6-dimethoxy-3-oxoindolin-2-ylidene)-2-(4-methoxyphenyl)acetonitrile (2ba): Red solid, mp (EtOH) 270.6–272.1 °C, Rf 0.2 (EtOAc/Hex, 3:2). Yield: 140 mg (0.42 mmol, 42%). 1H NMR (400 MHz, DMSO-d6) δ 10.05 (s, 1H), 7.57 (d, J = 8.9 Hz, 2H), 7.19–7.05 (m, 3H), 6.63 (s, 1H), 3.85 (d, J = 8.8 Hz, 6H), 3.75 (s, 3H). 13C NMR (101 MHz, DMSO-d6) δ 181.9, 159.7, 157.7, 150.1, 144.8, 143.0, 130.3 (2C), 124.2, 118.1, 114.8 (2C), 110.5, 105.7, 95.8, 89.0, 56.1, 56.0, 55.5. IR, vmax /cm−1: 3332, 2203, 1684, 1596, 1493, 1354, 1246, 1191, 1135, 1033. HRMS (ES TOF) calculated for (M + Na)+ C19H16N2NaO4 359.1002, found 359.0993 (2.7 ppm). (E)-2-(5,6-dimethoxy-3-oxoindolin-2-ylidene)-2-phenylacetonitrile (2bb): Purple solid, mp (EtOH) 204.9–206.6 °C (literature data: [23] mp 205.2–207.6 °C), Rf 0.47 (EtOAc/Hex, 1:1). Yield: 168 mg (0.55 mmol, 55%). 1H NMR (400 MHz, DMSO-d6) δ 10.15 (s, 1H), 7.64–7.60 (m, 2H), 7.56 (t, J = 7.7 Hz, 2H), 7.49–7.45 (m, 1H), 7.09 (s, 1H), 6.63 (s, 1H), 3.86 (s, 3H), 3.76 (s, 3H). (E)-2-(5,6-dimethoxy-3-oxoindolin-2-ylidene)-2-(p-tolyl)acetonitrile (2bc): Red solid, mp (EtOH) 281.6–282.7 °C, Rf 0.3 (EtOAc/Hex, 3:2). Yield: 148 mg (0.46 mmol, 46%). 1H NMR (400 MHz, DMSO-d6) δ 10.09 (s, 1H), 7.52 (d, J = 8.2 Hz, 2H), 7.36 (d, J = 8.3 Hz, 2H), 7.07 (s, 1H), 6.62 (s, 1H), 3.85 (s, 3H), 3.75 (s, 3H), 2.38 (s, 3H). 13C NMR (101 MHz, DMSO-d6) δ 181.9, 157.7, 150.2, 144.8, 143.5, 138.8, 129.9 (2C), 129.3, 128.7 (2C), 118.1, 110.4, 105.8, 95.8, 88.8, 56.1, 55.9, 20.9. IR, vmax /cm−1: 3273, 2207, 1688, 1598, 1485, 1360, 1202, 1169, 1129, 1081. HRMS (ES TOF) calculated for (M + Na)+ C19H16N2NaO3 343.1053, found 343.1046 (2.2 ppm). (E)-2-(5,6-dimethoxy-3-oxoindolin-2-ylidene)-2-(4-isopropylphenyl)acetonitrile (2be): Red solid, mp (EtOH) 201.4–202.2 °C, Rf 0.4 (EtOAc/Hex, 3:2). Yield: 181 mg (0.52 mmol, 52%). 1H NMR (400 MHz, DMSO-d6) δ 10.12 (s, 1H), 7.55 (d, J = 8.3 Hz, 2H), 7.43 (d, J = 8.4 Hz, 2H), 7.08 (s, 1H), 6.63 (s, 1H), 3.85 (s, 3H), 3.75 (s, 3H), 2.96 (hept, J = 6.9 Hz, 1H), 1.24 (d, J = 6.9 Hz, 6H). 13C NMR (101 MHz, DMSO-d6) δ 182.0, 157.7, 150.2, 149.6, 144.8, 143.6, 129.7, 128.8 (2C), 127.3 (2C), 118.1, 110.4, 105.8, 95.9, 88.7, 56.1, 56.0, 33.4, 23.7 (2C). IR, vmax /cm−1: 3293, 2211, 1682, 1598, 1491, 1358, 1324, 1202, 1167, 1135. HRMS (ES TOF) calculated for (M + Na)+ C21H20N2NaO3 371.1366, found 371.1360 (1.7 ppm). (E)-2-(5,6-dimethoxy-3-oxoindolin-2-ylidene)-2-(3,4,5-trimethoxyphenyl)acetonitrile (2bm): Purple solid, mp (EtOH) 182.1–184.0 °C, Rf 0.17 (EtOAc/Hex, 3:2). Yield: 99 mg (0.25 mmol, 25%). 1H NMR (400 MHz, DMSO-d6) δ 10.13 (s, 1H), 7.08 (s, 1H), 6.86 (s, 2H), 6.61 (s, 1H), 3.86 (s, 9H), 3.74 (d, J = 10.8 Hz, 6H). 13C NMR (101 MHz, DMSO-d6) δ 181.8, 157.8, 153.3 (2C), 150.2, 144.8, 143.9, 138.0, 127.5, 117.9, 110.4, 106.3 (2C), 105.8, 95.8, 88.8, 60.1, 56.1, 56.0 (3C). IR, vmax /cm−1: 3285, 2207, 1670, 1600, 1493, 1340, 1310, 1212, 1125, 996. HRMS (ES TOF) calculated for (M + Na)+ C21H20N2NaO6 419.1214, found 419.1208 (1.3 ppm). (E)-2-(benzo[d][1,3]dioxol-5-yl)-2-(5,6-dimethoxy-3-oxoindolin-2-ylidene)acetonitrile (2bn): Red solid, mp (EtOH) 240.7–242.7 °C, Rf 0.24 (EtOAc/Hex, 3:2). Yield: 140 mg (0.40 mmol, 40%). 1H NMR (400 MHz, DMSO-d6) δ 10.08 (s, 1H), 7.16 (s, 1H), 7.15–7.08 (m, 2H), 7.06 (s, 1H), 6.63 (s, 1H), 6.14 (s, 2H), 3.86 (s, 3H), 3.75 (s, 3H). 13C NMR (101 MHz, DMSO-d6) δ 181.9, 157.7, 150.2, 147.9 (2C), 144.8, 143.4, 125.8, 123.4, 118.1, 110.5, 109.1, 108.8, 105.8, 101.8, 95.8, 88.7, 56.1, 56.0. IR, vmax /cm−1: 3297, 2211, 1686, 1592, 1487, 1364, 1316, 1248, 1202, 1173, 1135. HRMS (ES TOF) calculated for (M + Na)+ C19H14N2NaO5 373.0795, found 373.0795 (2.8 ppm). (E)-2-(4-methoxyphenyl)-2-(8-oxo-2,3,6,8-tetrahydro-7H-[1,4]dioxino [2,3-f]indol-7-ylidene)acetonitrile (2ca): Red solid, mp (EtOH) 269.8–271.9 °C, Rf 0.32 (EtOAc/Hex, 1:1). Yield: 147 mg (0.44 mmol, 44%). 1H NMR (400 MHz, DMSO-d6) δ 10.05 (s, 1H), 7.62–7.51 (m, 2H), 7.14–7.09 (m, 2H), 7.08 (s, 1H), 6.49 (s, 1H), 4.34 (dd, J = 5.6, 2.7 Hz, 2H), 4.24–4.18 (m, 2H), 3.83 (s, 3H). 13C NMR (101 MHz, DMSO-d6) δ 182.3, 159.7, 152.0, 148.2, 142.8, 139.0, 130.2 (2C), 124.3, 118.1, 114.8 (2C), 112.6, 112.3, 100.4, 88.3, 65.2, 63.5, 55.5. IR, vmax/cm−1: 3344, 2914, 2211, 1685, 1642, 1586, 1470, 1310, 1242, 1170, 1138. HRMS (ES TOF) calculated for (M + Na)+ C19H14N2NaO4 357.0846, found 357.0837 (2.6 ppm). (E)-2-(8-oxo-2H-[1,4]dioxino [2,3-f]indol-7(3H,6H,8H)-ylidene)-2-phenylacetonitrile (2cb) Red crystals, mp (EtOH) 290.3–291.7 °C, Rf 0.23 (EtOAc/Hex, 1:2). Yield: 188 mg (0.62 mmol, 62%). 1H NMR (400 MHz, DMSO-d6) δ 10.15 (s, 1H), 7.62–7.53 (m, 4H), 7.46 (t, J = 7.1 Hz, 1H), 7.08 (s, 1H), 6.48 (s, 1H), 4.34 (dd, J = 5.6, 2.7 Hz, 2H), 4.22 (dd, J = 5.4, 2.7 Hz, 2H). 13C NMR (101 MHz, DMSO-d6) δ 182.4, 152.1, 148.2, 143.7, 139.1, 132.3, 129.36 (2C), 128.96, 128.73 (2C), 118.1, 112.47, 112.43, 100.5, 87.8, 65.2, 63.5. IR, vmax/cm−1: 3300, 2975, 2218, 1699, 1608, 1490, 1333, 1208, 1164, 1067, 930, 896. HRMS (ES TOF) calculated for (M + Na)+ C18H12N2NaO3 327.0735, found 327.0740 (1.5 ppm). (E)-2-(1-hydroxy-3-oxoindolin-2-ylidene)-2-phenylacetonitrile (10ab): A 5-mL round bottom flask equipped with a magnetic stirring bar was charged with 2′-nitroacetophenone (1 mmol, 165 mg), benzaldehyde (1 mmol, 106 mg), KCN (2 mmol, 130 mg), H2O (130 mg), and MeOH (1 mL) and refluxed for 30 min (TLC control). The reaction mixture was cooled to room temperature, and after that, MeOH (2 mL) and phenacyl bromide (1 mmol, 199 mg) were added and the reaction mixture was allowed to stand overnight. The precipitate that had formed was filtered off and washed with methanol to obtain (E)-2-(1-hydroxy-3-oxoindolin-2-ylidene)-2-phenylacetonitrile as a yellowish solid, mp (MeOH) 197.3–198.6 °C (decomposition), Rf 0.23 (EtOAc/Hex, 1:2). Yield: 123 mg (0.47 mmol, 47%). 1H NMR (400 MHz, DMSO-d6) δ 8.38 (d, J = 7.9 Hz, 2H), 7.27 (t, J = 7.7 Hz, 2H), 7.11 (t, J = 7.0 Hz, 2H), 6.99 (t, J = 7.3 Hz, 1H), 6.63 (d, J = 8.1 Hz, 1H), 6.39 (t, J = 7.2 Hz, 1H). 13C NMR (101 MHz, DMSO-d6) δ 197.0, 170.0, 155.9, 137.7, 136.4, 127.7 (2C), 127.4 (2C), 123.9, 123.7, 122.7, 121.8, 116.6, 116.5, 75.1. IR, vmax/cm−1: 3308, 2219, 1708, 1600, 1465, 1445, 1389, 1328, 1208, 1141. HRMS (ES TOF) calculated for (M + Na)+ C16H10N2NaO2 285.0634, found 285.0629 (1.9 ppm). 4-Oxo-2,4-diphenylbutanenitrile (12): A 5-mL round bottom flask equipped with a magnetic stirring bar was charged with acetophenone (1 mmol, 120 mg), benzaldehyde (1 mmol, 106 mg), KCN (2 mmol, 130 mg), H2O (130 mg), and MeOH (1 mL) and refluxed for 120 min (TLC control). The reaction mixture was cooled to room temperature, AcOH (1 mmol, 60 mg, 57 µL) was added, and reflux was continued to another 30 min. Then, the reaction mixture was diluted with 100 mL of EtOAc and washed twice with 30 mL of concentrated NaHCO3 solution. The organic layer was concentrated and purified by column chromatography (eluent EtOAc:Hex 1:4). Then, 4-Oxo-2,4-diphenylbutanenitrile was obtained as a white solid. NMR spectra were in agreement to previously published [26]. Yield: 35 mg (0.15 mmol, 15%). 1H NMR (400 MHz, Chloroform-d) δ 7.93 (dt, J = 7.1, 1.4 Hz, 2H), 7.64–7.55 (m, 1H), 7.52–7.30 (m, 7H), 4.56 (dd, J = 8.1, 6.0 Hz, 1H), 3.73 (dd, J = 18.0, 8.0 Hz, 1H), 3.51 (dd, J = 18.0, 5.9 Hz, 1H). 13C NMR (101 MHz, CDCl3) δ 194.7, 135.7, 135.3, 134.0, 129.4 (2C), 128.9 (2C), 128.5, 128.2 (2C), 127.6 (2C), 120.8, 44.6, 32.0. HRMS (ES TOF) calculated for (M + Na)+ C16H13NNaO 258.0889, found 258.0888 (0.6 ppm). Supplementary Materials The following are available online at https://www.mdpi.com/article/10.3390/molecules27092808/s1, 1H and 13C NMR spectral charts (Figures S1–S52), HRMS spectral charts (Figures S53–S78), and X-Ray crystallography data (Figure S79, Tables S1–S7). References [27,28,29] are cited in the supplementary materials. Click here for additional data file. Author Contributions N.A.A. (Nicolai A. Aksenov)—conceptualization, supervision, data analysis, funding acquisition; A.V.A.—supervision, investigation; I.A.K.—investigation; N.K.K.—investigation; D.A.A.—investigation, data analysis; N.A.A. (Nikolai A. Arutiunov)—investigation; D.S.A.—investigation; M.R.—conceptualization, supervision, data analysis, writing (original draft, review, and editing). All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Supporting Information data include NMR spectral charts. Conflicts of Interest The authors declare no conflict of interest. Sample Availability Not available. Figure, Schemes and Table molecules-27-02808-sch001_Scheme 1 Scheme 1 Cyclization of ortho-nitrochalcones into 2-alkylideneindolin-3-ones. molecules-27-02808-sch002_Scheme 2 Scheme 2 Preparation of 2-(3-oxoindolin-2-yl)acetonitriles 2. Figure 1 ORTEP drawing of X-Ray structures of (E)-2-(5-fluoro-2-methylphenyl)-2-(3-oxoindolin-2-ylidene)acetonitrile (2al, CCDC #2157035). The thermal ellipsoids are shown at 50% probability. Green:fluorine; blue:nitrogen; red:oxygen, gray:carbon. molecules-27-02808-sch003_Scheme 3 Scheme 3 Reactions with thiophenecarbaldehydes 4o-p. molecules-27-02808-sch004_Scheme 4 Scheme 4 Mechanistic rationale for cascade transformation providing 2-(3-oxoindolin-2-yl)acetonitriles 2. molecules-27-02808-sch005_Scheme 5 Scheme 5 Cyanide-induced Aldol condensation/Michael addition cascade. molecules-27-02808-sch006_Scheme 6 Scheme 6 Preparation intermediate cyclic hydroxylamine 10ab and its further conversion into 2-(3-oxoindolin-2-yl)acetonitrile 2ab. molecules-27-02808-t001_Table 1 Table 1 Optimization of the reaction conditions for one-pot conversion of 2-nitroacetophenone (3a) and p-anisaldehyde (4a) into (E)-2-(3-oxoindolin-2-ylidene)-2-arylacetonitriles (2aa). KCN a Temperature (°C)/Time (h) Methanol/Water/AcOH b Yield of 2aa, % c 1 130 65/1 2000/130/150 64 2 260 65/0.5 2000/260/300 65 3 130 65/0.5 1000/130/150 95(87) d 4 130 20/12 1000/130/150 58 5 130 65/0.5 1000/0/150 79 6 130 65/0.5 1000/130/195 e 38 7 130 65/0.5 1000/130/92 f 49 a Amount of KCN in mg per 1 mmol of starting material 3a is listed. b Volumes of methanol, water, and acetic acid in μL per 1 mmol of starting material 3a are listed. c NMR yields are reported. The best result is shown in bold. d Isolated yield of purified product 2aa is provided in parentheses. e H3PO4 was used instead of AcOH. f HCOOH was used instead of AcOH. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Hoessel R. Leclerc S. Endicott J.A. Nobel M.E.M. Lawrie A. Tunnah P. Leost M. Damiens E. Marie D. Marko D. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093421 sensors-22-03421 Review Progress in Sensors for Monitoring Reinforcement Corrosion in Reinforced Concrete Structures—A Review https://orcid.org/0000-0003-4480-787X Shevtsov Dmitry 1* https://orcid.org/0000-0002-5493-092X Cao Nhat Linh 2* https://orcid.org/0000-0002-7428-4814 Nguyen Van Chi 2 Nong Quoc Quang 2 https://orcid.org/0000-0001-8285-9553 Le Hong Quan 2 Nguyen Duc Anh 2 Zartsyn Ilya 1 Kozaderov Oleg 1 Laracca Marco Academic Editor Maio Leandro Academic Editor Memmolo Vittorio Academic Editor 1 Faculty of Chemistry, Voronezh State University, Universitetskaya pl. 1, Voronezh 394018, Russia; zar-vrn@mail.ru (I.Z.); kozaderov@vsu.ru (O.K.) 2 Coastal Branch, Vietnam-Russia Tropical Centre, Nguyen Thien Thuat St., 30, Nha Trang 57127, Khanh Hoa, Vietnam; nguyenvanchirvtc@gmail.com (V.C.N.); nquocquang5@gmail.com (Q.Q.N.); quanttndvn@gmail.com (H.Q.L.); nda.ttndvn@gmail.com (D.A.N.) * Correspondence: shevtsov@chem.vsu.ru (D.S.); cnlinh0812@vrtc.org.vn (N.L.C.); Tel.: +7-(952)-554-14-31 (D.S.); +84-(86)-245-26-09 (N.L.C.) 29 4 2022 5 2022 22 9 342111 3 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Non-destructive monitoring methods and continuous monitoring systems based on them are crucial elements of modern systems for the management and maintenance of assets which include reinforced concrete structures. The purpose of our study was to summarise the data on the most common sensors and systems for the non-destructive monitoring of reinforced concrete structures developed over the past 20 years. We considered systems based on electrochemical (potentiometry, methods related to polarisation) and physical (electromagnetic and ultrasonic waves, piezoelectric effect, thermography) examination methods. Special focus is devoted to the existing sensors and the results obtained using these sensors, as well as the advantages and disadvantages of their setups or other equipment used. The review considers earlier approaches and available commercial products, as well as relatively new sensors which are currently being tested. corrosion of reinforcement reinforced concrete non-destructive monitoring smart constructions Vietnam–Russia Tropical Centre931/QĐ-TTNĐVN This research was funded by the Vietnam–Russia Tropical Centre (project number 931/QĐ-TTNĐVN). ==== Body pmc1. Introduction Reinforced concrete (RC) structures serve as the basic composite material for modern civilisation. This composite material is used to construct industrial buildings, energy and transportation infrastructure, and social facilities. The unique properties of RC make it possible to implement any technical and architectural solution and construct buildings of virtually any size, form, and function. RC is a durable material with an expected repair-free service life of up to 100 years, according to EN 1991 (2002–2006 Eurocode 1: Actions on structures). However, there have been numerous cases, when such structures required maintenance long before the end of the design service life. Without timely and proper maintenance, RC structures are prone to collapse. The most common cause of premature destruction of RC structures is the corrosion of reinforcements [1]. Corrosion is most often induced by chlorides being the components of salt water and antifreeze reagents, as well as products of the chemical industry [2]. Carbonation of concrete is also a common cause of corrosion of steel reinforcement in concrete [3]. The destruction of RC structures proceeds in several stages, the most common being the following [4]: loss of passivity, the cracking and flaking of the protective layer accompanied by impaired adhesion between the reinforcements and the concrete, and finally, collapse. At the moment, countries with developed market economies spend up to 3–5% GPD to mitigate the consequences of the corrosion of steel reinforcement bars [5]. One of the main tasks facing studies of metal corrosion in the 21st century is to reduce these costs. The corrosion of steel reinforcement bars can be minimized by adjusting the composition of concrete (selecting the type of cement, additives, and corrosion inhibitors) and proper construction works. These measures provide for the primary protection of RC during the manufacturing stage. The secondary protection (polymer and cement coatings, hydrophobisators, migrating corrosion inhibitors) is applied after manufacturing and during operation in corrosive environments. The cost of the service life of any structure comprises the costs required during all stages, from construction to disposal. It can be reduced by selecting technological solutions that would ensure optimal construction and operational costs [6]. In order to do this, it is necessary to employ methods for assessing the effectiveness of the primary and secondary protection, and forecasting the repair-free service life with regard to the level of corrosivity of the environment and the condition of the RC. Non-destructive monitoring methods (and continuous monitoring systems based on them) are a crucial element of the systems for the management and maintenance of assets which include RC structures. Corrosion of reinforcement steel in concrete proceeds as an electrochemical process [7]. Therefore the most common methods used to identify the state of reinforcement (passivity or corrosion) or the corrosion rate are electrochemical methods. Physical methods are also growing in popularity, since they can be used to assess the development of the corrosion process based on indirect parameters such as permeability change, reduction of the level of adhesion at the steel/concrete boundary, and cracking caused by the accumulation of corrosion products. Currently, there are a significant number of reviews on the topic of sensors for assessing the corrosion condition of reinforcement in concrete. Some of them are aimed at a detailed discussion of specific methods: electrochemical [8], control of chloride content [9], fiber-optic [10], piezoelectric [11], etc. Other reviews cover groups of methods and discuss in detail the basics of methods, as well as give some specific applications of sensor designs, most often in laboratory conditions [12]. At the same time, researchers often play insufficient attention to systems that are used on real structures and ready-made commercial solutions. The purpose of our research was to review the existing literature on the most common sensors and non-destructive monitoring systems for RC structures. Of particular interest are sensors which are used or can be used for remote data collection and transfer systems, i.e., systems that do not require the constant presence of engineers close to the examined structures. The article focuses on specific sensors based on the existing principles. The scope of our study does not include a detailed discussion of the physicochemical principles of particular methods. The article covers a period of the past 20 years and reviews earlier approaches and available commercial products, as well as relatively new sensors during various stages of testing. The review consists of the following sections: 2. Electrochemical Methods. ○ 2.1. Half-Cell Potential (HCP) Sensors. ○ 2.2. Concrete Resistivity (CR) Measurement Sensors. ○ 2.3. Macro- and Microcell Sensors. ○ 2.4. Linear Polarisation Resistance (LPR) Sensors. ○ 2.5. Galvanostatic Pulse Technique (GPT) Sensors. ○ 2.6. Electrochemical Impedance Spectroscopy (EIS) Sensors. ○ 2.7. Chloride Monitoring Sensors. ○ 2.8. pH-Sensors. 3. Physical Methods. ○ 3.1. Fibre Optic Sensors (FOS). ▪ 3.1.1. Fibre Bragg Grating (FBG) Strain Sensors. ▪ 3.1.2. Long Period Fibre Grating (LPFG) Refractive Index Sensors. ▪ 3.1.3. Brillouin Optical Time Domain Reflectometry Sensors. ○ 3.2. Elastic Wave Sensors. ▪ Piezoelectric sensors. ○ 3.3. Hall Effect Sensors in an Electromagnetic Field. 4. Integrated Sensor Systems. 2. Electrochemical Methods Electrochemical assessment methods allow identifying the corrosion of steel reinforcement bars in concrete either directly or indirectly, based on the changes in the properties of the concrete cover. The principles underlying these methods are based on the quantitative relations between the assessed parameters such as, for instance, the relation between the circuit voltage and the concentration or activity of particular chemical elements, or the presence of ions in the corrosive environment, etc. [13]. Described below are the most common electrochemical methods for monitoring the health of reinforcements and sensor systems based on them, including commercially available ones. The methods are listed from the most to the least popular. 2.1. Half-Cell Potential (HCP) Sensors Measuring the free corrosion potential of steel reinforcementsts (Ecor) on the surface of concrete is one of the earliest methods for assessing the corrosion condition of RC. The first articles on the topic were published in the 1970s [14,15]. On the whole, half-cell potential measurements present a reliable qualitative method, which has been proved by a number of laboratory [16,17] and field [18,19,20] studies. This method has been adopted as standard in a number of countries [21,22,23] and is widely used. The generally accepted values of Ecor and the corresponding corrosion conditions of reinforcement are given in Table 1. The drawbacks of the HCP method include the lack of a fixed range for the measured potential, the dependence of the results on the temperature and the level of moisture in concrete, and the effect of the films of the coatings and hydrophobisators on the concrete surface. At the moment, portable sensors are the most popular. They consist of a voltmeter with high input impedance and a reference electrode providing for the consistency of the measurements performed during in situ studies. The most common reference electrodes are copper/copper sulphate and calomel electrodes. The devices have different commercial names in different countries: Canin+ or Profometer Corrosion produced by Proceq, Switzerland; Elcometer 331T by Elcometer, the UK; Giatech iCOR by Giatec Scientific Inc., Ottawa, ON, Canada; Armkor-1 by InterPribor, Russia, etc. The devices differ in their functions, which range from simply measuring and displaying the circuit voltage to mapping the potentials and determining the areas most prone to corrosion on site (the data are not processed by a computer). The application of such devices requires engineers to be in proximity to the examined structures [19]. Of more convenience for remote continuous monitoring systems are sensors, which can be embedded into concrete in the areas most prone to corrosion [24]. There are studies describing sensors based on copper/copper sulphate and silver chloride [25] electrodes that were embedded into concrete. However, the problem of maintaining the stability of such reference electrodes when used with liquid electrolyte solutions have not been solved yet; stability can be lost and some elements can even be destroyed by the alkaline medium of concrete, resulting in the contamination of the concrete with the components of the solution. Jin et al. [26] suggested using a solid MnO2-based reference electrode, which allows for polarisation measurements by means of the HCP method, linear polarisation, and electrochemical impedance spectroscopy. Muralidharan et al. [27] confirmed the effectiveness of MnO2-based electrodes for concrete embedded sensors. Later, Karthick et al. [28] suggested a modified reference electrode based on graphene oxide-manganese oxide (GO-MnO2), which demonstrated the ability to function stably for at least two years in concrete. Chand et al. [29] suggested a new method of HCP measurement by means of two coils functioning according to Faraday’s law of electromagnetic induction. Although this approach can hardly become widespread, it demonstrates that researchers today have a wide range of instruments to solve the problem. We should note that there are hardly any sensors that only monitor the free corrosion potential. Most commonly, integrated systems are used which monitor several parameters simultaneously (pH, chloride concentration, microcell current, etc.) or systems with reference electrodes for polarisation methods. In the latter case, the free corrosion potential is an additional parameter. Taking into account the qualitative nature of the method and the presence of undefined values, this approach is quite reasonable. Integrated systems are discussed in a separate paragraph at the end of this review. 2.2. Concrete Resistivity (CR) Measurement Sensors Measuring the electrical resistivity of concrete is another popular method for monitoring the corrosion condition of reinforcement bars [30,31,32]. There is a linear dependence between the electrical resistivity of concrete, moisture content, and the concentration of soluble salts (including chlorides) in concrete [33]. It is known that under otherwise equal conditions, low resistivity is related to rapid electrochemical processes. However, the dependence of CR on a number of factors, including temperature, relative humidity, amount of atmospheric precipitation, etc., significantly impairs the interpretation of the results obtained during the monitoring of resistivity [34]. Therefore, it is only possible to estimate the probability of corrosion. The criteria are given in Table 2. Nevertheless, the CR measurement technique is widely used [35] and is traditionally considered complementary to the HCP technique [30,36]. There was a long-term study (over 5 years long) on a specimen of cracked concrete performed in Rødbyhavn (Denmark) [37]. The concrete was subjected to spraying and immersed into water and studied using the HCP and CR techniques based on multicoil electrodes with additional temperature control. The Wenner probe is becoming more popular for CR measurements performed on site [38]. The probe consists of four metal electrodes arranged on the same line at a specific distance from each other. An electric current (alternating or direct) is applied to the first and the last electrode, and the difference in the potentials is measured for the other electrodes. Thus, a dependence between the current values and the difference in potentials is obtained for several pairs, and the resistivity of the concrete is determined based on the cell constant. There are also in situ sensors embeddable in concrete. Thus, Priou et al. [39] used a multi-electrode sensor in combination with a ROTRONIC humidity sensor and a Pt100 temperature sensor to monitor the corrosion process in an RC wharf. The authors used an ABEM terrameter LS, designed for geophysical studies [40], to collect data under direct current for 18 months. The study was designed to provide detailed information on the rate of penetration of chlorides into concrete in various regions of the wharf without taking the core samples and violating the integrity of the structure. It also helped to test the method for assessing the effect of the distance between reinforcement bars on the results of the measurements of electrical resistivity of concrete. Corva et al. [41] detailed the functioning of a four-electrode probe on a breadboard with USB connection performing measurements under direct current (Figure 1). The USB interface allowed for data transfer to any PC and thus removed the need to design special means of data transfer. Halabe et al. [42] used sensors comprised of two plain carbon steel plates embedded within concrete cubes. The concrete cubes were tested in a laboratory environment and on site (rehabilitated bridge columns). The study suggested using resistivity sensors in conjunction with commercially available temperature and humidity sensors for a more accurate assessment of the potential for corrosion. A similar device based on two metal rods was suggested by Chi et al. [43]. Simultaneous control of the listed parameters made it possible to increase the accuracy of forecasting the corrosion condition of the reinforcement. Kamat et al. [44] used embeddable multi-ring sensors with stainless steel electrodes. Their construction made it possible to measure the CR at different depths simultaneously, both in laboratory conditions and on site. As a result of their study, the authors elaborated the dependences of the chloride penetration rates obtained earlier. The above described monitoring techniques based on HCP and CR measurements are of a qualitative nature and cannot be applied to estimate the rate of destruction of reinforcements in concrete. It is probably for this reason that the sensors based on these techniques are not often used in systems for the continuous monitoring of the condition of steel reinforcements in concrete structures. When installing HCP and CR sensors on existing structures, the detection of fittings by non-destructive methods is required beforehand. One of these is the pacometric test, which is described in detail by Biondi and Frunzio [45]. However, the simplicity of the required equipment and the possibility to examine large regions over short time intervals make these techniques an important tool that can be used for the non-destructive identification of regions with high potential for the corrosion of steel reinforcements in concrete. 2.3. Macro- and Microcell Sensors Sensors based on galvanic macro- and microcells are widely used to estimate the intensity of steel reinforcement corrosion and the depth of corrosion from the concrete surface. Corrosion intensity is defined as a value which cannot be used to directly estimate the rate at which the metal deteriorates as compared to the degradation due to the gravimetric factor, but can be used to estimate how much faster the corrosion process is. After the calibration with regard to control samples, it is possible to recalculate the results by applying the proportionality constant and obtain approximate values for the corrosion rate of reinforcement [4]. Sensors based on microcells are comprised of metal bars with a length of a few centimeters. Anodes are made of low-carbon steel with a composition similar to that of the reinforcement steel. Cathodes can be made of stainless steel [46,47], titanium [48], copper [46,48], etc. One cathode is placed in close proximity to several anodes. There can also be additional reference electrodes for the monitoring of HCP (see Section 2.1) of certain half cells. The earliest and most popular version of such sensors is the so-called anode-ladder system [49,50,51,52]. The steel bars are arranged in a “ladder” with regard to the reference electrode and can be used to estimate the depth of corrosion in concrete (Figure 2). Such devices also monitor the location of the corrosion front by the difference in the free corrosion potentials between each bar. Another interesting option is presented by sensors based on macrocells in the form of multi-ring sensors [53]. The sensors are comprised of small ring electrodes isolated from each other and a measurement circuit. The ring electrodes are made of low-carbon steel. When placed in proximity to a cathode made of a more noble metal, the sensor can measure the current in the galvanic cell, and estimate the spread of corrosion spots and their depth depending on the distance from the concrete’s surface. Valdés et al. [54] studied macrocells based on reinforcing steel and copper with a ratio of areas being 1:1 and also estimated the depth of corrosion. The sensor was comprised of a metal plate (base) and several steel rods placed at various distances from the base. The authors also calibrated the sensor and determined the ranges of values of the current in the galvanic cell corresponding to the passive state and active corrosion of reinforcements based on HCP measurements. Pereira et al. [55] suggested a sensor for measuring the galvanic current based on a pair of steel/stainless steel reinforcing bars. The sensor included only one galvanic cell and could not be used to estimate the spread of the corrosion front along the concrete cover. However, the results of the experiments with a stainless steel electrode demonstrated that the metal can be used as a cathode. Replacing copper with stainless steel may be a promising solution, since in this case, the concrete does not become contaminated with copper ions, as copper is susceptible to corrosion in chloride environments. On the other hand, the values of galvanic current for a pair of copper/steel electrodes are higher, which makes the sensors based on it more sensitive. Thus, at the moment, there is no common solution for the construction of sensors based on macrocells and the most effective metal pairs. We should assume that the sensors which can be used to estimate the probability of corrosion at various depths of the concrete cover are more universal, and therefore, more promising. Sensors based on microgalvanic cells are comprised of plates of different metals with a thickness of several millimetres. They come in the form of packages of alternating cathodes and anodes (Figure 3) [56]. Such sensors can also be made of single-composition metal plates, with the difference in their potentials being artificially maintained at 20–100 mV [3]. Qiao et al. [57] used model alkali solutions to test microcell sensors based on Mg/graphite and Zn/graphite. Their advantage is that they can generate electric current which serves both as the measurement signal and as a source of power required to transmit this signal over a wireless network. It is assumed that such a system can make the monitoring process much easier, since it does not require numerous meters of connection wires to be embedded into concrete structures to transfer the signal from the recorder to the sensors. The above described sensors based on macro- and microcells are installed on bridges, in tunnels, and other infrastructure and industrial facilities operating in adverse environments. The sensors are also used to control the quality of maintenance and repair [58], as well as to control the application of corrosion inhibitors, including migrating corrosion inhibitors [59]. The sensors can be embedded either in new or rehabilitated RC constructions. In the former case, the sensors are fixed on the reinforcing bars, after which the form is filled with concrete as usual. In the latter case, cores are drilled in the concrete. The sensors are put in the cores, which are then sealed with a special repair composition. The first method is more preferable with regard to the accuracy of the results, since the concrete cover is homogeneous and has the same properties. The described sensors were initially designed to be used in remote monitoring systems for a number of reasons. They are simple, inexpensive, and quite compact, and often generate signals without additional polarisation. State-of-the-art technologies can be used to arrange for either the wired or wireless collection, recording, storage, and transmission of data at a relatively low cost. Up to now, there have been a number of various-scale studies, both laboratory [60] and in situ [55], which tested the systems of data collection, storage, and transmission. Thus, installing such sensors in regions susceptible to corrosion and wiring them into a single network enables building management organisations to analyse the condition of the structures as well as plan maintenance and repair works. 2.4. Linear Polarisation Resistance (LPR) Sensors LPR method is based on the fact that there is an inverse relationship between the corrosion current density (icor) and the polarisation resistance of the electrochemical reaction. The method has been used to estimate the rate of metal corrosion since the middle of the 20th century [61]. Knowing how icor changes over time, we can estimate the weight loss of the metal or the cross-section loss (Δl) according to Faraday’s law. The most common variant of this method used for the system “steel in concrete” is the LPR method which has been widely used both in laboratories and in situ studies since the 1970s [62]. The measurements are performed with a three-electrode cell (the working electrode is a reinforcement bar, the auxiliary electrode is usually stainless steel, and the reference electrode is the same as used in the HCP technique described in Section 2.1). The substantiation of the method can be found in a large number of studies [62,63,64,65]. These determined the range of values of the corrosion rate which characterise the danger of utilizing RC structures (Table 3). LPR takes significantly more time than HCP and CR. Therefore, to optimise the time costs, all the three methods can be used together. Qualitative HCP and CR measurements help to determine the regions with a high probability of corrosion. Then the LPR method is used to determine the rate of deterioration of steel reinforcements. There are a number of commercially available devices for in situ measurements of the corrosion rate, such as the Gecor 8 by James Instruments; Giatec iCOR by Giatec Scientific, etc. These devices require the presence of a corrosion engineer during the measurements. Pereira et al. [55] suggested using an embeddable electrode-based sensor together with a commercially available GEOCOR 06 device to estimate the rate of corrosion. Activated titanium was used as a reference electrode. The corrosion rate was estimated based on control samples rather than on the reinforcements of the structure. This means that the composition of the metal used with the sensor must be the same as the composition of the metal in the examined structure. Jin et al. [25] and Karthick et al. [28] suggested using embeddable LPR-sensors together with HCP measurements to estimate the corrosion rate. Brown et al. [66] suggested a sensor on a flexible substrate cable. The sensor itself is a standard three-electrode cell. The electrodes were made of corrosion-resistant materials, for instance, gold plated copper. It should be noted that the LPR method involves a measurement error (the estimated corrosion rate differs from the actual one by 2–4 times) resulting from a simplified calculation procedure [67]. The difference in the rate of uniform and localised corrosion (up to a factor of 10), the effect of the electrical resistivity of concrete, and a series of other factors impair the analysis of the obtained results [68]. However, the easiness and the speed of recording of the polarisation curve make the LRP method a powerful tool for the estimation of the rate of corrosion of steel reinforcements in concrete. 2.5. Galvanostatic Pulse Technique (GPT) Sensors The GPT involves recording the changes in the electrode potential over time, when applying a low galvanostatic pulse (below 50 µA) [69] or after switching off the pulse [70]. The analysis of the obtained potential transient is based on the assumption that the mechanism of the electrochemical reaction on steel reinforcement bars is described by a simple Randles equivalent circuit. The change in the potential can be described by the following expression:(1) E(t)=Iimp·[1 − e−t1/2Rp·Cdl]+RΩ where E(t) is the measured potential, Iimp is the impulse current, t½ is the transition time, Rp is the polarisation resistance of the electrochemical reaction, Cdl is the double-layer capacitance, and RΩ is the resistance of the medium. Determining the transition time, we can assess the corrosion condition of steel reinforcement bars in concrete (passive or active corrosion). In study [71], the following criteria are suggested: t½ > 40 s–passive state, t½ < 25 s–corrosion. GPT has been used to assess the corrosion of steel reinforcement bars in concrete since the late 1980s [72,73]. At the moment, there are commercially available systems based on the Gecor 8 system and the GalvaPulse sensor with a guard ring as those described in [74,75]. The device is used to determine the corrosion parameters for equation 1. The measurements are performed on site by a corrosion engineer. We did not manage to find any information on remote measurements performed using GTP. However, there are data showing that GPT is more stable and accurate than LPR measurements under adverse conditions, when there is no information about the examined region. It is also more stable and accurate than HCP, LPR, and EIS in the absence of a stable reference electrode [76]. 2.6. Electrochemical Impedance Spectroscopy (EIS) Sensors Similar to GTP, the EIS method has been used to monitor the condition of RC since the 1980s [77]. The range of parameters that can be determined using this method is quite wide and includes the electrical resistivity of concrete, the reaction mechanism, the polarisation resistance of the charge transfer reaction, the double-layer capacitance, etc. Lately, there have appeared sensor systems that can register EIS spectra without direct contact with steel reinforcement [78]. However, the inhomogeneity of concrete can cause noise and hinders the analysis of the results of EIS [79]. As we said earlier, Jin et al. [26] suggested a three-electrode sensor for registering the impedance spectra in concrete. Ahmadi et al. [80] obtained the impedance spectra using piezoelectric sensors without polarisation of the reinforcing bars. Sensor plates were installed on rebars embedded in concrete. The authors demonstrated that the suggested devices can be used to determine the time when corrosion starts, and the direction of the corrosion spread, as well as to calculate corrosion-induced weight loss more accurately than can be done by calculating the electrical charge according to the Faraday method. At the same time, the EIS method requires more complex equipment as compared to the LPR method, which makes its in situ application rather problematic. 2.7. Chloride Monitoring Sensors Sensors for chloride concentration or pH level can help to monitor the corrosion condition of RC prior to the beginning of the destruction process [81]. Although there are different views regarding the critical concentration of chloride ions which causes the corrosion of steel reinforcements [82,83,84], all the authors agree that there is such a critical concentration. Thus, monitoring the rate of penetration of chlorides through the concrete cover and determining their concentration on the reinforcement’s surface can be an important part of a comprehensive monitoring system. There are standard destructive methods used to monitor the concentration of chlorides in concrete. These methods involve core sampling followed by the analysis of aqueous extracts obtained from ground concrete either by titration or by means of the potentiometric method [85,86]. Such methods are not applicable for continuous monitoring because they destroy the concrete cover. Non-destructive chloride sensors can be divided into three major groups according to their operating principle: measuring the electric resistivity of concrete, chloride-selective electrodes, and fibre optic sensors. The first type was, to some extent, discussed in Section 2.2. Below, we will detail the other two types. Initially, the sensors embeddable into concrete were chloride-selective electrodes, for instance, silver/silver chloride electrodes [25,87,88,89]. Ion-selective electrodes are chemically stable in aggressive environments and easy to manufacture. However, there are a number of factors that can cause inaccurate measurements: changes in temperature and pH and the presence of the electric field. Some sensors deteriorate really fast due to the loss of the electrolyte solution [90]. Im et al. [91] suggested a sensor based on thin iron plates (1.5 mm), fixed in parallel 1 mm from each other on a polyethylene terephthalate substrate. The iron was coated with an anion-exchange membrane sensitive to chlorides. The study demonstrated that the concentration of chlorides can be determined up to ≈1.2 M. Despite the sensitivity and good calibration of the sensor, it has low mechanical resistance and degrades quickly when the concentration of chlorides is high. Therefore, such sensors cannot be embedded into concrete or mortar. The purpose of further studies will be to enhance the durability of such sensors. Leung et al. [92] developed fibre optic sensors based on a Nufern 780-HP fibre coated with an iron film with the thickness of 25 to 350 nm in order to measure the chloride concentration and determine the threshold value. The readings of the sensor were in good agreement with the results obtained using the galvanic method and macrocells. Unfortunately, the authors did not provide any information about the calibration of the sensors with regard to the actual concentration of free chlorides determined by any well-known method (titration or potentiometric method). Therefore, the study does not provide information on the sensitivity thresholds of the sensors. Laferrière et al. [93] described a sensor based on an indicator dye lucigenin, which is a blue-green, fluorescent chloride-sensitive ion indicator. The study demonstrated the possibility of accurately detecting chlorides within the concentration range from 0.030 to 0.35 M. 2.8. pH Sensors For Ordinary Portland Cement, pH ranges from 12.45 to 13.5 (at 20 °C), and a decrease in pH is expected in aged concrete due to alkali leaching, carbonation, and sulphate attacks for example [94]. A decrease in the pH of concrete resulting from carbonisation (a reaction between carbon dioxide and calcium hydroxide, a component of the cement brick), can lead to the development of the uniform corrosion of steel reinforcement [6]. When localised corrosion is induced by chlorides, there is a critical ratio between the concentrations of chloride and hydroxide ions [95,96]. Therefore, controlling the pH of concrete is important for the comprehensive monitoring of the condition of steel reinforcements. In their review, Behnood et al. [97] detailed various ways to control the pH of concrete, classified them, and considered the advantages and disadvantages of each method. Therefore, below, we are going to consider some of the well-known sensor designs: ion-sensitive field-effect transistor, fibre optic, hydrogel film, and solid-state pH sensors. Elements based on metal oxides, including iridium, platinum, palladium, rhodium, titanium, tin, aluminium, and rhenium oxides, are often used as ion-selective electrodes which provide for stable functioning of sensors in concrete. Huang et al. [98] suggested a flexible pH sensor based on iridium oxide. Some electrodes are prepared by means of deposition [99], electrochemical deposition [100], and oxidation [101]. Despite their high mechanical resistance, the effect of the overall ion strength on the readings of such electrodes is to be studied and discussed. Korostynska et al. suggested using fibre optic sensors for measuring the depth of the carbonisation of concrete [102]. Khalil et al. [103] investigated the use of mesotetraarylporpholactone as a chromophore and demonstrated that it can be used in the pH range of 11.5–13.2. McPolin et al. [104] suggested using a sol-gel based on cresol red with the pH of 8–13. The main limitation to the use of fibre optic sensors in concrete is the small range of pH values, often below 12, and, for some electrodes, the destruction of the chromophore [97]. Until recently, only the above described electrochemical methods were used to monitor the corrosion condition of steel reinforcement bars in concrete, in particular HCP, CR, and LPR methods. Therefore, they are documented by various standards and regulations, and there is a large assortment of commercially available devices based on these methods. However, at the moment, there are no regulatory documents that would describe the design and assessment criteria for the rest of the above considered electrochemical methods for the continuous non-destructive monitoring of RC and sensors based on them. On the one hand, this creates opportunities for new inventions. At the same time, it inhibits the development of new commercially available solutions. The next stage of research in this field should be the unification and standardisation of the existing approaches. 3. Physical Methods Physical methods of monitoring the corrosion condition of reinforcement bars have become the focus of research only recently (in the past 10–15 years), and many devices are still presented as laboratory samples or prototypes. Below, we consider the most common physical methods and sensors based on them. 3.1. Fibre Optic Sensors (FOS) FOS register changes in the properties of light (photons) transmitted through glass or organic fibres. Depending on the temperature or fibre deformations, there can occur changes in the wavelengths, the energy flux density, frequency, polarisation, or phase. Therefore, it is possible to assess the deformations occurring within concrete as a result of accumulation of corrosion products on the boundary steel reinforcement/concrete. As a rule, FOS is fixed to the reinforcement or in the immediate vicinity so that it is possible to track deformations at the steel/concrete boundary [105]. FOS are very promising for monitoring the corrosion of reinforcement bars in concrete due to their chemical and corrosion resistance, robustness to noise from external sources of electromagnetic radiation, accuracy, and simplicity [106]. The use of carbon nanotubes and the associated shielding of electromagnetic radiation can increase the sensitivity of some sensors [107,108]. At the moment, the most common FOS techniques are Fibre Bragg Grating (FBG) Strain Sensors, Long Period Fibre Grating (LPFG) Refractive Index Sensors, Brillouin Optical Time Domain Reflectometry Sensors, and Shape Memory Alloys (SMA) Sensors. 3.1.1. Fibre Bragg Grating (FBG) Strain Sensors FBG sensors are sensitive to changes in the reflection values and the lattice period along the optical fibre axis. Monitoring the changes in the reflection signal coming from the lattice makes it possible to estimate the increase in the amount of corrosion products. The application of FBG sensors for monitoring the corrosion of reinforcement bars in concrete has been actively discussed since the 2010s. The technique appears to be quite promising. Currently, most studies focus on the fibre materials and the ways of deploying the sensors in concrete samples. Thus, Mao et al. [109] suggested using Bragg grating fibre covered by epoxy resin for protection. The sensor proved to be mechanically resistant and capable of identifying cracks. However, the authors did not provide any calibration data regarding the relation between concrete deformation and the wavelength. Hu et al. [110] used sensors with double-layer coating (the inner layer was based on silver and the outer layer was based on Fe-C). The study demonstrated that the rate of corrosion varied depending on the source of chlorides (continuous complete immersion as opposed to capillary suction from a small volume). However, the authors did not provide the results of the calibration. Additionally, the film deposited on the surface of the sensor partially deteriorated because of corrosion. In the described studies, the sensors were fixed around reinforcement bars. Chen and Dong [111], although they did not specify the type of sensors they used, described the operation of the devices using the ANSYS software and proposed a conversion coefficient between wavelength and deformation. They pointed out that the coefficient depends on the thickness of the concrete cover, and is close to 0.829, when the concrete thickness is five times higher than the diameter of the reinforcing bars. Gao et al. [112] fixed the sensors perpendicular to the axes of the reinforcing bars. As a result, using the gravimetric weight loss method, they obtained the relationship between reflected wavelength change from the grating and the weight loss rate of rebars caused by the formation of corrosion products. The authors also determined the time of corrosion initiation (when the readings of the sensors did not change) and the time of corrosion development (the signal changed monotonously over time). Another important problem is the protection of embeddable sensors from mechanical damage occurring during the construction works and from the weight pressure of concrete [113]. Almubaied et al. [114] suggested putting an expanded polystyrene liner between the concrete and reinforcing bars. Although it helped to protect the sensor, the authors did not consider the reduction in adhesion between concrete and reinforcement and the effect of this factor on the load bearing capability of the whole structure. Jaafar et al. [115] used sensors by Photronix Technologies (M) Sdn. Bhd., with different types of concrete with a silicone gel protective coating. The authors determined the relationship between the Bragg wavelength and the changes in HCP, which was used for comparison. The suggested method ensured that the sensors functioned for at least a year under accelerated corrosion. Li et al. [116] tested FBG sensors with an epoxy protective layer and temperature sensor together with an acoustic emission sensor (AE, Micro-II Digital AE System, Physical Acoustic Corp, West Windsor Township, NJ, USA). The propagation of cracks in concrete was captured through a corresponding acoustic emission, and the surface strain was monitored by registering the increase on the amount of corrosion products using the FBG method. The study showed good agreement between the measurement results demonstrating that the method is promising for RC corrosion monitoring. By the end of the experiment, the sensors remained intact. We can thus assume that any protective coating resistant to the alkaline environment of concrete is able to protect FBG sensors from corrosion and destruction. Luo et al. [106] demonstrated that the sensors used could provide information about the initiation of the corrosion process only when the amount of corrosion products increased 3–4 times as compared to the initial state. In other words, it is only possible to estimate the degree of corrosion, when the process is in progress. It is impossible to register the transition moment between the passive state and corrosion initiation, and therefore, it is impossible to take preventive measures to restore the passivity. We should also note that FBG sensors have a limited range within structures. A large number of sensors are required to monitor large structures. At the moment, such sensors are rather expensive to produce. If FBG sensors are to be widely used for monitoring of RC structures, their construction and the type of material used should be optimised in order to reduce their cost. 3.1.2. Long Period Fibre Grating (LPFG) Refractive Index Sensors The functioning of LPFG sensors is based on the modulation of the core refractive index resulting in attenuation bands on the receiver. Resonant wavelength changes depending on the reflection value, and reflects the corrosion activity of the environment. In their review, James et al. [117] described a promising idea of integrated fibre optic sensors which can be used for the parallel independent monitoring of several observables (temperature, bending, deformation, etc.). Let us consider several examples below. Huang et al. [118] suggested a sensor coated with a thin layer of polyurethane and nanoscale iron/silica particles on single-mode optical fibres Corning SMFG28e. Quick laboratory tests allowed the authors to determine the correspondence between the accumulation of corrosion products and an increase in the resonant wavelength resulting from the corrosion of iron particles on the sensor. In other words, the authors used the nanoparticle coating as a sacrificial coating. The prediction error was 26%, which is a very good result for the localised corrosion of reinforcement bars in concrete. The polyurethane- coated sensor was used together with a sensor without polyurethane coating to control the temperature. In study [119], Chen et al. suggested a sensor with a double coating (with an inner layer based on silver and an outer layer based on Fe-C), with the thickness of the layers varying from a few to several dozen μm. The laboratory experiments with a chloride aqueous solution demonstrated a linear dependence between the changes in the resonant wavelength and the weight loss of the outer Fe-C layer in certain ranges. In their next study [120], the authors used the suggested sensors to examine steel bars in mortar and obtained promising results for the early diagnostics of corrosion. Further studies will focus on the agreement of the sensor measurements with the gravimetric measurements of corrosion rate. However, the short service life of the sensor (24 h in aqueous environment and 2 weeks in mortar) means that it cannot be used for the long-term monitoring of real structures. Thus, despite quite good results demonstrating the correspondence between the corrosion rate and the changes in the parameters of LPFG sensor signals, they cannot be used in the systems for the long-term monitoring of RC structures until the problem of corrosion resistance of nanomaterial coatings is solved. Since the use of such coatings is mandatory, one of the solutions may be the use of additional anti-corrosive protective films or more corrosion-resistant nanomaterials [121]. Although the considered sensors are more mechanically stable than FBG sensors, they still cannot be used to identify the moment of corrosion initiation, since they only register the accumulation of corrosion products on the surface of reinforcement bars. 3.1.3. Brillouin Optical Time Domain Reflectometry Sensors The distributed temperature and deformation can be measured by determining the dependence of the intensity of probe light on time and the distribution of the Brillouin frequency shift along the optical fibre. Lv et al. [122] used Brillouin optical fibre time domain analysis (BOTDA) Ditest SAT 200 sensors to monitor the expansion strain from steel corrosion. The authors tried a new way to install the sensor: instead of placing it on the surface of the steel, they fixed it on a 5 mm thick layer of mortar covering the reinforcing bar. The whole construction was then covered with an additional layer of mortar. The sensor appears to be durable, highly sensitive, and has a wide measurement range. As a result of the study, the authors developed a damage coefficient for qualitative assessment from the early stages of the spread of corrosion products to the cracking of the inner layers of concrete. The obtained results can facilitate the further development of systems for monitoring RC structures. In order to use such sensors on real structures, it is necessary to develop a convenient method of sensor installation (the sensor should wrap around a reinforcing bar) and application of the interfacial layer of concrete. Jagtap and Nayak [123] studied three different kinds of BOTDA sensors. The sensors differed in the way the fibre was fixed on the surface of reinforcement bars and the methods used to protect the sensors from the metal corrosion products (with a porous material layer serving as protection). The authors came to the conclusion that the described sensors can be used for long-term measurements. However, they did not specify which kind of sensors are the most optimal. Fan et al. [124,125] suggested a distributed fibre optic sensor deployed in a helix pattern on a steel bar. The authors obtained dependencies which can be used to calculate the cross-section loss of reinforcement bars. The need to deploy the sensor along the whole surface of steel bars and the high possibility of deformation or destruction on a construction site are the main problems preventing its in situ application. Scott et al. [126] suggested low-cost sensors that can be used to perform measurements, either on the surface of RC structures or within the concrete. The authors used two sensors simultaneously (one sensor for the deformation monitoring, and the other for the temperature monitoring). Both sensors were protected by a layer of polymer adhesive. The sensors were successfully tested in a laboratory environment. Seo [127] described an experiment with BOTDA sensors used to monitor the strain and temperature of pile foundations on site. Monsberg et al. [128] reported comprehensive work on the design and implementation of a monitoring system where the fibre was installed in the above mentioned piles situated in a fault zone of the Semmering Base railway Tunnel (Austria). The above considered BOTDA sensors are some of the first devices based on physical methods and are applied for the remote monitoring of steel reinforcement corrosion of real structures. They are quite popular because they allow for complete spatially distributed monitoring and sampling at distances of less than 1 mm [129]. However, the technology for winding such sensors around reinforcing bars, the serious influence of the temperature factor, and the high possibility of destruction during cracking of concrete limit the use of such sensors [106]. Shape Memory Alloys (SMA) Sensors are an increasingly popular subject for considerations and developments. Martensitic transformations triggered by temperature and stress occur in these metallic alloys. SMA are a class of alloys which can memorize their original shape. When the alloy is deformed, it can return to its original shape under the effect of temperature as a stimulus [130]. The pre-strained SMA is then embedded in a matrix in fresh concrete. After hardening of the matrix, the SMA is heated through resistive heating to activate. With an increase in tensile deformation, the electrical resistance of the alloy increases linearly, which makes it possible to estimate the crack width [131,132]. 3.2. Elastic Wave Sensors The relative magnetic permeability of steel is significantly (by over 100 times) higher than that of other components of RC, including the products of corrosion of reinforcing steel. By monitoring this difference, it is possible to identify the initiation and development of the corrosion process [133]. This is the effect that the functioning of elastic wave sensors is based on. Xie et al. [134] tested surface acoustic wave (SAW) sensors. The sensor consisted of two printed circuit boards with reflecting meshes and an interdigital transducer (both made of gold) covered with a protective coating to prevent short circuits (in case the film of corrosion products grows). The sensor was embedded within concrete in close proximity to the reinforcement bars. The authors presented the results of quantitative assessment of the corrosion rate. However, they did not perform any calibration with regard to the actual weight and cross-section loss of the reinforcement. The construction of the sensor allows it to function without additional power sources and transfer data via a wireless channel. In order to use such sensors on site, it will probably be necessary to optimise their construction to make it easier to deploy them on reinforcing bars. It would also be reasonable to investigate other materials (less expensive than gold) for the mesh. Sharma et al. [135] analysed the combined use of acoustic emission (AE) sensors and the ultrasonic guided waves (UGW) technique. The UGW sensors were deployed on reinforcement bars during the production of RC. These sensors proved to be more effective during the early stages of corrosion, when there are no cracks in concrete and the AE method does not work. During the formation of microcracks and their further growth, the AE method proved to be highly accurate in determining the regions with inner defects prior to the moment when they emerge on the surface. The authors only deployed the AE sensors for the duration of the study. The results of the laboratory experiments demonstrated the further prospects for this method. The laboratory experiments performed by Amiri et al. [136] demonstrated good agreement between UGW and HCP methods. Although this above mentioned method looks promising, it is extremely sensitive to noise from external sources. It is also difficult to determine the relationship between the degree of corrosion and the level of signal, especially during the early stages of corrosion, when the defects in concrete are not yet visible [106,137]. A number of studies have been carried out to solve these problems [138,139,140], but the ultimate solution remains to be found. These problems must be solved before such sensors are used in real-life engineering structures. It is also necessary to determine the most optimal way to deploy the sensors, interpret data from large constructions, etc. Du et al. [141] analysed all-optical photoacoustic sensors converting light energy into ultrasound waves. The sensors are based on nanocomposites of gold and a multimode optical fibre. The sensors were mounted on the surface of reinforcement bars. The sensitivity of the suggested system allowed the authors to determine corrosion loss starting from 0.02 g. Piezoelectric Sensors Ultrasonic methods and embeddable piezoelectric sensors are becoming a popular solution for the monitoring of corrosion of reinforcement steel in RC structures. Piezoelectric sensors generate acoustic vibrations induced by electric voltage and electric charges induced by acoustic waves. Peng et al. [142] suggested a piezoelectric sensor based on ceramics (lead zirconate titanate) and composite materials used to isolate the sensor, screen noise, and enhance the sensor’s signal. The authors assessed the corrosion condition and the degree of damage to steel reinforcement based on the signal’s amplitude. The authors believe that the main drawback of this method is the unidirectionality of the output and input, which makes it difficult to interpret the data in real-life corrosive environments with reinforcement bars arranged in various directions. In their further studies, the authors plan to focus on this problem. Peng et al. [143,144] also analysed sensors based on lead zirconate titanate and modified Fe, Mn, Ca (PZT-4-type), and La, Nb (PZT-5-type). On the whole, their results are close to those obtained in [142]. Su et al. [145] suggested a self-powered wireless sensor network for the automated prediction of steel reinforcement corrosion. The authors used self-powered wireless piezoelectric sensors. They predicted the corrosion rate based on the modelling and monitoring of corrosion data for five years. Sriramadasu et al. [146] used piezoelectric wafer transducers based on ceramics by PI Ceramic (PIC 151) with ultrasonic guided waves. The study assessed the signal characteristics of the longitudinal and flexural-guided wave modes of the sensors attached to the surface of steel reinforcement bars. As a result, the authors established the stages of the initiation and development of corrosion. Quantitative characteristics of the destruction process of metal will be the focus of further studies. Kaur and Bhalla [147] investigated piezoelectric sensors based on AD5933 converters embedded within concrete. The suggested set-up provides information about the strain within concrete, and harvest enough energy to support the conversion and transfer of data without using external power sources. Kocherla et al. [148] demonstrated that this method can be used to identify crack openings smaller than 10 μm. Chen et al. [149] studied commercially available sensors Model No. GU14095A0-25TR-L42 (Shenzhen Yinghai, Ltd., Shenzhen, China). The authors believe that piezoelectric sensors are more promising than ultrasonic sensors due to their lower cost. Xu and Tang [150] used sensors based on PZT 5 ceramic. The authors obtained good agreement between the relative amplitude of the measured signal and the corrosion rate, when the rebar cross-section loss is from 0 to 7%. With a more serious destruction level, the agreement is not so good, because the adhesion between the reinforcement bars and concrete decreases due to the accumulation of corrosion products. Acoustic sensors are generally more sensitive than FOS during the early stages of corrosion, when the amount of corrosion products is still not very large. However, the use of such sensors in real engineering structures is problematic due to their high sensitivity to noise. Further studies may develop effective methods to eliminate noise and present new commercialised solutions. 3.3. Hall Effect Sensors in an Electromagnetic Field The magnetic permeability of carbon steel is 100 times higher than that of other components, i.e., concrete, water, air, and iron oxide. It is possible to estimate the formation of corrosion products on the surface of reinforcing bars by measuring the magnetic properties of the system. The most common sensors used for such measurements are Hall effect sensors. Zhang et al. [133] used a Hall effect sensor (SS495A) in an electromagnetic field (EMMA). The authors fixed three Hall effect sensors at a specific distance from the reinforcement bar and immersed the set-up in concrete. The first sensor was fixed closer to the reinforcing bar with the poles of the electromagnet located at the edges of the bar. HCP and AE methods were used for comparison. The authors obtained a linear dependence between the weight loss of the metal and the voltage increment detected by the Hall effect sensor due to the variation of magnetic induction. Li et al. [151] developed an EMMA system with 24 Hall effect sensors and a U-shaped electromagnet around the examined construction. The system was used to detect the formation of corrosion products on the surface of steel reinforcement. The results corresponded well with the results of EIS. However, the accuracy of detection of uniform corrosion was higher than the accuracy of detection of localised corrosion. The suggested U-shaped electromagnet can only be applied on site to columns and bars which allow arranging three sources of electromagnetic field around them. Otherwise, it will be necessary to modify the system or apply other monitoring techniques. The authors also analysed combined use of electromagnetic sensors and acoustic emission apparatus [152]. The AE sensors were used to detect cracks in concrete induced by the corrosion products. The study demonstrated that there was good agreement between the measurement results and the actual condition of the examined structures. In their latest study [153], the authors focused on combined application of electromagnetic sensors and digital image correlation technique. A combination of physical methods and computer processed images of the condition of the surface can enhance the diagnostic abilities of the system. We should also note that the authors elaborated the design of their sensors, which were, at first, too large. At the moment, they are presented as compact circuit boards and can be commercialised. The digital image correlation technique can be used to examine an arbitrary region selected by the operator, while the EMMA system only monitors the regions where the sensors are located. Considering the aforementioned, the digital image correlation technique is a more technologically advanced method of monitoring. Provided that the cameras recording images and videos are deployed correctly, the monitoring can be carried out remotely. Similar results are obtained when using the thermal image data technique described by Na et al. [154], Kobayashi and Banthia [155], and Kato [156]. Omar and Nehdi [157] used a thermal imaging system born by an unmanned aerial vehicle to monitor bridges. We should note, however, that such techniques are associated with specific problems that have to be solved. Namely, the effect of the electromagnetic field on the state of the reinforcement in concrete and the issue of monitoring large structures, which cannot be performed by using a portable electromagnet. The above considered physical methods and the sensors based on them allow for the accurate assessment of the accumulation of corrosion products on the surface of steel reinforcement bars in concrete during the stage of corrosion development. However, they are less sensitive during the stage of corrosion initiation when the rust film is yet small. Considering this, the most promising solutions appear to be integrated monitoring systems, which combine electrochemical sensors (used to detect the corrosion initiation moment) and physical methods (used to analyse and predict the development of corrosion, the beginning of cracking, flaking of the concrete cover, or collapse of the whole structure). 4. Integrated Sensor Systems The durability of RC depends on a number of factors. Therefore, the monitoring systems should combine several sensors measuring different physicochemical parameters in real time [158]. The best systems for monitoring RC structures are integrated modular systems [159]. The theoretical basis for such systems is a generalised model of corrosion for steel in concrete which comprises all the stages of the process. Duffó and Farina [160] suggested multifunctional sensors, which can be used to monitor the HCP, the corrosion rate (using the LPR methods), the electrical resistivity of concrete, the concentration of chlorides, and the temperature (Figure 4a). The authors designed special software which detects the moment of transition from a passive state to corrosion. The authors stressed that the system is low-cost and can be applied to both new and rehabilitated structures. The system was tested in situ on a large model structure. Titanium rods from a standard cathodic protection system were used as the reference electrode. The surfaces of the rods were modified with iridium and tantalum. Martínez and Andrade [161] experimented with sensors that had similar functions. The sensors were embedded in concrete cubes and in a real bridge. The reference electrodes were systems based on Ti, MnO2, Ag, and Pb. Lu and Ba [162] performed laboratory testing of a multi-sensor system, which can monitor the HCP, the macrocell current, the concentration of chlorides, the electrical resistivity of concrete, and the rate of corrosion (with the LPR method) at the same time. Yu and Caseres [163] developed a multi-electrode system that can be used to monitor the chloride concentration, the HCP, pH, the electrical resistivity of concrete (with a Wenner probe), and the rate of corrosion (with the LPR method). The control samples were used to monitor the weight loss. Laboratory testing was performed on concrete specimens. Qiao et al. [164] used five-electrode sensors with two graphite electrodes (a reference electrode and an auxiliary electrode) and three working electrodes made of low-carbon steel. They were used to estimate HCP, GPT, and electrochemical noise (Figure 4b). The authors also designed software to process the noise signal. The results are in good agreement. There are also examples of effective combinations of electrochemical and physical methods. Thus, Arndt et al. [165] used microcells in an anode-ladder sensor, ground penetrating radar, and active inductive thermography to detect the corrosion of steel reinforcement bars in concrete specimens. The HCP technique was used for comparison. The obtained results were combined and assessed together, which allowed for a more accurate evaluation. Figure 4 Examples of multifunctional sensors: (a) Duffó and Farina [160]; (b) Qiao et al. [164]; (c) Jeong and Kim [166]. Jeong and Kim [166] tested a four-electrode sensor for the simultaneous monitoring of HCP, CR, and current density by means of the LPR method (Figure 4c). A platinized titanium electrode was used as the reference electrode. Ramón et al. [167] suggested an Integrated Network of Sensors for Smart Corrosion Monitoring (INESSCOM) and tested it on a model. The system is comprised of a standard three-electrode cell (with several working electrodes), and a new mode of signal transmission which allows for simultaneous measurement of the electrical resistivity of concrete and the corrosion current density. The present study does not consider a number of methods, for instance, harmonic analysis and electrochemical noise, because there are no monitoring sensors based on them. The list of monitoring systems presented here may be incomplete. At the moment, we continue to collect and analyse information about the current state of research in the field. 5. Conclusions Non-destructive electrochemical methods for assessing the health of steel reinforcement bars in concrete have been studied for 50 years. As a result, a number of standards and regulations have appeared, as well as a series of commercially available systems for in situ monitoring. This is why sensors based on electrochemical methods are more often used to monitor real-life structures. Quality electrochemical methods (HCP, CR) allow to quickly and fairly accurately determine areas with a high probability of corrosion of the reinforcement. This is convenient for inspections with the participation of an inspector, but it gives less valuable information for sensors implanted in concrete, because information about the rate of destruction of reinforcement is more valuable. The electrochemical LPR method is the most common and allows to estimate the corrosion rate of steel reinforcement. This is reflected in a significant number of sensors and monitoring systems. Other quantitative electrochemical methods (GPT, EIS) are less common due to more complex equipment and complex integration into data analysis. Non-destructive physical examination methods for the detection of corrosion in steel reinforcement bars have been studied in detail for the last 10–15 years. The principle of operation of sensors based on physical methods is often based on more indirect measurements than for electrochemical ones. The most common physical fibre optic sensors demonstrate good results at the stage of corrosion development, when adhesion at the reinforcement/concrete boundary deteriorates and cracks of various opening widths form. A large number of design options and applied principles (FBG, LPFG, BOTDA, SMA) give a good prospect for the practical implementation of these devices in the form of ready-made commercial solutions. The situation is similar with sensors based on elastic waves and the Hall effect. The studies should result in a number of standards regulating the principles of application of the most effective approaches. The most effective monitoring systems combine electrochemical and physical methods and sensors based on them. The choice of a particular combination is based on the complexity and importance of the structure, the adversity of the operating conditions, and the economic feasibility of the methods. Author Contributions Conceptualization, D.S. and N.L.C.; validation, O.K.; formal analysis, H.Q.L. and D.A.N.; writing—original draft preparation, D.S. and N.L.C.; writing—review and editing, I.Z.; visualization, D.S.; supervision, V.C.N. and Q.Q.N.; project administration, N.L.C. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Examples of sensors for determining CR. A four-electrode sensor with USB connection: the external electrodes are designed to generate electric current, and the internal ones are to register the potential difference [41]. Figure 2 Examples of sensors based on macrocells in the “ladder” type: the inner electrodes are made of reinforcing steel, the outer one is made of stainless steel [52]. Figure 3 Examples of sensors based on microcells in a bimetallic batch sensor: plates of mild steel and copper with a thickness of 0.1–0.25 mm are separated by a layer of mica with a thickness of 0.1–0.2 mm [56]. sensors-22-03421-t001_Table 1 Table 1 Range of HCPs of steel reinforcements in concrete used for the assessment of the corrosion condition (with regard to the copper/copper sulphate reference electrode at 20 °C). Range of Values Ecor, mV Corrosion Condition of the Reinforcement >−200 Passivity with a probability of 90% −200…−350 Undefined state <−350 Corrosion with a probability of 90% sensors-22-03421-t002_Table 2 Table 2 Range of CR values used to estimate the probability of corrosion of steel reinforcement bars in concrete [34]. Range of Values CR, Ωm Risk of Corrosion of Reinforcement (for 20 °C) <100 high 100…500 moderate 500…1000 low >1000 negligible sensors-22-03421-t003_Table 3 Table 3 Values of corrosion current density (icor) and cross-section loss (Δl) of reinforcement for assessing the condition. Range of Values Corrosion Rate icor, μA·cm−2 Δl, μm·y−1 ≤0.1 ≤1.16 passive state 0.1…0.5 1.16…5.80 low 0.5…1.0 5.8…11.6 moderate >1.0 >11.6 high Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Gulikers J. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091655 polymers-14-01655 Article Analysis of Correlation between Structure and Properties of Carboxymethyl Cellulose Film Loaded with Eu3+ and Tb3+ Fluorescence by Rheology at Different Drying Stages Ye Jun 1 Fu Zichang 1 Rao Jiawei 12 Xiong Jian 3* Alves Luis Academic Editor 1 School of Light Industry and Engineering, South China University of Technology, Guangzhou 510640, China; jye@scut.edu.cn (J.Y.); fzcyouxiang@hotmail.com (Z.F.); jasperrao@outlook.com (J.R.) 2 College of Light Industry and Food Engineering, Guangxi University, Nanning 530004, China 3 School of Food Science and Engineering, South China University of Technology, Guangzhou 510640, China * Correspondence: lcjxiong@scut.edu.cn; Tel.: +86-1364-2628-134 20 4 2022 5 2022 14 9 165525 3 2022 13 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The influences of interactions between carboxymethyl cellulose (CMC) and CMC/europium (III)–terbium (III) (CET) on the structure and properties of the resultant CMC/CET films were investigated by rheology at three stages of the film-drying process. According to the water content at different drying times, the kinetics curves during the film-drying process were drawn. Then, the rheology properties of film-forming solutions during the drying process were characterized by dynamic modulus, Han plots, zero shear complex viscosity and relaxation time. When the water content was 90%, the film contained either 0.1 or 0.2 g of CET, which had good fluidity, while the film with 0.3 g of CET was elastic-dominated. Han plots and XRD analyses showed that the interactions between the CMC and CET were not hydrogen bonds but random entanglements. The zero-shear complex viscosity and relaxation time spectrum confirmed that the entanglements enhanced as the CET content increased. Meanwhile, aggregation formed in the solution of CMC with 0.3 g of CET. When CMC-CET films with different CET additions were compared, the film with 0.2 g of CET had an even and tight sheet structure, the greatest fluorescence intensity, and superior tensile strength of 78.76 MPa. fluorescent carboxymethyl cellulose rheological properties mechanical property polymer chain ==== Body pmc1. Introduction Carboxymethyl cellulose (CMC), as a good film-forming material, has excellent biodegradation and bioactivity and has been extensively sought after for numerous applications. For example, Noshirvani et al. prepared a new nanocomposite film as a packaging material by incorporating chitosan-CMC-oleic acid with ZnO nanoparticles, which inhibited the growth of fungi and improved the shelf life of food [1]. Likewise, Saadiah et al. prepared a composite film using CMC/PVA hybrid polymers to serve as the host of polymer electrolytes, which has increased the ion conductivity by one magnitude order from 10−7 to 10−6 S/cm [2]. Ye et al. prepared fluorescent paper sheets by coating CMC/Eu(III) or CMC/Tb(III) onto a cellulosic paper, which offered a simple and effective route to prepare quality fluorescent papers in the paper-making industry [3]. Bowers et al. designed hyaluronic acid/CMC films as a barrier to prevent adhesions and evaluated the effects on perianastomotic adhesions in previously irradiated rats [4]. Drying processes, such as heating, can affect the quality of the films during their formation [5,6,7,8,9,10,11]. For example, Gregorova et al. revealed that increasing the drying temperature can initiate the interaction between CMC and PVP, resulting in an increase in stiffness by physical cross-linking [12]. With an NMR scanner, Ghoshal et al. followed gelatin from porcine skin solutions dissolved in D2O during the drying process until complete solidification occurred [13]. They also investigated the progress of molecular mobility of starch suspension until the final stage of film drying by spin–spin relaxation times, which revealed that the network formation and precipitation resulted in an increased crystallinity at the bottom surface [14]. Perez-Gago and Krochta reported that drying temperature increased the lipid crystalline morphology and lipid distribution within the whey protein isolate (WPI) matrix [15]. In the meantime, the water vapor permeability decreased for WPI-lipid films as compared to the WPI films. Additionally, during the drying process, there might be a phase transformation. For example, Denavi et al. reported that the thermal gelation transited from a rubbery to a vitreous phase. Nevertheless, some of these reported methods were costly and needed a tedious operation and long test cycle [16]. More importantly, although these studies have suggested how films structures impact their properties, the fundamental understanding of the structure–function relationship is still not well understood. As the heating and drying process proceeded, evaporation of the solvent changed the rheology of the film-forming solution. Rheological properties were sensitive to the film formation process, so most researchers focused on investigating the rheology of the initial film-forming solution and the performance of films. Chun and Ko [17], La Mantia et al. [18], Münstedt et al. [19], Yusova and Lipatova [20], El Miri et al. [21] studied the rheology of film-forming solutions and tensile properties of CMC/starch films reinforced with cellulose nanocrystals. They affirmed a transition from Newtonian behavior to shear thinning, which occurred when cellulose nanocrystals were added. Silva-Weiss et al. reported that viscosities of the film-forming solutions of chitosan and chitosan-corn starch with the extract from murta leaves increased with the addition of the extract, leading to a gel-like structure and thixotropic behavior [22]. Löfflath and Gebhard examined the rheological changes during the initial drying stages of a water-based latex film at different pH, with different co-solvents and neutralizing agents [23]. They found that water and co-solvent evaporation rates were determined as a function of temperature and humidity. However, they did not show the results regarding the film structure and property. In this research, samples with different water contents during different drying times were selected. The interactions between the CMC and CET of the samples at the different drying stages, such as crystallization, entanglement and chain conformations of macromolecules, were then investigated by rheology properties during the process of film-forming, such as dynamic modulus, complex viscosity, Ham plot and Maxwell model on relaxation spectra. Our objective of this research could be, finally, to reveal the relationship between the structure and properties of the blend films by the rheology properties during the process of film-forming. 2. Materials and Methods 2.1. Materials CMC (degree of substitution DS = 0.89, food grade, number-average molar mass Mn = 2.17 × 105 by GPC with wide angle laser light scattering photometer) was produced by Yingte Chemical (Shijiazhuang, Hebei Province, China) Co., Ltd.; Eu2O3 (AR grade), TbCl3·6H2O (AR grade) was produced by Aladdin Chemical Reagent (Shanghai, China) Co., Ltd.; NaOH (AR grade) and Guangzhou Donghong Chemical Plant (Guangzhou, Guangdong Province, China), respectively; HCl (AR grade) and ethanol (AR grade) were purchased from Guangzhou Chemical Reagent Factory (Guangzhou, Guangdong Province, China); KBr (spectral pure) was produced by Tianjin Komio Chemical Reagent (Tianjing, China) Co., Ltd.; Dialysis membrane (MWCO: 2000), Shanghai Yuanye Biotechnology (Shanghai, China) Co., Ltd. Products. All reagents were used without further purification. 2.2. Methods 2.2.1. Preparation of CET Composite CET was prepared as reported by Ye et al. [24]. In our experiments, the amount of CMC was 1.000 g. The concentrations of both TbCl3 and EuCl3 solutions were 0.0318 mol/L, and the amount of both solutions was 15 mL. The reaction was carried at pH = 7.00 and 70 °C for 30 min with stirring by a magnetic stirrer. Then, the mixture was dialyzed with deionized water until there was no AgCl precipitation when AgNO3 was added, and then the mixture was dried in an oven at 70 °C. The structure of CET is shown in Scheme 1 [24], which displayed that CMC connected with Tb3+ and Eu3+ both by inonic and cooerdinate bonds. 2.2.2. Preparation of Blend Films CMC was dissolved in 30 mL of deionized water. The CET sample was added according to Table 1 and stirring was continued. The prepared S0, S1, S2, S3 film-forming solutions (see Table 1) were cast onto a mold of 10 cm × 10 cm and cured at 50 °C, respectively. All of the films were stored in a desiccator until use. In Scheme 1, a, b, c, d, e, f indicate the structure of CMC, the structure of CET, the film-forming solution, the film, the AMF micrograph of the blend film, the SEM image of cross section of the blend film, respectively. 2.2.3. Characterization S0, S1, S2, S3 film-forming solutions (see Table 1) were poured into Petri dishes and dried in an oven at 50 °C. Water loss was periodically weighed by taking out the Petri dishes and weighing them on the electronic balance with a precision of 0.0001 g. The end point of drying was taken to be when no further changes in weight were observed. All weighing processes were completed in less than 10 s during the drying process. According to the data of time and weight, the drying kinetics curves were drawn; rheological properties were measured with an ARES-G2 rheometer equipped with a parallel plate (60 mm diameter; 500 μm gap). A dynamic frequency sweep was conducted by applying an oscillation amplitude within the linear viscoelastic region (5% strain) over a frequency range between 0.1 and 100 Hz at 50 °C. The relaxation spectra were obtained by TRIOS software. The crystallization performance was measured by a D8 Advance X-ray diffractometer manufactured by Bruker, Germany. The Cu target, Kα ray, tube pressure was 40 kV, tube flow was 40 mA, and the scanning range was 5° to 50°. The AFM test was performed by a Multimode IIIA atomic force microscope, manufactured by Veeco, USA. The probe material model was RTESP, the cantilever elastic constant was 40 N/m, the scanning speed was 1 Hz, and the tapping mode was used to measure at room temperature. Morphology analysis was carried out using an EVO18 scanning electron microscope from Carl Zeiss, Germany. The PL test was carried out by Fluorolog-3 fluorescence spectrometer produced by the JY Company of the United States. The fluorescence excitation spectrum was measured at a wavelength of 545 nm, and the fluorescence emission spectrum was measured at an excitation light of 374 nm. The number-average molecular weight (Mn) of CMC was determined by GPC on a WATERS 515 equipped with wide angle laser light scattering photometer (DAWN HELEOS) (laser wavelength is 658.0 nm) and laser differential refractive photometer (Optilab rEX). The mechanical testing was measured using the INSTRON 5565 tensile and compression material testing machine produced in the United States. The film was cut into a rectangular strip of 10 cm × 10 cm. The initial setting was 50 mm, and the stretching speed was set to 1 mm/s. The temperature is 24.54 °C, and the humidity is about 43.2% RH; the variance analysis was performed on the data using R software, p < 0.05. 3. Results and Discussion 3.1. Dynamic Rheological Properties of Film Forming Solutions 3.1.1. Drying Kinetics Curves of Film-Forming Solutions Blend film-forming solutions with initial water contents in the range of 97.72–98.68 w% were dried until their equilibrium moisture contents, which was to construct the drying kinetics curves. The drying kinetics curves of all of the film-forming solutions during the drying processes are shown in Figure 1. It was also observed that all curves had three stages during the drying process. The same result was described as the latex film-forming solution by Löfflath and Gebhard [23]. In the first stage, the rates of water evaporation of all samples were constant. Water contents of all film-forming solutions decreased linearly and evenly. When the water contents of the film-forming solutions reached 90%, all curves turned to the second stage, where the rates of water evaporation of all samples decreased dramatically. The water contents of these film-forming solutions decreased sharply from 90% to 10%. After that, these curves turned to the third stage, where the film formed. Finally, the water contents of these films were kept constant at about 8%. The content of CET has little effect on the film formation time, indicating that CET did not destroy the combination of CMC and water. In the first stage, all of the film-forming solutions were still in a fluid state. Notably, in the second stage, all of the film-forming solutions developed solid-like behavior (yield value). The yield value increased until the films were essentially a solid with the water contents of these film-forming solutions at 10%. The third stage involved the slow diffusion of water out of the films. 3.1.2. Dynamic Modulus Characterization of the Film-Forming Solutions during the Drying Processes Figure 2a–d shows the loss moduli (G″) and storage moduli (G′) of the initial film-forming solutions. Within the tested frequency range (1 to 100 rad/s), the G″s of all initial film-forming solutions were higher than the corresponding G’s of these initial film-forming solutions, with a tendency to approach each other at high frequencies (around 100 rad/s). The dynamic moduli (G′and G″) of all film-forming solutions with water content of 90% are presented in Figure 2e–h. Figure 2e–h clearly indicate that the rheological properties of the film-forming solutions during the drying processes were significantly different. For S1-90, liquid-like viscoelastic behavior was still observed (Figure 2f). The rheological behavior of S2-90 was similar to that of CMC, meaning their G’s increased more rapidly with frequency than G″s. When the film-forming solutions dried to a water content of 30%, dynamic spectra of S1-30 and S2-30 at a given frequency exhibited a significant difference (Figure 2i–l). The G″ of S1-30 was higher than G´ at low frequencies (Figure 2j). Notably, the G’s of S2-30 and S3-30 were significantly higher than their G″s from 1 to 100 rad/s (Figure 2k,l). It can be seen from the dynamic modulus information that these initial film-forming solutions with CET exhibited typical liquid-like viscoelastic behavior such as a CMC solution. When the film-forming solutions dried to a water content of 90%, the curves of S0-90 intersected at about 10 rad/s, and G´ of S2-90 was almost equal to its G″ in the frequency range 1–10 rad/s, which means that the liquid–solid transition happened for S0-90 and S2-90 [25]. Moreover, the interactions of the polymers in S2-90 were stronger than that in S0-90. G´ of S3-90 was higher than G″, showing obvious elastic behavior (see Figure 2h). It was worth noting that, from S1-90 to S3-90, as CET water content decreased, the frequency at which G″ almost intercepted G´ was lower. These results indicated that CET contributed to a larger extent to elasticity at this stage of the drying process. This might be attributed to the increase in inter-chain couplings between CET and CMC. When the film-forming solutions dried to a water content of 30%, the curves of S1-30 intersected at 40.5 rad/s, showing a typical concentrated polymer solution [26]. At this time, entanglements between chain segments of CMC and chain segments of unbonded CET were observed in S1-30. G’s of S2-30 and S3-30 were significantly higher than their G″s, indicating that these systems were predominantly elastic. Furthermore, G´ and G″ were almost parallel to each other and slightly frequency-dependent. This might be attributed to weak gel formation after inter-chain entanglements between chain segments of CMC and chain segments of unbonded CET at this drying stage [27]. 3.1.3. Han plots of the Film-Forming Solutions during the Drying Processes The Han plot is to reveal the lgG′–lgG″ relationship, which has been widely used to judge the compatibility and phase separation of the blend system of Hao et al. [28,29]. If the relationship matches Equation (1), there is less phase separation between the polymers in the film-forming solution. (1) lgG′ ∝ 2lgG″  Figure 3 shows the Han plots of the film-forming solutions dried to different water content and the XRD diffraction pattern of the S0–S3 film. As shown in Figure 3, for the initial film-forming solutions, the Han plots of the four samples overlapped, suggesting that there were some interactions between the CMC chain and the unbonded chain segment in the CET macromolecule. [30]. At the low-frequency region, the slope of the Han plots was close to 2, which indicated good compatibility between CET and CMC for all of the initial film-forming solutions. The Han plots of S0-30, S0-90, and S1-30, S1-90 still overlapped well, respectively (Figure 3), indicating that they still had good compatibility. For S2-90, the slope of the Han plot was close to 2. However, the slope of the Han plot of S3-90 deviated from 2. This result indicated that there was phase separation in the S3-90 [28]. Moreover, this phenomenon was more significant in the S2-30 and S3-30. The phase separation contributed to different chain structures between CMC and CET in which CMC was cross-linked by Eu3+ ions or Tb3+ ions. The XRD pattern of the S0–S3 film is shown in Figure 3d. The crystallinity can be obtained by the following formula [31]:(2) Cr.I.=(I002−Iam)/I002×100% where Cr.I. is the degree of crystallinity, I002 is the maximum intensity of the (002) lattice diffraction and Iam is the intensity diffraction at 2θ = 18°. The crystallinity of the S0–S3 film was calculated to be 23.1%, 23.0%, 18.6%, and 15.8%, respectively. Obviously, a small amount of CET macromolecules in the S1 film just entangled with the chains of the CMC in amorphous rather than destroyed the order of the CMC chains. Increasing the addition of CET led to an increase in the interactions of CMC chains with CET in crystalline during the film-forming solutions, resulting in a decrease in crystallinities of other films. This indicated that the interaction is not a hydrogen bond but a random entanglement during the drying process. 3.1.4. Complex Viscosity of the Film-Forming Solutions during the Drying Processes The complex viscosity (η*) of different film-forming solutions dried to different water content was plotted against the frequency, as shown in Figure 4. η* values at a given frequency increased significantly as drying progressed. Moreover, for all initial film-forming solutions, a Newtonian plateau occurred only at lower frequencies and shear thinning was observed at higher frequencies. For dried film-forming solutions, the Newtonian plateau disappeared, and shear thinning was observed over the tested range. In Table 2, n is the nth sample of the different film-forming solutions, while I, 90 and 30 represent the initial water content, 90% water content and 30% water content of the film-forming solutions, respectively. The zero-shear complex viscosity (η0* ) of each sample calculated according to the Cross model is shown in Table 2 [32]. As mentioned above, the addition of CET resulted in entanglement between the CMC chain and the unbonded chain segment in CET. However, the addition of CET weakened the interaction between the CMC chains. The addition of CET made the distance between the CMC chains increase, which greatly weakened the interactions between the CMC chains. This result was similar to the CMC solution diluted by CET; thus, we named the result the “dilution effect”. The η0* of the S1-I, S1-90 and S1-30 was less than that of S0-I, S0-90 and S0-30, respectively. Despite the good compatibility, the tiny content of CET may lead to less entanglement in the S1 film-forming solution. Once the entanglement was weaker than the “dilution effect”, a decrease in η0* occurred. The η0* of S2-I was less than that of S0-I, while during drying, η0*s of S2-90 and S2-30 were higher than that of S0-90 and S0-30, respectively. This result indicated that CET can “dilute” the CMC solution for the S2-I, and the entanglement increased as the water content decreased. The interactions of entanglements took over major roles as drying processes, which resulted in an increase in the η0*s [33]. For the S3 film-forming solution, all samples had larger η0* than that of S0, indicating that more entanglement existed in the S0 film. 3.1.5. Study on Relaxation Spectra of the Film-Forming Solutions during the Drying Processes The viscoelastic behavior of the film-forming solutions can be analyzed using the generalized Maxwell model, and the expression of the relaxation modulus is as follows:(3) G(t)=∑i=1NGiexp(−t/λi) where Gi and λi are the relaxation moduli and relaxation time of the ith element of the total number N of the modes [34]. Figure 5 shows the relaxation time spectrum of all film-forming solutions dried to different water contents. As shown in Figure 5, the relaxation modulus increased gradually with the decreasing water content of film-forming solutions. As the CET content increased, the relaxation modulus of film-forming solutions shifted to higher values in the long-range relaxation area due to the increased influence of the dissolved CET chain segment, which has slower relaxing modes. If the film-forming solution is regarded as composed of multiple parts, each part can be regarded as a Maxwell unit. With that being said, the sum of the contributions of each Maxwell unit to the relaxation modulus is the relaxation modulus of the film-forming solution [34]. The relaxation tendency of S0-I, S0-90, S0-30, S1-I, S1-90, S1-30, S2-I and S3-I was found to be similar in that the relaxation modulus decreased with increased relaxation time, while the relaxation tendency of S2-90, S2-30, S3-90 and S3-30 changed. As compared to S3-30, the relaxation modulus of S2-30 changed very gently. Moreover, the contributions by each Maxwell unit to the modulus of S2-30 were consistent. However, there was a significant decrease in the modulus of S3-30 in the small-scale relaxation region. Especially, the contribution of each Maxwell unit to the modulus is largely different. This suggested that the distributions of the interactions between of CMC and CET macromolecules of S3-30 were more nonuniform than that in the S2-30. As the CET content increased, CET macromolecules aggregated severely and nonuniformly, but some of the unbonded chain segments in CET did not entangle with CMC chains. Thus, great changes in the contributions of each Maxwell unit to the modulus may infer the nonuniform distribution of interactions. From the rheology research, we confirmed that there were entanglements rather than crystallization between the CMC chain and the unbonded chain segment in CET macromolecule during the drying process, but the interactions were nonuniform at higher CET contents. The mechanism of interactions between the CMC chain and the unbonded chain segment in the CET macromolecule during the drying process is shown in Figure 6. 3.2. Relationship between the Structure and Properties of Blend Films and the Rheology of Film-Forming Solution 3.2.1. The Relationship between the Morphological Structure of Blend Films and the Rheology of the Film-Forming Solution The AFM morphology of the S0–S3 films is shown in Figure 7. The average roughness (Ra) of the films was 1.42, 3.79, 5.03, 5.60 nm, respectively, showing that the surface of the blend film was rougher. When the amount of CET added was 0.3 g, the roughness of the film reached the highest value. Compared with Ra of S0 (pure CMC), the Ra increments of S1, S2, and S3 films were 2.37, 1.24, and 0.57, respectively. The reason might be that the entanglement enhanced with the increase in CTE contents; however, the increments were not linear, which can be supported by the results of Figure 2, Figure 3 and Figure 4. Therefore, the roughness of the film increased as the CET increased. Figure 8a−d shows the SEM images of the top surface of S0, S1, S2, and S3 films, respectively. It can be seen from the figure that the top surfaces of all films were relatively flat. There were no obvious holes, indicating that CET is uniformly distributed in the CMC matrix, but the degree of uniformity of the distribution was related to the CET content. Moreover, there were tiny cracks in the S0 and S1 films, but the cracks disappeared in the S2 and S3 films. The entanglement made cracks disappear on the top surface of the S2 and S3 films. Figure 8e−h shows the SEM images of the cross section of S0, S1, S2, and S3 films, respectively. In Figure 8e−h, the tight sheet structure was observed for the S0 and S1 films. Moreover, as the CET content increased, sheet structures interpenetrated each other to show a tight fiber aggregate structure for S2 and S3 films. This suggested that blend films inherited the neatly arranged sheet structure in the CMC film, and the sheet structure in the blend films was neater and extended in the Y axis. Among blend films, the S2 film showed a more uniform sheet structure, while the thickness of the sheet in S3 film appeared more uneven. The uneven sheet thickness in Figure 8h can be explained by the nonuniform distribution of interactions between CMC and CET macromolecules mentioned in Figure 5. 3.2.2. The Relationship between the Morphological Structure of Blend Films and the Rheology of Film-Forming Solution Figure 9a shows the excitation spectra of the S1–S3 films. The excitation spectra of the S1~S3 film measured at a wavelength of 545 nm peaked at 353, 371 and 380 nm, respectively, showing the 4f8→4f75d1 transition peak of Tb(III). Figure 9b shows the emission spectrum of the S1~S3 film measured at an excitation light wavelength of 374 nm. When the excitation wavelength was 374 nm, the S1~S3 film exhibited an emission peak at 488, 543, 590 and 615 nm. In addition, the strongest fluorescence intensity was exhibited by the S2 film, followed by the S3 film and then the S1 film. The emission peak at 543 nm had the highest fluorescence intensity, assigned to the characteristic 5D4→7F5 transition of Tb(III). The emission peak at 488 nm belonged to the 5D4→7F6 transition of Tb(III). The emission peak at 590 nm was formed by the superposition of 5D4→7F4 transition of Tb(III) and 5D0→7F1 magnetic dipole transition of Eu(III). The emission peak at 615 nm was formed by the superposition of the 5D4→7F3 transition of Tb(III) and 5D0→7F2 electric dipole transition of Eu(III). Moreover, the fluorescent intensities of the S1 and S2 films increased with the increase in the CET content but decreased in the fluorescent intensity of S3. From the analysis in Figure 2, Figure 3, Figure 4 and Figure 5, the reason for this phenomenon might be the nonuniform aggregation that was caused by the concentration quenching [35,36]. Table 3 shows the effect of CET content on the tensile stress–strain curve of all of the blend films. Compared with the S0 film, the tensile strength of the blend films was improved. Especially, the S2 film was significantly improved to reach the maximum value of 78.76 MPa (see Table 3). The tensile modulus of the blend film increased with the addition of CET, and the blend film with 0.3 g of CET had the largest value. The elongation at the break decreased overall with the increase in the CET content. As compared to other blend films (see Table 4), the blend films reported in this paper had excellent mechanical properties. It was worth noting that the increase in tensile strength was often accompanied by a decrease in elongation at the break, while when the CET content was increased from 0.1 to 0.2 g, the tensile strength of the film was significantly improved, but there was no significant change in elongation at break. The tensile moduli of the blend films increased with the strength in the entanglements, while tensile strengths and elongation at the break of the S3 film decreased because of the nonuniform aggregation in this S3 film. Based on the analysis of the structures and properties of the blend films, we can infer that there were interactions between the CMC chain and the CET macromolecule in the blend films to improve the structure and properties of the films. For example, the disappearance of cracks was observed in S2 and S3 films, and a tight fiber aggregate structure was observed in Figure 8. However, as the CET content increased, these interactions had complex effects on the structure and properties of the films rather than a simple promotion. For example, the fluorescence intensity and the tensile strength of the S3 film decreased at the maximum content of CET, as shown in Figure 9 and Table 3 Rheology was involved in investigating the detail of these interactions to illuminate the mechanism of influence on the structure and properties of the films, which can be supported by the results from Figure 2, Figure 3, Figure 4 and Figure 5. 4. Conclusions The AFM micrograph showed the average increase in the roughness of blend films with the increase in the CET amount. The SEM image showed that the cracks on the top surfaces disappeared in the S2 and S3 films. The fluorescence spectra showed that the strongest fluorescence intensity was exhibited by the S2 film, which is followed by the S3 film and then the S1 film. The mechanical test results showed that the S2 film had the strongest tensile strength of 78.76 MPa. Based on the above observations, we assumed the existence of the interactions between the CMC chain and the CET macromolecule in the blend films. Through the Han plot, we confirmed our hypothesis that there were indeed some interactions. The XRD results further confirmed that the interactions were not hydrogen bonds but random entanglements during the drying process. It could be known from the degree of the increment in the roughness of the blend films that the entanglements did not increase linearly with the increased CET content. Moreover, the relaxation time spectrum revealed that the distributions of the interactions between the CMC and CET macromolecules in the S3 film were more nonuniform, which was caused by nonuniform aggregation. The distinct features of traditional films are responsible for the interest in using them for separation processes. During the past several decades, cellulose-based films have already been dramatically utilized in food, pharmaceutical, and medicine fields, etc. Due to inheriting some unique structures and characterizations, cellulose-based materials with inorganic compounds are exerted in biological systems and advanced applications successfully [36,40,41,42]. In this study, the fabrication of CMC incorporation of cellulose–based nanocomposite can give rise to synergistic functions, that is, the blend films not only have good mechanics properties but also develop fluorescent functionality with narrow emission profiles of rare earth metal ions. This enables potential applications for the films such as probes, sensors and labels, anti-counterfeit technology and monitoring of drug release. Author Contributions Methodology, J.Y. and J.X.; software, Z.F. and J.R.; validation, J.Y. and J.X.; data analysis, J.Y., Z.F. and J.R.; data curation, Z.F.; writing—original draft preparation, Z.F. and J.R.; writing—review and editing, Z.F. and J.R.; supervision, J.Y. and J.X.; project administration, J.Y. All authors have read and agreed to the published version of the manuscript. Funding The National Natural Foundation of China (project 31270617) and State Key Laboratory of Pulp and Paper Engineering (project 2016C12). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. The funders had no role in the writing of the manuscript or in the decision to publish the results. Figures, Scheme and Tables polymers-14-01655-sch001_Scheme 1 Scheme 1 The process of CET synthesis and film-casting. Figure 1 The drying kinetics curves of S0–S3 film-forming solutions. Figure 2 Loss modulus and storage modulus of (a) S0-I, (b) S1-I, (c) S2-I, (d) S3-I, (e) S0-90, (f) S1-90, (g) S2-90, (h) S3-90, (i) S0-30, (j) S1-30, (k) S2-30, (l) S3-30. Note: I, 90 and 30 represent the initial water content, 90% water content and 30% water content of the film-forming solutions, respectively. The following legends are the same. Figure 3 Han plots of the film-forming solutions dried to different water content (a) initial, (b) 90%, (c) 30%, (d) the XRD diffraction pattern of the S0–S3 film. Figure 4 Complex viscosity of (a) S0, (b) S1, (c) S2, (d) S3 film-forming solutions dried to different water content. Figure 5 Relaxation time spectrum of (a) S0, (b) S1, (c) S2, (d) S3 film-forming solutions dried to different water contents. Figure 6 The mechanism diagram of entanglement between the CMC chain and the unbonded chain segment in CET macromolecule during the drying process. Figure 7 AFM micrograph of the blend films (a) S0 film, (b) S1 film, (c) S2 film, (d) S3 film. Figure 8 SEM image of top surface of (a) S0 film, (b) S1 film, (c) S2 film, (d) S3 film, and cross section of (e) S0 film, (f) S1 film, (g) S2 film, (h) S3 film. Figure 9 Fluorescence spectra of the blend films: (a) excitation spectra, (b) emission spectra. polymers-14-01655-t001_Table 1 Table 1 Compositions of the different film-forming solutions. Film-Forming Solutions WCMC/g WCET/g Vwater/mL Initial Water Content/% S0 0.4 0 30 98.68 S1 0.4 0.1 30 98.36 S2 0.4 0.2 30 98.04 S3 0.4 0.3 30 97.72 polymers-14-01655-t002_Table 2 Table 2 Zero shear complex viscosity η0* (Pa·s) of the different film-forming solutions. Sn-I * Sn-90 * Sn-30 * S0 1.49 160.00 562.75 S1 0.64 27.74 84.60 S2 1.00 280.35 911.58 S3 1.56 1372.68 5339.03 *: n = 0, 1, 2, 3. polymers-14-01655-t003_Table 3 Table 3 Mechanical properties of S0~S3 films. Film Tensile Modulus (GPa) Tensile Strength (MPa) Elongation at Break (mm/mm)% S0 2.603 d ± 0.38 56.90 b ± 3.47 4.26 a ± 0.96 S1 3.126 bc ± 0.30 57.18 b ± 10.13 3.68 b ± 1.04 S2 4.037 ab ± 0.37 78.76 a ± 3.49 3.74 b ± 0.35 S3 4.179 a ± 0.16 59.25 b ± 6.83 2.70 c ± 0.09 Mean of five measurements ± SD values in the same column with different letters are significantly different (p < 0.05). polymers-14-01655-t004_Table 4 Table 4 Comparison of mechanical properties of the blend films with the other CMC films. Film Tensile Modulus (GPa) Tensile Strength (MPa) Thickness (μm) References CMC/SPI film 1.228 65.40 109.0 Su et al. [37] PVP/CMC hydrogel film 1.423 20.93 - Roy et al. [38] starch/CMC film - 16.11 80.0 Ghanbarzadeh et al. [39] Our work 4.037 78.76 41.2 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Noshirvani N. Ghanbarzadeh B. Mokarram R.R. Hashemi M. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093351 sensors-22-03351 Review Metal Oxide Chemiresistors: A Structural and Functional Comparison between Nanowires and Nanoparticles https://orcid.org/0000-0001-9955-5118 Ponzoni Andrea 12 Taurino Antonietta Academic Editor 1 National Institute of Optics (INO) Unit of Brescia, National Research Council (CNR), 25123 Brescia, Italy; andrea.ponzoni@ino.cnr.it; Tel.: +39-030-3711440 2 National Institute of Optics (INO) Unit of Lecco, National Research Council (CNR), 23900 Lecco, Italy 27 4 2022 5 2022 22 9 335116 3 2022 25 4 2022 © 2022 by the author. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Metal oxide nanowires have become popular materials in gas sensing, and more generally in the field of electronic and optoelectronic devices. This is thanks to their unique structural and morphological features, namely their single-crystalline structure, their nano-sized diameter and their highly anisotropic shape, i.e., a large length-to-diameter aspect ratio. About twenty years have passed since the first publication proposing their suitability for gas sensors, and a rapidly increasing number of papers addressing the understanding and the exploitation of these materials in chemosensing have been published. Considering the remarkable progress achieved so far, the present paper aims at reviewing these results, emphasizing the comparison with state-of-the-art nanoparticle-based materials. The goal is to highlight, wherever possible, how results may be related to the particular features of one or the other morphology, what is effectively unique to nanowires and what can be obtained by both. Transduction, receptor and utility-factor functions, doping, and the addition of inorganic and organic coatings will be discussed on the basis of the structural and morphological features that have stimulated this field of research since its early stage. metal oxides nanowires nanoparticles chemiresistors self-heating surface functionalization surface termination Fondazione CariploRegione LombardiaEMPATIA@LECCO This research was funded by Fondazione Cariplo and Regione Lombardia through the project EMpowerment del PAzienTe In cAsa (EMPATIA@LECCO). ==== Body pmc1. Introduction Metal oxide nanowires have become a popular class of nanostructures for the development of electronic and optoelectronic devices. Their single-crystalline structure free from extended defects, together with their nm-sized cross section and μm-sized length, make them optimal candidates to merge the potentialities of the nanoscale with efficient electrical transport over much longer distances, with no constrictions from grain boundaries, which typically affect films composed by nanostructures with spherical shapes. In 2001, a fundamental milestone for the technological exploitation of nanowires was reached with the development of a cheap method suitable to synthesize metal oxide nanowires in large amounts and with controlled length, cross-section shape and size [1]. About one year later, these materials were exploited for the first time to develop chemiresistors based on a disordered network of SnO2 nanowires [2]. These papers inspired intense research in the field of gas sensors, and more generally, in the areas of electronics and optoelectronics. The field has rapidly grown and a broad range of papers have been published about all the relevant aspects of this technological branch. Dedicated reviews have been and are still being regularly published to track the progress in the synthesis of these nanostructures [3,4,5], their effective integration into functional substrates and devices [6,7,8] and their exploitation in different applicative fields [9,10,11]. This witnesses the potentialities and the appeal of these materials for science and technology in a broad sense, and specifically for chemosensing. About 20 years after the aforementioned pioneering papers, the present work aims at reviewing the state of the art of nanowire-based chemiresistors, emphasizing the comparison with nanoparticles. These are the traditional materials employed in the field but are also the target of recent research. In particular, the manuscript will continue with a section dedicated to the structural and morphological features of nanowires and nanoparticles, from which their potentialities and suitability for technological solutions stem. Synthesis methods will also briefly be reviewed. Gas-sensing models will then be introduced, starting from the knowledge developed with nanoparticles and showing how these concepts have been extended to properly account for shape effects. Finally, some relevant experimental results will be reported to highlight how ideas initially developed for nanoparticles have been further developed to work with nanowires and to discuss those solutions that are unique either to nanoparticles or nanowires. 2. Nanowires: Structure, Synthesis and Gas-Sensor Configurations This paragraph will briefly overview the structural and morphological features of nanowires, highlighting the differences with respect to nanoparticles. Synthesis methods to prepare single-crystalline nanowires and chemiresistor layouts adopted to exploit these nanostructures will also be resumed. Electrical transport across the circuit elements of chemiresistors based on metal oxide (MOX) nanostructures will finally be introduced in this section. Their dependence from the elementary crystallites will be discussed as the basis for gas-sensing models, and results summarized in Section 3 and Section 4, respectively. 2.1. Structural and Morphological Features of Nanowires The high crystalline quality and the well-defined morphology of nanowires has been widely reported in the literature, particularly by means of electron microscopy techniques, emphasizing the novelty of these features with respect to traditional nanoparticles. An overview of the morphological features of nanowires is reported in Figure 1a–c; images of nanoparticles are reported for comparison in Figure 1d,e. The large length-to-diameter aspect ratio, the absence of grain boundaries through the whole length of the wire, and the well-defined surface termination are the main differences with respect to nanoparticles and nanoparticle assemblies. Nanowires used in gas sensing are typically a few μm or tens of μm long; their cross section has a well-defined polygonal shape, typically a rectangle, a square or a hexagon, with a size of the order of a few tens of nm. The axis of the wire corresponds to a crystalline direction; the exposed surfaces correspond to crystalline planes and are often found to feature an almost atomically flat termination. In addition to nanowires, terms such as nanobelts, nanorods are also used to refer to elongated, single-crystalline nanostructures. A nanobelt is typically used for crystallites with a rectangular cross section, such as those shown in Figure 1; nanowire is used in case of a nearly squared cross section; while nanorod is used for single crystals with a smaller length-to-diameter aspect ratio, though much larger than unity. Nanocubes and nanoprisms will also be considered in this review; these are interesting as links between the ideal nanoparticle and nanowire morphologies. Indeed, nanocubes and nanoprisms feature an almost isotropic shape, typical of nanoparticles, together with faceted surfaces, typical of nanowires. Before to conclude this section, it is worth mentioning that the name nanowire, or porous nanowire, is sometimes used in the literature to indicate elongated structures composed by assemblies of elementary spherical nanoparticles. In the present work, these nanostructures will not be considered in the family of nanowires; this is because they lack a single-crystalline nature, and concerning electrical properties, their electrical transport within each individual assembly is affected by grain boundaries. Owing to these characteristics, they will be regarded as members of another family of materials, namely the hierarchical assemblies, and will be used for comparison with single-crystalline nanowires. 2.2. Synthesis of Metal Oxide Nanowires The first paper about nanowire chemiresistors employed a disordered network of SnO2 nanowires synthesized through the vapor–solid (VS) mechanism [2]. The setup was based on a tubular furnace in which a temperature gradient was realized. MOX powders were used as source material and placed in the warmest region of the tube. The vacuum background was of the order of 10 mbar, and the temperature in this region was adjusted to sublimate the powders (around 1350 °C). An Ar flow was used to transport vapors toward the colder region where condensation over substrates occurred [1]. A similar setup has also been widely used for the synthesis through the vapor–liquid–solid (VLS) mechanism. In this case, the substrate is precoated by metallic nanoparticles that catalyze the growth of the nanowire structures. The growth of the oxide nanowire arises from the formation of a eutectic alloy between the metal and the vapor species. As a consequence of the continuous fed of vapors, the alloy reaches a supersaturation condition and the MOX nanowire grows below the alloy nanoparticle [1]. A schematic representation of the VLS mechanism is provided in Figure 2a. The choice of the catalytic nanoparticles, their size and density over the substrate are useful parameters to control the diameter, distribution and density of nanowires [15,16]. Chemical vapor deposition (CVD) techniques have been developed to synthesize nanowires via VS and VLS mechanisms [17,18,19]. With respect to the aforementioned physical methods, CVD techniques allow for the reduction of the synthesis temperature since they do not require the sublimation of the source powders. To match the requirements of different device layouts, techniques for deposition over selected areas have been further developed. These include lift-off processes employing high-temperature resists [20], the patterning of the catalyst (for VLS growth) [21] or of the substrate roughness (for VS. growth) [22]. In some papers, the electrodes of the gas-sensor device have been used as catalysts to selectively promote the growth of the nanowires directly from the electrodes [18]. An additional effective method for the patterned growth of nanowires is thermal oxidation. In this case, a metallic film is initially deposited over the desired regions and it is further oxidized at high temperature in an oxidative atmosphere to induce the growth of wire-shaped nanostructures [23,24]. It is worth mentioning that the process often starts with the formation of a polycrystalline oxide layer from the metallic film. This polycrystalline layer is typically composed by rounded grains and nanowires grow out from this layer in a second time, as the oxidation process continues. In principle, a residual polycrystalline film may remain beneath the nanowires, resulting in a device whose properties are given by both morphologies. A similar situation may also occur with nanowires grown by VS and VLS mechanisms directly over the sensor substrate. Indeed, if the synthesis is not properly controlled, spurious depositions may occur, and in the worst cases, a continuous polycrystalline layer may deposit under the nanowire mat. In turn, the undesired film may introduce a non-negligible contribution to the final electrical and sensing properties of the device. In these situations, electron microscopy investigation of gently scratched areas and imaging of cross-section samples are useful methods employed to assess whether the electrical and gas-sensing properties of the device are dominated by nanowires or nanoparticles [20,25]. An example is shown in Figure 2b. In this specific case, the nanoparticles underlying the nanowire network were disconnected one another and they were reasonably supposed to not contribute to the electrical and sensing properties of the device [25]. Figure 2 (a) Schematic representation of the vapor–liquid–solid (VLS) growth mechanism employed in the preparation of single-crystalline nanowires; (b) SEM image showing the residual nanoparticles underlying a network of WO3 nanorods synthesized by oxidation of a polycrystalline film. In this case, nanoparticles are disconnected and are not expected to contribute to the electrical and sensing properties of the macroscopic layer. Figure 2a is from [26]; Figure 2b is reprinted from [25], Copyright (2011), with permission from Elsevier. Wet chemistry techniques, such as, for example, hydrothermal and sol-gel methods, have also been developed to prepare nanowires. These are appealing approaches owing to their reduced cost, low synthesis temperature and the possibility to finely control the nanowire properties, including the addition of dopants, by properly selecting the basic chemicals and tuning the solution composition [27,28]. Gas sensors are typically realized by collecting the nanowires from the synthesis apparatus, dispersing them into a solution or a paste that is further directly applied over the sensor substrate [29,30] or used as ink for screen printing [31]. In some cases, MOX nanoparticles have been used as seed crystals to promote the selective growth of nanowires directly over the desired areas of the sensor substrate [32,33]. 2.3. Nanowire-Based Chemiresistors: Device Configurations The simplest way to exploit nanowires in chemiresistor devices is by dispersing a disordered ensemble of these nanostructures over a substrate already provided with electrodes and the heating element. A schematic representation of such a kind of device is shown in Figure 3a together with the main components of its equivalent electrical circuit. Identifying the role of these components in the electrical transport of the device is fundamental to understand its sensing properties, which may be regarded as a gas-induced perturbation to the transport properties of the device. In the equivalent circuit, three main components can be found: nanowires, nanowire–nanowire junctions and the nanowire–electrode contacts, each represented by its equivalent resistor, namely Rnw, Rj and Rc [34]. In principle, these three elements are the same as in chemiresistors based on nanoparticles; nonetheless, the different morphological features of spherical and wire-shaped nanostructures introduce differences in the relative weight of these equivalent resistors. Indeed, for nanoparticles, it is widely reported that junctions dominate over crystallites, Rj >> Rnp, where Rnp is the resistance of nanoparticles and Rj is more often named the grain-boundary resistance. Similarly, considering the large number of grain boundaries in nanoparticle layers, it is often found that Rj >> Rc. As a result, at least in a first approximation, grain boundaries are usually regarded as the dominant elements in nanoparticle-based devices [35,36]. As far as wire-shaped crystallites are considered, there is a large amount of experimental and computational evidence that electrical transport may be no more junction-dominated and crystallites may play a non-negligible role [37,38,39,40]. As will be discussed in Section 3, this opens the interesting prospective for the exploitation of the sensing properties of the nanowire body, in addition to nanowire–nanowire junctions. Moreover, considering the reduced number of junctions in the network with respect to the nanoparticle networks, the condition Rj >> Rc may be no more valid. This will be particularly important in the case of non-Ohmic electrode–semiconductor contacts. In this situation, the reversely biased contact may feature a contact resistance, Rcr, that may be comparable with or even larger than Rj and Rnw [19]. The role of electrodes in metal oxide chemiresistors has been recently reviewed based on results achieved with thin-film and thick-film technologies [41]. It provides a complete overview of the different conduction regimes that may take place at the metal–semiconductor interface and the related equations, which are also valid in the case of nanowires. Another important configuration exploited with nanowire materials is the chemiresistor based on a single nanowire, in which the two electrodes are directly connected through the metal oxide nanowire. A schematic representation of this kind of device is shown in Figure 3. This class of devices is very appealing for both fundamental studies and applications, which will be discussed in the next paragraphs. Before addressing these arguments, it is worth briefly discussing the electrical circuit of the single-nanowire device. The absence of nanowire–nanowire junctions and the high crystalline quality of the nanowire ease the electrical transport through the semiconducting material. This means that the value of Rnw may become comparable with, if not lower than, Rcr [42]. To decouple Rnw from Rcr, the electrical characterization is often carried out using the four-probe configuration instead of the two-probe one [19,43]. As an example, Rnw ≈ 76 MΩ and Rcr ≈ 200 MΩ (for an applied voltage of 1 v) were measured at room temperature with a device based on a single SnO2 nanowire. The wire had a diameter of 50 nm and was placed orthogonally between two parallel Pt electrodes spaced by about 5 μm [19]. 3. Gas-Sensing Mechanism A chemiresistor is a gas sensor based on a sensitive layer that undergoes a variation in its electrical properties upon the interaction with gaseous molecules. The basic concepts underlying the working mechanism of these devices were mainly developed working with thick-film materials and have later been extended to properly account for the nanowire morphology. The sensing mechanism is usually schematized by means of two functions: the receptor function, which recognizes a chemical substance at the surface of the semiconducting MOX; and the transducer function, which transduces the chemical reactions at the semiconductor surface into the electric output signal [44]. The utility factor, namely the effectiveness of the layer in allowing a proper diffusion of gas through the layer itself, has also been considered in more recent years [45]. These three concepts will be separately introduced in Section 3.1, Section 3.2 and Section 3.3 as the basis of the framework underlying the scientific and technological solutions explored in the literature of nanowire-based chemiresistors. 3.1. Receptor Function Red-ox reactions occurring between the gas molecules and the MOX layer represent the widest-acknowledged phenomena underlying the receptor function of MOX materials. In these interactions, a key role is played by oxygen ions populating the surface of any metal oxide exposed to ambient air. At low temperatures (below 150 °C), oxygen is mainly adsorbed molecularly, either in its neutral physisorbed form (O2), or in its ionic chemisorbed form (O2−). Further increasing the temperature, the population of oxygen species becomes dominated by chemisorbed ions in their atomic forms, namely O− or O2−, the latter dominating above 400–450 °C [35]. With the aim to focus on aspects responsible for the modulation of the electrical properties of the oxide material, gas-sensing papers typically adopt the following scheme to describe the chemisorption process [35]:(1) β2O2,gas+αe−↔ Oβ,surf−α where O2,gas is the molecular oxygen adsorbed from the gas phase; e− is the elementary electrical charge withdrawn for the MOX conduction band; Oβ,surf−α is the ionic species chemisorbed over the oxide surface; β assumes the values of 1 or 2 for the molecular and atomic forms, respectively; while α is 1 or 2 for singularly and double-ionized ions. Though Equation (1) provides an oversimplified view of the chemisorption process, it clearly shows the capability of the oxygen chemisorption process to modulate the electrical properties of the MOX semiconductor by modulating its density of charge carriers. This mechanism is further exploited to detect molecules other than oxygen. Indeed, chemisorbed oxygen ions work as active species promoting the oxidation of other gas molecules (and the reduction of the oxide surface). For example, using carbon monoxide (CO) as prototypal molecule, its interaction with metal oxide gas sensors is typically described according to Equation (2) [35]:(2) βCOgas+Oβ,surf−α→kβCO2,gas+αe− where COgas is the CO molecule in the gas phase that adsorbs over the oxide surface; Oβ,surf−α, e−, α and β are as above; CO2,gas is the carbon dioxide molecule released back in air after the interaction between CO and the oxide semiconductor; and k is the reaction constant. However, despite its oversimplification, Equation (2) shows how the oxidation of the adsorbed CO molecule causes electrons previously withdrawn by oxygen chemisorption to be released back in the conduction band of the semiconductor. As far as gases other than CO and O2 are concerned, reducing gases, such as, for example, ethanol and acetone, are oxidized by the interaction with chemisorbed oxygen following a reaction similar to the one reported in Equation (2) [46]. Oxidizing compounds, such as ozone and nitrogen dioxide, will oxidize the metal oxide surface through a reaction similar to Equation (1) [47]. The receptor function may feature gas specificity; hence, it is useful to address selectivity. 3.2. Transducer Function As discussed in Section 2.3, the electrical transport thorough the sensitive layer can be decomposed into two contributions: one arising from elementary nanostructures and one from nanostructure–nanostructure junctions. At microscopic level, junctions are characterized by an energy barrier that hinders the transport of charge carriers. In the case of nanoparticles, this barrier makes the junction conductance much lower than the particle conductance. As a consequence, the macroscopic resistance of traditional gas sensors based on nanoparticle networks, the so-called thick films, features junction-type characteristics, i.e., it is barrier-limited as described by Equation (3) [48]:(3) R≈Rj=R0expEbkBT where R0 is a pre-exponential factor with the dimension of a resistance, which also includes the geometrical details of the effective cross-sectional area for the thick film and its effective length; and Eb is the macroscopic energy barrier characterizing the system. Until microscopic junctions can be considered almost equal to one another, Eb coincides with the barrier developed at individual microscopic junctions. If not, a statistical picture linking the microscopic and the macroscopic expressions of Eb should be used [49]. A schematic representation of the thick-film chemiresistor and the barrier arising at the nanoparticle–nanoparticle junction is provided in Figure 4. The sensing mechanism of thick films is typically explained in the literature on the basis of this scheme, Equation (3), and relating Eb to the chemisorption and red-ox reactions described by Equations (1) and (2). The chemisorption of oxygen over the MOX material creates acceptor-surface states, which in turn capture electrons from the semiconductor conduction band. As a consequence, charges accumulate at the particle–particle interface, developing an electric field that repels electrons from this region. From a mathematical point of view, these effects are usually treated by adopting the following assumptions: (i) surface states capture electrons within a layer of width, W, which remains completely depleted by electrons; (ii) treating the surface of MOX crystallites as a flat plane, the mathematical problem can be solved in one dimension (ignoring the effects of the crystallite shape). Such a flat geometry is a good approximation for large grains, i.e., for grains with a diameter much larger than the depletion layer, D >> W. In this case (other situations will be considered in Section 3.4), in the inner part of the grain (up to W) the density of charge carriers is constant and equal to the bulk value of the semiconductor, N0. As detailed in Ref. [35], this abrupt distribution of carriers implies a parabolic bending of the semiconductor band structure, with the surface barrier reaching its maximum value Eb at the particle surface and restoring the unperturbed semiconductor properties in the core portion of the particle (between W and the center of the particle). The relationship between Eb and W is given by Equation (4), while the relationship between Eb and the density of surface sites NS created by oxygen chemisorption is provided by Equation (5):(4) Eb=q2N02εW2=kBT2W2λD2 (5) Eb=q2NS22εN0 Here, q is the electron charge and ε is the dielectric constant of the semiconductor, λD=εkBT/q2N0 is the Debye length, which is a characteristic length of the semiconductor expressing the distance over which mobile charge carriers screen a charge-induced perturbation. Its usefulness will be further discussed in Section 3.4 and Section 3.5, dedicated to size- and shape-effects. A schematic representation of the depletion layer and the band bending is reported in Figure 4b. Equation (3) combined with Equation (5) shows that the macroscopic resistance of the metal oxide layer depends exponentially from the squared density of surface states, whose value is modulated by the red-ox reactions with gaseous molecules. This relationship explains the high sensing capability of MOX chemiresistors and links this capability to the features of microscopic grain boundaries. The intensity of the sensing response, hereafter shortened as S, is also dominated by grain boundaries. S is calculated as the ratio between the resistances during exposure to the air background and air background with a given diluted amount of the target gas. For an n-type MOX exposed to reducing gases, S is expressed by Equation (6):(6) S=RairRgas≈Rj,airRj,gas=expWair2−Wgas22λD2=expEb,air−Eb,gaskBT Following the convention often adopted in gas sensing [51], which calculates the response in such a way that S > 1 during gas exposure, for n-type semiconductors exposed to oxidizing gases it is S = Rgas/Rair, while for p-type materials it is the opposite, i.e., S = Rgas/Rair for reducing compounds and S = Rair/Rgas for oxidizing ones. As discussed in Section 2.3, the resistance of elementary nanowires Rnw may play a significant role both in single-nanowire and nanowire-mat-based devices. Rnw further depends on the interaction with gases and its relationship with the depletion layer developed at the nanowire surface can be retrieved based on geometrical considerations (conduction takes place only in the inner portion of the wire that is not depleted) [52]. This relationship is here reported in Equation (7) and it is the equivalent of Equation (3) for the nanowire body:(7) Rnw=1qμN0LnwπD2−W2=1qμN0LnwπD2−2εEbq2N02 Lnw is the nanowire length and μ is the bulk mobility of the metal oxide material. Nanowire–nanowire junctions are typically modeled as grain boundaries of thick film materials, i.e., using Equation (3). For the nanowire body and the nanowire–nanowire junctions, Eb depends on surface states and the depletion layer, as described by Equations (4) and (5). For an n-type MOX exposed to a reducing gas, the sensing response of the nanowire body then becomes:(8) S=Rnw,airRnw,gas=D2−Wgas2D2−Wair2=D2−2εEb,gasq2N02D2−2εEb,airq2N02 3.3. Utility Factor With reference to Equation (2), an efficient receptor function implies a large k, which in turn means a large consumption of the target gas. If the sensing layer is thick, only the outermost portion of the sensitive film is exposed to the nominal concentration of the target gas, whose amount rapidly decreases proceeding toward the substrate. These phenomena have been mathematically analyzed in terms of diffusion and surface reaction rates, obtaining the following expression for the gas concentration profile C as a function of the layer thickness, z [53]:(9) Cz=C0coshL−zk/DkcoshLk/Dk In Equation (9), L is the thickness of the sensitive film; C0 is the gas concentration in air and at the outermost layer of the sensitive film; and C0 = C(z = 0), Dk=4rp32RTπM is the Knudsen diffusion coefficient, which depends on the pore radius rp, the molecular mass M, the working temperature T and the gas constant R. This has been a leading concept in the development of several sensing layers and its optimization was identified as one of the key factors in some very remarkable achievements. Some examples will be discussed in Section 4. 3.4. Size Effects The beneficial effects arising from a reduced diameter of elementary nanostructures is among the widest-acknowledged results in gas sensing. It has been widely reported in experimental works [44,54], and theoretical papers have explained its relationship with the optimization of the transducer function at the level of individual nanostructures [55,56,57]. Depending on the ratio between W and the diameter D of the elementary nanostructure, the electrical and sensing properties enter in different regimes. Among these, only the extreme cases admit an analytical solution, namely (i) D >> W and (ii) D ≤ λD (which also means D < W, according to Equation (4)). The intermediate conditions need numerical solutions [57]. In the first case, as evident from Equations (4) and (5) and from Figure 4, the interaction with gases alters the electrical properties of a given metal oxide only within a surface layer of thickness W. In the region beyond W, the properties of the material are insensitive to interaction with gases. If the diameter of the nanostructure is further reduced below the extent of W, till the size of λD, the material enters into the second regime, in which grains are fully depleted from electrons (apart those that are thermally promoted into the conduction band). In this condition, the barrier height at junctions is lower than the thermal energy (flat band condition). Phenomena described in Section 3.1 modulate the conductance of the semiconductor by modulating the position of its Fermi level, and the resulting transducer function is even more efficient than the one described by Equations (4) and (5). In particular, being D < W, D becomes the relevant characteristic length in the response intensity. More specifically, the response S increases with D−1/2 and with D−1 for reducing and oxidizing gases, respectively. The complete mathematical description of these regimes can be found in dedicated papers [55,56]. 3.5. Shape Effects Equation (4) is valid for plate-shaped crystallites or for other shapes in the case of large grains (D >> λD), so that the surface of the crystallite can be considered almost flat. If grains are not large, as is often the case in nanostructures, shape effects apply to the Eb = Eb(W) relationship modifying it with respect to Equation (4). As already introduced in Section 3.4, only a few regimes can be solved analytically, and this also holds for shape effects [57,58]. Despite such intrinsic difficulties, the proposed models and simulations agree that the spherical shape is the most effective to enhance the transduction mechanism [55,56,58]. In this sense, it is useful to compare two crystallites having the same diameter and electronic properties, undergoing the same interactions with gases, in particular oxygen, but featuring a spherical and cylindrical shape, respectively. Theories predict that the depletion layer is larger in the spherical crystallite than in the cylindrical one and that the former enters in the full-depletion regime earlier (for lower oxygen concentrations in the atmosphere) than the latter [55,56,58]. Moreover, spatially-resolved scanning tunneling spectroscopy (STS) experiments have shown that oxygen adsorption occurs preferably at grain boundaries and to a lesser extent over the crystallite surface [59]. This will reasonably have important implication in the comparison between nanowires and nanoparticles, since nanowire networks intrinsically feature a much lower density of junctions than thick films composed by nanoparticles. From this point of view, the spherical shape emerges once again as potentially more effective than the wire shape. 4. Approaches Adopted to Control the Sensing Properties of Gas Sensors This section reports an overview of different strategies reported in the literature to exploit the morphological and structural features of MOX nanostructures to emphasize and tune their sensing capabilities. Considering the vastness of the literature about nanowire chemiresistors, a complete overview of the field is out of the scope of the present work. Results will be shown referring mainly to SnO2, ZnO and WO3, which have been suitable benchmark materials to compare nanowires and nanoparticles since the early times of nanowire literature. 4.1. Porosity (Utility Factor) and Network Effects As discussed in Section 3, small grains and large pores are fundamental features to optimize, respectively, the transduction mechanism and the efficiency of gas diffusion through the whole volume of the sensing layer. Concerning traditional thick films, it has often been observed that these two morphological features may conflict one another, with small nanoparticles often implying small pores [60]. To solve this issue, several authors focused their work on the development of hierarchical nanostructures. These exploit nanoparticles organized in μm-sized assemblies that are further distributed in a disordered network connecting the electrodes [60]. Hollow spheres [61,62,63], fibers [64,65,66] and hollow fibers [67,68] are some popular examples. Nanowire networks, on the other hand, intrinsically offer the possibility to merge these two requirements thanks to their nm-sized diameter and elongated shape, which often result in large voids, allowing an efficient gas diffusion. Some remarkable results obtained through the optimization of these parameters will be reported in the following part of this section and will be summarized at the end of it in Table 1. For example, Kida et al. compared the sensing properties of different SnO2 nanowire networks with varying length-to-diameter aspect ratios, including nanocubes with a size of ≈13 nm and nanorods with a diameter and length of ≈25 nm and ≈500 nm, respectively [69]. These nanostructures revealed excellent sensitivity to both ethanol and H2, demonstrating a very effective structure for the optimization of the transducer function and the utility factor. Concerning ethanol, a recent review highlighted that the response of these nanorods, ≈105 to an ethanol concentration of 100 parts per million (ppm), emerged as the largest response reported in the literature among more than recent 80 papers about chemiresistors based on pure SnO2. In addition, nanocubes were identified as remarkable outliers in such a review [70]. The situation is similar for H2: these nanostructures revealed a very high response, at least comparable with state-of-the-art nanostructures such as thick films and hierarchical nanostructures with very thin, fiber-like shapes [71,72]. In addition to the excellent sensitivity in a general sense, the authors observed that size effects are emphasized with small molecules, in particular H2, while for larger molecules such as ethanol, the nanorods exhibited the best performance despite their diameter being larger than the nanocube size. This was ascribed to diffusion phenomena. For ethanol, this could take place in an effective manner only in the case of nanorods, whose length allowed for a larger porosity. Differently, for H2, the lower porosity featured by the nanocube layer was sufficient for an efficient diffusion of such a small molecule, thus emphasizing the beneficial size effects of the small nanocubes. Other remarkable ethanol responses have been reported in the literature exploiting hierarchical assemblies of elementary nanowires. In this sense, it is worth mentioning the work by Firooz et al. that reports the response to 300 ppm of ethanol increasing from 1600 to 4000 when the structure is changed from a disordered network of nanowires to flower-like assemblies of nanowires [73]. These values are competitive with the largest responses recorded with nanoparticle-based devices, including thick films and hierarchical nanospheres with trimodal porosity [71,74,75]. In addition to the intrinsic porous structure, nanowire networks feature the presence of nanowire–nanowire junctions, which have often been invoked as key elements for the improvement of nanowire-based chemiresistors. This reasonably stems from the more efficient transduction mechanism of junctions with respect to the nanowire body (exponential vs. quadratic response, as depicted in Equations (6) and (8)), and to the preferential adsorption at grain boundaries discussed in Section 3.5. for example, junctions have been proposed to explain the remarkable responses to NO2 recorded at room temperature with In2O3 nanowire networks, in contrast with the much lower responses obtained with chemiresistors exploiting the single-nanowire configuration [76]. In particular, it is worth noting that the mentioned nanowire network revealed the ability to respond to NO2 concentrations lower than 50 parts per billion (ppb), which is the threshold limit for outdoor applications and is often used as a benchmark for sensing technologies, including thick films and hierarchical nanostructures [77,78,79]. On the other hand, such outstanding room-temperature sensitivity was obtained, exploiting an almost irreversible interaction with NO2, which required UV illumination to quickly restore the baseline. Other remarkable NO2 responses at the level of 50 ppb and below were achieved, exploiting hierarchical WO3 nanostructures [80]. In this case, the devices exploited an embedded heater to work at the temperature of 300 °C, which ensured an effective compromise between intense response and acceptable response and recovery times. Though several papers report the abundance of junctions as beneficial for nanowire chemiresistors [25,76,81,82], some works have recently pointed out the need for controlling the density of the nanowire network to achieve an optimal compromise between the density of junctions and the size of pores, which have opposite dependencies from the network density [83,84]. sensors-22-03351-t001_Table 1 Table 1 Chemiresistors based on pure (neither doped nor functionalized) metal oxide (MOX) nanoparticles and nanowires. The response intensity S is calculated as S = Rgas/Rair for NO2 and as S = Rair/Rgas for other compounds at the sensor temperature T. Gas concentrations are expressed in parts per million (ppm) or parts per billion (ppb), and, in the T column, RT stands for ‘room temperature’. MOX, Morphology T (°C) Gas, Concentration S Ref. SnO2, nanowire network 250 Ethanol, 100 ppm 105 [69] SnO2, nanowire network 300 H2, 200 ppm 200 [69] SnO2, nanocubes network 250 Ethanol, 100 ppm 6000 [69] SnO2, nanocubes network 300 H2, 200 ppm 270 [69] SnO2, nanowire network 300 Ethanol, 300 ppm 1600 [73] SnO2, hierarchical flower-like assemblies of nanowires 275 Ethanol, 300 ppm 4000 [73] SnO2, thick films 300 Ethanol, 100 ppm 1520 [71] SnO2, thick films 300 H2, 200 ppm 87 [71] SnO2, thick films 300 Ethanol, 100 ppm 2400 [74] SnO2, hierarchical nanospheres of nanoparticles 400 Ethanol, 5 ppm 316 [75] SnO2, hierarchical fibers of nanoparticles 250 H2, 100 ppm 25 [72] SnO2, hierarchical fibers of nanoparticles 150 NO2, 125 ppb 90 [78] WO3, 3D hierarchical assembly of nanowires 300 NO2, 50 ppb 6 [80] WO3, thick film 300 NO2, 50 ppb 1.5 [77] WO3, nanolamellae 200 NO2, 200 ppb 70 [79] In2O3, nanowire network RT NO2, 50 ppb 2 [76] 4.2. Surface Termination As discussed in Section 2.1, the surface termination of nanowires with well-defined crystalline planes is one of its unique features with respect to traditional nanoparticles. Indeed, different facets exhibit different densities of atomic edges, steps and unsaturated coordination sites that are all relevant for the interaction with gases [85,86]. Low-index surfaces of macroscopic SnO2 single crystals have been widely studied in surface science, showing their different behavior with respect to oxygen chemisorption [85]. Similarly, broad evidence has been collected demonstrating the surface-termination dependence of the interaction between macroscopic TiO2 single crystals and gaseous molecules [86]. Atomically resolved scanning tunneling microscopy studies have been applied to SnO2 nanowires pre- and post-surface-oxidation and surface-reduction treatments. Results have shown a similar ordering of surface atoms for these nanocrystals and macroscopic crystals typically studied in traditional surface science, providing an important conceptual link between the two disciplines [87]. More recently, attention has been dedicated to high-index surfaces, which are expected to be more suitable than low-energy ones for gas sensing owing to their richness in atomic edges, steps and unsaturated coordination sites. This has been confirmed both computationally and experimentally. Density functional theory (DFT) calculations confirmed that the exothermic oxygen chemisorption over SnO2 surfaces is more favorable (larger energy reduction) for high-energy facets such as (221) than for low-energy ones, such as (110) [88,89,90]. From an experimental point of view, the comparison between the gas sensing properties of low- and high-index surfaces has been realized through the synthesis of nanopolyhedra exposing high-energy facets and the synthesis of elongated nanopolyhedra, laterally bonded by low-energy facets, using SnO2 as example material [91]. In this way, the control over the length of the elementary nanostructures allowed for tuning of the balance between the areas of high- and low-energy planes, and in turn, their relative contributions to the sensing response. Similar improvements have also been recorded for other oxides, including Fe2O3 [92], Cu2O [90], NiO [30] and TiO2 [93]. In 2018, these results were analyzed as a whole. It was concluded that in most of cases, facets revealed more effectiveness than surface area in the enhancement of the sensor response in a broad sense, i.e., the improvement was observed toward different gases [94]. In other papers, the surface termination was proposed to explain the observed differences about the partial selectivity exhibited by nanowires and nanoparticles. For example, comparing SnO2 nanowire and nanoparticle networks, it was observed that none of the two morphologies could be indicated as more sensitive than the other in an absolute way, but these considerations are gas-dependent. As reported in Figure 5, nanowires were found to be more sensitive to compounds such as acetone, dimethyl methylphosphonate (DMMP) and dipropylene glycol monomethyl ether (DPGME), while nanoparticles were more sensitive to CO and NH3 [95]. Similar results were also obtained with In2O3 nanowires and nanoparticles [95]. Differences were also observed in the case of WO3, for which nanoparticles and nanorods exhibited similar responses to ammonia; while in the case of ethanol and acetone, the response of nanoparticles was about one order of magnitude larger than the response of nanorods [25]. 4.3. Doped Nanostructures In this paper, the term ‘doping’ will be intended according to its meaning from the field of semiconductors, in which a dopant is an additive element introduced in the lattice of the host material as an interstitial or substitutional atom/ion [96]. Those cases in which the additive element is deposited in the form of cluster over the supporting MOX are referred to as surface functionalization and will be treated in Section 4.4. In the field of thin- and thick-film chemiresistors, the dispersion of dopants has been widely used to address several objectives: (i) to increase the thermal stability of the film micro/nanostructure by hindering grain-coarsening phenomena; (ii) to decrease the electrical resistivity of the film; (iii) to control its Debye length and the space-charge-layer depth; (iv) to tailor the response and the partial selectivity of a given material according to specific requirements. Between these objectives, the former is probably the most shape-dependent. For thin and thick films composed by more or less compact aggregates of spherical nanostructures, grain coarsening was recognized as an important limiting factor for the long-time stability of MOX-based chemiresistors [97]. Electron microscopy investigations revealed that these phenomena occur at grain boundaries and involve the rearrangement of atom ordering at these interfaces. Reordering is such that larger grains coarsen by subtracting atoms from smaller grains [98]. Considering the importance of the grain size for the sensing properties of MOX, these microscopic phenomena affect the macroscopic properties of the device, such as its baseline and sensing response. To suppress these microstructural drifts, dopants have been widely used in the field of ceramic materials. These additives act as blocking elements for grain-boundary migration and phase transition [97,99]. As for nanowires, their elongated single-crystalline structure has already been proposed as a possible solution for such microstructural drifts in the first publication about nanowire-based gas sensors [2]. This is intrinsic for devices based on a single nanowire or aligned nanowires owing to the total absence of grain boundaries. For nanowire networks, which feature the presence of nanowire–nanowire junctions, an increased stability with respect to nanoparticles was reported after a study lasting about 1 month [100]. From this point of view, Ga2O3 is of particular interest. It has been largely studied as polycrystalline-film gas sensor owing to its thermal and chemical stability, which make it an appealing material for gas sensing [101,102]. In its pristine form (not intentionally doped), grains about 20 nm large remain stable at the temperature of 900 °C [103], while for pristine SnO2 the stable diameter is of the order of 100 nm [104]. Ga2O3 nanowires have also been investigated for H2 and volatile organic-compound sensing [105,106]. In addition to grain coarsening, doping also affects all the other aforementioned features of the device. Doped materials typically exhibit a baseline that is different from that of the pristine host. From this viewpoint, dopants are often used to decrease the baseline of materials that exhibit a large resistivity. Indeed, it may happen to deal with materials/sensors whose baselines approach or even exceed the GOhm [97,107,108]. The electrical signals of these devices are easily read by laboratory equipment but may be hard to read with cheap commercial electronics. Research in readout systems is studying innovative and cheap approaches suitable to read resistances spanning a broad range [109,110]. Nonetheless, avoiding very large values may be an appealing feature for easy and effective device exploitation [111]. Doping of TiO2 is a typical example: In its pure form, titania features the presence of oxygen vacancies but its resistivity remains quite large, and dopants such as Nb are often used to mitigate this drawback owing to the donor properties of Nb in TiO2. Nb-doped TiO2 has been studied both in the form of thick films [97], nanorods [112] and hierarchical structures such as nanotubes [113]. Ga2O3 is also highly resistive at the typical gas-sensing temperatures (below 600 °C), and dopants such as, for example, Si and Sn, are often used to increase the charge-carrier density [114,115]. Altering the baseline of the host material through the introduction of dopants also affects the transduction mechanism through the modulation of the Debye length. According to semiconductor theory (see Section 3.1), the increase in charge carriers implies shorter Debye length (and space-charge layer), which in turn implies a reduced sensor response. Generally speaking, a balance between an easy-to-read baseline and an effective Debye length should be considered. In some cases, doping may also cause the material to switch from n- to p-type or vice-versa. Using TiO2 as example, this is the case for Cr doping, which enters in the TiO2 lattice as electron acceptor, hence introducing holes. P-type Cr-doped TiO2 has been synthesized introducing Cr at the concentration of ca 8% at. [107]. In addition, dopants also affect the receptor function of the host material and can be exploited to tune the partial selectivity of MOX. For example, concerning Ga2O3, Sb doping enhances its response to O2, while Na-K co-doping improves its sensitivity to humidity [116,117]. Zn doping in In2O3 nanowires was reported to modify the response spectrum of the guest oxide by decreasing its response to NO2 and increasing the response to reducing gases, more specifically to H2, CO, ethanol and acetone [118]. A general increase in the response toward reducing compounds was also observed for Zn-doped In2O3 hierarchical nanospheres composed by spherical crystallites, with emphasis on the enhancement of the response to triethylamine [119]. Both for nanowires and other nanostructures, the effect of Zn doping on In2O3-sensing properties is usually ascribed to the formation of point defects, such as Zn interstitials, oxygen and In vacancies in the host matrix [118,120]. In some papers, Zn has also been proposed to stimulate the phase transition of the host oxide. This is the case, for example, of the In2O3 transition from body-centered cubic (bcc) to rhombohedral (rh). Introducing Zn in single-phase pristine bcc-In2O3, the material changed into a polycrystalline mixture of bcc/rh, with the portion of the rh phase increasing with the increase in the dopant [120]. This structural modification was accompanied by a morphological evolution from single-crystalline nanocubes to hierarchical polycrystalline nanoflowers. The joint structural and morphological modification implied more point and extended defects, including bcc-rh interfaces, and an increased surface area, which were identified as the key features underlying the increased sensitivity of doped samples to NO2. Morphological changes promoted by Zn doping were also observed in hierarchical spheres composed by SnO2 nanorods, which turned to assemblies of sheet-shaped crystallites. Zn-doped SnO2 nanostructures revealed more sensitivity in general toward reducing gases, including ethanol, glycol and acetone [121]. 4.4. Inorganic Heterostructures The functionalization of MOX surfaces with inorganic additives is probably the widest-used method to improve and tune the properties of MOX chemosensors. Both metallic nanoparticles and MOX nanoparticles have been widely investigated as additives to MOX thick films and have also further been applied to MOX nanowires. Over the years, several review papers have been specifically dedicated to this topic. In the past, these were mainly concerned with chemiresistors based on thick and thin films [122], while in more recent years, results obtained with other morphologies such as nanowires, nanosheets and hierarchical structures have also been included [123,124]. 4.4.1. Functionalization with Metallic Nanoparticles As for metallic additives, their effect (often termed sensitization) on gas-sensing properties occurs through two mechanisms: electronic and chemical [125]. The former arises from the different work functions of the metal and the MOX semiconductor (ϕm and ϕs). It is generally explained starting from the ideal situation of the two isolated materials that are further brought into contact. Referring to an isolated n-type MOX, its Fermi level lies inside the bandgap, close to the conduction band owing to oxygen vacancies. As a result, it is usually observed that ϕm > ϕs, meaning that it is easier to extract electrons from the semiconductor than from the metal. When the two systems are coupled, the Fermi levels’ alignment implies the transfer of electrons from the semiconductor to the metal, resulting in a depletion layer in the MOX side of the interface [35,126]. This effect adds up with the depletion layer induced by oxygen chemisorption described in Section 3 [125]. A schematic representation of the electronic sensitization is shown in Figure 6a. The second mechanism stems from the catalytic properties of nanosized metallic particles, which are able to dissociate molecules into byproducts that are further spilled over the supporting oxide (spill-over effect) facilitating the overall response of the composite material [122]. For chemosensing, the dissociation of molecular oxygen into reactive O− ions promoted by metals such as Au and Pd is of particular relevance [127,128,129]. It acts by increasing both the density of chemisorbed oxygen ions and the depletion-layer extension in the surrounding of the metallic nanoparticle, as schematically shown in Figure 6b. In addition to oxygen, other molecules are also directly spilled over by suitable metallic nanoparticles. A widely known example is the spill-over of H2 molecules promoted by Pd and Pt, which has been largely exploited in thick-film technology [122] as well as with nanowire chemiresistors, both in the single-nanowire [129] and nanowire-network configurations [18,69]. In other papers, the synergy between the intrinsic catalytic activity of metallic nanoparticles and the increased density of reactive oxygen arising from oxygen spillover is proposed as the main reason for the increased response. This is the case of ethanol responses enhanced by Au or Pt nanoparticles. To cite a few examples, Au functionalization was employed with SnO2 layers, both in the form of thick films [130] and nanowires [131]; Pt was used with SnO2 hollow spheres [132] and networks of SnO2 nanorods [133]. The same combination of metal-promoted effects was also proposed to explain the improved response to H2S observed for several Au-supported MOX, including WO3 nanorods [134] and nanoparticles [135]. A summary of the numerical responses recorded with these materials, both in their pristine and functionalized form, is reported in Table 2. In addition to the improved response intensity toward the target gas, it is worth mentioning the beneficial effect about the optimal working temperature, which is often lowered by the functionalization with metallic nanoparticles. In recent years, bimetal nanoparticles composed by alloyed metals have attracted large attention owing to their coupling with the supporting oxide, which is different from the coupling of the respective monometallic components. For example, the AuPd system has been studied by different research groups using different supporting metal oxides. Considering SnO2 thick films as support, the different balance between oxygen spillover and electronic sensitization has been reported for Au, Pd and AuPd nanoparticles [128]. Despite these insightful results, the effects of the bimetallic functionalization with respect to the functionalization with individual metal is still in an early stage. Different results have been published so far, without reaching a comprehensive, uniform picture of the involved phenomena. For example, enhanced performances have been reported for AuPd-functionalized SnO2 thick films to a broad range of compounds, including CO, CH4 and NH3, [136]. However, other papers reported AuPd functionalization as less effective for CO, ethanol and CH4 with respect to the pristine and single-metal-functionalized SnO2 [128]. Morphologies other than thick films have also been considered for this kind of functionalization. Some examples are SnO2 flower-like hierarchical assemblies of nanosheets, which showed an increased sensitivity to several compounds, including formaldehyde and acetone [137]; SnO2 hollow spheres, for which the effectiveness of the functionalization was proven against dimethyl disulfide; and WO3 nanowires, tested against butanol [138]. Before concluding this paragraph, it is worth mentioning another approach to exploit the metal–semiconductor interface for gas sensing, which was proposed by Wei et al. [139] working with a single-nanowire device. In this case, the metallic structure was the electrode, which formed a reverse biased Pt-ZnO junction in the single-nanowire device. Based on the discussion reported in Section 2.3, though such an interface is also present in devices based on MOX thick films and nanowire networks, it is particularly meaningful in the single-nanowire device, for which such a junction may dominate the overall device resistance. In particular, Wei et al. [139] proposed a sensing mechanism based on the modulation of the Schottky barrier Ems at the metal–oxide interface induced by gas adsorption:(10) R≈Rcr+Rnw≈Rcr∝expEmskBT exp−qVcrkBT (11) S≈Rcr,gasRcr,air=expEms,gas−Ems,airkBT  Vcr is the voltage drop over the reversely biased metal–oxide interface and other symbols are defined as above. Although in this case the metal is not dispersed in the form of nanoparticles—hence neither the electronic nor the catalytic sensitization are optimized—its transduction mechanism features an exponential dependence from Ems, which is more efficient than the quadratic form of the nanowire body described by Equation (8). sensors-22-03351-t002_Table 2 Table 2 Chemiresistors based on metal oxide (MOX) nanostructures functionalized with metallic nanoparticles. The response intensity S is calculated as S = Rair/Rgas at the sensor temperature T. In case of no response at this temperature, ‘no resp.’ is reported in the S column. Gas concentrations are expressed in parts per million (ppm). Supporting MOX, Morphology Functionalization T (°C) Gas, Concentration S Ref. SnO2, nanowire network -- 250 Ethanol, 100 ppm 105 [69] SnO2, nanowire network Pd 250 Ethanol, 100 ppm 1.1 × 105 [69] SnO2, nanowire network -- 300 H2, 200 ppm 200 [69] SnO2, nanowire network Pd 250 H2, 200 ppm 800 [69] SnO2, nanocubes network -- 250 Ethanol, 100 ppm 6000 [69] SnO2, nanocubes network Pd 250 Ethanol, 100 ppm 6000 [69] SnO2, nanocubes network -- 300 H2, 200 ppm 270 [69] SnO2, nanocubes network Pd 250 H2, 200 ppm 300 [69] SnO2, nanowire network -- 150 H2, 40 ppm no resp. [18] SnO2, nanowire network Pd 150 H2, 40 ppm 3 [18] SnO2, single nanowire -- 100 H2, 1 ppm no resp. [129] SnO2, single nanowire Pd 100 H2, 1 ppm 5 [129] SnO2, thick film -- 270 Ethanol, 200 ppm 28 [130] SnO2, thick film Au 220 Ethanol, 200 ppm 128 [130] SnO2, hollow spheres -- 325 Ethanol, 5 ppm 95 [132] SnO2, hollow spheres Pt 325 Ethanol, 5 ppm 1400 [132] SnO2, nanorod network Pt 300 Ethanol, 200 ppm 40 [133] WO3, nanorod network -- 350 H2S, 1 ppm 4 [134] WO3, nanorod network Au 350 H2S, 1 ppm 100 [134] WO3, thick film -- 300 H2S, 1 ppm 3 [135] WO3, thick film Au 300 H2S, 1 ppm 7 [135] SnO2, thick film -- 300 CO, 50 ppm 10 [128] SnO2, thick film Au 300 CO, 50 ppm 100 [128] SnO2, thick film Pd 300 CO, 50 ppm 100 [128] SnO2, thick film AuPd 300 CO, 50 ppm 2.5 [128] SnO2, thick film -- 300 Ethanol, 10 ppm 50 [128] SnO2, thick film Au 300 Ethanol, 10 ppm 500 [128] SnO2, thick film Pd 300 Ethanol, 10 ppm 150 [128] SnO2, thick film AuPd 300 Ethanol, 10 ppm 40 [128] SnO2, thick film -- 300 CH4, 1000 ppm 12 [128] SnO2, thick film Au 300 CH4, 1000 ppm 30 [128] SnO2, thick film Pd 300 CH4, 1000 ppm 90 [128] SnO2, thick film AuPd 300 CH4, 1000 ppm 12 [128] SnO2, thick film -- 350 CO, 20 ppm 3 [136] SnO2, thick film Au 225 CO, 20 ppm 5 [136] SnO2, thick film Pd 100 CO, 20 ppm 3 [136] SnO2, thick film AuPd 350 CO, 20 ppm 9 [136] SnO2, thick film -- 500 CH4, 50 ppm 3 [136] SnO2, thick film Au 450 CH4, 50 ppm 4.5 [136] SnO2, thick film Pd 450 CH4, 50 ppm 3 [136] SnO2, thick film AuPd 400 CH4, 50 ppm 6.5 [136] SnO2, thick film -- 375 NH3, 10 ppm 2 [136] SnO2, thick film Au 350 NH3, 10 ppm 4 [136] SnO2, thick film Pd 350 NH3, 10 ppm 2 [136] SnO2, thick film AuPd 350 NH3, 10 ppm 6 [136] SnO2, nanosheet network -- 300 Acetone, 50 ppm 20 [137] SnO2, nanosheet network Au 275 Acetone, 50 ppm 80 [137] SnO2, nanosheet network Pd 250 Acetone, 50 ppm 40 [137] SnO2, nanosheet network AuPd 250 Acetone, 50 ppm 110 [137] WO3, nanowire network -- 300 n-butanol, 100 ppm 26 [138] WO3, nanowire network Pd 200 n-butanol, 100 ppm 69 [138] WO3, nanowire network AuPd 200 n-butanol, 100 ppm 93 [138] 4.4.2. Functionalization with Metal Oxide Nanoparticles Concerning the use of MOX nanostructures as catalysts, their beneficial effects arise both from the catalytic properties of the additive and from the formation of electrical junctions between the two MOX semiconductors. CuO is a p-type semiconductor and the formation of p-n junctions at the interface with n-type MOX is often indicated as one of the key reasons for the observed enhanced sensitivity with respect to the pure n-type MOX. For example, this is the case of ethanol sensing with pristine and CuO-functionalized SnO2 hollow spheres [61]. In addition, CuO catalyst is particularly effective for H2S detection, which is probably its most popular use in gas sensing [140]. This is thanks to the suitability of CuO to react with H2S forming CuS according to the reaction CuO+H2S →CuS+H2O, which is reversible in an oxygen-rich environment [141]. Considering the metallic character of CuS, the p-n junction formed at the CuO–MOX interface is turned into a metal–semiconductor junction upon H2S exposure, resulting in a very effective transduction mechanism. This mechanism is reported as being specific to H2S. Indeed, as shown in Table 3, large H2S response enhancements have been published for SnO2-CuO composites based both on nanoparticles and nanowires as supporting material, while weaker enhancements have been indicated for other compounds such as CO and NH3 [46,141]. In addition to p-n junctions, n-n junctions have been exploited. A remarkable example is given by SnO2 and ZnO, which are widely used as individual material in a variety of morphologies, but they are also used combined with one another. Hierarchical morphologies are also widely employed to combine the beneficial morphological and functionalization effects. Concerning nanoparticle-shaped crystallites, SnO2 nanofibers increased their response to ethanol and acetone by a factor of about three when decorated with ZnO nanoparticles [64]. This is thanks to the combined morphological effect, arising from the hierarchical structure, and the functionalization one. Porous opals with 1:1 SnO2−ZnO composition were also realized [142]. In this case, the response to acetone was increased with respect to the same morphology realized with a single component, SnO2 or ZnO, while for ethanol the composite material featured a response comparable with the one exhibited by pure SnO2 and larger than the pure ZnO response, hence also providing benefits in terms of partial selectivity. Concerning nanowires, Zhao et al. studied the effect of SnO2-nanoparticle loading over the surface of ZnO nanowires for NO2 detection. Their results indicated that the functionalization improves the response intensity to NO2 by a factor of about six with respect to the performance of pristine ZnO [143]. NO2 was also the target gas for SnO2 nanowires cofunctionalized with ZnO nanoparticles and Pd nanoparticles, finding a nearly 3-times increase in the response of the composite material with respect to the base SnO2 nanowires [144]. sensors-22-03351-t003_Table 3 Table 3 Chemiresistors based on metal oxide (MOX) nanostructures functionalized with metal oxide nanoparticles. The response intensity S is calculated as S = Rair/Rgas for reducing gases and as S = Rgas/Rair for NO2 at the sensor temperature (T). Gas concentrations are expressed in parts per million (ppm). In those cases in which information is not available from the original reference, the related cell reports ‘na’. Supporting MOX, Morphology Functionalization T (°C) Gas, Concentration S Ref. SnO2, hollow spheres -- 300 Ethanol, 300 ppm 11 [61] SnO2, hollow spheres CuO 300 Ethanol, 300 ppm 35 [61] SnO2, thick film -- 350 H2S, 2 ppm 100 [46] SnO2, thick film CuO 200 H2S, 2 ppm 600 [46] SnO2, thick film -- 350 CO, 40 ppm 3 [46] SnO2, thick film CuO 350 CO, 40 ppm 4 [46] SnO2, thick film -- 300 NH3, 20 ppm 2 [46] SnO2, thick film CuO 250 NH3, 20 ppm 2 [46] SnO2, nanowire network -- 400 H2S, 2 ppm 8 [141] SnO2, nanowire network CuO 200 H2S, 2 ppm 3261 [141] SnO2, nanowire network -- na CO, 50 ppm 7 [141] SnO2, nanowire network CuO na CO, 50 ppm 7 [141] SnO2, nanowire network -- na NH3, 17 ppm 4 [141] SnO2, nanowire network CuO na NH3, 17 ppm 4 [141] SnO2, single nanowire -- 250 H2S, 10 ppm 1.5 [141] SnO2, single nanowire CuO 250 H2S, 10 ppm 26 [141] SnO2, porous fiber -- 260 Ethanol, 100 ppm 120 [64] SnO2, porous fiber ZnO 260 Ethanol, 100 ppm 360 [64] SnO2, porous fiber -- 260 Acetone, 100 ppm 10 [64] SnO2, porous fiber ZnO 260 Acetone, 100 ppm 30 [64] SnO2, porous opal -- 250 Acetone, 100 ppm 13 [142] SnO2/ZnO, porous opal -- 275 Acetone, 100 ppm 45 [142] ZnO, porous opal -- 350 Acetone, 100 ppm 17 [142] SnO2, porous opal -- 250 Ethanol, 100 ppm 22 [142] SnO2/ZnO, porous opal -- 250 Ethanol, 100 ppm 23 [142] ZnO, porous opal -- 325 Ethanol, 100 ppm 12 [142] SnO2, nanowire network -- 300 NO2, 5 ppm 2 [144] SnO2, nanowire network Pd 300 NO2, 5 ppm 4 [144] SnO2, nanowire network ZnO 300 NO2, 5 ppm 4 [144] SnO2, nanowire network ZnO + Pd 300 NO2, 5 ppm 6 [144] 4.4.3. Core–Shell Nanostructures The exploitation of the MOX/MOX interface is also at the basis of core–shell nanostructures, in which the inner nanowire material (core) is completely coated by the shell layer, which typically has a polycrystalline structure. An example is reported in Figure 7, with reference to a SnO2-ZnO core–shell sample. In this type of nanostructure, the outer layer is usually compact, and the composite material exposes the shell material to the gaseous environment. From the functional point of view, this is particularly evident in the case of core–shell nanostructures composed by a p-n (or n-p) couple of semiconductors, for which the sensing mechanism of the core is clearly distinguishable from that of the shell [145,146]. The transduction mechanism of the composite is strongly dependent on the core–shell electrical coupling, which in turn depends on the shell thickness. Investigations carried out with different nanowire-core/polycrystalline-shell systems, including CuO/ZnO [145], SnO2/Cu2O [146] and SnO2/ZnO [147], indicated that the sensing response is optimized for a shell thickness approaching the Debye length (λD). The explanation of these results is typically carried out starting from the band structure of the two materials in contact with one another, assuming both materials are thick enough to restore the bulk properties outside the space-charge layer [145,147,148,149]. Figure 7 reports an example of this band structure using SnO2-ZnO as a reference system. Starting from this framework, thickness arguments can be further briefly discussed as follows [147]: decreasing the shell thickness increases the sensitivity of the shell material to the interaction with gases, according to space-charge-layer arguments similar to those described in Section 3.2. Nonetheless, as the shell is thinned, an increasing amount of electrical transport takes place in the core instead of in the shell, thus decreasing its coupling with gas interaction occurring at the external surface of the shell. These results have been reported to apply in general to reducing gases and to core–shell systems, whichever their n-n, p-n or n-p character [145,146,147]. Such a general improvement, with poor specificity among reducing compounds, has also been confirmed by other authors [149]. The Ga2O3/SnO2 system has also been exploited in the core–shell configuration, obtaining a reduction in the optimal temperature for ethanol detection with respect to the pristine Ga2O3 nanowire (400 vs. 600 °C) [150]. Joint functionalization with MOX and with metallic nanoparticles is also reported in the literature. For example, a partial specificity to triethylamine was obtained by further functionalizing the ZnO-SnO2 core–shell composite with Au nanoparticles, which allows to jointly exploit the gas specificity of the catalyst and beneficial effects of the core–shell structure on the transduction mechanism [149]. The sensing properties of these materials are resumed in Table 4 in terms of sensor response intensity S and type of sensing mechanism (n- vs. p-type). 4.4.4. Hierarchical, Branched Nanostructures An additional opportunity offered by nanowires, which does not find an equivalent in the nanoparticle case, is the possibility to grow branched heterostructures composed by a nanowire backbone, with other nanowires extending from its surface. This is a very interesting configuration because it allows at the same time for the exploitation of more phenomena described in the previous sections. An example of this morphology is shown in Figure 8a. The two main features of this particular type of hierarchical structure for gas sensing are: (i) the very open structure, which enhances the utility factor, and (ii) the MOX–MOX interface, which may be in the core–shell form or as dispersed branches growing out from the backbone nanowire, providing the functionalities already introduced in Section 4.4.3 and Section 4.4.2, respectively. Using the SnO2-ZnO system as a case study, Tharsika et al. compared the performance of three different morphologies, namely the SnO2 nanowire network in its pristine form, the same base material in a core–shell configuration using a ZnO film coating (Figure 7) and in a hierarchical configuration in which branched ZnO nanorods grow out the ZnO shell. Results indicate that this latter configuration is the most effective both in terms of response intensity to different compounds and in terms of partial selectivity to ethanol [148]. The CuxO-ZnO system has also been investigated as a branched structure for acetone sensing, obtaining a response about four times larger with respect to the individual nanowires [151]. In this case, ZnO branches are also not dispersed over the CuxO backbone but directly emerge from a uniform ZnO shell coating the underlying CuxO nanowire core. The response type indeed switches from p- to n-type as the backbone is coated by ZnO. Branched nanowire heterostructures have also been exploited as hierarchical support for further functionalization with metallic or MOX nanoparticles. For example, the structure composed by SnO2 nanowire as backbone, dispersed Bi2O3 branches and Pt nanoparticles has been used to detect NO2 at sub-ppm levels [152]. The branched nanostructure (without Pt functionalization) revealed the best performance, with optimal performances reached at the working temperature of 300 °C owing to the SnO2-Bi2O3 interface exposed to gases. Benefits arising from the addition of Pt consisted in reducing the optimal temperature to 50 °C for an almost room-temperature sensing [152]. Other branched, ternary materials have also been studied for NO2 sensing. These are hierarchical structures composed by branched ZnO nanorods grown over SnO2 nanowire and further functionalized with either Au [153] or Cr2O3 [154] nanoparticles. As for the SnO2-ZnO-Au system, optimal performances were obtained at the working temperature of 300 °C, with the response of the ternary compound being about four times larger than the response of the branched nanostructure without Au nanoparticles and about six times the response of pristine SnO2 nanowires. The increase is even larger for the SnO2-ZnO-Cr2O3 system, its response to NO2 being about ten and twenty times the response of the branched SnO2-ZnO and the pure SnO2 nanowires. These results were ascribed to the synergic effects of the branched structure of SnO2-ZnO hierarchical support, their n-n interfaces and the effects of Au and Cr2O3, which act through the electronic and chemical sensitization mechanisms discussed in Section 4.4.1 [153,154]. Doping has also been used in conjunction with branched nanostructures. For example, branched SnO2 nanowires were grown over metallic Sb-doped SnO2, realizing a hierarchical structure characterized by semiconductor–metal interfaces. This system was used for ethanol sensing [155]. The response intensities S and the type of sensing mechanisms of the branched nanomaterials discussed in this section are compared with those of the respective backbone nanostructures in Table 5. Figure 8 Branched hierarchical nanowires. SEM image of branched Bi2O3 nanowires grown over SnO2 nanowires (a). Sensor response to 1 ppm of NO2 vs. temperature for the branched Bi2O3–SnO2 nanowires functionalized with Pt nanoparticles, branched Bi2O3–SnO2 nanowires and the backbone SnO2 nanowires (b). Figure 8a,b are reprinted from [152], Copyright (2021) with permission from Elsevier. 4.5. Inorganic–Organic Heterostructures In addition to inorganic materials, organic layers have also been considered to functionalize metal oxides. The goal is to exploit the variety of interactions offered by the organic chemistry to control the sensitivity and the partial selectivity of the composite material. 4.5.1. Graphene and Related Materials Nowadays, talking about organic functionalization immediately recalls graphene (G) and related materials such as graphene oxide (GO) and reduced graphene oxide (RGO). Indeed, several works have been published exploiting both nanoparticles, nanowires and hierarchical nanostructures coupled with these carbon-based materials. Similarly to other functional interfaces mentioned in the previous sections, the electrical coupling between the two materials is a fundamental concept to explain the gas-sensing properties of the composite. For 2D materials, the band structure is depicted through Dirac cones, with the Fermi level and the shape of cones strongly depending on defects and number of layers, as well as bending and corrugation that may affect the 2D film [156]. These parameters, in turn, exhibit a large dependence from the synthesis conditions of the carbon-based material and from the morphological features of the surface over which it is deposited/synthesized. As a consequence, the electrical properties of these materials are still the subject of study and a unified picture is still to be achieved, especially for those situations in which many of the aforementioned effects may be involved at the same time, as is the case in gas-sensor devices. In addition, G, GO and RGO are intrinsically sensitive to gases, hence they may also directly contribute to the overall response of the device [157,158]. The result is a large number of parameters that may be tuned to control the sensing properties of the composite material. An example of hybrid material composed by ZnO nanorods and RGO sheets is reported in Figure 9a. At the interface between the two materials, electrical coupling gives rise to the formation of p-n inorganic–organic junctions, which are adopted to interpret the electrical and sensing properties of the composite. Figure 9b reports the schematic representation of the band structure of the RGO-ZnO interface in three conditions: the two separated materials, the coupled materials exposed to air and the coupled materials exposed to NO2. Considering the work functions of RGO (≈4.4–5 eV) and of ZnO (≈4.2–4.3 eV), the alignment of the Fermi levels implies electrons transferring from ZnO to RGO, hence a depletion layer extending inside the ZnO nanorods [159]. Already at low RGO concentrations, this material was proposed to facilitate the electrical transport between connected nanorods owing to the reduced resistance of RGO with respect to the ZnO-ZnO interface. On the other hand, considering the low RGO concentration in the optimal device, the sensing mechanism was interpreted as the NO2 molecules adsorbing mainly on ZnO. The proposed transduction mechanism involved the consequent downshift of the ZnO Fermi level, which in turn resulted in the electrons’ withdrawal from the RGO phase and the hole concentration increase therein. This explained the experimentally observed p-type response (electrical resistance decrease upon gas exposure) of the system at room temperature [159]. On the other hand, n-type sensing is also reported in the literature for the RGO–ZnO system. For example, this is the case with ZnO hierarchical spheres composed of elementary nanoparticles deposited over RGO [160,161]. P-type response and room-temperature sensitivity were also reported for RGO functionalized with ZnO nanowires exposed to ammonia [162] and for RGO layers functionalized with hierarchical ZnO sheets composed by elementary nanoparticles exposed to NO2 [163]. In this latter case, the p-type conductivity of the RGO phase was suggested as the motivation of the p-type sensing mechanism exhibited by the composite, whose intensity is further enhanced by interface effects. A similar mechanism may be inferred for the first case owing to the large amount (about 50% wt) of RGO. The transition from n- to p-type sensing was observed in NH3 sensing with an RGO-loaded SnO2 film as the RGO load was increased [164]. SnO2 nanowires have also been used with RGO for H2S sensing at room temperature. In this case, the functionalization process left Cu contamination, and this was proposed as an important feature for the observed selective response to H2S [165]. Tests carried out with other compounds, including NO2 and ethanol, indicated an n-type response, differently from the previously discussed ZnO-RGO system; however, in the SnO2-RGO case, the presence of Cu may also have a relevant role for these compounds, not only for H2S. Cu was intentionally added as dopant in the ZnO film of the RGO-ZnO composite for H2S sensing at room temperature [166]. Room-temperature NO2 sensing was also achieved with RGO-functionalized In2O3 nanorods [167]. In this case, the response was n-type and ascribed to a dominant role of the MOX nanowire, which is n-type, with the MOX-RGO interface having the role of promoting sensing capability at room temperature [167]. Nanowires have been further exploited in the form of hierarchical structures in conjunction with RGO. An example is given by hierarchical 3D mesocrystals composed by Cu2O nanowires deposited over RGO [168]. The sensing mechanism is based on p-p junctions, which featured p-type responses to NO2 at room temperature [168]. Numerical values of the measured responses S and the type of sensing mechanisms are resumed in Table 6. Figure 9 RGO–MOX interface. SEM image (top view) of the RGO–ZnO nanorods composite (a). Schematic representation of the electrical coupling mechanism between RGO and ZnO nanorods; energy bands are shown for the following three situations: disjointed materials, coupled materials exposed to air, and to NO2 (b). Figure 9a is reprinted from [169], Copyright (2018) with permission from Elsevier. Figure 9b is reprinted with permission from [159], Copyright 2016 American Chemical Society. An additional mode to exploit the RGO–MOX interface has been proposed by Van Quang et al. They deposited SnO2 nanowires over both the electrodes of the device, leaving an empty gap between them (no nanowires between the electrodes). They further realized the connection between the two electrodes by means of a graphene sheet; in particular, the G sheet was not directly in contact with the metallic part of the electrodes but with the nanowires coating the electrodes. Owing to the metallic properties of graphene, the macroscopic electrical resistance of the device is given by the reversed biased junction formed at the G-SnO2 nanowire interface [170]. Similarly to the solution proposed by Wei et al. [139] discussed in Section 4.4.1, the sensing response is governed by the modulation of the Schottky barrier formed at the metal–semiconductor interface. In particular, the proposed G-SnO2 interface revealed the ability to detect NO2 at the ppb level. At the optimal temperature (150 °C), the device showed a response of about 6–50 ppb of NO2, which is comparable with the best performance reported in Table 1. Before the advent of graphene and related materials, carbon was also widely exploited in the form of 1D carbon nanotubes. A recent article compared the SnO2-RGO and SnO2-CNT systems, in particular using semiconducting multiwalled CNTs. RGO functionalization revealed higher performance than that with CNTs, and this result was ascribed to the larger density of carbon–MOX interfaces for the 2D material. This enhancement was observed for SO2 detection, while both types of functionalization revealed similar improvements with respect to the pristine SnO2 using compounds such as CO and CH4 [171]. The motivation of this selectivity was not explained; it may reasonably arise from the complex dependence of both RGO and CNTs properties on their synthesis, defects, stresses and bending [172,173]. 4.5.2. Organic Receptors In addition to carbon allotropes and their (partially)oxidized nanostructures, other organic materials have been exploited to functionalize the MOX support. For example, ZnO functionalization was studied with tris(hydroxymethyl)aminomethane (THMA). The supporting material was a network of ZnO nanowires, over which a coating of dispersed ZnO nanoparticles was applied. Nanoparticles were fundamental to increase the surface area of the supporting ZnO layer available for the THMA molecules with respect to the case of ZnO nanowires [174]. Indeed, the responses to NO2 recorded with ZnO nanowires directly functionalized with THMA were quite similar to those recorded with the pristine oxide, while an increase of about two times was observed with the intermediate nanoparticle layer [174]. Recently, organosiloxanes have also been employed as organic coatings for acetone detection. A three- and a five-fold enhancement with respect to the pristine ZnO nanowire network were obtained using 3-glycidoxypro-pyltrimethoxysilane (GLYMO) and (3-aminopropyl)trimethoxysilane (APTMS), respectively [175]. As for GLYMO, the enhancement was ascribed to electronic and chemical sensitization. Similar to the mechanism described in Section 4.4, it arises from electrons transferring from the ZnO to the organic layer owing to Fermi levels’ alignment. Concerning APTMS, it also involves the receptor function of the device thanks to the ammine functional groups of APTMS. These groups remain available for interaction with gases and in particular with the carbon of acetone: C3H6O + (NH2)− ⇔ (H3C)2–C=N + H2O, [175]. N-[3-(Trimethoxysilyl)propyl]ethylenediamine (en-APTAS 1) has been used to functionalize SnO2 nanowires for NO2 sensing, reaching a response of about 10 to 250 ppb [176]. From the calibration curve, a response of about 2 is extrapolated for the reference concentration of 50 ppb discussed in Section 4.1, which is comparable with the most performing NO2 chemiresistors reported in the literature and resumed in Table 1. Another important class of organic materials for gas sensing is the family of porphyrins. These materials have been widely exploited in the field, with optical and mass-sensitive transducing mechanisms often preferred to the electrical one owing to the poor conductivity of these materials [177]. To exploit the receptor functionalities of these organic materials through an electrical transduction mechanism, porphyrin-functionalized MOX chemiresistors working at room temperature have been developed, using visible light illumination to activate the electrical coupling between the supporting MOX and the receptor. These devices have been developed based on ZnO nanoparticles [178] and ZnO nanorods [179], showing the capability of the organic receptor to tune the sensitivity of the material to selected chemicals. For example, while pure ZnO revealed similar responses to ethanol and triethylamine, its functionalization with the H2TPPCOOH-porphyrin revealed suitability for increasing the partial selectivity in favor of the latter compound [179]. Such a capability was further exploited to discriminate off odors released by beef meat during the spoilage process [180]. The results discussed in this Section are resumed in Table 7. 4.6. Self-Heating Effect This working mode aims to exploit the current flowing through the sensitive layer to warm it by the Joule effect, without the use of an external heater. It was first tested in 2003 using a porous, polycrystalline film composed by μm-sized particles for CO detection [181]. At that time, besides the technological interest for the layout simplification achieved by avoiding the external heating element, the high density of grain boundaries intrinsic in the polycrystalline material was not effective in reducing power consumption. Indeed, the device required a power supply of the order of 1 W to activate CO sensing. On the other hand, this idea revealed its full potential when it was tested with nanowire materials, in particular with chemiresistors based on a single-crystalline nanowire contacted by two electrodes. Indeed, as discussed in more detail in Section 2 and Section 3, the nanowire is an excellent conductive channel for charge carriers thanks to its single crystalline structure, free from current-limiting elements such as grain boundaries [1]. Considering the small mass of the nanowire, a small current flowing through the nanowire itself is enough to heat it till the typical working temperature of gas sensors. To achieve a suitable self-heating effect, the device structure should also be designed to reduce heat dissipation as much as possible. Indeed, efficient self heating is typically achieved working with suspended nanowires, i.e., nanowires that are not in direct contact with the substrate but are separated from it by a gap of air [54]. A schematic representation of the suspended nanowire exploiting the self-heating mode is shown in Figure 10a. With this configuration, a few tens of μW (with currents of a few tens of μA) allow to reach the desired temperature [182]. Simulations for the temperature distribution along the suspended and the not-suspended nanowire is shown in Figure 10b [183]. The power consumption is lowered by about four orders of magnitude with respect to ordinary, bulky sensor substrates, whose power consumption is typically of the order of a few 100 mW, and by about 3 decades with respect to MEMS-based chemiresistors, which require a few 10 mW [184,185]. Such impressive power saving enables the development of sensing systems that are able to exploit the thermally activated reactions described in Section 3.1, and at the same time are energetically autonomous. For example, a proof-of-concept device was realized by coupling the self-heated chemiresistor with a thermoelectric microgenerator [186]. Despite the most effective exploitation of the self-heating effect being achieved with a single-nanowire device, interfaces have also been introduced in a controlled way to take advantage of their functional properties. For example, depositing nanowires with controlled density between two closely separated electrodes, the self-heating effect was exploited with a networked layer. Figure 10c shows the schematic representation of this type of self-heated device. The reduced gap between electrodes (2 μm) and the wire morphology of the MOX nanostructures were important to ensure a network with a few crystallite–crystallite junctions, which are the current-limiting elements [187]. The reduced number of junctions allowed the exploitation of the self-heating effect to detect NO2 with about 10 μW. Increasing the density of the network results in an increase in the required power supply to the order of 1 mW due to the larger number of junction elements [187]. Self heating was also exploited with composite metal oxides to merge the low power consumption with benefits arising from the heterointerfaces of the composite. As an example, a low-density network composed of ZnO nanowires coated with a SnO2 shell and further functionalized by Pt nanoparticles was integrated in a device characterized by a short gap (a few μm) between the electrodes. The C7H8-sensing performance were optimized by tuning the ZnO coverage thickness, resulting in sub-ppm sensing capability with a power supply of about 30 μW [188]. Self-heated devices are also appealing owing to their fast thermal dynamics, which feature time constants of the order of ms [189], comparable with those exhibited by microhotplates [190]. This allows for the use of temperature-modulation techniques that have been widely investigated and exploited with both bulky and MEMS substrates to increase the selectivity of individual sensors [191,192]. Since the response of MOX strongly depends on the working temperature, the periodic modulation of this parameter allows the exploration of a range of different sensitivities and to emphasize the partial selectivity of MOX based on gas-interaction transients [193]. For example, a self-heated SnO2 single nanowire was used to track the CO concentration in a background with different humidity levels using a square-wave bias characterized by a duty cycle of 50% and period of 1 s [194]. The fast thermal time constants exhibited by self-heated devices have also been exploited to further reduce power consumption from the μW range down to the pW level. In particular, applying a short bias pulse (a few ms) every few seconds to a single SnO2 nanowire device, Meng et al. achieved sub-ppm sensing of NO2 using less than 40 pW [183]. 5. Conclusions The main approaches to develop resistive gas sensors exploiting metal oxide nanowires have been reviewed. About 20 years have passed since the first publications proposing the use of these materials for chemosensing as an alternative to polycrystalline layers composed by nanoparticles. The goal of this paper is to review the results achieved so far in the field, starting from the structural and morphological features that attracted the attention of the scientific community since the early works. The overall impression is for the most effective exploitation of the nanowire structure and morphology being achieved in terms of power-supply saving and utility-factor optimization. Regarding the former, the best results have been obtained through the self-heating effect, which exploits the absence of grain boundaries in the single-nanowire device and the Joule effect to reach the temperature required to activate the gas-sensing mechanism. This is probably the most impressive result, since it offers the opportunity to reduce the power consumption by 3–4 orders of magnitude with respect to state-of-the-art MEMS-based chemiresistors, hence opening enormous potentialities for integration in portable devices, including smartphones, and the development of networks of energy-autonomous sensors. Its prospects are mainly in two directions: (i) device integration, i.e., the development of methodologies and processes to integrate these sensors into effective sensing systems, often exploiting the electronic nose and/or the temperature-modulation approach; (ii) lowering the preparation cost. The exploitation of nanowire networks with a low and well-controlled number of nanowire–nanowire junctions is particularly promising in this sense, allowing for a suitable compromise between power saving and ease of device fabrication, which is still unsatisfactory with the actual preparation technology of single-nanowire devices. The latter is an intrinsic feature in nanowire mats, which are typically characterized by wide-open volumes between wires, suitable for an efficient gas diffusion. Nonetheless, it should be noted that porosity optimization is intrinsic but not unique for nanowire mats. Indeed, several papers reported successful results achieved with nanoparticle layers, both organized in hierarchical structures and traditional thick films. Unique to nanowires is the branched structure, a particular form of hierarchical architecture, which allows for the merging of the open structure of nanowire networks with the functional properties of the MOX–MOX interfaces. Apart from this particular configuration, functionalization has not emerged as revealing something particular arising from the nanoparticle or nanowire morphology. Functionalization with metallic or metal oxide nanoparticles as well as organic materials has been applied with both types of MOX morphologies and may be used in combination with all the aforementioned configurations of nanowires to extend the possibilities to tune the sensing properties of the considered device. Especially concerning metallic and MOX catalysts, approaches traditionally developed with thick films as support are being further exploited and adapted for application with nanowire-based devices. Opportunities seem almost equally open for both nanowires and nanoparticles. As for the different surface termination of nanowires and nanoparticles, flat vs. rounded, this was already highlighted in the first gas-sensing publications. Since then, it has been proposed as an appealing opportunity to merge the results of surface science, typically obtained by working with macroscopic single crystals, with those of applicative fields such as catalysis and gas sensing, which employ films composed by interconnected nanostructures. Despite the remarkable results obtained both in terms of sensor response and partial selectivity, a proper understanding of surface termination effects is still to be achieved and there is much room for both fundamental and device-oriented studies in order to reduce the gap between these two fields. The main results reviewed in the present paper are briefly summarized in Table 8. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Comparison between the morphological features of nanowires and nanoparticles: (a) Scanning electron microscopy (SEM) picture of SnO2 nanowires/nanobelts; (b) transmission electron microscopy (TEM) image of a single ZnO nanobelt, the inset reports the selected area electron diffraction (SAED) pattern of the nanobelt; (c) high-resolution TEM (HR-TEM) image of the surface of a ZnO nanowire; (d) SEM image of a network of SnO2 nanoparticles; (e) TEM image of a few In2O3 nanoparticles. Figure 1a is reprinted from [12]. Figure 1b,c are from [1], reprinted with permission from AAAS. Figure 1d is reprinted from [13]. Figure 1e is reprinted from [14], Copyright (2016), with permission from Elsevier. Figure 3 Types of nanowire-based chemiresistors: (a) Schematic representation of a chemiresistor based on a disordered network of nanowires and the main components of the equivalent electrical circuit; (b) SEM image of a chemiresistor based on a single nanowire contacted by two electrodes. Figure 3a is reprinted from [34], Copyright (2010), with permission from Elsevier. Figure 3b is from [12]. Figure 4 Schematic representation of the sensing mechanism of a thick-film-based gas sensor. The thick-film layout, composed by a network of interconnected particles (a) and the barrier Eb arising at the particle–particle junction (b). In each particle, the surface region depleted from charge carriers is distinguished by light-blue color from the inner portion (blue color) that maintains its unperturbed charge-carrier density. Figure 4a,b are adapted from [50]. Figure 5 Surface termination effects in nanowire- and nanoparticle-based chemiresistors. Polar plot comparing the response of SnO2 nanowires (SnNW) and nanoparticles (Sn) against different chemicals. © 2008 IEEE. Reprinted, with permission, from [95]. Figure 6 Schematic representation of metal oxide sensitization by means of metallic nanoparticles. (a) Electronic sensitization: the depletion layer is extended at the metal–metal oxide interface as a consequence of Fermi level alignment between the two materials; (b) spillover of oxygen molecules, O2, promoted by the metallic nanoparticle: oxygen molecules from the gas phase are dissociated by the metallic nanoparticle and further chemisorbed over the metal oxide surface. In the surrounding of the nanoparticle, this causes both an increase in the density of active ions and an extended depletion region. Figure 7 SEM (a) and TEM (b) images of SnO2-ZnO core–shell composite material and the schematic representation of the band structure for the SnO2-ZnO system (c). For both materials, the bulk values of their energy gap (Eg), work function (ϕ), electron affinity (χ) are reported; profiles of the vacuum (Evac), valence band (Ev), conduction band (Ec) and Fermi level (EF) energies are also schematically reported. Figure 7a,b are reprinted from [148]. Figure 10 Self-heating. Schematic representation of the self-heating nanowire (a); simulation of temperature distribution along the nanowire for the suspended and not-suspended configurations (b); schematic representation of a self-heated nanowire network (c). Figure 10a,b are reprinted with permission from [183], Copyright 2016 American Chemical Society. Figure 10c is reproduced from Ref. [187] with permission from the Royal Society of Chemistry. sensors-22-03351-t004_Table 4 Table 4 Chemiresistors based on metal oxide (MOX) core–shell nanostructures. The response intensity S is calculated as S = Rgas/Rair for NO2 and as S = Rair/Rgas for other compounds in the case of n-type sensing response. The contrary is in the case of p-type sensing response. The n- or p- type response is reported in the S column. Gas concentrations are expressed in parts per million (ppm) and the sensor temperature is T. Core MOX, Morphology Shell MOX T (°C) Gas, Concentration S (type) Ref. CuO, nanowire network -- 300 CO, 1 ppm 2.2 (p) [145] CuO, nanowire network ZnO 300 CO, 1 ppm 6 (p) [145] ZnO, nanowire network -- 300 CO, 1 ppm 1.3 (n) [145] CuO, nanowire network -- 300 C6H6, 1 ppm 2.3 (p) [145] CuO, nanowire network ZnO 300 C6H6, 1 ppm 5.8 (p) [145] ZnO, nanowire network -- 300 C6H6, 1 ppm 2.3 (n) [145] SnO2, nanowire network -- 300 C7H8, 10 ppm 2 (n) [146] SnO2, nanowire network Cu2O 300 C7H8, 10 ppm 12 (p) [146] SnO2, nanowire network -- 300 C6H6, 10 ppm 2 (n) [146] SnO2, nanowire network Cu2O 300 C6H6, 10 ppm 13 (p) [146] SnO2, nanowire network -- 300 NO2, 10 ppm 130 (n) [146] SnO2, nanowire network Cu2O 300 NO2, 10 ppm 2 (p) [146] SnO2, nanowire network -- 300 CO, 10 ppm 5 (n) [147] SnO2, nanowire network ZnO 300 CO, 10 ppm 80 (n) [147] SnO2, nanowire network -- 300 C6H6, 10 ppm 5 (n) [147] SnO2, nanowire network ZnO 300 C6H6, 10 ppm 80 (n) [147] SnO2, nanowire network -- 300 NO2, 10 ppm 160 (n) [147] SnO2, nanowire network ZnO 300 NO2, 10 ppm 25 (n) [147] ZnO, nanowire network -- 40 Triethylamine, 50 ppm 4 (n) [149] ZnO, nanowire network SnO2 40 Triethylamine, 50 ppm 7 (n) [149] ZnO, nanowire network SnO2 + Au 40 Triethylamine, 50 ppm 12 (n) [149] ZnO, nanowire network -- 40 Acetone, 500 ppm 2 (n) [149] ZnO, nanowire network SnO2 40 Acetone, 500 ppm 5 (n) [149] ZnO, nanowire network SnO2 + Au 40 Acetone, 500 ppm 6 (n) [149] ZnO, nanowire network -- 40 Ethanol, 50 ppm 2 (n) [149] ZnO, nanowire network SnO2 40 Ethanol, 50 ppm 4 (n) [149] ZnO, nanowire network SnO2 + Au 40 Ethanol, 50 ppm 6 (n) [149] Ga2O3, nanowire network -- 600 Ethanol, 1000 ppm 100 (n) [150] Ga2O3, nanowire network SnO2 400 Ethanol, 1000 ppm 65 (n) [150] sensors-22-03351-t005_Table 5 Table 5 Chemiresistors based on branched metal oxide (MOX) nanostructures. The response intensity S is calculated as S = Rgas/Rair for NO2 and as S = Rair/Rgas for other compounds in the case of n-type sensing response. The contrary is in the case of p-type sensing response. The n- or p- type response is reported in the S column. Gas concentrations are expressed in parts per million (ppm) and the sensor temperature is T. Backbone MOX Coating, Morphology T (°C) Gas, Concentration S (type) Ref. SnO2 ZnO, shell 400 Ethanol, 20 ppm 20 (n) [148] SnO2 ZnO, shell + branch 400 Ethanol, 20 ppm 32 (n) [148] CuxO -- 250 Acetone, 50 ppm 1.2 (p) [151] CuxO ZnO, shell 250 Acetone, 50 ppm 1.5 (n) [151] CuxO ZnO, shell + branch 250 Acetone, 50 ppm 6.5 (n) [151] SnO2 -- 50 NO2, 1 ppm -- [152] SnO2 Bi2O3 branch 50 NO2, 1 ppm 3 (n) [152] SnO2 Bi2O3 branch + Pt nanoparticles 50 NO2, 1 ppm 28 (n) [152] SnO2 -- 250 NO2, 1 ppm 10 (n) [152] SnO2 Bi2O3 branch 250 NO2, 1 ppm 50 (n) [152] SnO2 Bi2O3 branch + Pt nanoparticles 250 NO2, 1 ppm -- [152] SnO2 -- 300 NO2, 20 ppm 2 (n) [153] SnO2 ZnO branch 300 NO2, 20 ppm 4 (n) [153] SnO2 ZnO branch + Au nanoparticles 300 NO2, 20 ppm 13 (n) [153] SnO2 -- 300 NO2, 10 ppm 2 (n) [154] SnO2 ZnO branch 300 NO2, 10 ppm 5 (n) [154] SnO2 ZnO branch + Cr2O3 nanoparticles 300 NO2, 10 ppm 58 (n) [154] Sb-doped SnO2 SnO2, branched 300 Ethanol, 100 ppm 51 (n) [155] sensors-22-03351-t006_Table 6 Table 6 Chemiresistors exploiting composite materials based on metal oxide (MOX) and 2D carbon nanostructures, namely graphene (G) and reduced graphene oxide (RGO). The response intensity S is calculated as S = Rgas/Rair for NO2 and as S = Rair/Rgas for other compounds in the case of n-type sensing response. The contrary is in the case of p-type sensing response. The n- or p- type response is reported in the S column. In this column, ‘sb’ stands for the response arising from the metal–semiconductor Schottky barrier modulation (Equation (11)). Gas concentrations are expressed in parts per million (ppm); the sensor temperature is T; ‘RT’ stands for ‘room temperature’. MOX, Morphology 2D Carbon Material T (°C) Gas, Concentration S (type) Ref. ZnO, nanorods -- RT NO2, 1 ppm 1.8 (n) [159] -- RGO RT NO2, 1 ppm 1.2 (p) [159] ZnO, nanorods RGO RT NO2, 1 ppm 2.2 (p) [159] ZnO, nanosheets -- RT NO2, 50 ppm 6 (n) [160] ZnO, nanosheets RGO RT NO2, 50 ppm 9 (n) [160] ZnO, hierarchical spheres -- 110 NO2, 1 ppm 4 (n) [161] ZnO, hierarchical spheres RGO 110 NO2, 1 ppm 20 (n) [161] ZnO, hierarchical porous sheets RGO RT NO2, 1 ppm 10 (p) [163] ZnO, nanoparticles RGO RT NH3, 1 ppm 1.07 (p) [162] Cu doped SnO2, nanowires RGO RT H2S, 50 ppm 33 (n) [165] Cu doped SnO2, nanowires RGO RT NH3, 50 ppm 1.25 (n) [165] Cu doped SnO2, nanowires RGO RT NO2, 50 ppm 1.5 (n) [165] Cu doped ZnO, nanorods RGO RT H2S, 50 ppm 1.05 (p) [166] In2O3, nanorods RGO RT NO2, 97 ppm 2.5 (n) [167] Cu2O, hierarchical mesocrystals -- RT NO2, 2 ppm 1.4 (p) [168] -- RGO RT NO2, 2 ppm 1.2 (p) [168] Cu2O, hierarchical mesocrystals RGO RT NO2, 2 ppm 1.7 (p) [168] SnO2, nanowire G RT NO2, 0.1 ppm 11 (sb) [170] sensors-22-03351-t007_Table 7 Table 7 Chemiresistors based on metal oxide (MOX) nanostructures functionalized with organic molecules. The response intensity S is calculated as S = Rgas/Rair for NO2 and as S = Rair/Rgas for other compounds in the case of n-type sensing response. The contrary is in the case of p-type sensing response. The n- or p- type response is reported in the S column. Gas concentrations are expressed in parts per million (ppm) or parts per billion (ppb) and the sensor temperature is T. (*): under solar illumination, (+): under UV illumination; (§): under visible light illumination. MOX, Morphology Organic Coating T (°C) Gas, Concentration S (type) Ref. ZnO, nanowires -- 190 NO2, 2 ppm 1.3 (n) [174] ZnO, nanowires THMA 190 NO2, 2 ppm 1.2 (n) [174] ZnO, nanowires + nanoparticles -- 190 NO2, 2 ppm 1.22 (n) [174] ZnO, nanowires + nanoparticles THMA 190 NO2, 2 ppm 1.44 (n) [174] ZnO, nanowires -- 300 Acetone, 50 ppm 30 (n) [175] ZnO, nanowires GLYMO 300 Acetone, 50 ppm 90 (n) [175] ZnO, nanowires APTMS 300 Acetone, 50 ppm 160 (n) [175] SnO2, nanowires en-APTAS 1 RT (*) NO2, 250 ppb 10 (n) [176] ZnO, nanoparticles H2TPPCOOH porphyrin RT Pentanol, 60 ppm 1.1 (n) [178] ZnO, nanorods -- RT (+) Ethanol, 104 ppm 1.01 (p) [179] ZnO, nanorods H2TPPCOOH porphyrin RT (§) Ethanol, 104 ppm 1.002 (n) [179] ZnO, nanorods -- RT (+) Triethylamine, 104 ppm 1.01 (p) [179] ZnO, nanorods H2TPPCOOH porphyrin RT (§) Triethylamine, 104 ppm 1.8 (n) [179] sensors-22-03351-t008_Table 8 Table 8 Resumed comparison between nanowires (NWs) and nanoparticles (NPs), relationship between the structural/morphological properties and the related gas-sensing phenomena and properties. Single-Phase Materials Feature Nanowires (NWs) Nanoparticles (NPs) Surface termination Well-defined crystalline planes:Receptor/transducer functions (response intensity and partial selectivity) related to specific crystalline planes. Rounded shape:Receptor/transducer functions (response intensity and partial selectivity) related to spherical/irregular surfaces. Length-to-diameter aspect ratio Large, often >10:Less effective than NPs in terms of surface-to-volume ratio and transducer function; NW Network: open morphology for optimal diffusion (enhanced response intensity and diffusion-related partial selectivity owing to enhanced utility factor); NW network: porosity does not decrease with decrease in the NW diameter; Individual NW: no grain boundaries for improved stability (no grain coarsening); Individual NW and low-density NW networks: self-heating effect for extremely low power consumption. Almost unitary, ≈1:Most effective morphology for optimization of surface-to-volume ratio and transducer function (transition from surface-to-volume depletion regimes as the NP diameter decreases); Porosity may decrease with decrease in the NP diameter; Large density of grain boundaries (elements featuring the most effective transducer function and preferential gas adsorption). Doping Modulation of charge-carrier density (and Debye length); Modulation of transducer and receptor functions. Eventual dopant-induced phase transition will turn the NW structure from single- to polycrystalline. Increased thermal stability owing to hindered grain-coarsening phenomena; Possible dopant-induced phase transition. Hierarchical structures More open morphology for optimal diffusion (enhanced response intensity and diffusion-related partial selectivity owing to enhanced utility factor; Heterostructure-based materials Surface functionalization Nanowires (NWs) Nanoparticles (NPs) metallic nanoparticles Electronic and chemical sensitization for lowering optimal sensing temperature, increasing response intensity and partial selectivity. MOX nanostructures Exploitation of the interface and the additive sensing capabilities for enhanced response intensity and partial selectivity. Branched NW heterostructure merging the open hierarchical morphology with the interface and additive sensing capabilities. -- Organic materials Exploitation of the interface and the additive sensing capabilities to reduce the optimal working temperature, enhancing the response intensity and the partial selectivity. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092929 molecules-27-02929 Article Conducting the RBD of SARS-CoV-2 Omicron Variant with Phytoconstituents from Euphorbia dendroides to Repudiate the Binding of Spike Glycoprotein Using Computational Molecular Search and Simulation Approach https://orcid.org/0000-0002-2631-5831 Hassan Heba Ali 1 Hassan Ahmed R. 2 https://orcid.org/0000-0002-0314-0725 Mohamed Eslam A.R. 3 https://orcid.org/0000-0001-9626-6302 Al-Khdhairawi Ahmad 4 Karkashan Alaa 5 https://orcid.org/0000-0003-4938-0553 Attar Roba 5 https://orcid.org/0000-0002-6486-9835 Allemailem Khaled S. 6* https://orcid.org/0000-0003-2984-9262 Al Abdulmonem Waleed 7 https://orcid.org/0000-0001-5960-1503 Shimizu Kuniyoshi 8 https://orcid.org/0000-0002-2586-9023 Abdel-Rahman Iman A. M. 9 https://orcid.org/0000-0003-0286-581X Allam Ahmed E. 10* Nakagawa-Goto Kyoko Academic Editor 1 Department of Pharmacognosy, Faculty of Pharmacy, Sohag University, Sohag 82524, Egypt; heba.ali@pharm.sohag.edu.eg 2 Desert Research Center, Medicinal and Aromatic Plants Department, Cairo 11753, Egypt; drarahdrc@gmail.com 3 Department of Chemistry, Faculty of Science, Minia University, Minia 61511, Egypt; eslamahmedragabmohamed@gmail.com 4 Department of Biological Science and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; ahmadayad@outlook.my 5 Department of Biology, College of Sciences, University of Jeddah, Jeddah 21959, Saudi Arabia; askarkashan@uj.edu.sa (A.K.); rmattar@uj.edu.sa (R.A.) 6 Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia 7 Department of Pathology, College of Medicine, Qassim University, Buraydah 51452, Saudi Arabia; dr.waleedmonem@qu.edu.sa 8 Department of Agro-Environmental Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan; shimizu@agr.kyushu-u.ac.jp 9 Department of Pharmacognosy, Faculty of Pharmacy, South Valley University, Qena 83523, Egypt; emanabdelraheem@svu.edu.eg 10 Department of Pharmacognosy, Faculty of Pharmacy, Al-Azhar University, Assiut 71524, Egypt * Correspondence: k.allemailem@qu.edu.sa (K.S.A.); ahmedallam@azhar.edu.eg (A.E.A.) 04 5 2022 5 2022 27 9 292907 4 2022 02 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). (1) Background: Natural constituents are still a preferred route for counteracting the outbreak of COVID-19. Essentially, flavonoids have been found to be among the most promising molecules identified as coronavirus inhibitors. Recently, a new SARS-CoV-2 B.1.1.529 variant has spread in many countries, which has raised awareness of the role of natural constituents in attempts to contribute to therapeutic protocols. (2) Methods: Using various chromatographic techniques, triterpenes (1–7), phenolics (8–11), and flavonoids (12–17) were isolated from Euphorbia dendroides and computationally screened against the receptor-binding domain (RBD) of the SARS-CoV-2 Omicron variant. As a first step, molecular docking calculations were performed for all investigated compounds. Promising compounds were subjected to molecular dynamics simulations (MD) for 200 ns, in addition to molecular mechanics Poisson–Boltzmann surface area calculations (MM/PBSA) to determine binding energy. (3) Results: MM/PBSA binding energy calculations showed that compound 14 (quercetin-3-O-β-D-glucuronopyranoside) and compound 15 (quercetin-3-O-glucuronide 6″-O-methyl ester) exhibited strong inhibition of Omicron, with ΔGbinding of −41.0 and −32.4 kcal/mol, respectively. Finally, drug likeness evaluations based on Lipinski’s rule of five also showed that the discovered compounds exhibited good oral bioavailability. (4) Conclusions: It is foreseeable that these results provide a novel intellectual contribution in light of the decreasing prevalence of SARS-CoV-2 B.1.1.529 and could be a good addition to the therapeutic protocol. SARS-CoV-2 Omicron Euphorbia dendroides molecular docking molecular dynamics This research received no external funding. ==== Body pmc1. Introduction In October 2021, a new SARS-CoV-2 B.1.1.529 variant emerged in South Africa, designated Omicron by the World Health Organization (WHO). By early 2022, over 100,000 Omicron genomes had evolved as Omicron begun to dominate SARS-CoV-2 infections around the world [1]. Because it contains more mutations than any other strain, it is more transmissible than previous strains. Many of the changes are found in the spike protein, which is involved in the transmission of the virus. Natural ingredients offer a wide range of chemical properties, including antiviral activity, and thus could be used to treat coronavirus infections. Plants and their secondary metabolites that act against targets associated with SARS-CoV-2 infection could be useful leads for developing drugs against the newly emerged Omicron. The discovery of antiviral drugs and effective therapeutic techniques is lengthy and laborious. Therefore, natural chemicals are often considered as attractive alternative treatment solutions because they are also the main sources of antibacterial and antiviral drugs. Recently, it was suggested that dietary flavonoids could regulate the severity of SARS-CoV-2 disease by affecting ACE2 prevalence and function [2,3]. Several publications have also stated that polyphenols target the renin–angiotensin system by modulating angiotensin II levels in mice [4,5]. In silico studies recently reported that flavonoids such as quercetin and rutin and polyphenols such as epigallocatechin gallate, myricetin, and quercetagetin showed a high rate of inhibition against the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 [6]. Euphorbia dendroides L. (family Euphorbiaceae), one of the species with high polyphenol content, is a perennial small tree growing in Sollum and Mersa Matruh in Egypt [7]. The genus Euphorbia is unique in that it includes highly reputed species used in traditional medicine against various human diseases, such as respiratory diseases, inflammation, skin diseases, diarrhea, migraine, gonorrhea, warts, and intestinal parasites and exerts a laxative effect [8,9,10]. In addition to E. dendroides, other plants in the genus Euphorbia and their constituents have also recently gained medicinal importance and are used for various diseases, including as anticancer [11,12,13], antioxidants [14], and antiviral agents, and to target multidrug resistance [15,16] and COVID-19 [17,18,19]. Our previous phytochemical studies on E. dendroides’ aerial parts revealed the presence of ten phenolic compounds, including six flavonoids, one phenolaldehyde, and three phenolic acids [20], as well as six cycloartane triterpenes and lupeol triterpenes, in addition to β-sitosterol and three fatty acids [13]. The constituents of medicinal plants may serve as targets for the development of therapeutic candidates against some SARS-CoV-2 proteins [21,22]. Consequently, in this study, seventeen phytoconstituents from the categories of flavonoids, phenols, and triterpenes that we had previously obtained from this plant were evaluated by in silico studies as potential candidates against the Omicron variant of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In this study, we performed computational molecular screening against the Omicron receptor-binding domain (O-RBD) to explore and develop drugs from natural plant constituents that are effective against the current pandemic virus. 2. Results and Discussion 2.1. Identification of Phytoconstituents from E. dendroides The phytochemicals of E. dendroides (1–17) were elucidated using NMR (Figures S1–S52) as well as LC-ESI-MS/MS spectra and by comparison with the literature data. These compounds were identified as 24-methylene cycloartan-3β-ol (1) [11], cycloart-23-ene-3β,25-diol (2) [23], cycloart-23-ene-3β,25-diol monoacetate (3) [23], 3β-hydroxy-cycloart-23-ene-25 methyl ether (4) [24], 24 R/S-3β-hydroxy-25-methylene cycloartan-24-ol (5) [24], 23 R/S-3β-hydroxycycloart-24-ene-23-methyl ether (6) [13], lupeol (7) [25], gallic acid (8) [26], vanillin (9) [27], protocatechuic acid (10) [28], trans-caffeic acid (11) [29], luteolin (12) [30], kaempferol-3-O-β-D-glucuronopyranoside (13), quercetin-3-O-β-D-glucuronopyranoside (14) [31], quercetin-3-O-glucuronide 6″-O-methyl ester (15) [31,32], kampferol-3-O-glucuronide 6″-O-methyl ester (16) [32], and quercetin-3-O-β-D-glucopyranoside (17) [33]. 2.2. Molecular Docking An important aspect of the drug discovery strategy is molecular docking analysis, which can be used to specify protein–ligand interactions in the active site of the target protein. In this context, all seventeen compounds were subjected to molecular docking calculations to investigate their potency as anti-Omicron drugs. For each compound, only the docking pose with the highest docking score was selected from the nine docking poses. The estimated docking scores for all seventeen compounds are shown in Table S2. As shown in the data deposited in Table S2, eight compounds had docking scores of less than −7.0 kcal/mol. For the remaining compounds, three compounds were in the range of −7.2 to −7.7 kcal/mol and six compounds were in the range of −7.9 to −8.8 kcal/mol. The average docking score for the seventeen compounds was calculated to be −7.04 kcal/mol. Remdesivir is a nucleotide analog prodrug and has been recently subjected to in vitro experiments as an anti-Omicron drug [34]. In order to assess the potentiality of the discovered compounds, the binding features and affinities of the top six compounds were compared to those of remdesivir against Omicron. Table 1 provides a deeper understanding of the binding characteristics for the top six compounds, as well as remdesivir, with Omicron. The docking features in Table 1 reveal that remdesivir demonstrated four hydrogen bonds with TYR453, SER494, and TYR501 with bond lengths ranging from 2.27 to 3.01 Å. The six top-ranked compounds exhibited similar binding modes and abundant hydrogen bonds with three main residues: SER496, TYR501, and HIS505. For instance, compound 15 achieved the highest value of docking score towards Omicron equal to −8.8 kcal/mol, forming multiple hydrogen bonds with SER496 (2.99, 3.01 Å), in addition to TYR501 (2.92 Å) and HIS505 (3.17 Å). Compound 14, the second-highest-ranked compound, had a docking score equal to −8.7 kcal/mol, forming triple hydrogen bonds with SER496 (2.13, 2.93, 3.01 Å), as well a single bond with TYR501 (2.89 Å) and HIS505 (3.18 Å). To recognize the other types of interactions, two-dimensional representations of the interactions of these six potent compounds with the main active site residues are displayed in Figure 1. It is worth mentioning that compound 15 exhibited a pi–alkyl interaction with LEU455 (4.96 Å) and ARG493 (4.17 Å). Moreover, it also formed a pi–cation interaction with ARG403 (4.92 Å), as well pi–pi stacked and pi–pi T-shaped with TYR501 (4.24 Å) and HIS505 (4.92, 4.99 Å), respectively. In the case of compound 14, it formed two extra types of interactions, pi–pi T-shaped with HIS505 (4.92, 5.05 Å) and a pi–cation interaction with ARG403 (4.90 Å). Similar to compound 15, compound 16 formed pi–alkyl, pi–pi stacked, and pi–cation interactions with the same residues but different bond lengths. Excluding pi–alkyl interaction, compound 17 showed the same types of interactions that existed in compound 16. Notably, compound 13 was the only one that exhibited no pi–cation interaction. Compound 12, the lowest-ranking of the six compounds in terms of docking score, showed only two additional interactions other than hydrogen bonding. It is worth highlighting that residues ARG403, TYR501, and HIS505 favored the formation of pi–cation, pi–pi stacked, and pi–pi T-shaped interactions, respectively, with RBD residues. 2.3. Molecular Dynamics (MD) Simulations The main purpose of applying molecular dynamics (MD) simulations is to investigate the conformational flexibilities and stabilities of studied protein–ligand complexes. Accordingly, MD simulations up to 200 ns were performed for the best-ranking six complexes. Binding energy calculations were also run using the MM/PBSA approach. Data of estimated binding energies are represented in Figure 2. Notably, compounds 14 and 13 showed similar binding energies over 50 ns with values equal to −44.8 and −46.4 kcal/mol, respectively. Furthermore, compound 15 and compound 16 showed identical values of binding energy. Interestingly, compound 12, the lowest of the six compounds in terms of docking score, also exhibited the lowest binding energy, with a value equal to −18.6 kcal/mol. To further check the stability of the six compounds inside the Omicron active site, MD simulation was performed up to 100 ns. Remarkably, compounds 15 and 12 exhibited higher binding energies than in the 50 ns MD simulations, with ΔGbinding of −22.9 and −21.8 kcal/mol, respectively. Compounds 14 and 13, those that were the highest-ranked compounds in the 50 ns MD simulations, were also the highest-ranked in the 100 ns MD simulations, with a slight decrease in binding energies, with values equal to −42.4 and −38.4 kcal/mol. Through analyzing the binding energies for the six compounds over the whole 200 ns MD simulations, compound 13 showed a continuous decrease in binding energy with increasing simulation time. Conversely, compound 15 exhibited increasing binding energy over the 200 ns MD simulations. Interestingly, compound 17 showed very similar results of binding energy over the 50 ns, 100 ns, 150 ns, and 200 ns MD simulations. 2.4. Post-MD Analyses The structural stability and conformational variations of protein–ligand docked complexes over the MD simulation process can be evaluated by root-mean-square deviation (RMSD) measurement. Lower values of RMSD give an indication of tight binding. The following equation describes how RMSD was estimated. RMSD=∑i=1nRi∗Rin where Ri is the vector connecting the positions of atom i [of N atoms] in the reference snapshot and the current snapshot after optimal superposition. Obtained results of RMSD analysis over 200 ns MD simulation are plotted in Figure 3. It is worth mentioning that compound 14 exhibited overall stability compared to the other five compounds, with an average RMSD value of 2.84 Å. Comparatively, the RMSD values of the other compounds were relatively high. These results showed that compound 14 was tightly bound and had no effect on the overall topology of Omicron. Root-mean-square fluctuation (RMSF) analysis was conducted to investigate the flexibility of the Omicron residues over the 200 ns MD simulations. Briefly, higher RMSF values indicate greater flexibility of protein residues, whereas low RMSF values imply limitations of residues’ movement, and accordingly less flexibility. RMSF can be estimated using the following equation:RMSF=∑j=13(1N∑k=1NPijk2−P¯ij2) The RMSF of the atom i with j from 1 to 3 for the x, y, and z coordinate of the position vector P of the atom and k over the set of N evaluated snapshots was calculated. The RMSF data are shown in Figure 4. Compound 14 complexed with Omicron had fewer fluctuations over 200 ns, with RMSF of 1.47 Å, which is consistent with the RMSD findings. Other compounds had values of RMSF ranging between 1.48 and 1.68 Å. In order to assess whether the protein–ligand complexes were stably folded or not, radius of gyration (Rg) analysis was performed over the 200 ns MD simulations. Values of Rg give an indication of the compactness of the protein structure within the system [35] A more compact protein structure can be observed in the case of lower values of Rg. During the 200 ns simulation course, all compounds exhibited acceptable behavior of Rg, which can be observed in Figure 5. The range of Rg values for the six compounds was from 18.68 to 19.14 Å. What also can be noticed from Figure 5 is that compounds 14 and 15 showed constant Rg behavior during the simulation, which indicates high compactness of the protein structure. Solvent-accessible surface area analysis (SASA) was performed to represent the area of protein exposed to solvent. The averaged SASA values of the six structures were 10,501, 10,428, 106,30, 11,007, 10,553, and 10,886 for compounds 12 to 17, respectively. Despite the compact folding of 7QNW, the increase in SASA in compound 15 compared to the other compounds indicated obvious conformational changes due to ligand binding. Consequently, SASA may provide details about the protein’s ability to interact. As shown in the data in Figure 6, all six compounds exhibited relative stability and compactness over the course of the 200 ns MD simulation. 2.5. In Silico Drug Likeness Based on Lipinski’s rules, physicochemical properties were investigated to understand the studied compounds’ molecular features better. The Molinspiration tool https://www.molinspiration.com (30 April 2022) was utilized to compute the in silico molecular features of compounds. The investigated Lipinski’s parameters, topological polar surface area (TPSA), as well percentage of absorption (% ABS) of Lipinski’s parameters were anticipated and are presented in Table 2. As revealed from the data in Table 2, the investigated two compounds showed lower values of miLogP, indicating that these compounds possess adequate permeability via the cell membrane. The molecular weights of the inspected compounds did not exceed 500 (calc. 478.4 and 492.4). Hydrogen bond donors (nON) were found to total 13. Furthermore, the number of hydrogen bond acceptors (nOHNH) was found to range between 7 and 8. Although the values of nON and nOHNH were higher than the ideal values, it was reported that this defect did not exert a remarkable effect on the compound’s diffusion and transportation, as many FDA-approved drugs transcend the optimum Lipinski values of nON and nOHNH [36]. The estimated %ABS values were in the range of 30.0–35.0%. The TPSA values were also detected in the range of 215.0–230.0, which indicates the high bioavailability of these discovered compounds. 3. Materials and Methods 3.1. Plant Material The aerial parts of E. dendroides were collected in April 2017 in Mersa Matruh on the northwestern coast of Egypt. The plant material was authenticated by Dr. Omran Ghaly, with a PhD in Plant Taxonomy, at the Desert Research Center. A voucher sample (CAIH-30-12-2017-R) was deposited in the herbarium of the Desert Research Center, Cairo, Egypt. 3.2. Phytochemical Constituents of E. dendroides The phytochemical study of the aerial plant parts of E. dendroides revealed approximately seventeen secondary metabolites, as indicated by previous studies [13,20]. These natural components have been divided into six flavonoids (12–17), four phenolic compounds (8–11), and seven triterpenes (1–7). In particular, the triterpenes were extracted from the methanol plant extract by extensive chromatographic methods, and their structure was elucidated by 1D and 2D NMR spectroscopic methods. Four phenolics and three flavonoids were also isolated and identified from the poly-phenolic-rich fraction of the plant using chromatographic and spectroscopic tools, while the remaining three flavonoids were determined from the polyphenol-rich fraction of the plant using LC-ESI-MS/MS. All the phytochemical components of E. dendroides (1–17) were tested in silico for their ability to inhibit Omicron. 3.3. Protein Preparation The three-dimensional crystal structure of the receptor-binding domain (RBD) of the SARS-CoV-2 Omicron variant (PDB ID: 7QNW, resolution: 2.40 Å) was retrieved and used for all in silico analyses. The downloaded viral target was prepared by removing ions, water molecules, and hetero-atoms. To identify the protonation states of the protein residues, the H++ web server [37,38] was used. Accordingly, all missing hydrogen atoms were successfully added. For the H++ calculations, physiological parameters such as pH, salinity, internal dielectricity, and external dielectricity were set to 6.5, 0.15, 10, and 80, respectively. 3.4. Inhibitor Preparation Chem3D Pro 12.0 software (version 12.0.2) was utilized to sketch and analyze the seventeen extracted compounds’ chemical structures. All studied compounds were subjected to energy minimization using the MM2 force field. Before a molecular docking study, such an energy minimization step is required to reduce the influence of any potential unfavorable torsion angles, bond angles, bond lengths, or undesirable non-bonded interactions [39]. The names of the investigated compounds and their 2D chemical structures are illustrated in Table S1. 3.5. Molecular Docking Molecular docking is considered the best tool in computational drug discovery to determine the efficacy of the compounds under study [40]. AutoDock Vina was used to study the binding affinities for these compounds [41]. All parameters were left in their default modes in this study, except for the exhaustiveness parameter, which was set to 200. Residues of the O-RBD were enclosed by a docking grid box with XYZ dimensions of 25 × 25 × 25 (Å). In addition, the grid spacing was set to 1.0 Å. The generated nine poses of the docked inhibitors were evaluated and the best one was selected. BIOVIA Discovery Studio was used to visualize the protein–ligand interactions [42]. 3.6. Molecular Dynamics Simulations Molecular dynamics (MD) simulations were conducted using the YASARA Structure (version 21.12.19) protocol [43] to obtain a better understanding of the stability of the protein–ligand complexes. Within the MD simulations, the AMBER14 force field was the utilized force field. Execution of the initial energy minimization was performed using the steepest descent algorithm. The MD simulations were conducted for amino acid residues at the default physiological value of pH (7.4). Water molecules were successfully introduced into the system at constant temperature and pressure conditions. Counter ions (Na+ or Cl−) with a concentration equal to 0.9% were included to maintain the neutral state of the systems. To maintain the pressure value at 1 atm, the Berendsen barostat technique [44] was used. The long-range coulomb forces were computed employing the particle-mesh Ewald (PME) method [45,46]. The cut-off radius was set to 8 Å for the non-bonded interactions. The Langevin thermostat method was employed to hold the value of temperature at 300 K [47]. The periodic boundary conditions were also taken into account. The cubic simulation cell was chosen to be larger than the studied protein–ligand complexes by 20 Å in every instance. With a multiple time step of 1.25 fs, a regular simulation speed was preserved for intramolecular processes. At an integration step of 2 fs, all intermolecular bonds, including hydrogen bonds, were constrained using the SHAKE algorithm [48]. As a final step, production stages were accomplished over simulation times of 50 ns, 100 ns, 150 ns, and 200 ns. Snapshots of the simulation trajectory were held every 100 ps after an equilibration time of 1–2 ns, determined by the root-mean-square deviations (RMSDs) of the solutes from the initial structure. Simulation steps were executed using a pre-installed macro (md_run.mcr) within the YASARA package. The YASARA program (version 21.11.16) uses the Poisson–Boltzmann approach [49] (called “PBS”). The surfcost parameter, used to calculate the entropic cost of exposing an Å2 to the solvent, was set to 0.35. The Amber14 force field was used to calculate the binding energy of the inhibitor. To ensure consistency with the empirically determined values, the binding energies derived from PBS were divided by a factor of 20 [50]. The root-mean-square deviation (RMSD), radius of gyration (Rg), root-mean-square fluctuation (RMSF), and solvent-accessible-surface-area (SASA) were all used to analyze the best compounds at the end of the 200 ns MD simulations. 3.7. Binding Energy Calculations Binding free energies of the investigated drugs against Omicron were computed using the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) approach [51]. The below-illustrated equations were used in the process of MM/PBSA binding free energy calculations. ΔGbinding= ΔGC−ΔGP− ΔGL ΔGbinding=ΔH−TΔS=ΔEMM+ΔGSol−TΔS Values of the binding energy of the complex, protein, and ligand are described by ΔGC, ΔGP, and ΔGL, respectively. In addition, ΔGSol, ΔEMM, and −TΔS stand for the solvation Gibbs energy, gas-phase molecular mechanics change, and conformational entropy, respectively. The term ΔEMM can be determined through summation of the van der Waals and electrostatic interactions. The term ΔGSol can readily be defined as adding the polar and non-polar solvation values. The entropic contribution is denoted by the term −T∆S. 3.8. Drug Likeness Properties In order to assess the physicochemical parameters of the specified compounds, the online Molinspiration cheminformatics software (http://www.molinspiration.com 30 April 2022) was employed. For each discovered compound, various descriptors were checked, including the octanol/water partition coefficient (milogP), topological polar surface area (TPSA), molecular weight (MWt), number of hydrogen bond donors (nOHNH), number of hydrogen bond acceptors (nON), number of rotatable bonds (Nrotb), and percentage of absorption (%ABS). The equation that was used in computing %ABS is displayed below [52]:%ABS=109−[0.345×TPSA] 4. Conclusions In the current pandemic, with the emergence of Omicron, in silico strategies may be useful in discovering potent inhibitors for this disease. In the present study, molecular docking calculations were performed for seventeen isolated compounds from Euphorbia dendroides. The results showed that six flavonoid compounds are the best anti-Omicron drug candidates, further supporting their efficacy. Combined molecular dynamics simulations (MD) and molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) binding energy evaluations over 200 ns were performed for these six compounds. Two compounds, namely quercetin-3-O-β-D-glucuronopyranoside and quercetin-3-O-glucuronide 6″-O-methyl ester, showed promising binding affinities, with ΔGbinding of −41.0 and −32.4 kcal/mol, respectively. As for drug-like properties, both compounds also proved their potential, with a good percentage of absorption (%ABS). Acknowledgments The researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules27092929/s1, NMR spectra; Figures S1–S52: spectrum of compounds 1–14; Table S1: Compound name, 2D-chemical structure, and IUPAC name of the seventeen studied compounds; Table S2: Calculated docking scores (in kcal/mol) for the SARS-CoV-2 Omicron drug candidates. Click here for additional data file. Author Contributions Conceptualization, A.E.A. and H.A.H.; validation, A.R.H.; formal analysis, E.A.R.M., A.A.-K. and K.S.A.; investigation, K.S., A.K. and R.A.; data curation, H.A.H. and I.A.M.A.-R.; methodology, E.A.R.M. and A.R.H.; writing—original draft preparation, A.R.H.; writing—review and editing, I.A.M.A.-R., K.S.A. and E.A.R.M.; visualization, A.E.A., H.A.H., A.R.H. and I.A.M.A.-R.; supervision, A.E.A.; project administration, K.S. and W.A.A. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Two-dimensional representations of the anticipated binding poses of the best-investigated drugs inside the active site of Omicron. Figure 2 Estimated MM/PBSA binding energies for the best six drug candidates as Omicron inhibitors. Figure 3 Root-mean-square deviation (RMSD) of Omicron backbone atoms from the initial structure complexed with the highest-ranked drugs over 200 ns MD simulations. Figure 4 Root-mean-square fluctuation (RMSF) of the six selected complexes (Omicron-12, Omicron-13, Omicron-14, Omicron-15, Omicron-16, Omicron-17). Figure 5 Radius of gyration (Rg) plot of six identified complexes through 200 ns MD simulations. Figure 6 Solvent-accessible surface area (SASA) of the best six compounds for 200 ns MD simulations. molecules-27-02929-t001_Table 1 Table 1 Docking scores (in kcal/mol) and binding features for best six compounds and remdesivir against Omicron. Molecule 2D Chemical Structure Docking Score (kcal/mol) Binding Features (Hydrogen Bond Length in Å) 15 −8.8 ARG403 (3.23 Å), TYR453 (2.94 Å), SER496 (2.99, 3.01 Å), TYR501 (2.92 Å), HIS505 (3.17 Å) 14 −8.7 ARG403 (3.18 Å), SER496 (2.13, 2.93, 3.01 Å), TYR501 (2.89 Å), HIS505 (3.18 Å) 16 −8.4 GLU406 (2.94 Å), TYR453 (2.87 Å), SER496 (2.92, 3.01 Å), TYR501 (2.90 Å), HIS505 (3.17 Å) 17 −8.3 TYR453 (2.97 Å), SER496 (2.99, 3.03, 3.08 Å), TYR501 (2.94 Å), HIS505 (3.17 Å) 13 −8.1 TYR453 (2.26 Å), SER496 (2.93, 2.98, 3.01 Å), TYR501 (2.87 Å), HIS505 (3.15 Å) Remdesivir −8.0 TYR453 (2.91 Å), SER494 (2.27, 2.89 Å), TYR501 (3.01 Å) 12 −7.9 ARG403 (3.04 Å), TYR495 (2.57 Å) molecules-27-02929-t002_Table 2 Table 2 Predicted physiochemical parameters of the best identified compounds and their structural descriptors. 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==== Front Nutrients Nutrients nutrients Nutrients 2072-6643 MDPI 10.3390/nu14091946 nutrients-14-01946 Article Antilipidemic and Hepatoprotective Effects of Ethanol Extract of Justicia spicigera in Streptozotocin Diabetic Rats Murillo-Villicaña Marina 1 Noriega-Cisneros Ruth 2 https://orcid.org/0000-0001-5802-488X Peña-Montes Donovan J. 1 https://orcid.org/0000-0003-2962-1954 Huerta-Cervantes Maribel 1 https://orcid.org/0000-0003-0326-2068 Aguilera-Méndez Asdrubal 1 https://orcid.org/0000-0002-4850-772X Cortés-Rojo Christian 1 https://orcid.org/0000-0001-5920-6562 Salgado-Garciglia Rafael 1 Montoya-Pérez Rocío 1 https://orcid.org/0000-0003-0194-2537 Riveros-Rosas Héctor 3 https://orcid.org/0000-0002-0811-2950 Saavedra-Molina Alfredo 1* Alisi Anna Academic Editor 1 Instituto de Investigaciones Químico Biológicas, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58030, Mexico; 0850421k@umich.mx (M.M.-V.); 0618853j@umich.mx (D.J.P.-M.); marzy112@yahoo.com.mx (M.H.-C.); amendez@umich.mx (A.A.-M.); christian.cortes@umich.mx (C.C.-R.); rafael.salgado@umich.mx (R.S.-G.); rocio.montoya@umich.mx (R.M.-P.) 2 Facultad de Enfermería, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58030, Mexico; ruth.noriega@umich.mx 3 Departamento de Bioquímica, Facultad de Medicina, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Cd. Universitaria, Ciudad de Mexico 04510, Mexico; hriveros@unam.mx * Correspondence: francisco.saavedra@umich.mx; Tel.: +52-443-326-5790 06 5 2022 5 2022 14 9 194627 3 2022 04 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Oxidative stress is a factor that contributes to the development of complications in diabetes; however, its effects can be counteracted using exogenous antioxidants that are found in some plants, which is why people turn to traditional medicines in the search for therapeutic treatment. Justicia spicigera has been demonstrated to have the capacity to reduce glycemic levels; however, its effects on non-insulin-dependent organs such as the liver have not been reported. During 30 days of administration of Justicia spicigera ethanol extract, the blood glucose and weight of rats were measured every 5 days. Once the treatment was concluded, the rats were sacrificed. Corporal weight, blood glucose, cholesterol, very-low-density lipoprotein (VLDL), triglycerides, total lipids, and liver profile were reduced in the diabetic condition and normalized with the application of ethanol extract from J. spicigera (EJS). Additionally, there was a significant increase in catalase and superoxide dismutase activity in the control diabetic rats, a decrease in their activity with the extract administration, and no effect on normoglycemic rats. In conclusion, EJS is considered to be capable of reducing oxidative stress by maintaining diminished lipid and liver function profiles in male Wistar rats with streptozotocin-induced diabetes. antioxidant bioactive compounds diabetes Justicia spicigera liver streptozotocin Coordinación de Investigación Científica (UMSNH)2.16 UNAM-DGAPA-PAPIITIN218819 IN219022 This research was funded by Coordinación de Investigación Científica (UMSNH), grant number 2.16 to A.S.-M. and UNAM-DGAPA-PAPIIT grants IN218819, and IN219022 to H.R.-R. ==== Body pmc1. Introduction Diabetes mellitus (DM) is a heterogeneous set of multifactorial pathogenetic syndromes with a common nexus of metabolic disorder, mainly chronic hyperglycemia and alterations in lipid and protein metabolism. Glucose transportation through the plasma membrane of mammalian cells is one of the most important events of nutrient transport, since this monosaccharide has a central role in metabolism and cell homeostasis. To transport glucose inside the cells, the organs in the body have different GLUT proteins, and they are divided into two groups. One group is insulin-dependent, including skeletal muscle, adipose tissue, and heart tissue, which have GLUT4 transporters [1]. The other group is tissues that do not depend on insulin to transport glucose inside them, such as the brain, kidney, and erythrocytes, and the epithelial cells of the intestine and liver [1]. The liver performs several biochemical functions of synthesis and excretion, so there is no test that can define the state of total liver function. The National Academy of Clinical Biochemistry and the American Association for the Study of Liver Diseases recommend a specific panel of tests to be used in the initial evaluation of patients with known or suspected liver disease, designated as the liver function profile, which is composed of the following analyses: total proteins, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (AP), total bilirubin, and direct and indirect bilirubin [2]. As an example, liver necrosis transaminases, AST, and ALT are sensitive and specific for hepatocytes, as well as for total, direct, and indirect bilirubin [3]. Liver AP is found on the canalicular surface and is therefore a marker of biliary dysfunction [4]. In patients with diabetes, the prevalence of non-alcoholic hepatic steatosis or non-alcoholic fatty liver disease (NAFLD) is 50–75% because the liver is a key organ that contributes to the development of insulin resistance and type 2 diabetes mellitus [5,6]. Therefore, dyslipidemia is a major side effect of diabetes [6]. Mitochondria, the major producers of reactive oxygen species (ROS) in the electron transport chain, also contribute to oxidative and nitrosative stresses when ROS are generated and lipid peroxidation occurs in diabetes [7,8]. Similarly, the mitochondrial glutathione pool is affected in diabetes due to oxidative stress [7]. Plants are among the most promising sources for discovering new antioxidant agents [9]. Since antiquity, humans have consumed the seeds, roots, stems, flowers, and fruits of plants to alleviate disorders due to their effectiveness in healing, their availability, and their low cost. Polyphenols are metabolites characterized by the attachment of one or more hydroxyl groups to one or more aromatic rings [10]. Among polyphenols, flavonoids represent a wide variety of metabolites [11]. including flavonoid glycosides, which are mainly found as their 3- or 7-O-glycosides [12]. In particular, flavonoids are considered as potential antidiabetic agents because they have multiple actions that are both hypoglycemic (insulinomimetic action) and antihyperglycemic (insulin secretagogue) [13]. Previous studies have shown that extracts of Justicia spicigera have antioxidant properties [14]. because they contain significant concentrations of flavonoids, mainly kaempferol glycosides, the most important being: kaempferitrin (kaempferol-3,7-dirhamnoside) and astragalin (kaempferol-3-β-D-glucopyranoside) [15], of which their antidiabetic properties have been demonstrated [16,17], and anticancer [18,19]. Consequently, we aimed to study the hepatic function and lipid profiles of an insulin-independent organ and analyze the antioxidant activity of Justicia spicigera in liver mitochondria of Wistar rats with streptozotocin-induced diabetes. 2. Materials and Methods 2.1. Plant Material and Extraction Justicia spicigera plants were collected in the spring, during April and May 2021, in Morelia, Michoacán, Mexico, from the greenhouse of the Instituto de Investigaciones Químico Biológicas of the Universidad Michoacana de San Nicolás de Hidalgo. Briefly, Justicia spicigera leaves were collected and the fresh plant material was prepared by maceration with ethanol to obtain higher yields in the recovery of flavonoids [20,21], for 6 days at 4 °C, with 10 mL of solvent added per 1 g of plant material, for effective dissolution and extraction of polyphenolic compounds. After filtration, the extracts were evaporated to dryness in a rotary evaporator with reduced pressure at 55 °C and dissolved in DMSO (5%) to a final concentration of 100 mg/mL. The extracts were stored at 4 °C until use. 2.2. In Vitro Antioxidant Assays 2.2.1. DPPH Radical Scavenging Assay The DPPH• (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity of the J. spicigera extract (100 mg/mL) was determined according to Lee et al. [22]. In brief, 0.1 mL of extract was made up to 1 mL with deionized water and mixed with 1 mL of DPPH solution (0.2 mM in absolute ethanol). Next, samples were incubated for 30 min in the dark at room temperature. A positive control was prepared with ascorbic acid (0.3 mg/mL). Absorbance was measured spectrophotometrically at 517 nm in a Perkin Elmer Lambda 18 UV-VIS spectrophotometer. The percentage of radical scavenging activity was calculated using the following formula: % RSA = ((Abs 517 control − Abs517 sample)/Abs517 control) × 100 2.2.2. Antioxidant Capacity Assay by Phosphomolybdenum Antioxidant capacity by phosphomolybdenum was determined according to Prieto et al. [23]. For this assay, 0.1 mL of extract was mixed with 0.2 mL of deionized water, then 3 mL of the reactive phosphomolybdenum solution (0.6 M of H2SO4, 28 mM of Na2HPO3, and 4 mM of ammonium molybdate) was added and mixed. Then the reaction mix was incubated for 90 min at 95 °C. A positive control was prepared with ascorbic acid (0.3 mg/mL). Next, the reaction mix was cooled to room temperature, and absorbance was recorded at 695 nm in a Perkin Elmer Lambda 18 UV-VIS spectrophotometer. Antioxidant capacity of the extract was calculated as follows:% AC = ((Abs 695 sample/Abs 695 control)) × 100 2.2.3. Reducing-Power Assay The reducing-power activity of the J. spicigera extract (100 mg/mL) was assayed according to Cell Biolabs [24]. Briefly, 0.1 mL of extract was made up to 1 mL with deionized water. Next, 2.5 mL of phosphate buffer (0.2 M, pH 6.6) and 2.5 mL of 1% (w/v) potassium ferrocyanide were added and thoroughly mixed. Next, the reaction mix was incubated for 20 min at 50 °C. Then 1.5 mL of 10% (w/v) trichloroacetic acid was added, and the mix was centrifuged for 10 min at 3000 rpm. Finally, 2.5 mL of the supernatant was mixed with 2.5 mL of deionized water and 0.5 mL of 0.1% FeCl3. A positive control was prepared with ascorbic acid (0.3 mg/mL). The absorbance of the final reaction was measured at 700 nm in a Perkin Elmer Lambda 18 UV–VIS spectrophotometer. The reducing-power activity of the extract was calculated as follows:% Reducing power = (Abs 700 sample × 100)/Abs 700 control 2.2.4. Antilipid Peroxidation Assay The antilipid peroxidation assay is a modified thiobarbituric acid reactive substances (TBARS) assay for measuring lipid peroxidation, as described by Ohkawa et al. [25]. First, 0.5 mL of the J. spicigera extract (100 mg/mL) was made up to 1 mL with deionized water, 5 µL of 7 mM FeSO4 was added to induce lipid peroxidation, and the mixture was incubated for 30 min. Then, 1.5 mL of 20% (v/v) acetic acid (pH 3.5 adjusted with NaOH), 1.5 mL of 0.8% (w/v) thiobarbituric acid in 1.1% (w/v) sodium dodecyl sulfate, and 0.5 mL 20% (w/v) trichloroacetic acid were added, and the mixture was incubated in a boiling-water bath for 60 min, and centrifuged at 5000 rpm for 5 min. Ascorbic acid was employed as a positive control. Absorbance was measured at 532 nm in a Perkin Elmer Lambda 18 UV–VIS spectrophotometer. The percentage of antilipid peroxidation of the extract was calculated as follows:% Antilipid peroxidation = (Abs 532 sample × 100)/Abs 532 control 2.3. Animals The studied animals were male Wistar rats (90 days old, 327–373 g). They were housed under standard laboratory conditions and maintained at room temperature in a room with a 12 h light/dark cycle, and fed a standard rodent diet and purified water ad libitum. We followed the recommendations of the regulatory standard for the use of animals issued by SAGARPA in the federal regulations for the use and care of animals (NOM-062-ZOO-1999). All protocols were approved by the Institutional Committee for the Use of Animals of the Universidad Michoacana de San Nicolás de Hidalgo (# 09/2018). 2.4. In Vivo Study 2.4.1. Diabetes Induction Diabetes was induced in overnight fasted rats by single intraperitoneal administration of streptozotocin (STZ) (50 mg/kg body weight) dissolved in fresh citrate buffer (pH 4.5). Control rats were injected with citrate buffer alone. Five days after induction, glucose levels were determined to confirm diabetes, and levels >300 mg/dL were considered for the study. 2.4.2. Experimental Protocol Rats were randomly divided into 4 groups: group I (normoglycemic + DMSO 5%) of 8 rats, group II (diabetic + DMSO 5%) of 5 rats, group III (normoglycemic + J. spicigera extract) of 6 rats, and group IV (diabetic + J. spicigera extract) of 5 rats. The ethanolic extract of Justicia spicigera (100 mg/mL) was administered at a dose of 100 mg/kg by oral gavage, the dose was obtained according to the results reported by Ortiz-Andrade et al. [26]. The treatment was continued daily for 30 days. 2.4.3. Blood Glucose and Body Weight Determination Blood glucose concentration was estimated by the enzymatic glucose oxidase method using a commercial glucometer (Accu-Chek Active, Roche) through tail tip puncture. Glucose estimation was started just before extract administration, and was done every 5 days for 30 days. Animal weight was recorded during the 30 days. 2.4.4. Evaluation of Biochemical Parameters At 30 days of treatment, the animals were fasted overnight and sacrificed by decapitation. Blood samples were obtained in BD Vacutainer® dry tubes with coagulation activator, and serum was separated through centrifugation at 3500 rpm for 5 min for biochemical estimations. The lipid profile (total cholesterol, high-density cholesterol (HDL), low-density cholesterol (LDL), very-low-density lipoprotein (VLDL), triglycerides, total lipids, and atherogenic index) and the liver profile (total proteins, total bilirubin, direct bilirubin, indirect bilirubin, alkaline phosphatase, gamma glutamyl transferase, aspartate amino transferase, and alanine amino transferase) were determined in serum using DRI-CHEM NX500i dry chemistry equipment. 2.5. Mitochondrial Isolation Mitochondria were isolated from the livers of male Wistar rats according to Hogeboom (1948) with some modifications [27]. The liver was cut into small pieces in a solution (220 mM mannitol, 70 mM sucrose, 2 mM MOPS, 1 mM EGTA, pH 7.4) at 4 °C. The suspension was homogenized and subjected to differential 2000 rpm centrifugation in a J2-MC device (Beckman) for 10 min at 4 °C, and the supernatant was centrifuged at 7500 rpm for 10 min. The last pellet was resuspended in a solution (220 mM mannitol, 70 mM sucrose, 2 mM MOPS, pH 7.4), and centrifuged at 9000 rpm for 10 min. Protein concentration was determined by the modified Biuret method [28]. 2.6. Superoxide Dismutase (SOD) Determination in Liver Mitochondria SOD activity, expressed in U/mg protein, was determined using a commercial analysis kit (Sigma-Aldrich, St. Louis, MO, USA). Readings were taken at 450 nm using a Multiskan Go microplate reader (Thermo Fisher Scientific, Vantaa, Finland). 2.7. Catalase (CAT) Activity in Liver Homogenate Catalase enzyme activity in tissue was assayed, following the procedure of Jeulin et al. [29], by measuring the conversion of hydrogen peroxide to oxygen with a Clark-type electrode connected to a YSI 5300A Biological Oxygen Monitor (Yellow Springs, OH, USA). 2.8. Statistical Analysis Results are expressed as mean ± standard error (SE). Statistical analyses were performed with one-way or two-way analysis of variance (ANOVA), with calculations done using GraphPad Prism (version 7) (GraphPad Software, San Diego, CA, USA). p < 0.05 was considered statistically significant. 3. Results 3.1. Evaluation of In Vitro Antioxidant Assays The in vitro antioxidant assay results are presented in Table 1. DPPH• and phosphomolybdate reduction results show 100% antioxidant activity of J. spicigera ethanol extract (100 mg/mL), the same value obtained for the ascorbic acid used as control, while the results of antilipid peroxidation and Fe-reduced assay (reducing power activity) show 78 and 25% activity, respectively, compared to 100% for ascorbic acid. 3.2. Evaluation of Body and Liver Weight in Experimental Animals with Hyperglycemia Experimental induction of diabetes was confirmed by elevated blood glucose levels (Figure 1A), decreased body weight (Figure 1B), and signs of polyphagia and polydipsia in diabetic groups (data not shown). The normoglycemic control group maintained normal glycemic levels of throughout treatment, starting at 80.5 ± 1.3 mg/dL glucose, latter 75.6 ± 2.6 mg/dL at 15 days, and 86.4 ± 3.4 mg/dL at 30 days after the first measurement. Treatment with J. spicigera ethanol extract for 30 days did not change blood glucose levels in the treated normoglycemic group, which presented glycemic levels of 81 ± 3 mg/dL at the beginning of treatment, 79 ± 1 mg/dL at the halfway point of treatment, and 74 ± 2 at the end of treatment; while glucose levels decreased significantly (p < 0.05) in the treated diabetic group that showed a glycemic level of 351 ± 43 mg/dL at the beginning of treatment, 266 ± 46 mg/dL, 15 days after the start, and 219 ± 39 mg/dL at the end of treatment, compared to the untreated diabetic control group, which presented elevated blood glucose levels throughout the experiment with 408 ± 19.1 mg/dL at the beginning, 438.7 ± 19 at the middle of treatment and a significant increase in glycemic levels of 427.3 ± 11.10 mg/dL at the end of treatment. Changes in body weight were recorded in grams (g) at the beginning and end of treatment. Figure 1B shows that the normoglycemic control group gained weight throughout the treatment, with an initial weight of 371 g ± 5 reaching 380 ± 8 g (2% weight gain). The normoglycemic group treated with the extract also gained weight, starting at 380 ± 9 g and reaching 396 ± 9 g at the end of treatment, a weight gain of 4%. The diabetic control and treated groups had significant (p < 0.05) decreases in body weight; the former had an initial weight of 373 ± 8 g and a final weight of 314 ± 12 g, showing a 16% decrease in body weight, while the latter started at 327 ± 13 g and ended at 293 ± 28 g, presenting a 10% decrease in body weight. The observed weight loss in the diabetic groups indicates good establishment of the diabetic model. The weight of the liver obtained in the control group did not show significant differences when compared with the diabetic group treated with the extract; in the same way, there were no significant differences between the normoglycemic groups (Table 2). 3.3. Lipid Profile Evaluation The effects of J. spicigera ethanolic extract on the lipid profile are shown in Table 3. The results obtained for serum HDL cholesterol from diabetic and normoglycemic rats showed no significant changes with administration of the extract. The same results were obtained for both groups treated with the extract. In relation to LDL cholesterol, the diabetic group exhibited significantly lower levels compared to the normoglycemic control group (p < 0.05); unexpectedly, treatment with J. spicigera ethanol extract lowered LDL cholesterol levels in normoglycemic animals (p < 0.05), but increased LDL cholesterol levels in diabetic animals (p < 0.05). On the other hand, VLDL cholesterol levels showed higher values in the diabetic group compared to the normoglycemic group (p < 0.05), and treatment with J. spicigera ethanol extract reduced VLDL cholesterol values in both groups (p < 0.05). Triglycerides exhibited higher values in the diabetic group compared to the normoglycemic group (p < 0.05), and both were significantly reduced (p < 0.05) in control glycemic and diabetic groups treated with the extract. The same significant results (p < 0.05) were obtained in the assay of total lipid content. 3.4. Liver-Profile Evaluation 3.4.1. Total Proteins The effects of J. spicigera ethanol extract on the liver profile are presented in Table 4 and Figure 2. The results show a high serum total protein content in the diabetic control group compared with the normoglycemic control group; however, the diabetic group treated with the extract showed reduced serum total protein compared with the diabetic control group without treatment, but there were no differences between the normoglycemic groups. 3.4.2. Total Bilirubin Total bilirubin in the normoglycemic group treated with the extract was reduced compared to the untreated normoglycemic group. The same results were obtained for the diabetic group treated with the extract. There were no significant differences in direct bilirubin for the normoglycemic groups; however, there was a decrease in the diabetic group treated with the extract compared to the diabetic group without the extract. In the normoglycemic control group treated with J. spicigera ethanol extract, significantly reduced indirect bilirubin values were obtained compared with the untreated control group (p < 0.05). In the diabetic group treated with the extract, there was a tendency toward reduced levels of indirect bilirubin compared with the untreated diabetic group. 3.5. Serum Liver Enzyme Activities Alkaline phosphatase activity (Figure 2A) in the diabetic group treated with the extract showed a 31.15% reduction compared with the untreated diabetic group. Similarly, gamma glutamyl transpeptidase activity (Figure 2B) in the diabetic group treated with the extract showed a 19.11% reduction compared with the untreated diabetic group. With regard to alanine aminotransferase (Figure 2C) and aspartate aminotransferase (Figure 2D), the diabetic group treated with the extract showed a 73.24 and 68.26% reduction, respectively, compared with the untreated diabetic group. 3.6. Evaluation of Superoxide Dismutase and Catalase Activity Figure 3A shows that treatment with ethanol extract of J. spicigera induced an increase in superoxide dismutase (SOD) activity in the normoglycemic group compared to the untreated group (p < 0.05). However, the diabetic group treated with the extract showed reduced SOD activity (p < 0.05). Catalase activity was the same in the liver homogenate from the treated and untreated normoglycemic groups, whereas catalase activity in the diabetic group treated with the extract was significantly reduced compared with the untreated diabetic control group (p < 0.05) (Figure 3B). 4. Discussion Patients with DM have increased oxidative stress and inflammatory processes, which are greater in those who present with diabetes complications [30,31,32]. The liver plays a fundamental role in oxidative and detoxification processes. Therefore, to prevent or control the occurrence of complications, such as liver disease in patients with diabetes, the use of an antioxidant compound that complements treatment should be considered. There are reports on the presence of flavonoids, such as kaempferitrin and its bis-ramnoside, kaempferol, in the leaves of J. spicigera [33]. Kaempferol is a potent antioxidant that prevents oxidative damage of cells, lipids, and DNA [34], and has also shown hypoglycemic properties in in vitro and in vivo assays [35]. Studies of J. spicigera by García-Márquez et al. [36] reported that extracts obtained using solvents with greater polarity showed more effective radical scavenging activity than extracts obtained using solvents of lower polarity. Sepúlveda-Jimenez et al. [37] analyzed the aerial part of J. spicigera and observed that its methanolic extract had higher free-radical scavenging activity and a greater number of phenolic compounds and flavonoids. In this work, we observed that ethanol extract of J. spicigera had antioxidant capacity similar to ascorbic acid (see Table 1), and therefore could provide protective effects against oxidative liver damage in diabetes when administered orally for 30 days. Similar antioxidant activity was reported by Awad et al. [14] with an ethanol extract of J. spicigera. On the other hand, treatment with ethanol extract of J. spicigera significantly decreased glycemic levels in streptozotocin-induced diabetic rats (Figure 1A). This is consistent with the results reported by Ortiz-Andrade et al. [26], who observed that J. spicigera ethanol extract had a hypoglycemic effect when administered to animals with experimental diabetes. This was probably due to the extract’s increased ability to assimilate glucose, which would explain the small increase in weight gain in normal rats (2% in untreated animals and 4% in treated animals) and the lower weight loss in rats with diabetes (Figure 1B), since these rats were probably able to take advantage of the ingested food, unlike the diabetic rats without the extract, which presented greater weight loss. Patients with diabetes frequently present a combination of hyperglycemia and dyslipidemia [38,39]. This is because when insulin is lacking, the insulin-sensitive lipase enzyme in fat cells undergoes great activation. Thus, stored triglycerides are hydrolyzed, and large amounts of fatty acids and glycerol are released into the circulating blood. The fatty acids entering the hepatocytes, together with fatty acids derived from de novo lipogenesis, are used for the synthesis of triglycerides and other complex lipids [40]. The hormone-sensitive lipase catalyzes the hydrolysis of triglycerides in adipose tissue, which results in the release of fatty acids into the circulation. Normally, insulin suppresses this release and blocks the release of fatty acids. However, in insulin resistance states, insulin fails to suppress the release, which results in enhanced lipolysis and increased fatty acid flux to the non-esterified fatty acid plasma pool [41]. Sheweita et al. [42] reported that plasma levels of triglycerides, total cholesterol, LDL, and VLDL were increased in diabetic rats induced with STZ compared with control rats, while there was a decrease in HDL, thus maintaining an adequate model of diabetes. Streptozotocin-induced diabetic rats showed an increased serum content of total lipids and triglycerides in comparison to control rats (Table 3). This agrees with previous published works reporting an increase in hepatic synthesis of triglycerides and subsequent hypertriglyceridemia in diabetic subjects [43]. Treatment with ethanol extract of J. spicigera in both the normoglycemic control group and streptozotocin-diabetic group showed a significant decrease in serum triglycerides and total lipids (Table 3), producing a hypolipidemic effect that was reflected in a decrease in the plasma atherogenic index. This improvement could be due to the presence of some component in the extract capable of normalizing triglyceride levels, and hence total lipids, in diabetes, as the results show a similar a total cholesterol content in all experimental groups, suggesting that J. spicigera ethanol extract does not participate in inhibiting cholesterol synthesis (Table 3). This is related to the results obtained when quantifying HDL cholesterol, where no significant changes were observed in any group (Table 3). On the other hand, a significant increase in VLDL cholesterol was observed in the untreated diabetic control group (Table 3), probably due to the excessive production of fatty acids in the liver that are secreted as VLDL components; this metabolic situation is also responsible for the increased hepatic synthesis of triglycerides and subsequent hypertriglyceridemia [34] observed in the control diabetic group (Table 3). The percentage of total liver weight was calculated based on body weight to denote the size of the liver after receiving treatment with ethanolic extract of J. spicigera (100 mg/mL). The total liver/body weight, we observed that the diabetic group treated with the extract showed increased liver weight in relation to body weight (Table 2); these results are consistent with those obtained by Noriega-Cisneros et al. [7], suggesting that an increase in liver size may be attributable to the administered extract acting at the liver level on lipid storage and mobilization, thus modifying blood lipids, as was already reported [44]. Because liver diseases are more frequent in the diabetic population [45], the present investigation was carried out to evaluate the potential protective effect of J. spicigera on this organ. The determination of total proteins and bilirubin allows us to directly identify changes in liver metabolic function, while the activity of liver enzymes in plasma is a reliable marker for assessing liver damage [46], including oxidative damage. Albumin is the main protein produced by the liver, and it can be altered when there is liver damage, a catabolic state, malnutrition, or loss of proteins; it is also responsible for transporting numerous endogenous substances such as bilirubin [47,48]. In our study, the groups treated with J. spicigera ethanol extract, both control and diabetic groups, showed decreased total protein levels. The liver secretes most of the proteins in blood plasma, including sex-hormone-binding globulin (SHBG). The circulating concentration of SHBG is associated with glucose metabolism, adiposity, and components of the metabolic syndrome [49]; therefore, liver disease can affect the plasma proteome [50]. This is probably what caused the increased serum levels of total proteins in the diabetic control group; however, further studies are required to corroborate the presence of this globulin in our study (Table 3). The increase in total and direct bilirubin occurs when there is some alteration at the hepatic level or of the bile ducts, while the increase in indirect bilirubin may reflect the presence of hemolysis. In our investigation, the levels of total, direct, and indirect bilirubin were reduced with the administration of the extract in the normoglycemic and diabetic groups (Table 4); direct bilirubin was unchanged in normoglycemic rats but diminished in the diabetic group. The latter suggests that J. spicigera extract offers protection from pathophysiological liver defects as observed in the streptozotocin-induced diabetic group. Alkaline phosphatase can be present in organs other than the liver, so a supplementary test is required to confirm whether an increase in this enzyme comes from the biliary system or the liver [51,52], which is why its concomitant measurement with gamma glutamyl transferase is essential, since that enzyme comes almost exclusively from the liver [53,54]. The results obtained in this investigation indicated cholestasis and biliary dysfunction in the diabetic group without J. spicigera extract treatment, since both were increased; however, with administration of the extract, the levels were significantly improved in the diabetic condition and in normoglycemic rats (Table 4). These results are consistent with those obtained by Olayinka et al. [55]. The enzymes ALT and AST are the most commonly used indicators to assess the presence of liver necrosis [56]. Transaminases are sensitive but not very specific to hepatocyte damage, and ALT is more specific than AST, since it is not only found in the liver but also in skeletal and cardiac muscle, and increased levels of both enzymes have been reported [53,54,56,57]. These results are consistent with those obtained in our research for ALT and AST in diabetic rats without J. spicigera treatment. With extract administration, there was a significant decrease in those parameters in diabetic rats (Table 3); however, the results for LDH in the normoglycemic control group showed a significant increase compared to the control diabetic group, perhaps due to the variety of LDH isoenzymes that can be determined in serum, making this a nonspecific test. There was also a decrease in LDH activity in treated rats in both groups, indicating a positive effect with administration of the extract (Table 4). It is worth mentioning that there was a hepatotoxic effect of STZ. Ohaeri [58] observed that STZ-induced diabetic rats presented hepatic necrosis. Therefore, the increased activity of alkaline phosphatase, GGT, ALT, AST, and LDH in plasma could also be due to leakage of these enzymes from the hepatic cytosol into the bloodstream [59]. However, in this study, the ethanol extract of J. spicigera showed hepatoprotective activity by reducing the activity of these enzymes in plasma. High levels of oxidative stress in diabetic animals are due to glucose autoxidation, protein glycation, lipid peroxidation, and low antioxidant enzyme activity [60]. High glucose in diabetes promotes higher production of reactive oxygen species in the presence of transition metal ions that cause oxidative damage to the lipids of cell membranes. However, the extent of damage seems to be specific, with the heart, liver, and kidney being more susceptible. The harmful effect of superoxide anion (O2•−) and hydroxyl radical (HO•) can be counteracted by antioxidant enzymes SOD and catalase (CAT). An increase in these enzymes has been indicated as a possible response mechanism in the early stages of diabetes [61]; however, intense long-term production of this radical exhausts the stimulation of enzymatic activity, since the reaction product can inhibit it [62]. This can occur at high CAT concentrations, since the cell uses the enzymes CAT and glutathione peroxidase for reduction of H2O2 [63]. Based on the above, we can deduce that the values obtained in our research for SOD in the diabetic control group (Figure 3A) may be due to inhibition of the enzyme by H2O2, since the observed CAT levels were increased in this group (Figure 3B). However, with the administration of J. spicigera ethanol extract in the diabetic group, the levels of both enzymes were reduced. 5. Conclusions In conclusion, ethanol extract of Justicia spicigera exerted a protective effect on the livers of diabetic rats by reducing some characteristic symptoms of diabetes, such as hyperglycemia, body weight loss, serum triglycerides, and total serum lipids and by significantly reducing markers of hepatocyte injury such as gamma-glutamyl transpeptidase, alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase. However, since J. spicigera extract comprises a complex mixture of biological active compounds, some of its effects may potentially be undesirable, such as the reduced total serum protein content or reduced hepatic activity of catalase and superoxide dismutase observed in the streptozotocin-diabetic group. Further analysis of the individual effects of the main biological compounds contained in J. spicigera extract must be performed. Acknowledgments The authors acknowledge the support of the animals and some materials for the experiments to Biochemistry Department, Medicine Faculty, UNAM. Author Contributions Conceptualization, M.M.-V. and A.S.-M.; investigation, M.M.-V., R.N.-C., A.S.-M. and A.A.-M.; supervision, R.N.-C., R.S.-G. and A.S.-M.; formal analysis, R.N.-C. and A.A.-M.; methodology, M.M.-V., D.J.P.-M., M.H.-C., C.C.-R., R.S.-G. and R.M.-P.; software, D.J.P.-M., M.H.-C. and H.R.-R.; data curation, C.C.-R. and R.M.-P.; writing-original draft preparation, M.M.-V.; validation, R.S.-G. and A.S.-M.; writing—review and editing, M.M.-V. and A.S.-M.; project administration, A.S.-M.; funding acquisition, A.S.-M. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement All procedures used in the present study were carried out according to the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 80–23) and the Official Mexican Standard for the use of experimental animals (NOM-062-ZOO-1999). Informed Consent Statement Not applicable. Data Availability Statement The data used to support the findings of this study are available from the corresponding author upon request. Conflicts of Interest The authors declare no conflict of interest. Abbreviations ALT Alanine aminotransferase AP Alkaline phosphatase AST Aspartate aminotransferase CAT Catalase DC Diabetic control DE Diabetic with extract DM Diabetes mellitus DMSO Dimethyl sulfoxide DPPH 2,2-diphenyl-1-picrylhydrazil EGTA [ethylenebis(oxonitrilo)]tetra-acetate GLUT Glucose transporter HDL High-density lipoprotein LDL Low-density lipoprotein MOPS 3-(N-Morpholino) propanesulfonic acid NAFLD Nonalcoholic fatty liver disease NC Normoglycemic control NE Normoglycemic with extract NOM Norma Oficial Mexicana pH Potential hydrogen ROS Reactive oxygen species SAGARPA Secretaría de Agricultura y Desarrollo Rural SHBG Sex hormone binding globulin SOD Superoxide dismutase STZ Streptozotocin v/v Volume/volume VLDL Very-low-density lipoprotein w/v Weight/volume Figure 1 (A) Blood-glucose levels and (B) body weight during treatment with ethanolic extract of Justicia spicigera (100 mg/mL) for 30 days. Control (NC) and diabetic control (DC) groups were treated with DMSO; control administered (NE) and diabetic administered (DE) groups were treated with J. spicigera ethanol extract. Obtained values were analyzed by one-way ANOVA. Data represent mean ± SE (n = 5–8). Significant differences using Tukey’s multiple comparison test (p < 0.05) are indicated by different lowercase letters above each point; same letter indicates no significant differences. Figure 2 (A) Alkaline phosphatase, (B) gamma glutamyl transpeptidase, (C) alanine aminotransferase, and (D) aspartate aminotransferase activity at the end of treatment with ethanolic extract of Justicia spicigera (100 mg/mL) for 30 days. Control (NC) and diabetic control (DC) groups were treated with DMSO; control administered (NE) and diabetic administered (DE) groups were treated with J. spicigera ethanol extract (100 mg/mL). Values were analyzed by one-way ANOVA. Data represent mean ± SE (n = 5–8). Significant differences using Tukey’s multiple comparison test (p < 0.05) are indicated by different lowercase letters above each point; same letter indicates no significant differences. Figure 3 (A) Superoxide dismutase and (B) catalase activity from liver mitochondria and liver homogenate, respectively. Normoglycemic control group untreated (NC) and treated with J. spicigera ethanol extract (100 mg/mL) for 30 days (NE). DC, untreated diabetic group; DE, diabetic group treated with J. spicigera ethanol extract (100 mg/mL). Values were analyzed by one-way ANOVA. Results represent mean ± SE (n = 5–8). Significant differences using one-way ANOVA (p < 0.05) are indicated by different lowercase letters above each point; same letter indicates no significant differences. nutrients-14-01946-t001_Table 1 Table 1 Evaluation of in vitro antioxidant assays. Assay Control Ascorbic Acid (%) Justicia spicigera Ethanol Extract (%) DPPH• 100 ± 2.0 a 105 ± 3.0 a Total antioxidant activity 100 ± 2.0 a 101 ± 3.0 a Reducing power activity 100 ± 2.0 a 25 ± 2.0 b Antilipid peroxidation 100 ± 2.0 a 78 ± 4.0 b Obtained values of Justicia spicigera extract (100 mg/mL) were analyzed by one-way ANOVA. Data represent mean ± SD. (n = 3). Significant differences are denoted by superscript letters using Tukey’s multiple comparison test (p < 0.05); no significant differences are denoted by the same letter. nutrients-14-01946-t002_Table 2 Table 2 Liver weight percentage. Group Liver Weight (%) NC 3.46 ± 0.18 bc NE 2.99 ± 0.07 c DC 3.86 ± 0.11 ab DE 4.42 ± 0.33 a Control (NC) and diabetic control (DC) groups were treated with DMSO; control administered (NE) and diabetic administered (DE) groups were treated with ethanol extract of J. spicigera (100 mg/mL). Obtained values were analyzed by one-way ANOVA. Data represent mean ± SE. (n = 5–8). Significant differences are denoted by superscript letters using Tukey’s multiple comparison test (p < 0.05); no significant differences are denoted by the same letter. nutrients-14-01946-t003_Table 3 Table 3 Lipid profile evaluation. Lipid Profile Group Total Cholesterol [mg/dL] HDL Cholesterol [mg/dL] LDL Cholesterol [mg/dL] NC 88.33 ± 8.76 a 36.67 ± 3.33 a 19.80 ± 3.70 ab NE 75.40 ± 4.92 a 45.00 ± 3.03 a 14.56 ± 2.88 ab DC 73.67 ± 4.10 a 42.00 ± 5.29 a 7.40 ± 1.72 b DE 81.58 ± 2.86 a 40.75 ± 0.48 a 24.97 ± 3.85 a VLDL-cholesterol [mg/dL] Triglycerides [mg/dL] Total lipids [mg/dL] NC 31.87 ± 6.15 a 110.00 ± 17.83 ab 270.00 ± 15.34 b NE 15.84 ± 0.92 b 79.22 ± 4.59 b 278.33 ± 13.28 b DC 24.27 ± 4.62 ab 172.33 ± 29.01 a 440.70 ± 48.70 a DE 15.86 ± 1.48 b 79.28 ± 7.41 b 289.54 ± 12.04 b Atherogenic index of plasma NC 0.48 NE 0.25 DC 0.61 DE 0.29 Control (NC) and diabetic control (DC) groups were treated with DMSO; control administered (NE) and diabetic administered (DE) groups were treated with ethanol extract of J. spicigera (100 mg/mL). Obtained values were analyzed by one-way ANOVA. Data represent mean ± SE. (n = 5–8). Significant differences are denoted by superscript letters using Tukey’s multiple comparison test (p < 0.05); no significant differences are denoted by the same letter. nutrients-14-01946-t004_Table 4 Table 4 Liver-profile evaluation. Group Total Protein [g/dL] Total Bilirubin [mg/dL] Direct Bilirubin [mg/dL] Indirect Bilirubin [mg/dL] NC 6.82 ± 0.20 b 0.65 ± 0.10 a 0.15 ± 0.07 ab 0.50 ± 0.07 a NE 6.55 ± 0.53 b 0.34 ± 0.05 a 0.12 ± 0.02 ab 0.22 ± 0.04 a DC 7.87 ± 0.30 a 0.63 ± 0.12 a 0.20 ± 0.00 a 0.43 ± 0.12 a DE 5.90 ± 0.21 b 0.33 ± 0.06 a 0.10 ± 0.00 b 0.23 ± 0.06 a Control (NC) and diabetic control (DC) groups were treated with DMSO; control administered (NE) and diabetic administered (DE) groups were treated with ethanol extract of J. spicigera (100 mg/mL). Obtained values were analyzed by one-way ANOVA. Data represent mean ± SE. (n = 5–8). 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15092984 materials-15-02984 Article Deep Learning for Type 1 Diabetes Mellitus Diagnosis Using Infrared Quantum Cascade Laser Spectroscopy https://orcid.org/0000-0001-6827-1761 Fufurin Igor 1* https://orcid.org/0000-0001-5235-5303 Berezhanskiy Pavel 2 https://orcid.org/0000-0001-5961-0901 Golyak Igor 1 Anfimov Dmitriy 1 Kareva Elizaveta 1 Scherbakova Anastasiya 1 Demkin Pavel 1 Nebritova Olga 1 https://orcid.org/0000-0002-8022-990X Morozov Andrey 1 Kim Dokyoung Academic Editor 1 Physics Department, Bauman Moscow State Technical University, Moscow 105005, Russia; igorgolyak@yandex.ru (I.G.); dimananfimov97@gmail.com (D.A.); elisabethkareva@gmail.com (E.K.); nastya_schs@mail.ru (A.S.); demkin.pavel1996@yandex.ru (P.D.); o.nebritova@outlook.com (O.N.); amor59@mail.ru (A.M.) 2 Morozov Children’s Clinical Hospital, State Budgetary Healthcare Institution, Moscow Healthcare Pulmonology Department, Moscow 119049, Russia; p.berezhanskiy@mail.ru * Correspondence: igfil@mail.ru; Tel.: +7-903-611-75-04 20 4 2022 5 2022 15 9 298425 3 2022 17 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). An estimated 10.5% of the world’s population aged 20–79 years are currently living with diabetes in 2021. An urgent task is to develop a non-invasive express-diagnostics of diabetes with high accuracy. Type 1 diabetes mellitus (T1DM) diagnostic method based on infrared laser spectroscopy of human exhaled breath is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We propose a method for collecting and drying an exhaled human air sample and have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers with an accuracy of 99.7%, recall 99.6% and AUC score 99.9% was used. The demonstrated results require clarification on a larger dataset and series of clinical studies and, further, the method can be implemented in routine medical practice. diabetes breath analysis deep learning infrared spectroscopy quantum cascade laser biomarker ==== Body pmc1. Introduction Non-invasive diagnostics is one of the most important directions for the development of modern medicine. An estimated 537 million adults aged 20–79 years worldwide (10.5% of all adults in this age group) have diabetes, the International Diabetes Federation (IDF) reported in 2021. IDF estimated the number of children (0–19 years) and adolescents with type 1 diabetes to be about 1.2 million in 2021. This number is projected to rise to 643 million by 2030 and 783 million by 2045 [1]. The ability to monitor blood glucose non-invasively by monitoring compounds in breath and emitted through the skin has been demonstrated [2,3]. Recently, the interest has been focused on a compendium of the volatile organic compounds (VOCs) emanating from the human body [4]. VOCs were shown to be isolated from the breath (872 compounds), saliva (359 compounds), blood (154 compounds), milk (256 compounds), skin secretions (532 compounds), urine (279 compounds), and feces (381 compounds) in apparently healthy individuals. Exhaled breath contains many different volatile organic compounds. However, the final list of such substances has not yet been published. A list of compounds that have been observed in breath was published, e.g., by Manolis [5], Philips [6], and Selvaraj [7], including volatile inorganic [8] and organic compounds (VOCs) [9]. For many of these substances it is unknown whether they are produced endogenously, i.e., whether some of them are associated with smoking [10]. Quite a number of volatile compounds may be related to food consumption or medication [11], but some of them can be identified with a possible human disease. Despite the fact that acetone is a biomarker of diabetes mellitus [12], the analysis of acetone alone is insufficient [13]. Others volatile organic compounds such as isoprene and methyl nitrate were associated with diabetes mellitus [14]. The basis of diagnostics is related to disease-specific changes in the concentration of VOCs in exhaled air [15]. The combination of chromatography and mass spectrometry allows rapid identification of substances with high selectivity and sensitivity down to ppt levels [16]. These techniques require accurate calibration of the chromatographic column and manual sampling procedures [17]. Ion mobility spectrometry can be used for breath research [18], but has limitations in separating components in multi-component gas mixtures. Infrared femtosecond lasers can be used for thermal imaging including medical applications [19]. Modern quantum cascade lasers (QCLs) allow to study biomarker molecules with high sensitivity and in the future to create portable devices with low cost of “one measurement” [20,21]. A wide tuning range, emission in the “fingerprint” range, operation at room temperature, and the miniature size of the laser chip make it possible to highlight QC lasers for biomedical applications. Particularly promising is the use of QC lasers in portable devices [22]. In biomedical applications one typically study VOCs at ppb-ppm levels that requires highly sensitive methods. Spectroscopic methods like absorption spectroscopy are limited in sensitivity by the optical path length. Longer path length improves the sensitivity and detection limit. Richard [23] reported the usage of a distributed feedback quantum cascade laser (DFB QCL) at λ∼ 5.26 μm. The NO detection limit of 60 ppt is achieved in a single measurement of 140 ms and an average over 10 s shows sensitivity up to 8.3 ppt. Gorbani et al. [24] used the same system to identify carbon monoxide (CO) in human exhaled breath using a multi-pass gas cell and measured CO at 4.69 μm with a detection limit of 9 ± 5 ppbv and data acquisition time of 0.07 s. McManus [25] reported sensitivity at sub-ppb levels for a narrow band QCL and a 200 m Herriot multipass gas cell. Aerodyne Research, Inc. (Billerica, MA, USA ) has started commercial production of a compact gas analyzer based on mid-infrared QCL for recording trace amounts of CH4, N2O, NO, NO2, CO, CO2, formaldehyde, formic acid, ethylene, acetylene, ammonia, etc. [26]. In the study [27] using QCL tuning in the 1150–1250 cm−1 range, stable T1DM patients were shown to have concentrations in exhaled breath above the VOC concentration range for healthy individuals. The advantage of using a single biomarker present in high concentrations (e.g., acetone) is obvious, but it alone cannot directly correlate with blood glucose concentrations for all diabetics [3]. Tuzson [28] for a spectral range between 2950 and 2980 cm−1 showed that monitoring acetone in exhaled breath can indeed provide useful information for monitoring of lifestyle interventions. Trefz [29] showed a significant intersection of the values of acetone concentration in exhaled air for diabetic and healthy people, but T1DM patients have significantly higher isopropanol concentrations than their healthy peers. Another approach is to look at a number of biomarkers and correlate the biomarker pattern (i.e., biomarker combination and their concentrations) [30]. Simultaneous quantification of several gaseous substances enables to observe correlations in their excretion with the exhaled air and, thus, to investigate the interrelationships between various physiological and biochemical processes in the body [31]. E. van Mastrigt [9] for broadband QCL 832–1262.55 cm−1 showed prospects of machine learning methods for diagnosis of asthma and cystic fibrosis for children. Pearson correlation is used to analyze broadband infrared (IR) spectra analysis for remote sensing applications [32,33], but exhaled breath contains a huge number of components and the usage of such methods becomes quite challenging. Machine learning methods are a promising tool for VOC analysis in human breath [34,35]. Kistenev [36] used to apply machine learning for diagnosis of oral lichen planus. Zhu [37] published a current review of deep learning applications for diabetes. It is shown that 610 papers have been published as of 20 October 2020 (the first in 2016). Deep learning methods in medical research are actively developing. Song [38] used neural networks to classify imbalanced oral cancer image data, Zhang [39] used Convolutional Neural Network (CNN) to accurately estimate optical properties of breast tissue in the presence of the chest wall. Deep learning for diabetes diagnosis is a state-of-the-art technique, and it is necessary to conduct extensive experimental and clinical trials to verify the possibility of applying these methods for diabetes diagnosis using QC laser spectroscopy. Moreover, deep learning models are regarded as “black boxes” with a lack of model transparency; therefore, it is necessary to investigate the applicability of deep learning for spectral analysis. Deep learning [40,41] is one of the most effective methods focusing on learning features and building predictive models directly from large-scale datasets [42], and has demostrated success in chemistry, biology, physics, and spectroscopy [43], and metabolomics [44]. CNN is an important branch of deep learning technology inspired by the biological mechanism of visual cognition. For example, Fan et.al. [45] use CNN for Raman spectroscopy applications. In previous studies [46,47,48] we used machine and deep learning methods to classify and identify VOCs, including multicomponent gas mixtures. The estimated sensitivity of the proposed method was at levels of 10–100 ppb, which makes it possible to diagnose a wide range of diseases using IR laser spectroscopy of exhaled breath. Our current research is devoted to testing the feasibility of diagnosing T1DM using CNN and IR laser spectroscopy and evaluating the accuracy of the developed method. In the present paper, an infrared quantum cascade laser and Herriot multi-pass gas cell were used. Infrared spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group) and used 1-D CNN for volunteer classification were collected. We estimated the accuracy of the diagnosis of type 1 diabetes based on the analysis of exhaled air. We describe in detail the structure and parameters of the neural network and show its capabilities to give researchers an incentive for further work in this area. 2. Materials and Methods 2.1. Diabetes Fruity Exhaled Breath T1DM, previously known as juvenile diabetes, is a chronic autoimmune disease characterized by elevated blood glucose levels (hyperglycemia), which are due to the insulin deficiency that results from the loss of β-cells of the islets of Langerhans [49,50]. Type 1 diabetes is a condition in which your immune system destroys insulin-making cells in your pancreas, while type 2 diabetes is a condition in which your body does not respond to insulin the way it should. The pathogenesis of autoimmune destruction of β-cells is associated with not-fully understood interactions between predisposition genes, autoantigens, and environmental factors. In type 1 diabetes, there is an absolute or relative lack of insulin production. This leads to impaired carbohydrate metabolism as well as metabolic changes such as increased blood glucose levels and intense lipolysis [51]. During lipolysis, fatty acids are quickly mobilized and released from adipose tissue and the synthesis of fatty acids is suppressed in the liver. Frequently, patients with T1DM are hospitalized with the described symptoms as well as hyperglycemia and sometimes diabetic ketoacidosis (DKA) [52]. DKA most often occurs in patients with T1DM and develops when insulin levels are too low to meet basic metabolic needs. When insulin is deficient, the body receives energy from lipid and amino acid metabolism instead of glucose metabolism. Uncontrolled lipolysis results in increased serum glycerol and free fatty acid levels; the level of alanine also increases due to the catabolism of muscle tissue. Glycerin and alanine serve as substrates for hepatic gluconeogenesis, which is stimulated by excess glucagon accompanying insulin deficiency. At the same time, glucagon stimulates the conversion of free fatty acids into ketone bodies in the mitochondria. Normally, insulin blocks ketogenesis by inhibiting the transport of free fatty acid derivatives into mitochondria, but ketone bodies are formed in the absence of insulin. The main ones are acetoacetic and beta-hydroxybutyric acids that determine metabolic acidosis. Acetone formed from acetoacetic acid accumulates in the serum and then is slowly excreted through the lungs. The described mechanism causes the specific fruity exhaled breath. Figure 1 shows the mechanism of the appearance of certain VOCs (fruity smell) in T1DM human breath. 2.2. Experimental Setup T1DM diagnostics is based on the analysis of volunteers’ exhaled breath. Figure 2 shows the basic principle of the developed diagnostic method. An infrared laser spectroscopy to analyze human breath was proposed. The breath sample is collected in a Urine Bag ST 1300102 (Meridian, Moscow, Russia), it is then passed through a Nafion dryer and placed into a Herriot multipass gas cell. IR radiation is emitted by an external cavity quantum cascade laser then it enters to the gas cell and after the required number of reflections is collected at the photodetector. The measured IR spectrum undergoes preprocessing procedures and then comes to the convolutional neural network. A neural network trained on the control and the target groups can classify healthy and T1DM volunteers by their IR breath spectra. The two mass flow controllers (MFC) type FC-201CV and GE50A (Bronkhorst High-Tech B.V., Bronkhorst, The Netherlands), the pressure controller P-602CV (Bronkhorst High-Tech B.V., Bronkhorst, The Netherlands), and the vacuum pump MVP 015-2 DC (Vacuumbrand GMBH and CO KG, Wertheim, Germany) with pressures up to 3.5 mbar are used. The normal operating pressure is approximately 500 mbar. The exhaled breath must be dehydrated after collection and for this purpose a Nafion gas dryer MD series (Perma Pure LLC, 197 Lakewood, NJ, USA) is used. The pure nitrogen with a flow rate about 40 standard cubic centimetres per minute (sccm) to dry the breath sample and a flow rate of about 20 sccm to place the breath sample into a pre-vacuumed gas cell is used. The optical scheme of the experimental setup is shown in Figure 3. The experimental setup is based on the IR quantum cascade laser (Figure 3, pos. 1) and two thermoelectrically cooled photoconductive HgCdTe (MCT-TE) photodetectors (reference photodetector Figure 3, pos. 7, signal photodetector Figure 3, pos. 6, and laser pointer Figure 3, pos. 8). The teference photodetector is used only for adjusting to measure optical path length for the IR beam in the gas cell. Unfortunately, the LaserTune system does not allow to apply an external trigger to use the signal and reference photodetector simultaneously. One signal detector is used for gas analysis and two photodetectors (signal and reference) to determine the optical path by measuring the time delay of the beams traveling to the signal and reference photodetectors. The QC laser (LaserTune, Block Engineering, Southborough, MA, USA) emits in a pulsed mode with a peak power up to 150 mW, a pulse duration of about 50 ns and a repetition rate of about 1 MHz. The photodetector is an MCT-TE photodetector with a detectivity of D*∼6−8×109 cm·Hz1/2/W and time resolution of at least 4 ns. The principle of optical scheme operation is as follows. The laser beam from the QCL (Figure 3, pos. 1) through the mirror (Figure 3, pos. 2) enters the beam splitter (Figure 3 pos. 3), where it is divided into two beams. The first beam falls on the reference photodetector (Figure 3, pos. 7). The second beam through the mirror (Figure 3, pos. 4) enters the gas cell (Figure 3, pos. 5) and, after reflections in the cell, falls on the signal photodetector (Figure 3, pos. 6). The laser pointer (Figure 3, pos. 8) is used when setting up the system to obtain a given pattern of reflections [25], which allows you to obtain the required number of reflections in the gas cell. 2.3. Neural Network In the present paper, the shallow Convolutional Neural Network (CNN) that is a well-known deep learning architecture inspired by the natural visual perception mechanism of living organisms is used. Figure 4 shows the shallow convolutional neural network that was created in this work. The proposed CNN model contains an input layer, a single convolutional layer, a max-pooling layer, a fully connected MLP layer (FCL), and the output layer. In this model spectra (one-dimensional raw data arrays) are sent into the input layer. Then these spectra are filtered by the convolutional layer. A one-dimensional kernel is used, because each sample (i.e., spectrum) is represented as a one-dimensional array. The convolution layer uses the ReLU [53] activation function. Then extracted feature arrays are sub-sampled by the max-pooling layer, thus obtaining a reduced optimal feature set. These initial layers represent the feature extraction mechanism. Next comes the flatten layer, where a multidimensional array of features is transformed into a one-dimensional one. After that comes a fully connected multilayer perceptron (MLP) layer with the ReLU activation function and a fully connected output layer with the number of units equal to the number of classes. The use of the softmax activation function on this output layer allows obtaining the class prediction of the network in response to an input sample. The fully connected layers represent the classification mechanism. The stochastic gradient descent (SGD) [54] is used as an updating rule for weights in our neural network. The ‘Glorot’ initialization [55] is chosen for the convolutional kernels and output layer weights because it helps us to keep track of the seed which was used for randomization [56]. The neural network is created using the open-source machine learning library TensorFlow, developed by Google to solve various problems using machine and deep learning methods. API Keras (Google, Mountain View, CA, USA) is also used to build and train models. Therefore the parameters of our model and the range of their values are presented in Table 1. In order to find the best combination of parameter values, a random grid search cross-validation framework (RGS-CV) [57] is used during the training phase to select the configuration with the highest accuracy. Then the models are refitted using the whole training data and applied to the test data to obtain classification accuracy. Thus, the optimal parameters of our neural network have the following values: kernels = 48, N = 20; s = 1, momentum = 0.9, neurons = 256, lr = 10−3, epochs = 600. 2.4. Groups under the Study Figure 5 shows age and sex charts for the control and target groups. The experimental research was conducted from August to October 2021 on the basis of Bauman Moscow State Technical University (Moscow, Russia) and Morozov Children’s Clinical Hospital State Budgetary Healthcare Institution of Moscow Healthcare Department (Moscow, Russia). The experimental research was conducted in accordance with the principles of Good Clinical Practices. The protocol of the research was approved by the Ethics Committee of the Morozov Children’s Clinical Hospital State Budgetary Healthcare Institution of Moscow Healthcare Department (Moscow, Russia), Ref. number 174 on 18 January 2022. All participants were informed about details of the research and signed an “informed agreement” for the actions carried out. Control group: 60 healthy volunteers between the ages of 8 and 21 were examined. All volunteers from the control group had health group 1 based on in-depth preventive examinations. Health group 1 includes persons without any chronic diseases and risk factors for their occurrence. The results of medical examinations in this health group are within the normal range. This category includes citizens with the most favorable level of health. Based on the results of medical examination, preventive consultations and other medical and recreational activities are carried out for persons in this category, with the main purpose of promoting a healthy lifestyle and observing sanitary and hygienic standards. The target group: 60 patients aged 6 to 17 years were examined. All volunteers had an average degree of severity of the disease, four volunteers had acute ketoacidosis, the rest had decompensation stage without ketoacidosis. The average glucose level at admission is 13.05 mmol/L (from 7.3 to 38 mmol/L). Diabetes experience: Average 7.7 years (from 1 year to 15 years). 2.5. Sampling Protocol Exhaled breath samples were taken on an empty stomach without morning oral hygiene procedures at a room temperature of 20–22 °C. A disposable Urine Bag ST type 1300102 (Meridian, Moscow, Russia) was used for breath sampling. The volunteer exhaled the volume of their usual breath into the bag without taking a deep breath beforehand. Volunteers were asked to avoid inhaling through the nose while exhaling through the mouth due to the “lack” of air reflex. Volunteers exhaled as much as they could into two-liter valve bags. Since the volume of the gas cuvette is 0.5 liters and the operating pressure is about 500 mbar, the volume of one exhalation is sufficient for sample analysis. The preservation of the sample was checked in the urine bag and it was experimentally established that the correlation coefficient of the sample of the infrared spectrum of the volunteer’s air sample immediately after taking the sample and after 8 h of storage in the sample bag is 0.97. This allowed us to transport the breath sample from a medical facility to the laboratory. 3. Results 3.1. Sensitivity of Experimental Setup The diagnosis of T1DM by human breath analysis is based on detecting the presence of certain biomarker molecules (or its patterns), as well as on the excess of their concentrations of a certain threshold. Thus, the developed experimental setup must have a sensitivity (minimally detectable concentrations) no worse than the values of the certain threshold corresponding to certain VOC and diseases. The relationship between the components of exhaled air and human health pathologies is well known [3]. The average acetone concentration in healthy breath varies from 293 to 870 ppb and ethanol from 27 to 153 ppb [58]. Average acetone concentration may exceed 1800 ppb for diabetic patients [59]. P. Trefz et al [29] reported that T1DM patients exhaled significantly higher amounts of ethanol, isopropanol, dimethyl sulfide, isoprene, and pentanal compared with healthy controls (171, 1223, 19.6, 112, and 13.5 parts per billion by volume (ppbv) vs. 82.4, 784, 11.3, 49.6, and 5.30 ppbV). M. Simic [60] reported that endogenous ethanol correlates with increased glucose blood levels and can alert about T1DM. Acetone and ethanol as major biomarkers for T1DM are examined. A standard gas mixture with pure nitrogen with a concentration of 1000 ppm for acetone and ethanol is used. First, the gas cell is pumped to a pressure of 1 mbar. Then the gas mixture is fed from the cylinder at a given rate. The substance can be identified in the described experimental setup if the correlation coefficient of the experimental and base spectrum (registered at a high concentration of about 50–100 ppm) is at least 0.5 (corresponding to a time value of 2 s on Figure 6). The value of the minimum detectable concentration is determined by calculating the flow rate of the gas mixture (red straight line in Figure 6) and according to the Beer–Lambert law (box plot in Figure 6). To calculate the concentration according to the Beer–Lambert law, the absorption cross-sections for some VOCs at a given wavelength (Table 2) is experimentally determined. The systematic error and measurement techniques cause different slopes of experimental results in Figure 6. The minimum detectable concentrations for ethanol and acetone were experimentally obtained at levels 51 and 42 ppb using the gas mixture flow rate calculations (red line on Figure 6) and 157 and 67 ppb as median values for box plot Figure 6, with rms values equal to 63 and 41 ppb for ethanol and acetone, respectively. The obtained results allowed us to assert that the developed experimental setup makes it possible to detect typical T1DM molecule biomarkers at the required concentration. 3.2. Classification of Volunteers by Infrared Breath Spectra A balanced dataset consisting of 60 healthy volunteers and 60 T1DM volunteers aged from 6 to 21 years was used. Breath samples of T1DM volunteers with type I diabetes were taken in Morozov Children’s Clinical Hospital State Budgetary Healthcare Institution of the Moscow Healthcare Department. Ten measurements were carried out with each volunteer. Each measurement represents the spectrum of the exhaled air. A total of 1200 spectra were obtained, including 46 girls and 74 boys (34 children under 14 years old and 86 children over 14 years old). Of the total number, 60% was taken for training, 20% of the total number was taken for validation, and 20% of the total number of spectra was taken for testing. Accuracy, which is an estimate of the probability that an arbitrary object is classified correctly, was chosen as a metric for determining the quality of classification by a neural network. To achieve the highest accuracy of the neural network, calculations were conducted with the next optimum parameters: kernels = 48, N = 20, s = 1, momentum = 0.9, neurons = 256, lr = 10−3, epochs = 600. To evaluate the effectiveness of the obtained neural network parameters, cross-validation was performed on the entire training data. The results of CNN training on Figure 7 are shown. The graph from Figure 7 shows that the median accuracy was at least 99.5% on training dataset. After that, the trained neural network on the remaining 20% test sample consisting of 24 people (240 spectra) was applied. The results of T1DM and healthy volunteer classification by infrared breath spectra are shown in the Table 3. The Table 3 shows the probability that an arbitrarily taken T1DM volunteer is classified correctly (sensitivity, recall) is no less than 99%. Moreover, a probability that an arbitrarily taken volunteer is classified correctly (accuracy) of at least 99% was achieved. Area under the curve (AUC) score for convolutional neural network classification of healthy and T1DM volunteers for all sex and age groups achieved no less than 99.9%. This result shows that a randomly selected object can be positively classified with a high probability based on its IR spectrum. In contrast, in the spectra of healthy and T1DM volunteers, the neural network finds stable features necessary for classification. IR breath spectra using a neural network and selected optimal parameters for high accuracy were analyzed. As a result, the highest accuracy in the analysis of all volunteers was achieved, dividing them into healthy and T1DM volunteers (99.6%). The use of training and cross-validation on the entire data volume was shown. The expected reduction of the test group within one gender slice should increase the classification accuracy. However, the experiments showed that the classification accuracy for the entire dataset appears to be the highest compared to the slices. This can be explained by the group size, which directly affects the classification accuracy. It is possible to use data augmentation [61] to increase the dataset, which can improve the accuracy of the neural network. Advanced exhaled air diagnostic methods reveal a large number of VOCs. Changes in their levels are frequently linked to specific diseases or metabolic disorders in general. The determination of VOCs to search for prognostic markers for the development of metabolic disorders, particularly diabetes mellitus, is promising. The use of such predictors in screening large population groups and developing preventive measures on this basis is a significant social as well as biomedical issue, particularly when it comes to children’s health. Acetone is one of the potentially volatile compounds linked to metabolic abnormalities. Variations in its content in exhaled air or urine fairly accurately reflect changes in lipid metabolism, particularly lipid beta-oxidation. Type 1 diabetes mellitus occurs when the pancreatic β-cells that produce insulin are destroyed by the immune system, necessitating lifelong insulin therapy. Patients use home glucose meters to determine if they need to administer insulin, and the ISO 15197 standard for available glucose meters allows a margin of error of ±20% error. Therefore, it is very important to develop other ways to control diabetes. As a result of the conducted research, it is clear that the use of infrared laser spectroscopy is promising for the development of express methods for analyzing diabetes mellitus biomarkers in large-scale surveys in order to implement appropriate preventive and therapeutic interventions. However, more precise identification of the corresponding gas-metabolic profiles in diabetic patients representing systemic metabolic rearrangements under normal and pathological conditions is needed, because light hydrocarbons are intermediate or by-products of numerous metabolic cycles [62]. Acetone, for example, is formed as a result of the involvement of fatty acids in the energy metabolism of diabetes mellitus [63]. With starvation, prolonged intensive physical work [51], and changes in the enteric environment [64], more acetone can be formed in exhaled air [65]. The properties of light hydrocarbons in exhaled air can be used to predict individual metabolic features, including those associated with risk factors for metabolic disorders [6]. The assessment of acetone in exhaled air in conjunction with the clinical picture can be a reliable marker of liver damage and necrosis, representing the severity of oxidative stress along with the content of glycated hemoglobin in blood and products of lipid peroxidation. There are marked dysmetabolic disorders in diabetes mellitus patients’ connective tissue, in the endothelium, where active metabolites produced by oxidative stress potentiate the formation of volatile organic compounds such as ethane and pentane, the assessment of which will also be relevant in exhaled air, as it will help determine dysmetabolic changes in express mode without taking biochemical blood tests. It is possible to create a gas metabolic profile of diabetic-diabetes mellitus patients. The analysis of the whole spectrum of exhaled breath as a pattern of components as well as various biomarkers for human health check is promising. We understand that the target group contained children in the acute stage of diabetes. At this stage, we have tested the method and evaluated its accuracy. However, for early diagnosis, it is necessary to create a target group with blood glucose values close to the control group. 4. Conclusions Infrared laser spectroscopy to analyze human exhaled air was used. The experimental setup consisted of a quantum cascade laser emitting in a pulsed mode with a peak power up to 150 mW in the spectral range of 5.3–12.8 μm and a Herriot multipass gas cell with an optical path length 76 m. The control group included 60 healthy volunteers aged from 8 to 21 years; the target group included 60 volunteers with confirmed T1DM aged from 6 to 17 years. A method for collecting and drying an exhaled human air sample and collecting 1200 infrared breath spectra (10 spectra for each of 120 individuals) was proposed. The 1-D convolutional neural network to classify healthy and T1DM volunteers using IR breath spectra was used. The whole IR breath spectra of each volunteer for analysis was used. The optimal parameters of the neural network were obtained: kernels = 48, N = 20, s = 1, momentum = 0.9, neurons = 256, lr = 10−3, epochs = 600. With an optimally tuned neural network, we achieved the probability that an arbitrarily taken T1DM volunteer is classified correctly (recall) is no less than 99%. Moreover, the achieved probability that an arbitrarily taken volunteer is classified correctly (accuracy) is at least 99%. The area under the curve score for convolutional neural network classification of healthy and T1DM volunteers for all sex and age groups achieved no less than 99.9%. The obtained data require clarification on a larger sample as well as investigation of the possibilities of diagnosing other diseases. The most urgent task is to develop criteria for early rapid diagnosis of patients in prediabetic condition. We hope that the proposed experimental setup and neural network can be used to create devices that will be used in routine medical research as a doctor’s decision-making assistance system. Acknowledgments Data analysis and visualization was performed according to the development program of Bauman Moscow State Technical University. Author Contributions Conceptualization, A.M.; methodology, I.F.; software, D.A., E.K. and I.G.; data analysis, I.G., I.F. and O.N.; clinical study, P.B., P.D. and A.S.; writing, I.F., I.G. and O.N.; visualization, A.S. and O.N.; project administration, I.F. and A.S.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript. Funding The reported study was funded by RFBR according to the research project No.18-29-02024. Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Morozov Children’s Clinical Hospital State Budgetary Healthcare Institution of Moscow Healthcare Department (Moscow, Russia). Protocol code 174 on 18 January 2022. Informed Consent Statement All participants were informed about details of the research and signed “Informed agreement” for the actions carried out. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: AUC Area under the curve CNN Convolutional neural network DFB Distributed feedback DKA Diabetic ketoacidosis IDF International Diabetes Federation IR Infrared FCL Fully connected layer MIR Mid-infrared MCT-TE Mercury Cadmium Telluride thermoelectrically cooled MFC Mass flow controller MLP Multilayer perceptron QCL Quantum cascade laser ppb Parts per billion ppbv parts per billion by volume ppm Parts per million RGS-CV Random grid search cross-validation framework sccm standard cubic centimetres per minute SGD Stochastic gradient descent T1DM Type 1 diabetes mellitus VOC Volatile organic compound y.o. Years old Figure 1 The mechanism of VOC formation in T1DM human breath. Figure 2 Basic scheme for breath sample analysis method. Figure 3 Optical scheme of the experimental setup. Figure 4 Scheme of the shallow Convolutional Neural Network used in this paper. Figure 5 Groups under the study. Figure 6 Ethanol (a) and acetone (b) minimum detectable concentrations for test gas mixtures. Figure 7 CNN cross-validation results on the training dataset. materials-15-02984-t001_Table 1 Table 1 Ranges of values for the convolutional neural network model. Parameter Value Value Range Number of kernels of the convolutional layer kernels {24,48} Size of kernels of the convolutional layer N [10,20] Stride for the convolution and max-pooling s [1,2]; Momentum in the SGD updating rule momentum [0.1,0.9] Number of neurons in FCL neurons [128,256] Learning rate lr [10−3,10−4] Number of epochs epoches [100,600] materials-15-02984-t002_Table 2 Table 2 The molecular cross-section for some VOCs. No Substance Wavenumber, cm−1 Cross-Section, 10−19 cm2 1 Ammonia 930 4.28 2 Acetone 1217 3.37 3 Methanol 1033 7.16 4 Ethanol 1065 2.58 materials-15-02984-t003_Table 3 Table 3 Results of T1DM and healthy volunteer classification by infrared breath spectra. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092176 cancers-14-02176 Article EMT, Stemness, and Drug Resistance in Biological Context: A 3D Tumor Tissue/In Silico Platform for Analysis of Combinatorial Treatment in NSCLC with Aggressive KRAS-Biomarker Signatures Peindl Matthias 1† Göttlich Claudia 1† Crouch Samantha 2‡ Hoff Niklas 1 Lüttgens Tamara 1 Schmitt Franziska 1 https://orcid.org/0000-0002-1267-7049 Pereira Jesús Guillermo Nieves 1 May Celina 1 Schliermann Anna 1 Kronenthaler Corinna 1 Cheufou Danjouma 3 Reu-Hofer Simone 45 Rosenwald Andreas 45 Weigl Elena 1 https://orcid.org/0000-0002-5037-054X Walles Thorsten 6 https://orcid.org/0000-0003-1984-7343 Schüler Julia 7 https://orcid.org/0000-0003-1886-7625 Dandekar Thomas 28*‡ Nietzer Sarah 19§ Dandekar Gudrun 19*§ Alfieri Roberta Academic Editor 1 Chair of Tissue Engineering and Regenerative Medicine, University Hospital Würzburg, Röntgenring 11, 97070 Würzburg, Germany; matthias.peindl@uni-wuerzburg.de (M.P.); claudia.goettlich@crl.com (C.G.); nto.hoff@gmail.com (N.H.); tamara.luettgens@stud-mail.uni-wuerzburg.de (T.L.); schmitt_franziska@gmx.de (F.S.); jesus.nieves@uni-wuerzburg.de (J.G.N.P.); celina.may@stud-mail.uni-wuerzburg.de (C.M.); anna.schliermann@gmail.com (A.S.); ckronenthaler@aol.com (C.K.); elena.weigl@med.uni-muenchen.de (E.W.); sarah.nietzer@uni-wuerzburg.de (S.N.) 2 Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany; samantha.crouch@uni-wuerzburg.de 3 Department of Thoracic Surgery, Klinikum Würzburg Mitte gGmbH, Salvatorstr. 7, 97074 Würzburg, Germany; danjouma.cheufou@kwm-klinikum.de 4 Department of Pathology, University of Würzburg, Josef-Schneider-Str. 2, 97080 Würzburg, Germany; simone.reu@uni-wuerzburg.de (S.R.-H.); rosenwald@uni-wuerzburg.de (A.R.) 5 Comprehensive Cancer Center Mainfranken, Josef-Schneider-Straße 6, Building C16, 97080 Würzburg, Germany 6 Department of Thoracic Surgery, University Medicine Magdeburg, Leipziger Straße 44, 39120 Magdeburg, Germany; thorsten.walles@med.ovgu.de 7 Charles River Discovery Research Services Germany GmbH, Am Flughafen, 14, 79108 Freiburg, Germany; julia.schueler@crl.com 8 European Molecular Biology Laboratory (EMBL) Heidelberg, Structural and Computational Biology, Meyerhofstraße 1, 69117 Heidelberg, Germany 9 Fraunhofer Institute for Silicate Research (ISC), Translational Center Regenerative Therapies, Röntgenring 11, 97070 Würzburg, Germany * Correspondence: dandekar@biozentrum.uni-wuerzburg.de (T.D.); gudrun.dandekar@uni-wuerzburg.de (G.D.); Tel.: +49-931-3184551 (T.D.); +49-931-3182597 (G.D.) † These authors shared first authorship. ‡ These authors are first and last author, respectively, regarding the bioinformatics. § These authors shared last authorship. 27 4 2022 5 2022 14 9 217619 1 2022 15 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary The phenotypic transition of tumor cells from epithelial to mesenchymal characteristics is called EMT and is widely discussed in the scientific community as a game changer in drug resistance and metastasis formation. However, clinical studies could not prove the efficacy of EMT-interfering treatments, and in clinical routine, EMT is not investigated to assess invasion. To fill this gap between bench and bedside, we use in this study a lung tumor tissue model with a preserved basement membrane for investigation of EMT functions with respect to invasion across this membrane and drug resistance. Our results suggest EMT is more a marker of drug resistance than a maker. Invasion is enhanced by EMT but more dependent on intrinsic factors, and EMT is not detected in the center of invasive tumor nodules. An in silico signaling network model is used to integrate these in vitro results and to reveal determinants for drug response. Abstract Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRASG12C or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-β. To investigate further determinants of drug response, we tested several drugs in combination with a KRASG12C inhibitor in KRASG12C mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures. EMT drug resistance invasion stemness 3D lung tumor tissue models KRAS biomarker signatures boolean in silico models targeted combination therapy This research was funded from 2016 to 2019 by the BMBF in the call “Alternative methods to animal experiments” (GD, TD, JS project: 031L0129A-C) and from 2019 to present by the Bavarian Research Foundation (GD, TD: project: AZ-1365-18). Furthermore, this publication was funded by the University of Würzburg in the funding program Open Access Publishing. ==== Body pmc1. Introduction The cellular program of epithelial-to-mesenchymal transition (EMT) and its dynamic nature is believed in the tumor biology community to be of great importance for the development of strategies to fight advanced and therapy-resistant cancer [1]. Entering the keywords “epithelial mesenchymal transition cancer” in Pubmed (https://pubmed.ncbi.nlm.nih.gov, accessed on 1 December 2021) reveals nearly 30,000 publications starting in 1975 and gaining exponential growth about 10 years ago. Adding the term “invasion” results in over 15,000 publications or “resistance” in over 5000. As a major player, TGF-β is suggested to induce not only EMT but also drug resistance [2,3], stressed by over 400 publications starting from 2002 when adding “TGF” to “resistance”. As an approach going beyond EMT, the shut-down of cellular plasticity was suggested, which is illustrated by a search result of about 1500 publications since 2009 when adding the search term “stemness” to “EMT” and “cancer” in Pubmed. Regarding lung cancer, there has been a rising number of publications for about 10 years on the topic of EMT (in total, about 3700). A vast amount of work has been performed to translate EMT-related scientific results into the clinic with limited success. Several approaches to revert EMT to mesenchymal-to-epithelial transition (MET) ended up disappointingly in clinical trials (overview: the work of [4]). One conclusion was that EMT is a metastable stage, and therapeutic intervention strategies will lead to the activation of compensatory pathways to regain homeostasis and resistance. From the clinical perspective, EMT markers are not investigated except for colorectal cancer (CRC), where the translocation of β-catenin toward the nucleus is one hallmark of cancer progression. In the nucleus, β-catenin induces the expression of genes responsible for EMT induction and stemness [5]. However, a deep understanding of EMT and its correlation with invasion, stemness, and drug response and interdependent signaling pathways related to biomarkers is still needed for an effective translation into the clinic. For non-small cell lung carcinomas (NSCLC), especially adenocarcinomas, biomarker-guided therapies were successfully introduced into patient care about 17 years ago, mostly targeting activating EGFR mutations [6]. However, secondary resistance often arises after a few months of treatment, and the largest group of patients suffering from tumors carrying a KRAS mutation even shows high primary resistance to targeted treatment. For NSCLCs harboring KRASG12C mutations (representing more than 40% of the most frequent KRAS mutation in lung cancer), sotorasib, an allele-specific covalent inhibitor, received accelerated approval by the FDA in May 2021 and showed efficacy in certain subgroups during clinical trials [7,8]. Combinatorial treatments are under investigation [9]. The identification of frequent co-mutations paves the way for understanding mechanistically how to improve tailored therapies also in resistant groups [10]. A major restriction for effective translation is the availability of models including in vivo-like biological features capable of reliably evaluating drug efficacy. This is illustrated by the high attrition rates of existing preclinical models [11,12]. As a solid basis for the simulation of tumor biological mechanisms in vitro, we use a tissue-engineered tumor model based on an intestinal decellularized tissue matrix termed SISmuc (small intestinal submucosa with preserved mucosa). It allows tumor cell growth to reach homeostasis with changed expression patterns compared to 2D cultures, especially regarding markers of proliferation and apoptosis [13], but also (as demonstrated here) regarding carcinogenesis-related and clinical markers. Notably, our model shows a higher chemoresistance and a better predictivity compared to 2D and animal experiments in several biomarker-guided targeted test set-ups [14,15]. Furthermore, it allows discrimination between functional non-invasive and invasive cells due to its preserved basement membrane, which has been shown to be crossed by cells after EMT induction via TGF-β [16,17]. In this study, to gain insight into dependencies regarding EMT, invasion, stemness, and drug resistance, we induced EMT by TGF-β1 in different lung cancer cell lines grown in 3D tissue models and then treated those models with biomarker-corresponding targeted therapies. We analyzed changes in tissue morphology (cell invasion), differentiation (E-cadherin, muc-1), EMT (cytokeratins, vimentin), and stemness (CD44). While we could indeed demonstrate a higher EMT status in secondary resistant EGFR-mutated cells, which was also true for primary more resistant KRAS-mutated cells, no increase in resistance after induction of EMT via TGF-β1 was observed. Moreover, in four KRASG12C-mutated patient-derived xenograft (PDX)-derived cell lines, neither EMT status nor stemness consistently correlated with resistance in 2D and 3D cell culture. The environment’s intrinsic invasion properties surpass TGF-β1 induced, and thereby EMT-related, invasion. Evaluation of clinical lung adenocarcinoma specimens further underlines an EMT-independent invasion mechanism at least in the center of invasive tumor nodules. Changes in drug response under 3D culture conditions stress the high impact of the tissue environment on preclinical testing results. Our central hypothesis in this paper is that EMT is not a consistent predictor of drug response. Instead, we propose that it is the individual biomarker signature of a cell line that allows for predicting the effectiveness of individualized therapies targeting tumor-specific cascades in a complex network. A combined in vitro/in silico modeling approach was hence pursued to understand the complex pathway interdependencies in the cellular network. To improve the understanding of resistance mechanisms, we in silico simulated the sensitivity of the more epithelial KRASG12C-mutated cell line H358 and the higher resistance of the more mesenchymal KRASG12C-mutated cell line HCC44 toward a KRAS inhibitor. Before the simulation, we updated our previously established in silico topology of a cancer signaling network [14,15,16,17] by integrating frequent co-mutations from patients with KRAS-mutated lung cancer [10] and modified it in a cancer cell line-specific way. After incorporating experimental data, we had a cell type-specific model. We tested the KRAS inhibitor in several combination therapies in vitro and in silico as suggested by large screening experiments employing the more resistant HCC44 cell line exhibiting high EMT status, CD44 and c-MYC expression, and other common KRAS co-mutations. After adjusting the in silico model to three different in vitro combination treatments, we could, as proof of concept, reveal the combination of an aurora-kinase A (AURKA) inhibitor together with a KRASG12C inhibitor to be effective, predicted simultaneously by both in silico and in vitro experiments. The present tissue-based model combined with in silico analysis should pave the way for a better tumor biological understanding of co-mutations, EMT, stemness, and drug resistance and reveal biomarker signatures for patient stratification in clinical studies. 2. Materials and Methods 2.1. Matrix Preparation Chemical decellularization of jejunal segments was performed as previously published [18,19,20]: the intestinal segments were explanted, rinsed, and chemically decellularized with a sodium deoxycholate monohydrate solution; the vascular tree was manually removed; and the whole product underwent gamma sterilization. Tumor models were prepared as published before [13]. 2.2. Cells All cells were cultured under standard culture conditions (37 °C, 5% CO2). PDX-derived cell lines LXFA 983, LXFL 1072, LXFL 1674, and LXFA 2184 were kindly provided by Dr. Julia Schüler (Charles River, Freiburg, Germany). PDX-derived cell lines, A549 (purchased from DSMZ), H358 (purchased from ATCC), and HCC44 cells (purchased from DSMZ) were cultured in RPMI 1640 medium with GlutaMAX™ (Gibco/life technologies/ThermoFisher Scientific, Waltham, MA, USA) supplemented with 10% FCS (PAN-Biotech, Aidenbach, Germany). HCC827 cells (purchased from DSMZ) were cultured in RPMI 1640 medium with GlutaMAX™ (Gibco/Life Technologies/ThermoFisher Scientific, Waltham, MA, USA) supplemented with 20% FCS (PAN-Biotech, Aidenbach, Germany). HCCresA1, HCCresA2, and HCCresA3 were generated from the cell line HCC827 via the constant addition of 1 µM gefitinib (Selleckchem, Houston, TX, USA) to the cell culture medium. While HCCresA2 and HCCresA3 cells displayed resistance toward the treatment with gefitinib in concentrations up to 10 µM, HCCresA1 cells still exhibited an intermediate sensitivity in comparison (Figure S2). HCCresA2 and HCCresA3 cells displayed reduced sensitivity toward EGFR inhibition after about 4 months of permanent treatment with 1 µM gefitinib, whereas HCCresA1 still showed a partial sensitivity in this time period. Regarding gefitinib sensitivity, we are referring to Noro et al. cells with an IC50 lower than 1 µM as highly sensitive, cells with an IC50 between 1 and 10 µM as intermediately sensitive and cells with an IC50 greater than or equal to 10 µM as resistant, respectively [21]. Primary human lung fibroblasts were isolated from biopsies of healthy lung tissue with informed consent according to ethical approval granted by the Institutional Ethics Committee of the University Hospital Würzburg (protocol code 99/20-am) and cultured in DMEM with GlutaMAXTM (Gibco/life technologies/ThermoFisher Scientific, Waltham, MA, USA) supplemented with 100 mM sodium pyruvate and 10% FCS (PAN-Biotech, Aidenbach, Germany). 2.3. Preparation of Tumor Models Single pieces of the SISmuc matrix were fixed between two metal rings (cell crowns) and seeded with 100,000 tumor cells on the mucosal side (transwell in Figure 1A). Tumor models were placed in 12-well plates with 1 mL media in the inner compartment and 1.5 mL in the outer compartment of the cell crown. For static cell culture, the models were cultured for 11 days and afterward treated with the test substance for 3 days. During static and semi-dynamic cell culture, medium was changed every 2 to 3 days. All test substances were administered via the cell culture medium. Here, A549, H358, HCC44, and HCC827 tumor models were cultured statically, while PDX-derived cell lines tumor models were grown under semi-dynamic conditions on an orbital shaker at 100 rpm until day 10 of culture before the inhibitor treatment started. 2.4. Patient Tumor Samples Human tracheo-bronchial tissue samples for staining were obtained from adult patients undergoing elective pulmonary resection at the University Hospital Würzburg, the Hospital Würzburg Mitte gGmbH, and the University Hospital Magdeburg. Written informed consent was obtained beforehand, and the studies were approved by the institutional ethics committees on human research of the Julius-Maximilians-University Würzburg (protocol code 215/12 and 99/20-am) and Otto-von-Guericke University Magdeburg (protocol code 163/17), respectively. Patient material was derived from 10 NSCLC adenocarcinoma patients older than 18 years from both sexes. Ethnicity, previous treatments, and other medical disorders were neglected. 2.5. Treatment of Cells in 2D and 3D For 2D cell culture, cells were cultured in 12-, 24-, or 96-well plates for subsequent M30 Cyto Death™ ELISAs (PEVIVA®, TECOmedical, Sissach, CH), immunofluorescence stainings, or CellTiter-Glo® viability assays (Promega, Madison, WI, USA), respectively. Inhibitors dissolved in DMSO (Sigma-Aldrich, München, Germany) or water were applied in different concentrations to the cell culture medium for 3 days, 24 h after seeding the cells, with a medium change on the 2nd day of treatment. For 3D cell culture, cell lines were seeded on the matrix SISmuc and cultured for 11 days prior to adding medium containing the corresponding inhibitors. Medium containing the inhibitors was renewed after 48 h of treatment. The following inhibitors were used to treat the cells in 2D and 3D cell culture: alisertib (Selleckchem, Houston, TX, USA), ARS-1620 (Hycultec, Beutelsbach, Germany), crizotinib (Selleckchem, Houston, TX, USA), erdafitinib (Selleckchem, Houston, TX, USA), gefitinib (Selleckchem, Houston, TX, USA), metformin HCl (Selleckchem, Houston, TX, USA), and SHP099 HCl (Selleckchem, Houston, TX, USA). 2.6. Stimulation of Cells with hTGF-β1 Cells were seeded on the SISmuc as described above. After 3 days in culture, medium containing 2 ng/mL hTGF-β1 with a carrier (Cell Signaling, Danvers, MA, USA) was added to the models and renewed every 2 to 3 days for the remaining 11 days in culture. 2.7. Immuno/Histochemical Stainings Tumor models were fixed in a 4% PFA solution (Carl Roth GmbH, Karlsruhe, Germany) for 2 h, embedded in paraffin, and cut in a microtome at 3–5 µm. Hematoxylin and Eosin (H&E) staining (Morphisto, Offenbach am Main, Germany) was performed according to the manufacturer’s protocol. Non-immunofluorescent immunohistochemical stainings were performed using the 3,3′-Diaminobenzidine (DAB) system (DCS Innovative Diagnostik-Systeme, Hamburg, Germany) according to the manufacturer’s protocol resulting in the formation of a brown dye at areas of antibody binding. The following primary antibodies were used: rabbit anti-TTF1 (Abcam Cat# ab76013, RRID: AB_1310784, Cambridge, UK), rabbit anti-SPP1 (Abcam Cat# ab8448, RRID: AB_306566), mouse anti-Cytokeratin 7 (Abcam Cat# ab9021, RRID: AB_306947), rabbit anti-P63 (Abcam Cat# ab53039, RRID: AB_881860), rabbit anti-Collagen IV (Abcam Cat# ab6586, RRID: AB_305584), rabbit anti-vimentin (Abcam Cat# ab92547, RRID: AB_10562134), rabbit anti-Ki-67 (Abcam Cat# ab16667, RRID: AB_302459), rabbit anti-Mucin-1 (Abcam, Cat #ab84597, RRID_ AB_10672326), mouse anti-E-cadherin (BD-Biosciences, Cat#61081, RRID:AB_397581), rabbit anti-CD44 (Abcam, Cat#ab51037, RRID: AB_868936), rabbit anti-β catenin (Abcam, Cat#ab32572, RRID: AB_725966), and mouse anti-Cytokeratin, pan (Sigma-Aldrich Cat# C2562, RRID: AB_476839). Primary antibodies were diluted 1:100 in antibody diluent (DCS Innovative Diagnostik-. Systeme, Hamburg, Germany) and incubated overnight at 4 °C. Secondary antibodies donkey anti-mouse IgG (Thermo Fisher Scientific Cat# A-31571, RRID: AB_162542) conjugated to Alexa-647 or donkey anti-rabbit IgG (ThermoFisher Scientific, Waltham, MA, USA Cat# A-31572, RRID: AB_162543) conjugated to Alexa-555 (ThermoFisher Scientific, Waltham, MA, USA) were diluted 1:400 in antibody diluent and incubated for 1 h at room temperature (RT). Cell nuclei were counterstained using 4′,6-diamidino-2-phenylindole (DAPI), which was diluted in the embedding medium Fluoromount-G (ThermoFisher Scientific, Waltham, MA, USA). Images were taken using a digital microscope (BZ-9000, Keyence, Osaka, Japan). 2.8. Quantification of Proliferation and Cell Invasion For the determination of proliferation indices, Ki-67 stainings were quantified: at least 5 images of non-overlapping regions of each sample were taken with a fluorescence microscope (BZ-9000, Keyence, Osaka, Japan). DAPI and Ki-67-positive cells were counted manually using Image J (v1.53a, NIH, Bethesda, MD, USA). For the quantification of cell invasion, collagen IV immunofluorescence stainings were performed. Subsequently, DAPI-positive cells were manually counted on top and in the former crypts versus inside the biological matrix in at least 5 images of non-overlapping regions of each sample. For each image, the number of Ki-67-positive cells or cells inside the biological matrix was calculated in percent of total cell number, and the mean of each sample was used for subsequent statistical analysis. 2.9. M30 ELISA The M30 CytoDeath™ (PEVIVA®, TECOmedical, Sissach, CH) or M30 ApoptoSense™ (PEVIVA®, TECOmedical, Sissach, CH) assay was used for the quantification of epithelial apoptosis by measuring the caspase-cleaved product of cytokeratin 18 in the cell culture supernatant. Both ELISAs were performed according to the manufacturer’s protocol. In short, supernatants of cells cultured in 12-well plates or the 3D tumor models were collected at 4 different time points: directly before and 24, 48, and 72 h after the inhibitor treatment. Samples in duplicates were diluted in the corresponding cell culture medium to fit the range of the standard curve, and the M30 conjugate was added to the samples in each well. After incubation on an orbital shaker for 3 h, wells were washed 5 times prior to the addition of the substrate. The reaction was stopped after 20 min. Absorbance was measured after shaking for 10 s with a microplate reader (TECAN, Männedorf, Switzerland). M30 quantifications were analyzed with Origin (OriginLab, Northampton, MA, USA). To calculate the fold increase in apoptosis after treatment, each sample was firstly normalized to its baseline increase in apoptosis in the last 24 h before the initial treatment. Subsequently, these normalized values of the respective timepoints (24, 48, or 72 h) after treatment were divided by the values of the untreated controls at the same time points. 2.10. Viability Assays (MTT-Test and CellTiter-Glo®) To determine the viability of cells after inhibitor treatment in 2D cell culture, CellTiter-Glo® Luminescent Viability assays (Promega, USA) were used according to the manufacturer’s protocol. In brief, cells were seeded in 96-well plates and were allowed to attach for 24 h. Subsequently, cells were treated for 72 h with the indicated inhibitors and washed once with PBS before the addition of CellTiter-Glo® reagent diluted 1:2 in cell culture medium. Plates were mixed for 2 min and incubated at RT for 10 min before recording the luminescence with an integration time of 1000 milliseconds per well in a microplate reader (TECAN, Männedorf, Switzerland). IC50 values were calculated using Prism 8 (Graphpad Software, Inc., San Diego, CA, USA) by plotting the logarithmic concentrations versus the response with a variable slope. For the evaluation of viabilities after treatment in 3D tumor models, MTT assays were performed. Therefore, 3 mg/mL MTT (SERVA, Heidelberg, Germany) were diluted 1:3 in the corresponding cell culture medium and added to the models for 3 h at standard conditions. The biological matrix was removed from the cell crowns, and formazan was washed out of the SISmuc with 0.04 N HCl in isopropanol in 3 steps before measuring the absorbance at 570 nm with a microplate reader (Tecan, Männedorf, Switzerland). 2.11. Western Blotting Tumor cells cultured on the SISmuc were lysed in lysis buffer (137 mM NaCl (Carl Roth GmbH, Karlsruhe, Germany), 20 mM Tris-HCl (Sigma Aldrich, München, Germany) pH 8.0, 2 mM EDTA (ThermoFisher Scientific, Waltham, MA, USA), 50 mM NaF (Sigma Aldrich, München, Germany), 1 mM NaVO3 (Sigma Aldrich, München, Germany), 10% glycerol (Carl Roth GmbH, Karlsruhe, Germany), 1% NP-40 (AppliChem GmbH, Darmstadt, Germany), 0.5% DCA (Carl Roth GmbH, Karlsruhe, Germany), 0.1% SDS (Carl Roth GmbH, Karlsruhe, Germany), 1× Protease Inhibitor (Roche, Munich, Germany)) for 1 h at 4 °C on a rocking shaker. For each sample, 80 µg protein was loaded per lane on a 10% SDS gel and subsequently blotted on a 0.2 µm nitrocellulose membrane. Blots were blocked for 1 h at RT in 5% milk in TBS-T (Sigma Aldrich, München, Germany). Primary antibodies against c-MYC or alpha-tubulin were incubated overnight at 4 °C in 5% BSA or 5% milk in TBS-T, respectively. Secondary antibodies were incubated in 5% milk in TBS-T at RT for 1 h. For the development of the blots, the WesternBright Chemilumineszenz Substrat Quantum kit (Biozym, Hessisch Oldendorf, Germany) was used and visualized on the Imaging Station FluorChemQ (Biozym, Hessisch Oldendorf, Germany). The following antibodies were used: rabbit anti-c-Myc (Y69) (Abcam Cat# ab32072, RRID: AB_731658), mouse anti-alpha-tubulin (DM1A) (Cell Signaling, #3873:, RRID: AB_1904178), goat anti-mouse IgG (H + L)-HRPO (Jackson Immuno Research, West Grove, PA, USA, #115-035-146, RRID: AB2307392) and goat anti-rabbit IgG (H + L)-HRPO ((Jackson Immuno Research, #111-035-144, RRID: AB_2307391). Quantitative evaluation was performed using ImageJ, and each sample was normalized to the loading control before comparison. 2.12. Statistics Statistical significance was determined with Prism 8 (GraphPad, USA) using unpaired t-tests, assuming that data were distributed normally for 3 ≤ n ≤ 13. p-values ≤ 0.05 were considered significant; *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001. 2.13. Lung Cancer PCR Array For quantitative real-time PCR (qPCR), Human Lung Cancer PCR Array plates (PAHS-134D-12, Qiagen, Hilden, Germany) were used with the recommended RT2 SYBR Green Master Mix, according to the array’s protocol. cDNA was prepared using the iScript cDNA Synthesis Kit (Biorad, Hercules, CA, USA), and 20 μL of this cDNA sample was diluted 1:5 in H2O, subsequently added to 1350 μL of master mix and 1250 μL of water. This equals samples for 100 wells with cDNA from 10 ng of RNA per well. Of this master mix, 25 µL were pipetted into each well of a 96-well lung cancer PCR array plate (separate Supplementary file Gene_Table.xls). The qPCR was run as follows: 10 min at 95 °C, 15 s at 95 °C, 30 s at 55 °C repeated 40 times, 30 s at 72 °C, indefinitely at 4 °C. For analysis, the ΔΔCt value was determined, and the fold change 2-ΔΔCt was calculated by dividing the normalized gene expression (2-ΔCt) of a test sample by the normalized gene expression of a control sample. Fold change was then transformed into fold regulation; fold changes greater than one were equal to the fold regulation, while fold changes smaller than one were inverted and presented as negative fold regulation values (e.g., fold change 0.5 equals a –2-fold regulation). All genes with a fold regulation greater than 3 or smaller than −3 and with at least one of the two compared Ct values < 30 were considered considerable transcription differences. 2.14. Ultrastructural Analysis Samples were washed with pre-warmed PBS + calcium and magnesium (Sigma Aldrich, München, Germany). Cell-free edges were removed with a scalpel prior to fixation in a 6.25% or 2.5% solution of glutaraldehyde (Sigma Aldrich, München, Germany) overnight at 4 °C. Further sample preparation for raster electron microscopy (REM) as well as imaging was performed at the Imaging Core Facility, Biocenter, University of Würzburg. 2.15. In Silico 3D Tissue Simulations/Bioinformatics To model individual drug actions as well as effects of drug combinations, dynamic simulations of cellular pathways also considered known impacts of different drugs. Signaling network reconstruction was based on available literature and on biochemical and human interactome database sources such as KEGG. For data-driven modeling, signaling network reconstruction was combined with dynamic simulations of cellular pathways: first, the network topology was created and edited in CellDesigner (version 3.5.1, The Systems Biology, Tokyo, Japan, accessed on: 1 February 2021; https://celldesigner.org/download351.html) [22] and exported as an xml file (separate Supplementary file Topology.xml). Importantly, the Boolean logic of the network was considered, i.e., activating and inhibitory interactions between receptors, proteins, and protein cascades. Modifying crosstalk was also implemented. Next, dynamic simulations followed using the software SQUAD and using the network calculated to predict the activation or inhibition for every protein in the whole network as well as the resulting outcome parameters [23]. As kinetic information for individual protein nodes in the network is usually quite limited (not known, not measured), SQUAD automatically interpolates between the different network states to model signal propagation in the network. SQUAD assumes standard exponential functions but modifies the kinetic parameters of the function according to the network logic using concatenated exponential functions. The network is inevitably always a simplification regarding modulatory input from the remaining cell, as we model only about 30 proteins though the cell contains 5.000 proteins. To take this into account, the ground state (how strongly activated or inhibited at the start) was modified for several nodes in the simplified network according to available experimental data. This is given in Tables S1 and S2 in Supplement Materials. For all other nodes, the ground state was set to zero. Next, the trajectories of full or partial activation down to no inhibition were calculated for the whole network and for all included proteins. However, only selected ones are shown in the figures to avoid cluttering the figures. Different mutational profiles and treatments were integrated into the dynamic simulation via the SQUAD perturbation function. Outcome predictions (Tables S1B and S2B, “outcome”) were assessed by comparing to experimental readouts (proliferation and apoptosis in cell culture) as basic markers as well as considering all available co-mutations. The simulation initially describes only time units and activation strengths. Normalization of the activities and activation times was performed according to the collected experimental data: several iterative cycles between simulations, comparison with experimental data, and modifying the network topology and parameters accordingly. The resulting predictions are shown for the therapy-resistant cell line HCC44 and targeted combination therapy. All simulation protocols on parameters, simulated stimulation or inhibition, and stimulus time were saved in the SQUAD prt file format. 3. Results 3.1. Generation and Characterization of the 3D Lung Cancer Tissue Model To generate a more reliable and in vivo-like preclinical tumor model, we developed a lung cancer model on a decellularized tissue matrix from porcine jejunum termed SISmuc. From one single pig, about 150 transwell cultures can be produced and used as inserts for 12-well or 24-well plates (Figure 1A). After cell seeding, a homeostasis-like state is reached at about day 10 of 3D cell culture, and this state remains stable for at least one further week [13]. Dynamic culture conditions in bioreactors enhance cell growth (Figure 1B) and enable longer culture periods. A unique feature of our model is the preserved basement membrane enabling physiological anchorage of epithelial cells from which carcinomas derive. The addition of TGF-β1 induces EMT (increased vimentin staining, red in Figure 1D) and invasion across the basement membrane as a typical feature of carcinomas (arrows in Figure 1D). Cells grown in the tumor model are phenotypically characterized by established clinical markers. Regarding the expression of the lung adenocarcinoma markers thyroid transcription factor 1 (TTF1) and cytokeratin 7 (CK7), the adenocarcinoma-derived cell line HCC827 harboring an activating EGFR mutation well correlates with clinical adenocarcinoma specimens, whereas the KRAS mutated widely used cell line A549 isolated from an alveolar cell carcinoma is negative for both markers (Figure 1). However, the squamous cell carcinoma marker P63 (slightly positive in a single adenocarcinoma specimen) was strongly upregulated in both cell lines under 3D culture conditions (Figure S1A–C). Further, we investigated the expression of the experimental but clinically relevant marker osteopontin, also known as secreted phosphoprotein 1 (SPP1), which was recognized as an indicator of tumor aggressiveness and metastatic potential and is discussed as a diagnostic biomarker. Immunohistochemically, both cell lines mentioned above were positive for SPP1 in 2D and 3D cultures (Figure 1J,M). Comparing the expression of other lung cancer-associated genes in the HCC827 cell line with preserved adenocarcinomatous phenotype under 2D and 3D conditions by PCR arrays revealed an upregulated transcription of anterior gradient protein 2 homolog (AGR2), carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6), signal transducer and activator of transcription 2 (STAT2), transforming growth factor beta 1 (TGF-β1), TOX high mobility group box family member 3 (TOX3) and vascular endothelial growth factor A (VEGFA). All these genes were expressed at least four times higher in 3D than in 2D culture (Figure S1D). Most of them are involved in carcinogenesis (AGR2 (connected to Mucin-1), STAT2 [24], TGF-β1), and cell adhesion (CEACAM6). Taken together, these results illustrate the strong impact of cell culture conditions on tumor cell growth, signaling, and biomarker expression and suggest a more cancer-like state of tumor cells in our 3D model in comparison to 2D culture. 3.2. Assessment of EMT, Differentiation, Stemness, and Invasion For a more detailed biological characterization in the context of EMT (pan-cytokeratin (PCK), vimentin), we looked at differentiation (E-cadherin, Mucin-1) and stemness (CD44), and we quantified cell invasion after staining of the basal membrane component collagen IV. For this, two additional lung cancer cell lines were used besides HCC827 cells: H358 with a more epithelial phenotype and HCC44 with more mesenchymal features. Both cell lines harbor the KRASG12C mutation, which can be targeted by the KRAS inhibitor ARS-1620. The two cell lines with more epithelial characteristics (HCC827, H358) were additionally treated with TGF-β1 to reveal general EMT-correlating patterns, as shown in Figure 2. In line with HCC827, H358 cells without TGF-β1 stimulation show a clear expression of the adherence junction protein and epithelial marker E-cadherin at cell-to-cell contacts, which is not present in HCC44 cells. Furthermore, both cell lines express the epithelial marker Mucin-1. Here, we observed in HCC827 cells a polarized apical location, in H358 cells a strong but basolateral expression, and the same expression pattern (but only weakly present) in HCC44 cells. In contrast to HCC827 cells, H358 cells in 3D culture (as well as HCC44 cells) show not only PCK but also vimentin expression. The stemness marker CD44 is highly expressed in HCC44 cells corresponding with the highest EMT status, but not in HCC827 cells and only weakly in H358 cells. Next, we stimulated the more epithelial HCC827 and H358 cells with TGF-β1 and observed a shift to a more mesenchymal phenotype showing E-cadherin- and Mucin-1-negative cells with stronger vimentin and CD44 expression. EMT status correlated with the expression of the stemness marker CD44. Independent of its location, Mucin-1 expression is inversely correlated with TGF-β1-dependent EMT induction (Figure 2). TGF-β1 also led to the enhanced invasion of HCC827 and H358 cells. Interestingly, HCC44 cells were massively more invasive than HCC827 and H358 cells. We generated secondary resistant subclones named HCCresA2 and HCCresA3 by permanent EGFR inhibitor treatment of HCC827 cells with gefitinib (Iressa®, Absource Diagnostics GmbH, München, Germany). Drug resistance developed after about four months of treatment (Figure S2A). These subclones exhibited increased invasiveness compared to their parental cell line (Figure 3). While EMT and invasiveness were interconnected in the tested cell lines, pathological assessment of the EMT markers cytokeratin and vimentin in 10 clinical samples of invasive lung adenocarcinomas revealed exclusive cytokeratin expression in the center of invasive tumor nodules; however, it does not represent the invasive front (Figure S3). 3.3. EMT Correlation with Drug Response Next, we investigated how EMT correlates with drug response in our three cell lines carrying the clinically relevant KRASG12C (H358, HCC44) and EGFR mutation (HCC827). Since tumors with KRAS mutations exhibit a high chemoresistance, termed primary resistance, we selected H358 and HCC44 cells as they both harbor the KRASG12C mutation. This mutated protein is targetable with the allele-specific and covalent inhibitor ARS-1620, a derivative of which received accelerated approval by the FDA in May 2021. Our analysis shows a strong connection between EMT and drug response in both 2D and 3D cell cultures (Figure 4 and Figure S2). After low doses of ARS-1620, the more epithelial H358 cells show a reduction in cell number as well as in proliferation and dose-dependent apoptosis induction (Figure 4A–C). In the more mesenchymal and invasive HCC44 cells, no pronounced and dose-dependent effects on cell number, proliferation, and apoptosis could be observed (Figure 4A,B,D). Regarding the EGFR mutation, in the resistant subclones HCCresA2 and HCCresA3 permanently treated with the EGFR inhibitor, resistance correlates with EMT (PCK/VIM), loss of E-cadherin, downregulation of Mucin-1 and upregulation of CD44 (Figure 4E–G). 3.4. Set-Up of Combined In Vitro/In Silico Models with KRAS Signatures To unravel the underlying dependencies of drug responses in this setting more systematically, we further applied an in silico model that displays a network map of signaling pathways (topology) for our KRASG12C models. This topology is given in a machine-readable format (using CellDesigner [15]). It can be used to simulate systemic drug responses in specific mutational backgrounds by the application of the SQUAD software. To achieve a more clinically relevant model, we integrated into the topology common co-mutations of KRAS found in over 1000 clinical lung adenocarcinoma patients [10], most frequently being P53 (about 40%) and LKB1/STK11 (about 20%), and KEAP1, to enable the efficient patient-specific translation of the model from bench to bedside (Figure 5A). The resulting model topology of key pathways and interactions in lung adenocarcinoma is given as the Supplementary file Topology.xml. However, such a lung cancer network is always a simplification, and we wanted to include the effect of input from outside of the topology and consider differences between the H358 and HCC44 cell lines. Hence, to take these activities of important nodes into account that specifically acted differently in H358 and HCC44 cells looking at untreated and treated with the KRAS inhibitor ARS-1620, we pre-set ground state activities in our model estimated from the literature and experimental data (Table S1A, gray columns). In preparation for individual patient predictions, mutational differences of the cell line H358 (responder to the KRAS inhibitor ARS-1620) and HCC44 (non-responder) were adjusted to certain activation levels in order to stratify subgroups that could be relevant for drug efficacy. In detail, H358 cells display a TP53 and a PIK3CG mutation together with KRAS, but the LKB1 gene is wildtype [25]. On the other side, HCC44 cells harbor an LKB1-, KEAP1-, and SMARCA4 mutation in addition to P53 [25,26], and a higher expression of c-MYC is reported [27]. Further differences between these two cell lines are a lower EMT status, stemness (Figure 2), invasion (Figure 3), and proliferation (Figure 4) in H358 compared to HCC44 cells. For successful simulation, according to the experimental data, we had to add (next to EMT activation by vimentin) an activation link in the network topology from vimentin to EKR2 that, in turn, inhibits proliferation. One important node for therapeutic success in the network is GSK-3β (Table S1). In our simulation, a change in the state of the node shows a significant change in the apoptotic response for H358 cells (Table S1B). In our simulation, the co-mutation KEAP1, which shows a loss of function in HCC44, is indicated to be an important regulator in the apoptotic and proliferation pathway by hindering therapeutic responses. In contrast, P53 and other common co-mutations have similar effects on simulations in both cell lines. After these adaptations of the starting ground state (Table S1A), the experimentally measured drug responses in vitro toward ARS-1620 regarding proliferation, apoptosis, and EMT in H358 and HCC44 toward ARS-1620 treatment could be correctly simulated with the software SQUAD (Table S1B, simulation output). Important nodes were graphically represented regarding strength (y-axis) over time (x-axis) (Figure 5B). 3.5. TGF-β1-Induced EMT Does Not Mediate Resistance toward Targeted Therapies The correlation between EMT and drug resistance is widely shown in the literature and in our previous experiments (Figure 4). Next, we wanted to investigate whether EMT is a marker or a marker of resistance. As TGF-β1 is a strong inductor of EMT, we investigated changes in the drug response of the more epithelial H358 and HCC827 cells upon stimulation with this growth factor. As described above, the stimulation of H358 and HCC827 cells with TGF-β1 resulted in a progressed EMT phenotype, pointed out by a lower expression of epithelial markers and a stronger expression of mesenchymal markers (Figure 2 and Figure 6A,C). Both H&E and immunofluorescence stainings indicated reduced cell numbers in H358 and HCC827 tumor models after treatment with either ARS-1620 or gefitinib, independent of TGF-β1 (Figure 6A,C). While treatment with TGF-β1 alone already resulted in reduced proliferation indices for both cell lines compared to the untreated controls, additional treatment of the H358 and HCC827 cells with ARS-1620 or gefitinib, respectively, led to an even stronger decline in the proliferation rate (Figure 6B,D). The increase in apoptosis in cultures treated with both TGF-β1 and ARS-1620 or gefitinib was comparable to that in cultures solely treated with the corresponding targeted therapy (Figure 6B,D). In summary, the tissue architecture, the reduced proliferation indices, as well as the increase in apoptosis provide evidence that EMT induction with TGF-β1 alone is not sufficient to mediate resistance of H358 and HCC827 cells toward either ARS-1620 or gefitinib. 3.6. EMT Status and CD44 Expression Are No Predictors of Drug Response in PDX Cell Lines To investigate the correlation between EMT and stemness on a larger scale, we used four different KRASG12C-mutated patient-derived xenograft (PDX) lung cancer cell lines (LXFA 983, LXFL 1072, LXFL 1674, LXFA 2184), which were kindly provided by our collaborator Oncotest (Charles River, Freiburg). Interestingly, no clear interdependency between EMT phenotype and sensitivity toward the KRASG12C inhibitor ARS-1620 was observed in 2D or 3D cell culture (Figure 7). The most epithelial cell line LXFA 983 exhibited the highest IC50 value toward ARS-1620 in 2D (IC50 = 14.6 µM) and did not display sensitivity toward the inhibitor in 3D (viability about 95%). However, LXFL 1072 cells differentiated into a more epithelial phenotype when cultured in 3D. Remarkably, they switched from a resistant state in 2D (IC50 = 7.6 µM) to drug response in 3D (viability 65%) comparable to LXFL 1647 cells (viability 70%), which are clearly the most sensitive PDX cells in 2D (IC50 = 1.1 µM) but displayed a high grade of EMT in 2D and 3D conditions (Figure 7B,C). In the four PDX cell lines, we observe a black and white pattern regarding E-cadherin and CD44 staining (Figure 7A), which underlines a connection between a high EMT status and stemness in LXFL 1674 and LXFA 2184, but this does not correlate with drug resistance (Figure 7C). 3.7. Combination Strategies to Overcome Resistance in HCC44 Tumor Models Due to these inconsistent results for EMT status regarding resistance, we tested how we could break higher resistance in KRASG12C-mutated HCC44 cells compared to H358 cells by targeting other possible resistance determinants. Homeostasis is the equilibrium of the cell. In a healthy cell, the differentiation pathways reliably help the cell to carry out its specific functions. In lung cancer, the system state that is actively preserved in the cancer cell is high proliferation, low differentiation, and low apoptosis. A combinatorial treatment allows rectifying this system state by combining drugs targeting specific pathways, for instance, a kinase inhibitor blocking proliferation with another drug stimulating apoptosis. Therefore, we performed several combination tests together with the KRASG12C inhibitor ARS-1620 suggested by either genome-scale CRISPR interference (CRISPRi) studies, which identified essential genes when KRAS as a tumor driver is inhibited [28] or by described resistance mechanisms as responses to KRASG12C inhibition [29,30]. These combination strategies included metformin as an AMP-activated protein kinase (AMPK) activator as well as receptor tyrosine kinase inhibitors (RTK-Is) such as erdafitinib and crizotinib targeting the fibroblast growth factor receptor (FGFR) or anaplastic lymphoma kinase (ALK), respectively. In addition, SHP099 inhibiting the Src homology region 2 domain-containing phosphatase 2 (SHP2) was assessed, and to further hinder feedback loop activation, one triple therapy with gefitinib, SHP099, and ARS-1620 was tested. The combined use of an SHP2 inhibitor and RTK-Is in KRAS mutant NSCLC cells was promising by suppressing stemness in vitro [31]. While we could find crizotinib as a promising drug candidate with a low IC50 value for HCC44 cells in 2D, we could not find any enhanced effect when this therapy was combined with ARS-1620 (Figure S4). In 3D, MTT assays revealed that none of the monotherapies and also none of the combinatorial treatments with ARS-1620 could reduce the viability of HCC44 cells to less than 75% of the untreated and that metformin even resulted in increased viability (Figure 8A). In former publications, drug efficacy in our 3D models better correlated with clinical results than that in 2D and animal experiments [15,17]. Here, we again observe a difference in drug response between 2D and 3D cultures, demonstrating the severe impact of culture conditions. As we assume that our 3D model delivers improved preclinical predictions, we used 3D results of non-effective treatments to optimize our existing in silico network topology [15], which in the first place predicted effective drug responses in in silico simulations similar to 2D culture testing. Methodic details of how we achieved this optimization can be found in the Supplementary material (Table S2). Looking for further determinants of resistance, we came across the oncogenic cooperation between KRAS and c-MYC, driving invasion in KRASG12D mouse models [32]. This interaction potentially had an impact on our 3D test system’s sensitivity toward ARS-1620. Databases indicate that there is a higher expression of c-MYC in HCC44 cells than in H358 cells [33,34], which we could confirm on protein level in our 3D tumor model (Figures S5 and S8). After treatment with inhibitors of KRASG12D, AURKA mediates its reactivation [30]. Treatment with an AURKA inhibitor also leads to the degradation of c-MYC [35]. Hence, we tested the AURKA inhibitor alisertib as a promising candidate for sensitizing the HCC44 cells to treatment with ARS-1620. In 2D culture, the IC50 value of alisertib lay in the sub-micromolar range, with and without ARS-1620. Treatment of human primary fibroblasts from healthy tissue with alisertib and ARS-1620, even at high concentrations, merely resulted in minor growth inhibition, indicating cytocompatibility (Figure S4). In our 3D tumor model, the combination of ARS-1620 with alisertib proved to be most effective in reducing viability (Figure 8A). This finding was underpinned by a significantly reduced cell number and proliferation index (Figure 8B,C). In line with these results, the optimized in silico simulation predicted an enhanced response of the HCC44 cell line to the aforementioned combination therapy, thereby independently supporting the findings of the in vitro experiments. The in silico adjustments according to the experimental results for individually targeted therapies with crizotinib, SHP099, gefitinib, and alisertib at the start for the ground state are given in Table S2A. Simulation results for readout parameters (in particular apoptosis and proliferation) are given in Table S2B. The software could calculate the results for any combination therapy of choice. We see that some of the nodes change in comparison to the monotherapy within the ground state (blue color) (Figure 8D). Combination therapy simulations of SHP099 and gefitinib can be found in the Supplements (Figure S6). Furthermore, the correlation between the sensitizing effect of alisertib and c-MYC expression was supported by the treatment responses of the four PDX cell lines harboring the KRASG12C mutation. The cell lines LXFL 1072 and LXFL 1674 with the highest c-MYC expression displayed the most pronounced reduction in viability. Vice versa, the LXFA 983 cell line with the lowest c-MYC expression was the most therapy-resistant (Figures S7 and S8). 4. Discussion In this study, we investigated EMT, stemness, invasion, and drug resistance in several lung tumor cell lines and PDX-derived cell lines grown on a tissue matrix with the support of an in silico model. Several conclusions could be drawn: (i) EMT is more a marker than a maker of resistance, (ii) tissue context has an impact on EMT status, gene expression, and drug response, (iii) intrinsic factors are more important to invasion than EMT as confirmed by clinical specimens, and (iv) EMT correlates with stemness. Our results suggest that EMT is overestimated as a determinant of invasion and resistance, at least in lung cancer. We integrated frequent co-mutations of KRAS into cell line-specific in silico models to unravel complex interdependencies and define patient subgroups for clinical studies. As proof of concept, we demonstrated concordance between in vitro testing and in silico simulation of combinatorial treatments of the KRAS-mutated HCC44 cell line, further displaying high c-MYC expression and harboring LKB1, P53, and KEAP1 co-mutations. Hence, this work mimics individual patient tumor signatures for later clinical application. 4.1. D Tissue Models for More Realistic Preclinical Testing Models better representing tumor biological aspects are demanded by clinicians [36]. Different 3D tumor models are currently used in the field of cancer research, including spheroids and organoids. However, we see specific advantages of our biological scaffold SISmuc regarding this article’s issues as it allows for cell-ECM adhesion and the study of EMT processes and invasion due to apical-basal polarity of SISmuc-based models and a preserved basement membrane. We succeeded in generating different surrogate models for a variety of tumor entities, including breast, colorectal, and lung cancer [14,37,38]. Almost all commercially available cell lines tested so far are attached to the biological scaffold, and stable tumor models could be subsequently generated within 14 days. SISmuc tumor models are a versatile tool, also permitting co-cultures of tumor cells with fibroblasts or immune cells [13,39]. By successfully serving as a substitute for animal experiments in the testing of CAR T-cells, the SISmuc demonstrated its close correlation with in vivo conditions [39,40]. Still, the decellularized jejunum is not organotypic for the mentioned tumors, and ECM components highly differ between different organs (reviewed in the work of [41]), which is why characteristics of cancer cells might be differentially influenced. Additionally, the decellularization of the tissue is a time-consuming and labor-intensive process. Previous work has shown the importance of cues from the ECM and especially basement membrane proteins for the cellular phenotype of both normal and cancer cells [42,43,44,45]. Therefore, we investigated the expression of clinically established lung carcinoma markers in our lung tumor models [46]. The HCC827 cell line shows homogeneous staining for the lung adenocarcinoma markers TTF1 and CK7 in both 2D and 3D cultures, reflecting the maintenance of the adenocarcinomatous phenotype. Similarly, the A549 cell line’s expression pattern of the two markers was widely stable across culture conditions. However, these cells are negative for TTF1, and staining for CK7 is markedly weaker, especially in the 3D culture. Intriguingly, both cell lines exhibit the squamous cell carcinoma marker P63 in the 3D culture while they do not in the 2D culture. Reasons for P63 expression in tumors derived from P63-negative tissues might be a redifferentiation toward a squamous phenotype or the acquisition of stem cell properties [47,48]. We could previously show a higher expression of stem cell markers in cells cultured on the matrix SISmuc in colorectal cancer models [13,47]. In addition to the immunohistochemical markers relevant to clinical subtyping, we investigated the expression of SPP1 (also known as osteopontin). In various cancer types, including lung, breast, colon, and prostate cancer, overexpression of SPP1 is associated with tumor invasion, metastasis, and poor clinical outcome [49,50,51,52]. For NSCLC, SPP1 has been proposed as a biomarker used for diagnosis and surveillance [53,54]. Both cell lines, A549 and HCC827, showed largely homogeneous staining for SPP1 in 2D and 3D cultures, comparable to that of lung adenocarcinoma specimens. These preliminary data suggest SPP1 signaling in our model, which might promote its invasive and metastatic capacity [55,56,57], supporting its eligibility for our studies. In a more large-scale analysis, PCR array results showed the upregulation of several genes related to cancer progression, indicating that 3D culture better reflects the conditions found in in vivo carcinomas. CEACAM6, being important for cell adhesion during cancer progression [58], was detected to be upregulated in 3D culture, stressing the importance of growth conditions tumor cells are exposed to. 4.2. EMT Correlation to Drug Resistance and Invasion EMT and MET are fundamental processes during embryonal development and are discussed as key players in cancer [59,60,61,62]. However, it became evident that cancer cells do not necessarily pass through an EMT to spread to distant organs, and this process is highly context-dependent [63,64]. In line with this, we observe in our study a far higher invasion by one specific cell line (HCC44) than in other cell lines with a similar EMT status induced by TGF-β1, suggesting other intrinsic factors to be more important to driving invasion. Consistency of these findings with the clinic is shown by the fact that in samples of 10 lung adenocarcinoma specimens derived from the tumor center, however, all invasive tumor cells strongly expressed PCK but not vimentin. Next to PCK and vimentin, we investigated E-cadherin in our 3D models as a further marker of epithelial differentiation. This adherence junction protein is connected to β-catenin, which is observed to translocate to the nucleus for EMT induction at the invasive front of CRC [65]. The investigation of β-catenin localization is part of the clinical routine in CRC but not in NSCLC. A correlation or even a dependency of resistance on EMT is claimed in several publications [1,66,67]. Indeed, we could find a higher EMT status in secondary resistant cells (HCCres cells) after permanent gefitinib treatment, in addition to higher resistance in the more mesenchymal of two KRAS mutant cell lines (H358, HCC44). While TGF-β emerged as a central player in cancer drug resistance about 10 years ago [2], we could conversely not observe resistance in HCC827 and H358 cells after EMT induction by TGF-β. Interfering with TGF-β signaling has been a great hope in cancer therapy for a decade with limited success [68,69]. Finally, in PDX cell lines with four different degrees of EMT status, the most epithelial one surprisingly exhibited the highest resistance together with the most mesenchymal cell line. Taken together, these observations suggest EMT is neither necessary nor sufficient for drug resistance development. Notably, reversion of EMT must be handled carefully in the clinic as MET could promote metastasis formation at distal sites [4]. EMT is also discussed to be correlated with stemness [1,70]. There is some evidence of CD44 being a cancer stem cell marker [71]. CD44 is a significant and clinically relevant prognostic marker in NSCLC patients [72]. In combined in vitro and mathematical feedback-loop studies, CD44 was also confirmed as an important factor in maintaining an EMT hybrid status [73]. In line with this, we observed in all experiments a co-regulation of EMT and CD44 but no predictive value for drug resistance. The two most sensitive PDX cell lines were in between the most epithelial and mesenchymal cells with their EMT phenotype. Additionally, while one of these two cell lines expressed CD44, the other did not. We further observed the repression of transmembrane glycoprotein Mucin-1 in all cells displaying a progressed EMT phenotype in the 3D tumor model. 4.3. An In Vivo/In Silico Platform for Testing Targeted Therapies In order to generate a platform suitable for biomarker-signature-based testing, we connected our 3D tissue tumor model with an in silico model [13,15,17]. To adapt the model for specific tumor cell lines, common co-mutations in the large subgroup of KRAS-mutated NSCLC were also integrated, which correspond to patients’ tumor mutations observed in the clinic [10]. For optimization of the network, we used experimentally determined parameters of the two cell lines H358 and HCC44 as a responder and non-responder to the KRAS inhibitor ARS-1620, respectively. To find hints that higher resistance in HCC44 cells is not only based on multi-drug resistance (MDR) proteins, we checked the expression of MDR and ABC transporters in an available online database [74,75] and did not find any expression. The in silico model integrates “EMT” as a systemic output similar to “apoptosis” and “proliferation”. Signaling, which leads to EMT, is interconnected to several cascades. By this, changes in EMT levels can be observed, but the model does not support EMT as a marker or maker for drug resistance and serves primarily as an effective screening tool for combinatorial drugs. EMT is discussed to be a marker of KRAS independency [76,77]. In the case of KRAS independence, co-mutation signatures gain importance for combinatorial treatment. To integrate EMT in simulations correctly, the cell line HCC44, as a non-responder to the KRAS inhibitor, had to show a higher EMT than the responder cell line H358, as shown in Figure 5. Combinatorial treatments were then tested in HCC44 with high EMT status. The combination of RTK inhibitors together with KRAS inhibition is assumed to have combinatorial or even synergistic drug effects in the corresponding cancer cells. (Reviewed in [78]). There is further evidence that the inhibition of SHP2 can inhibit potentially negative feedback loops in KRAS signaling in a more general way and thereby increases the efficacy of KRAS inhibition in different KRASG12C-mutated cell lines [79]. Here, we could always observe a slightly increased effect when RTK inhibitors were combined with ARS-1620 on the 3D tumor models. Still, the effect of these combinations was marginal and not sufficient to overcome the primary higher resistance of HCC44 cells. Next, we met the challenge to simulate in silico the higher resistance of HCC44 to combinatorial treatments observed in 3D culture. Based on the fitting of HCC44 cells under treatment with ARS-1620 (Table S1), control simulations with three combinatorial targeted treatments (SHP2-inhibitor, crizotinib, gefitinib) together with the KRAS inhibitor were performed to optimize network connections in the signaling topology until in silico only marginal effects could be observed as it can be seen in the clinic. For focused optimization, we used the obvious difference between 2D and 3D culture: in 3D culture the proliferation and thereby also the metabolism is lower compared to 2D conditions. Since EMT is also regulated in the context of metabolism, we linked the mitosis regulator AURKA to EMT, c-MYC [80,81], and LKB1 [82] in the in silico topology. This enables us to simulate also the non-effective AMPK activation by metformin. To identify an effective combinatorial therapy, we looked for other possible vulnerabilities in HCC44. As HCC44 cells show a P53 mutation and a high c-MYC expression [27] which is also maintained under 3D conditions (Figure S5), we tested the AURKA inhibitor alisertib, which is reported to induce c-MYC degradation in P53 mutant cancer [83]. This inhibitor has been tested in clinical trials for multiple cancers and is the only one that reached phase III (Reviewed in [80,82,84]). The relevance of AURKA in lung cancer patients is stressed by the correlation of poor outcomes with AURKA expression, which could also be correlated to resistance toward the KRASG12C inhibitor in vitro [85]. Here, we could demonstrate that this inhibitor was more effective than the KRAS inhibitor alone (90% viability), whether used alone (75% viability) or together with the KRAS inhibitor ARS-1620 (60% viability). The benefit of this combinatorial treatment in NSCLC showing KRAS mutation and high c-MYC expression was supported by the differential efficacy in four PDX cell lines decreasing the viability to up to 50%. The two most sensitive PDX cell lines display both a KEAP-1 co-mutation but only one single cell line, a P53 mutation suggesting the KEAP-1 mutation to be more relevant for drug efficacy in this context. Furthermore, they display a completely different EMT status, which argues against EMT being a conclusive marker of KRAS independency. We are aware that this needs further investigation. The simulations of the combination therapy with ARS-1620 and alisertib could be predicted based on the network and were made independently from the experimentally determined results (Figure 8). Interestingly, KEAP-1 is one important ground state parameter of higher resistance in HCC44 cells compared to H358 cells (Table S1). Thereby, our platform is now available for preclinical application. In general, in silico modeling allows us to have fewer experiments by pre-testing various drugs and their combinations. We can achieve cell line- and thereby biomarker-specific in silico models. For clinical transfer, we would simply match the mutation profile from tumor biomarker analysis with optimal therapy strategies explored before in our in vitro/in silico platforms using insights from network modeling [86]. Our combined in silico/in vitro results are proof of concept that a systematic understanding of the tumor cell-specific network and its different pathway dependencies on the biomarker signature are the key to a rational combinatorial targeted treatment. In contrast, individual markers such as EMT do not correlate sufficiently well with the cellular systems response and treatment success. Though we focus our analysis on the treatment of lung cancer and, in particular, on NSCLC, our in vitro/in silico platform can be easily adapted to other cancer entities. We currently investigate this in breast cancer [13] and colon cancer [14]. The pathway-centric view of our platform is also very useful to reveal proliferative and apoptotic pathways and immune-modulator and immune-suppressive interactions between tumor and immune cells, along with implied cancer engines. A cooperative effect of KRAS mutation and the expression of c-MYC for invasion, as also observed in HCC44 cells in our model, and for immune modulation has been shown previously in animal studies for lung and pancreatic cancer [32,87]. We are aware that large-scale phospho-proteomic and expression data would add important value to our model. This and the comparison with patient data will be one focus of further studies. As a starting point, we also analyzed established clinical markers in our models and vice versa, EMT status and cell invasion in clinical specimens. Furthermore, we integrated common co-mutations of KRAS found in NSCLC patient cohorts into the in silico network topology. 5. Conclusions In this study, we investigated different functions of EMT related to the microenvironment of tumor cells, integrating parts from the biological context of carcinomas in a tissue tumor model for lung cancer. Of note, we think that the tumor biological dogma of EMT being the central factor for invasion and drug resistance should be put into perspective. This is supported by clinical observations, especially for lung cancer. For the reduction in the still high attrition rates of preclinical models, a paradigm shift is mandatory to models that could reflect characteristics of the tissue context of tumor cells. For the large and challenging subgroup of KRAS-mutated NSCLC patients, an in silico network was used for a systematic analysis of combinatorial effects, also considering common co-mutations found in the clinic. The pathways and effective targeting options and combinations revealed by our in vitro/in silico platform help to pave the road for patient stratification. This will involve clinical studies according to sequencing data in KRAS-mutated NSCLC patients. This strategy has not to be restricted to targeted therapies but should also involve strategies to overcome immune-suppressive hurdles in the tumor microenvironment. Acknowledgments We would like to thank Heide Häfner for technical assistance in the lab, Georg Krohne, Claudia Gehrig, and Daniela Bunsen for their support in the ultrastructural analysis, and Esther Meyer for identifying and guiding the study participants. Supplementary Materials The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cancers14092176/s1, Figure S1: Differences between 2D and 3D cultures; Figure S2: Differences between 2D and 3D culture: Drug testing of EGFR or KRASG12C mutated cells; Figure S3: Immunofluorescence staining of lung adenocarcinomas; Figure S4: Combination of ARS-1620 with crizotinib or alisertib in 2D; Figure S5: Expression of c-MYC in H358 and HCC44 cells in 3D; Figure S6: In silico combination therapy simulations of the HCC44 cell line; Figure S7: Combination of alisertib and ARS-1620 in PDX cell lines with differential c-MYC expression; Figure S8: Original, uncropped images of c-MYC Western Blots with loading controls; Table S1: Simulation parameters at the start (ground state) of the simulation of the KRASG12C Biomarker Model, untreated (gray) and with ARS1620 treatment; Table S2: Simulation parameters at the start of the simulation (ground state) for indicated individual combination targeted therapy of the HCC44 KRASG12C Biomarker Model with ARS-1620. Separate supplementary file: Gene_Table.xlsx (qPCR array plate information). Click here for additional data file. Author Contributions Conceptualization, G.D. and T.D.; methodology, M.P., C.G., S.N., S.C. and N.H.; software, T.D. and S.C.; validation, T.L., F.S., J.G.N.P., C.M., A.S., C.K. and E.W.; formal analysis, M.P., S.N., C.G. and S.C.; investigation, S.R.-H. and A.R.; resources, T.W., D.C. and J.S.; data curation, M.P., C.G., S.N. and T.D.; writing/original draft preparation, G.D. and T.D.; writing/review and editing, M.P., N.H., G.D. and T.D.; visualization, M.P., C.G., S.N. and S.C.; supervision, S.N., G.D. and T.D.; project administration, G.D. and T.D.; funding acquisition, G.D., T.D. and J.S. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki. Human material: Human adenocarcinoma biopsies were provided by the Department of Thoracic Surgery of the Magdeburg University Hospital and approved by the local Ethics Committee (vote 163/17, date of approval: 16 October 2017), by the University Hospital of Würzburg, approved by the Ethics Committee of the Julius Maximilian University of Würzburg (protocol code 215/12, date of approval: 20 May 2014) and by the Thoracic Surgery of the Hospital Würzburg Mitte gGmbH—location Missio—approved by the Ethics Committee of the University of Würzburg (protocol code 99/20-am, date of approval: 3 August 2020). Primary fibroblasts were provided as well by the Thoracic Surgery of the Hospital Würzburg Mitte gGmbH—location Missio and approved by the Ethics Committee of the University of Würzburg (protocol code 99/20-am, date of approval: 3 August 2020). Evaluation of EMT marker and invasion was performed by the pathology department at the University of Würzburg. Animals: The collagen matrix SISmuc (Small Intestinal Submucosa and mucosa) was prepared from porcine jejunal segments of young pigs (German landrace, local supplier). All explantations are in compliance with the German Animal Protection Laws (§4 Abs. 3), and all animals received humane care in compliance with the guidelines by the FELASA, WHO, and FDA (WHO-TRS978 Annex3 und FDA-OCTGT Preclinical Guidance) after approval from our institutional animal protection board (registration reference number #2532-2-12, Ethics Committee of the District of Unterfranken, Würzburg, Germany). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement All data for this publication are included in the manuscript and its supplements. The software used (SQUAD and CellDesigner) are publicly available as indicated in the manuscript, and the model files used (H358, HCC44) are given as supplements. Conflicts of Interest The authors declare no conflict of interest. Figure 1 A preclinical tissue tumor model reflecting EMT, invasion, clinical and biological markers. (A) 3D tumor model based on porcine jejunum. (B) Dynamic cell culture in a flow bioreactor. (C) HCC827 lung cancer cells on SISmuc (Pan-cytokeratin, green) and (D) HCC827 cells on SISmuc + TGF-β1 in a flow bioreactor, arrows: vimentin-positive cells (red) invading across the basement membrane the collagen matrix. DAB staining of TTF1, CK7, and SPP1 on paraffin sections: adenocarcinoma of the lung (E–G), HCC827 cells on SISmuc under static culture conditions (H–J), inserts: HCC827 in 2D, A549 cells on SISmuc under static culture conditions (K–M), inserts: A549 in 2D. Scale bar in (D,G) = 100 µm for (C–M); scale bar in m = 100 µm for (h–m). Figure 2 EMT correlates with stemness, is inducible by TGF-β1, and is inversely correlated with Mucin-1 expression, independent of its location. Three different 3D tumor models (HCC827, H358, HCC44) with and without TGF-β1 (2 ng/mL) treatment are stained for different markers of EMT and stemness: pan-cytokeratin (PCK, green), E-cadherin (E-CAD, light blue), Mucin-1 (MUC-1, yellow), vimentin (VIM, red), and CD44 (purple). Cell nuclei are counterstained with DAPI (blue). Scale bar = 50 µm for all images. Figure 3 EMT correlates to some extent with invasion, but the intrinsic invasion of HCC44 exceeds TGF-β1-induced invasion in H358 and HCC827. (A) Collagen IV (red) immunofluorescence staining with DAPI (blue) counterstaining of HCC44 tumor models or H358 and HCC827 3D models treated with 2 ng/mL TGF-β1. Invasive cells are indicated with white arrowheads. HCC827 gefitinib-resistant subclones A2 and A3 display a similar degree of invasion as parental HCC827 stimulated with TGF-β1. Scale bar = 100 µm (upper panel); 50 µm (lower panel); n = 4. (B) Quantitative evaluation of invasive cells; n = 4. Significance determined with unpaired t-tests versus H358 or HCC827, respectively. *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001. Figure 4 EMT, dedifferentiation, and stemness correlate with primary as well as secondary resistance. (A) Proliferation indices and (B) normalized cell numbers of H358 and HCC44 cells on 3D SISmuc tumor models with or without 1 µM ARS-1620 treatment; n = 4. Fold increase in apoptosis over untreated control evaluated by M30 ELISAs of (C) H358 and (D) HCC44 cells in 3D after the treatment with indicated concentrations of ARS-1620. Red line indicates the baseline apoptosis of the DMSO control. Triangles (▼) represent values from single biological replicates; n = 2. (E) Fold increase in apoptosis over untreated control (red line) and (F) proliferation indices in HCC827, HCCresA2 and HCCresA3 cells in 2D and 3D after treatment with 1 µM gefitinib; 4 ≤ n ≤ 13. (G) Immunohistochemistry staining of CD44 and immunofluorescence staining of EMT markers pan-cytokeratin (PCK, green), vimentin (VIM, red), E-cadherin (E-CAD, green), β-catenin (β-CAT, red), and Mucin-1 (MUC-1, green) of HCC827, HCCresA2, and HCCresA3; scale bar = 100 µm. Reflection electron microscopy (REM): white arrows indicate the elongated shape of resistant cells; scale bar = 50 µm. Significance determined with unpaired t-tests. **: p ≤ 0.01, ***: p ≤ 0.001. Figure 5 In silico signaling network topology used for H358 and HCC44 therapy simulations. (A) The network of protein–protein interactions (rounded rectangles) and cellular responses (hexagons) integrates common co-mutations of KRAS. Interactions: arrows activating interactions, blunted arrows inhibitory interactions; white rectangles: interacting nodes, gray rectangles: assumed to be constant, yellow rectangles: constant nodes tested experimentally, orange rectangles: KRASmt node. (All coding also applies to the following simulations) (B) SQUAD calculates the activity changes and responses for every node in the network in detail. H358 and HCC44 treated with ARS-1620, with H358 being a responder to KRAS inhibition seen by induction of apoptosis and slight reduction in proliferation, which is not the case in simulations for HCC44 cells. (a) Untreated H358, (b) H358 treated with ARS-1620, (c) untreated HCC44, and (d) HCC44 treated with ARS-1620. Only the readout of interesting nodes of our network is shown. However, all nodes of the network as given in (A) are simulated and available in their trajectories so that novel drugs, as well as the detailed response of the whole network, can be studied. Figure 6 EMT is more a marker than a maker of resistance. (A,C) H&E and immunofluorescence stainings of pan-cytokeratin (green) and vimentin (red) of 3D tumor models with H358 (A) and HCC827 cells (C) treated with 2 ng/mL TGF-β1 and 1 µM ARS-1620 or 1 µM gefitinib, respectively; n = 4. Scale bars = 100 µm. Proliferation indices and fold increase in apoptosis over untreated control of H358 (B) and HCC827 (D) cells in 3D after the treatment with 2 ng/mL TGF-β1 and 1 µM ARS-1620 (H358) or 1 µM gefitinib (HCC827). Red line indicates the baseline apoptosis of the corresponding controls. Significance determined with unpaired t-tests. *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001; 4 ≤ n ≤ 6. Figure 7 Neither EMT status nor CD44 expression correlates in PDX cell lines with drug response in 2D and 3D. (A) Pan-cytokeratin (PCK, green), vimentin (VIM, red), E-cadherin (light blue), and CD44 (purple) immunofluorescence staining of KRASG12C-mutated PDX-derived lung cancer cells in 2D (n = 2) and 3D (n = 2). Scale bar = 100 µm. (B) CellTiter-Glo viability assay of PDX-derived lung cancer cells after treatment with increasing concentrations of ARS-1620. Calculated IC50 values for 2D cultures are indicated. The picture shows the IC50 curve of one representative experiment of two independent assays; n = 2. (C) MTT-assay of 3D SISmuc tumor models seeded with the PDX-derived cell lines and treated with 1 µM ARS-1620 for 72 h. Significance determined with unpaired t-tests. *: p ≤ 0.05, ***: p ≤ 0.001; n = 4. Figure 8 Combination of ARS1620 with AURKA inhibitor alisertib as the most effective combination in 3D HCC44 tumor models. (A) MTT assays of HCC44 tumor models treated with 1 mM metformin or 5 µM SHP099, erdafitinib, gefitinib, crizotinib, or alisertib. Drugs were tested either in monotherapies or in combination with 1 µM ARS-1620. Triangles (▼) represent values from single biological replicates; n ≥ 2. (B) Relative cell numbers and (C) proliferation indices of HCC44 cells in 3D after the treatment for 72 h with 1 µM ARS-1620, 5 µM alisertib and the combination of both inhibitors; n = 4. (D) In silico combination therapy simulations of (a) HCC44 treated with crizotinib and ARS-1620 and (b) HCC44 treated with alisertib and ARS-1620. Color code for different readout parameters is given on the right side of the figure. Significance determined with unpaired t-tests. *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Zhang Y. Weinberg R.A. Epithelial-to-mesenchymal transition in cancer: Complexity and opportunities Front. Med. 2018 12 361 373 10.1007/s11684-018-0656-6 30043221 2. Brunen D. Willems S.M. Kellner U. Midgley R. Simon I. Bernards R. Tgf-beta: An emerging player in drug resistance Cell Cycle 2013 12 2960 2968 10.4161/cc.26034 23974105 3. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092097 cancers-14-02097 Review Improving Patients’ Life Quality after Radiotherapy Treatment by Predicting Late Toxicities Lapierre Ariane 12 https://orcid.org/0000-0003-2205-9196 Bourillon Laura 1 Larroque Marion 1 Gouveia Tiphany 1 Bourgier Céline 1 Ozsahin Mahmut 3 Pèlegrin André 1 Azria David 1 https://orcid.org/0000-0003-4205-7200 Brengues Muriel 1* Strojan Primož Academic Editor 1 IRCM, INSERM, University Montpellier, ICM, 34298 Montpellier, France; ariane.lapierre@chu-lyon.fr (A.L.); laura.bourillon@icm.unicancer.fr (L.B.); marion.larroque@icm.unicancer.fr (M.L.); tiphany.gouveia@inserm.fr (T.G.); celine.bourgier@icm.unicancer.fr (C.B.); andre.pelegrin@inserm.fr (A.P.); david.azria@icm.unicancer.fr (D.A.) 2 Department of Radiotherapy-Oncology, Lyon-Sud Hospital Center, 69310 Pierre-Bénite, France 3 CHU Vaudois, 1011 Lausanne, Switzerland; mahmut.ozsahin@chuv.ch * Correspondence: muriel.brengues@icm.unicancer.fr; Tel.: +33-411-283-182 22 4 2022 5 2022 14 9 209724 2 2022 16 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary Over 50% of patients with cancer will receive radiotherapy treatment. Five to ten percent of patients who received radiotherapy will develop side effects. Identifying these patients before treatment start would allow for treatment modification to minimize these effects and improve the life quality of these patients. Our team developed a test, which allows predicting these secondary effects before starting the treatment. This will help in proposing personalized treatments to improve the outcome. This review presents how this test is performed, its results, as well as its modification in order to be used in hospitals. Abstract Personalized treatment and precision medicine have become the new standard of care in oncology and radiotherapy. Because treatment outcomes have considerably improved over the last few years, permanent side-effects are becoming an increasingly significant issue for cancer survivors. Five to ten percent of patients will develop severe late toxicity after radiotherapy. Identifying these patients before treatment start would allow for treatment adaptation to minimize definitive side effects that could impair their long-term quality of life. Over the last decades, several tests and biomarkers have been developed to identify these patients. However, out of these, only the Radiation-Induced Lymphocyte Apoptosis (RILA) assay has been prospectively validated in multi-center cohorts. This test, based on a simple blood draught, has been shown to be correlated with late radiation-induced toxicity in breast, prostate, cervical and head and neck cancer. It could therefore greatly improve decision making in precision radiation oncology. This literature review summarizes the development and bases of this assay, as well as its clinical results and compares its results to the other available assays. biomarkers radiotherapy late toxicities prediction personalized treatment SIRIC of MontpellierINCa-DGOS-12553 This research was funded by the SIRIC of Montpellier: Grant “INCa-DGOS-12553”. ==== Body pmc1. Introduction Radiotherapy is one of the leading treatment modalities in oncology. Over 50% of patients will receive radiotherapy at some point during their treatment course [1]. Although it is a locoregional treatment, patients can exhibit toxicities in the treatment field or in the surrounding tissues. These toxicities can be defined either as early (occurring during or in the 3 months after treatment completion) or late (occurring more than 3 months after treatment completion). Depending on the prognosis and tumor type, the prescription dose and constraints to organs-at-risk are usually chosen in order to keep the risk of developing grade 3 or higher side effects below 5% [2,3]. However, even when keeping these constraints, 5 to 10% of patients will develop sever toxicities after radiotherapy. In breast cancer, severe toxicities can present as breast or lung fibrosis. In cerebral radiation therapy, cerebral radiation necrosis is the most frequent occurrence. In pelvic and abdominal radiotherapy, severe toxicities can be radiation enteritis and vesical or rectal bleeding. Patients displaying severe toxicities can be considered intrinsically radiosensitive [4]. The first clinical observation of individual radiosensitivity was described by Holthusen in 1936 [5], whereas the first in vitro display of individual radiosensitivity was shown on fibroblasts of ataxia telangiectasia patients in 1975 [6]. Early toxicities can usually be managed using symptomatic treatments and will most of the time resolve after treatment completion. On the other hand, late toxicities can be definitive, and severely affect quality of life, sometimes requiring extensive treatments such as surgery to alleviate the symptoms. Based on these observations, it appears crucial to identify the patients at risk of developing severe late toxicities early on, because severe toxicities in a minority of patients limit the dose for the majority of patients [7]. Furthermore, these patients need to be identified before treatment starts, because acute toxicities may not always predict late toxicities [8]. The first large scale clinical search of individual factors of radiosensitivity was performed in the 1970s by Turesson et al. [9]. However, in this study, clinical factors and early toxicities only explained 30% of late toxicities, leaving 70% unexplained. Although influenced by many exogenous factors (such as smoking habits, age or ongoing treatments), it seems rather unlikely that individual radiosensitivity should be caused by only one intrinsic factor. It seems reasonable to assume that clinical radiosensitivity should be regarded as a complex trait depending on the combined effect of several different genetic alterations [10]. Should these genetic traits be successfully found, early identification of patients at risk of severe late toxicities could allow physicians to suggest a more appropriate treatment course (such as radical mastectomy instead of conservative breast surgery) in cases where the risk of toxicity outweighs the benefits of the radiation treatment [11,12]. In the near future, this could lead to tailored treatment based on the risk profile of each patient, adapting treatment dose or technique to each individual situation. More recently, in the 2000s, several genetic profile studies have come up with gene expression models linked to tumor radiosensitivity in vitro [13,14]. When looking at healthy tissue toxicities, genomic signatures, single nucleotide polymorphisms (SNPs) variability, or apoptosis or cell cycle regulating gene expression changes after irradiation appear to have better potential at classifying patients [15,16]. Even though it has been widely discussed for over 20 years [17], the American Society for Radiation Oncology (ASTRO), the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI) have recently established guidelines on precision medicine in radiotherapy, mainly for breast, prostate, lungs and head and neck cancers [15]. Their conclusion is that genomically guided radiation therapy is a necessity that must be embraced in the coming years, to improve outcomes for numerous cancer patients. However, routine genomic signature and clinical tests still need to be brought into routine standard of care. 2. Development of the Radiation-Induced Lymphocyte Apoptosis (RILA) Assay The first correlation between in vitro assays and clinical findings was performed using skin fibroblasts of ataxia telangiectasia patients [6]. In this study, the authors observed a difference in in vitro response to radiotherapy of primary fibroblast cultures between ATM-mutated patients and healthy controls, showing that in vitro observation could translate to the clinic. Further studies, based on colony-forming assays or surviving fraction at 2 Gray (Gy) (SF2) showed a strong relation between fibroblast sensitivity in vitro and normal-tissue reactions, both acute effects and late fibrosis [18,19]. However, although these results were promising, both studies were performed on small groups of patients (respectively, 6 and 12 patients), and further validation on larger cohorts was needed to confirm these observations. Unfortunately, when performed on a larger group (79 patients), no significant correlation between fibroblast radiosensitivity and fibrosis could be found, because of significant inter-patient variation for SF2 values (over 40%) [20]. Other fibroblast-based assays such as comet assays or micronuclei formation were investigated [21,22]. However, in both cases, despite promising results in small study groups, no significant correlation was found between these in vitro tests and patient radiosensitivity in larger cohorts [23]. Based on these observations, and given the fact that fibroblast radiosensitivity assays have a long completion time (over one month), a simpler and more reliable in vitro assay was needed. Since fibroblasts assays were rather time-consuming, researchers turned to easily available cells: peripheral blood mononucleated cells (PBMCs). Out of these PBMCs, lymphocytes were soon selected as a study model because of their higher radiosensitivity compared to other cell types [24]. The first studies investigating peripheral lymphocytes irradiation gave inconsistent results [25,26,27]. Although comparing cell survival after irradiation, irradiation was performed at low-dose rate in all three studies. Lacking a clear standard for their tests, inter-patient and intra-patient variations were very high and no correlation to the clinic could be found because of the lack of reproducibility. High dose-rate irradiation for in vitro studies started to develop in the 1990s. At first, the assays used were the same as the fibroblast-based assays: colony formation, SF2, comet and micronuclei assays. Once again using ataxia telangiectasia patients, West et al. showed that peripheral blood lymphocytes from patients who suffered severe reactions to radiotherapy were more radiosensitive than those from normal donors [28]. However, micronuclei assay data showed large discrepancies between studies and no clear conclusion could be made [29,30,31]. The same goes for comet assays: although the test could identify patients with defective in vitro DNA repair mechanisms, no correlation could be made between these findings and radiation-induced toxicities in patients [32]. However, analysis of lymphocyte apoptosis after irradiation showed a different response to radiotherapy in patients with genetic disorders such as ataxia telangiectasia of neurofibromatosis when compared to healthy counterparts [33]. Apoptosis may not be the predominant death type after radiotherapy in most cancer cell lines; however, it is much more frequent in hematopoietic cell lines such as peripheral lymphocytes [34]. This particular cell death mechanism occurs rapidly after irradiation (6 to 72 h) and can be easily detected by flow cytometry [35]. Therefore, in the 1990s, Ozsahin et al. developed a rapid assay to detect peripheral lymphocyte apoptosis after irradiation [36]. This assay was based on the analysis of apoptosis of both CD4 and CD8 T-lymphocytes 48 h after 8 Gy irradiation using flow cytometry. The result was given as a percentage of apoptosis at 8 Gy, subtracting the apoptosis at 0 Gy (non-irradiated samples) as a control (Figure 1). CD4 and CD8 T-lymphocytes apoptosis was correlated in all adult donors, and inter-donor variations were higher than intra-donor variations, displaying a good reproducibility of this assay. This was later named the radiation-induced lymphocyte apoptosis (RILA) assay. Blood samples were collected from donors in Heparin tubes, diluted in RPMI medium (1:10) and then cultured in 6-wells plate at 37 °C, 5% CO2 for 24 h prior to ex-vivo irradiation (0 or 8 Gy). Irradiated whole blood was cultured for 48 h, red blood cells were lysed and the remaining cells were labeled with FITC-conjugated anti-CD8 monoclonal antibodies to select CD8 + T-lymphocytes that were then stained with propidium iodide (PI). Cells were analyzed by flow cytometry to determine the percentage of apoptotic cells. 3. Clinical Data The first prospective study using this RILA assay followed 399 patients with miscellaneous cancers (mostly breast, head and neck, genitourinary and gastrointestinal) treated with radiotherapy with curative intent [37]. The CD4 and CD8 RILA assays were performed before radiotherapy, and patients were assessed for both acute and late toxicity. With a median follow-up of 30 months, T-lymphocyte radiation-induced apoptosis did not correlate with either early toxicity or survival. However, more radiation-induced T-lymphocyte apoptosis was significantly associated with less grade 2 and 3 late toxicity (p < 0.0001). CD8-RILA was more sensitive and specific than CD4-RILA, and thus from this point on, most studies used CD8 T-lymphocytes apoptosis for the RILA assay. This was confirmed in a phase II multicenter prospective study: the CO-HO-RT trial [38]. A total of 150 breast cancer patients were tested with the RILA assay before breast adjuvant radiotherapy. With a median follow-up of 26 months, high RILA scores (i.e., a high level of CD8-T-lymphocyte apoptosis after 8 Gy irradiation) proved once again to be associated with fewer grade 2 or more toxicities. A longer follow-up of these patients, as well as another prospective multicenter study on 502 breast cancer patients, confirmed these results [39,40]. In both studies, a RILA score over 12% was significantly associated with lower grade 2 or more late breast fibrosis (p = 0.012). However, in these studies, late fibrosis was also correlated with hormonotherapy and, although both hormonotherapy and RILA independently influenced late breast fibrosis, RILA appeared to be a continuous risk-variable rather than a high or low risk discrete variable [41]. A recent review of the significance of the RILA in breast cancer summarizes these results [42]. RILA has also been assessed in two small prospective studies in cervical and head and neck cancer [43,44]. In both cases, a high RILA score was associated with lower severe late toxicities. Larger studies have been published on prostate cancer, using both CD4 and CD8 T-lymphocytes [45,46,47]. In all three studies, a higher RILA score was significantly associated with a lower-risk late toxicity. However, with rather small patient samples (45, 12 and 50 patients, respectively), the results were inconsistent between studies, one showing significantly lower genito-urinary toxicity, where the other only showed lower gastro-intestinal toxicities [46,47]. However, in a more recent prospective multicenter trial on a larger population (383 patients), a RILA score over 15% was associated with lower grade 2 or more toxicities, both genito-urinary and gastro-intestinal, confirming both earlier studies’ results [48]. Other cancer types, such as lung cancer, are currently being tested as part of a wide multicenter trial: the REQUITE project [49,50,51]. This study, including breast and prostate cancer patients, should also further validate the data already published on these cancer types. A summary of published studies and results by cancer types can be found in Table 1. All of these data suggest that a high RILA score is associated with a low risk of late toxicity after radiotherapy. Subsequently, low-RILA patients should be considered at higher risk of developing severe late toxicity after radiotherapy, and alternate treatment should be considered when available. For example, mastectomy could be proposed to patients with localized breast cancer in order to forgo postoperative radiotherapy. In cases where radiotherapy is still warranted but the patient has a high risk of severe toxicity, fractionation could be altered to protect healthy tissues. On the other hand, in the case of high-risk tumors in patients with a low risk of severe toxicities, treatment could be escalated by adding concurrent chemotherapy. Other treatment alterations are suggested in Azria et al. [12]. However, since no strong correlation has been found between low RILA and an increased risk of radiation-induced toxicities, radiotherapy should be maintained when it is the standard of care. Although the mechanism of this inverse association is not completely clear, it may possibly be related to the delay of cells in recognizing the radiation-induced cell damage and initiating apoptosis, with a consequently increased risk of toxicity and, theoretically, of cancer radioresistance and reduced tumor control for low-RILA patients [52]. However, to date, no correlation between low RILA values and low tumor control has been described in the literature. The RILA assay has been used in numerous studies, in various centers and countries. Where earlier radiosensitivity assays had low reproducibility, this test is robust, and its results have been confirmed in different centers with similar results for same patients, further validating its use in different centers [53,54]. Although prospective data to predict toxicities were similar between all studies, one retrospective study found rather contradicting results in prostate cancer [55]. This was a retrospective analysis of the Epinal radiation incident, where 409 prostate cancer patients received over 108% of the prescribed dose due to overexposure related to portal imaging. In this analysis, RILA did not correlate with inter-individual variations in maximum digestive or urinary toxicity. However, in this case, the magnitude of the overdosage may override the biological predictors of toxicity, including individual radiosensitivity. More interestingly, a prospective study investigating 120 patients who developed radiation-induced sarcomas (RIS) found that patients with a high RILA value were less likely to develop RIS. In this matched cohort study, the mean RILA value was lower in RIS than in control patients (18.5% vs. 22.3%, p = 0.0008), and patients with a RILA > 21.3% were less likely to develop RIS (p < 0.0001) [56]. In summary, with prospective data available in different clinical settings, the RILA assay shows great promise in predicting long-term toxicities after radiotherapy. 4. Molecular Rationale for the RILA Assay The molecular bases underlying the RILA assay are still unclear. Even though the mechanisms leading to radiation-induced fibrosis have been thoroughly investigated [57], the role of peripheral lymphocytes, specifically CD8 T lymphocytes remains unknown. However, some new hypotheses are starting to rise in an attempt to explain the differences of radiation-induced lymphocyte apoptosis among patients. Apoptosis does not appear to be the most important mode of cell killing by radiation in most cases in vitro and in vivo but it has been described in both tumor cells and normal tissues after irradiation. Although mitotic death is usually described as being the preferential mode of radiation-induced cell death in proliferating cells, several studies have shown that apoptosis may be induced preferentially in the S phase of the cell cycle [58]. However, in mature lymphoid cells and lymphocytes, apoptosis appears to be the leading cell death mechanism after irradiation. The role of apoptosis in normal tissue response to radiation has been investigated using p53-deficient mice. In this model, there is an increased survival of haemopoietic cells and fibroblastoid stromal precursor cells after irradiation, due to a larger shoulder in the survival curves [59]. This shows that a decrease in apoptosis affects not only apoptotic prone cells, but other tissues as well. Furthermore, the wider shoulder in survival curve could be correlated to increased DNA repair, but this may lead to increased acquired mutations and alter cell function overtime. As such, patients displaying lower levels of radiation-induced apoptosis in their lymphocytes may exhibit greater radioresistance in their connective tissues as well, leading to delayed reaction to radiation, such as fibroblast proliferation leading to fibrosis. CD8 T lymphocytes have been shown to produce basic Fibroblast Growth Factor (bFGF), while CD4 T lymphocytes produce both bFGF and heparin-binding epidermal growth factor-like growth factor (HB-EGF) [60]. These cytokines are potent mitogens for fibroblasts and endothelial cells. Furthermore, bFGF has been shown to protect endothelial cells from radiation-induced cell death both in vitro and in vivo [61]. As such, patients with decreased T cell apoptosis after radiation may have increased production of fibroblast growth factors, increasing radiation resistance and proliferation of fibroblasts in the treated region. Another hypothesis is that patients for whom a severe and late radio-induced side effect is associated with a low RILA, may have a pool of lymphocytes more resistant to radiation-induced apoptosis, which could therefore reflect the presence of cells in senescence that will be participating in the development of these late effects in the irradiated healthy tissue (fibroblasts). Ionizing radiations can induce a variety of cell death responses including apoptosis, but also senescence. Senescent cells have reduced sensitivity to apoptosis, and a pro-inflammatory secretory phenotype. In addition, ionizing radiations can induce the production of reactive oxygen species (ROS) that cause DNA damage in non-targeted tissue, and systemic effects associated with inflammation. It has recently been shown that, in healthy donors Th17 CD4 T lymphocytes are less sensitive to apoptosis and more sensitive to senescence than other subtypes of CD4 T lymphocytes [62]. Other groups have observed a high frequency of Th17 cells in murine radiation induced pneumonitis with fibrosis, in comparison with pneumonitis without fibrosis [63]. It has also been shown that the balance between Th17 and regulatory T lymphocytes (Treg) might modulate radiation induces lung fibrosis [64]. It can thus be hypothesized that patients with a low RILA value might have an imbalance in their Th17 ratio. In conclusion, the molecular rationale for the RILA assay is still very much unclear, but several hypotheses point towards a correlation between peripheral lymphocytes and radiation induced fibrosis. A summary of the hypotheses can be found in Figure 2. 5. RILA Compared to Other Radiosensitivity Assays As discussed above, RILA tests have been performed on different cell populations. Where the first CD4 results were less reproducible than CD8 results, a recent study on 272 breast cancer patients with over 10 years of follow-up showed that low CD4-RILA was associated with increased risk for both fibrosis and telangiectasia [65]. However, in this study, neither CD8 nor NK-RILA were correlated with late toxicity. Another comparison between CD4, CD8 and NK-RILA in breast cancer patients showed that both CD8 and NK lymphocytes were associated with late toxicity [66]. A last study compared CD8 RILA to CD4 and B-lymphocyte RILA in 94 cervical cancer patients [67]. In this study, both CD8 and B-lymphocyte RILA were significantly correlated with toxicities, whereas CD4-RILA was not. Overall, RILA seems to be applicable to different lymphocyte populations. However, as the largest studies were published using CD8-T-lymphocytes, the standard cell population for this assay remains CD8 lymphocytes. As seen before, RILA seems a robust and reproducible test to assess the risk of late radiation-induced toxicities and delayed complications in various cancer types. However, it seems important to compare it with other available radiosensitivity assays. As the only assay tested in a prospective multicenter study, RILA qualifies as the highest level of evidence. Only the SNP analysis in prostate cancer can also be considered level I, since a large meta-analysis has confirmed the link between their expression and radiosensitivity [68]. A summary of the different assays and their level of evidence in shown in Table 2. In breast cancer patients, RILA was compared to other lymphocyte-based assays: residual DNA double-strand breaks (DSB), G0 and G2 micronucleus assay [70]. In this case-control study, the RILA assay performed best to detect individual radiosensitivity, with a strong correlation between the RILA value and the clinical outcome (p < 0.01), followed by the residual DSB and both micronuclei assays. The same results were shown in prostate cancer patients. When comparing RILA to γ-H2AX and G2 micronuclei assays, lymphocyte apoptosis analysis appeared to be the most suitable test for patients’ radiosensitivity prediction [46]. In breast and head and neck cancer patients, CD3-lymphocyte radiation-induced apoptosis was compared to DNA strand breaks (Comet assay), γ-H2AX foci, and whole genome expression analyses [88]. Once again, inter-individual variations and inter-laboratories variation were very high for most of these tests, although lymphocyte apoptosis seemed the most robust assay. Initial DNA damage, measured by DSB, was also compared to RILA data in 26 breast cancer patients [90,91]. In this study, patients who presented lower levels of initial DNA damage had higher RILA scores and fewer late toxicities. The two assays’ results seemed correlated; although, the patient sample was small and a prospective analysis is still required to confirm those results. The only other radiosensitivity assay with a high level of evidence is the SNPs analysis for prostate cancer [68,92,93]. In 2008, Azria et al. compared RILA results and these known SNPs variability in late radiation-induced toxicity prediction in 399 patients with miscellaneous cancers [94]. In the low-RILA (<9%) patient group, where patients had higher toxicity rates, 94% of patients had four or more SNPs, whereas in the high-RILA group, only 33% had four or more SNPs. Although the numbers are rather small in this study, this points towards a good correlation between the two most robust assays for assessing individual radiosensitivity. Overall, with a higher level of clinical evidence than most tests, the RILA assay appears to be one of the most robust tests and its results correlate to other available radiosensitivity assays. Furthermore, cost wise, the RILA is a relatively cheap assay, around EUR 150 per test, making it easy to implement in a clinical routine; although, most available tests have a similar price range. Overall, with a higher level of clinical evidence than most tests, the RILA assay appears to be one of the most robust tests and its results correlate to other available radiosensitivity assays. 6. Use of RILA in Clinical Routine Due to considerable progress in cancer management in recent decades, the number of cancer survivors has dramatically increased, raising new challenges in the various phases of survivorship. Thus, post-treatment morbidity and quality of life have become a critical concern in the growing patient population. The medico-economic consequences of severe late side effects can also be consequential, as treatments to alleviate the symptoms range from lifelong pain medication to major surgery. The ultimate goal of any radiosensitivity assay is thus to identify the patients at risk for severe toxicity before radiation treatment to offer therapeutic alternatives. These depend on two main factors: tumor control probability (TCP) and normal tissue complication probability (NTCP). In the case of low-risk tumors, patients at risk for severe toxicity could be offered surveillance instead of radiation, or smaller fields of radiation. However, when tumor control is critical, alternative treatments such as surgery or chemotherapy should be discussed. A list of possible treatment adaptations based on TCP and NTCP has been proposed by Azria et al. [12]. Although alternatives to radiation therapy exist in many cases, when radiation is the standard of care, the radiation course can be tailored to fit the patient’s individual radiosensitivity. Clinical trials studying fractionation schedule alteration or long-term toxicities prevention through additional drugs are currently ongoing (NCT04282122, NCT04385433). Another aspect currently under investigation is the cost-utility of these models. This is being carried out in Europe through the ongoing REQUITE project, using the RILA assay, as well as other validated biomarkers [51]. In summary, although radically changing a treatment course based simply on radio-sensitivity assays should not be undertaken outside of clinical trial settings, available alternatives should be proposed when available and validated. 7. Conclusions Identifying patients at risk of severe radiation-induced toxicity before treatment is one of the cornerstones of precision medicine applied to radiotherapy. Although numerous assays have been developed over the last few decades, only a couple reach the highest level of evidence: SNPs analysis in prostate cancer and the RILA assay in several cancer types. Out of these, the RILA assay seems the easiest to use in clinical routine, especially without the need of using an X-ray irradiator like in the original version of RILA. By replacing the irradiation step by the addition of bleomycin, the procedure becomes transferrable in any clinical laboratory. The procedure itself is rather simple and results can be obtained under a week’s time. To date, the RILA has been validated in breast, prostate, cervix, head and neck cancer, and validation in lung cancer is pending. Although the mechanistic basis of this test still remains unclear, the RILA assay appears to be a robust help in deciding the best treatment course in radiotherapy planning. Taking into account tumor prognosis as well as late results and quality of life, the RILA assay, incorporated in a nomogram with the other independent factors, can be used safely in a clinical setting. Wider use of this test would allow for a personalized risk-adapted approach to provide more effective treatments for patients receiving radiotherapy. In case of high local relapse risk and low toxicities risk (high RILA value), new strategies could be considered as an increase in the total dose; in case of high local relapse risk and high toxicities risk (low RILA value), indication of radiotherapy should be discussed and alternative locoregional treatments should be preferred; in case of low local relapse risk and high toxicities risk (low RILA value), antifibrotic agents could be recommended in a mitigation approach in order to prevent or reduce the severity of late radio-induced toxicities (PRAVAPREV study, ClinicalTrials.gov Identifier: NCT04385433). 8. Future Directions Despite the clinical evidence, mechanistic rationale of the RILA assay remains uncertain. Further research is still warranted to identify the role of lymphocyte apoptosis in the development of fibrosis after radiation treatment. From a clinical point of view, the cost-utility of such markers is still under study, and the ongoing REQUITE project should shed a light on this aspect in the next few years. Their relevancy in clinical routine is also being assessed through two clinical trials studying adapted treatment modalities (fractionation schedule alteration or long-term toxicities prevention through additional drugs): NCT04282122, NCT04385433. Author Contributions Conceptualization, A.L., M.B. and D.A.; resources, A.L. and M.B. writing—original draft preparation, A.L. and M.B.; writing—review and editing, A.L., L.B., M.L., T.G., C.B., M.O., A.P., D.A. and M.B. supervision, M.B. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest D. Azria declares NovaGray, stock options. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Figure 1 RILA assay procedure (adapted from Brengues et al. [36]). Figure 2 Main hypotheses for RILA molecular rationale. cancers-14-02097-t001_Table 1 Table 1 Available data on RILA assay by tumor type. GU: genito-urinary, GI: gastro-intestinal. Tumor Type Data Type Patient Number Results References Breast Prospective multicenter 577 Correlation with fibrosis (RILA cutoff = 12%) (p = 0.001) [39,40,41] Prostate Prospective multicenter 692 Correlation with GU and GI toxicity (RILA cutoff = 15%) (p = 0.01) [45,46,47,48] Cervix Prospective 94 Correlation with sexual toxicity (p = 0.001) [43] Head and neck Prospective 79 Correlation with xerostomia (p = 0.035) [44] Lung Prospective multicenter 561 Data pending [50,51] cancers-14-02097-t002_Table 2 Table 2 Available radiosensitivity assays with their respective level of evidence (based on the REMARK guidelines [69]). SNP: single nucleotide polymorphism, RILA: Radiation-Induced Lymphocyte Apoptosis. Assay. Tissue Sample Level of Evidence References rs17599026 and rs7720298 SNPs for prostate cancer Blood sample I (meta-analysis) [68] RILA Blood sample I (prospective multicenter analysis) [37,39,43,44,45,46,66,70,71] SNPs for breast cancer Blood sample II (observational studies) [72,73] SNPs for lung cancer Blood sample II (observational studies) [74,75] Fibroblast-based assays Skin biopsy IV (retrospective studies) [18,21,22,76,77,78] G0 micronuclei Blood sample IV (retrospective studies) [79,80,81] G2 metaphase Blood sample IV (retrospective studies) [79,82,83] Residual γ-H2AX foci Blood sample IV (no validation studies) [46,70,84,85,86,87,88,89] Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Delaney G. Jacob S. Featherstone C. Barton M. The role of radiotherapy in cancer treatment Cancer 2005 104 1129 1137 10.1002/cncr.21324 16080176 2. Emami B. Lyman J. Brown A. Cola L. Goitein M. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092955 molecules-27-02955 Article Non-Invasive Detection of Anti-Inflammatory Bioactivity and Key Chemical Indicators of the Commercial Lanqin Oral Solution by Near Infrared Spectroscopy Ma Hui Xiao Lulu Xu Dongchen Geng Yingrui Liu Xuesong Chen Yong * https://orcid.org/0000-0003-0246-9067 Wu Yongjiang * Komsta Lukasz Academic Editor College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; 12119013@zju.edu.cn (H.M.); 22119162@zju.edu.cn (L.X.); xdcgg@zju.edu.cn (D.X.); 22019084@zju.edu.cn (Y.G.); liuxuesong@zju.edu.cn (X.L.) * Correspondence: chenyong1@zju.edu.cn (Y.C.); yjwu@zju.edu.cn (Y.W.) 05 5 2022 5 2022 27 9 295506 4 2022 04 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Quality control methods of current traditional Chinese medicine (TCM) preparation is time-consuming and difficult to assess in terms of overall efficiency of the drug. A non-destructive rapid near-infrared spectroscopy detection system for key chemical components and biological activity of Lanqin oral solution (LOS), one of the best-selling TCM formulations, was established for comprehensive quality evaluation. Near infrared spectral scanning was carried out on 101 batches of commercial LOS under the penetrated vial state and traditional state. RAW 264.7 cells were cultured to detect the anti-inflammatory ability of LOS, and the reference concentrations of epigoitrin, geniposide, and baicalin were obtained by HPLC. The quantitative models were optimized by three kinds of variable selection methods. The correlation coefficients of prediction value of the models were greater than 0.94. The system also passed the external validation. The performance of the non-invasive models was similar to the traditional models. The established non-destructive system can be applied to the rapid quality inspection of LOS to avoid unqualified drugs from entering the market and ensure drug effectiveness. The biological activity index of LOS was introduced and predicted by NIRs for the first time, which provides a new idea about the quality control of TCM formulations. Chinese medicine formulations non-invasive detection near infrared spectroscopy Lanqin oral solution anti-inflammatory epigoitrin geniposide baicalin Ministry of Industry and Information Technology of the People’s Republic of ChinaZ13506000902 This research was funded by the Industrial Transformation and Upgrading Project of the Ministry of Industry and Information Technology of the People’s Republic of China (Z13506000902). ==== Body pmc1. Introduction Enterprises and government departments have always been concerned with the quality consistency control of drugs. For finished pharmaceutical formulations, the current testing methods need to damage the container, which is destructive to the product [1]. As a result, it is impossible for the tested samples to enter the subsequent commercial circulation. Therefore, the finished products can only be analyzed by sampling [2]. At the same time, the traditional analytical method has the limitations of human and material resource consumption and is time-consuming, which reduces the production efficiency of enterprises and deviates from the demand for efficient and continuous production [3]. To prevent substandard drugs from entering the market and causing delays in the production cycle of manufacturers, it is necessary to develop a non-destructive, vial-penetrating, and rapid detection method. Near-infrared spectroscopy (NIRs) has been widely applied in the food, petroleum, and pharmaceutical fields due to its advantages of rapidity, non-destructiveness, and lack of a need for sample pretreatment [4,5,6]. The information of multiple indicators in the sample system can be parsed by NIRs, which meets the requirements of multiple efficacy indicators of traditional Chinese medicine (TCM) [7,8]. Combined with chemometric methods, the feasibility of NIRs in detecting TCM formulations has been demonstrated. Yan et al., created a rapid quality-assessment system for Chinese medicine preparation Honghua Oil [9]. Si et al., achieved qualitative and quantitative analysis of Yaobitong capsule without damaging capsule shell [10]. However, NIRs are mostly applied for rapid detection of chemical indicators of drugs, and its application potential in the detection of drug biological activity remains to be tapped. Compared with chemical formulations, components of TCM formulations are complex. TCM formulations are usually prepared by decoction of herbs, and the system contains hundreds of components. At the same time, the mechanism of TCM has yet to be explored [11]. It is difficult to reflect the overall efficiency of the drug by analyzing the concentration of specific components [12]. Lanqin oral solution (LOS) is famous for its heat-clearing and detoxifying properties, and is commonly used in the treatment of pharyngitis. Clinical experiments have shown that taking LOS has a significant therapeutic effect on children with herpangina, and it can shorten the fade time of fever and herpes without increasing the occurrence of adverse reactions [13]. There is research finding that LOS can shorten the healing time of acute pharyngitis [14]. For patients with chronic pharyngitis, Li et al., conducted a randomized controlled trial on 1642 patients. The results of the meta-analysis revealed that LOS can effectively inhibit the increase of various inflammatory factors and is beneficial to the relief of patients’ symptoms [15]. However, the therapeutic efficiency mechanism of LOS has not yet been elucidated. LOS is made of Isatidis Radix, Gardeniae Fructus, Scutellariae Radix, Phellodendri Chinensis Cortex, and Sterculiae Lychnophorae Semen. The quality-control method research of LOS mainly focuses on three substances: epigoitrin, geniposide, and baicalin [16]. The efficiency of TCM is considered to be the result of the synergistic effect of multiple components [17]. It is incomplete to assess the quality by focusing on single or several chemical substances in TCM as single substance is inadequate to ensure the efficiency of LOS. The material basis of LOS is complex. Studies have found that there are at least 175 chemical components in the LOS system [18]. It is impractical to separate and analyze all substances in LOS. Therefore, it is worthwhile to evaluate the quality of LOS directly through pharmacodynamic indicators. The aim of this study was to achieve a comprehensive quality assessment of LOS without destroying the vial. In this study, an inflammatory model was constructed by lipopolysaccharides (LPS)-stimulated cells to investigate the anti-inflammatory ability of LOS. At the same time, HPLC analysis was performed on 101 batches of drugs to analyze the concentration of epigoitrin, geniposide, and baicalin in the sample. The NIR spectra of the LOS collected with the container and under the traditional state were collected to construct the optimal partial least squares regression (PLSR) models. It was expected that this research was the first attempt at a non-invasive rapid detection system for drug activity of LOS. 2. Results 2.1. Raw Spectra Analysis The raw NIR spectra of 101 samples in scanning tubes or commercial vials are shown in Figure 1a,b. It was apparent that the raw spectra of samples collected from different production batches were overall similar in both states. The obvious bands around 7000 cm−1 were generated by the first overtone of the O-H stretching vibration of water. Additionally, its combined absorption band with the second overtone can be observed near 5100 cm−1. The broad band extending from 8800 cm−1 to 8000 cm−1 was consistent with the second overtone region of bonded C-H. The NIR spectra of the empty vials are displayed in Figure 1c. The main components of the glass bottles were silica and other inorganic substances, so their NIR spectra were gentle lines without obvious absorption bands. The spectra of 101 vials fluctuated due to certain differences in quality between vials. It corresponded to the realistic scenario of spectral applications. The purple line in Figure 1d is the difference spectrum, calculated by subtracting the spectrum of the corresponding empty vial from the sample spectrum penetrated the vial. Compared with the red line in the figure, there was no significant difference between the two. It suggested the feasibility of non-invasive models. The directly observable bands in Figure 1 were caused by C, H, O elements that were widely present in various substances. It was difficult to directly correlate spectral features with the concerns. Therefore, it was necessary to introduce a chemometric method to further mine the information in the spectra to realize the non-destructive detection of LOS. 2.2. Reference Data Analysis The production years of the collected samples spanned 3 years, from July 2019 to July 2021. The anti-inflammatory ability and content distribution of key chemical indicators of 101 batches of LOS are displayed in Figure 2. The color of the sample gradually changed from red to blue as the value decreases, which was shown on the right side of the figure. Sample No. 100 and the samples on the left side were 35 batches of samples without pre-dilution, and the samples to the right of No. 100 were sorted to the right by increasing dilution. It can be observed that the reference value of the sample decreased roughly with the increase of the dilution factor. The distance correlation analysis was performed between the inhibition rate of nitric oxide production (ANTI-NO) and key chemical indicators of the 101 samples, and the p values of epigoitrin, geniposide, and baicalin were 0.8610, 0.901, and 0.912, respectively. The three chemical indicators selected based on the experience had significant correlations with the biological activity of the sample. It was proved that the selection of chemical detection indicators was reasonable. From the distribution of the reference values of the first 35 batches of samples, it can be found that even without human intervention, the quality of the LOS still fluctuated from batch to batch. The concentration of epigoitrin, geniposide, and baicalin in these 35 batches ranged from 0.02546–0.0702 mg/mL, 2.388–7.413 mg/mL, and 0.9230–3.131 mg/mL, respectively. In contrast, ANTI-NO fluctuated steadily in the range of 75.55% to 88.97%. The concentration of epigoitrin, geniposide, and baicalin fluctuated within a 3-fold range, while the corresponding biological activity was stable in the range of 15%, which revealed that the current detection method based only on chemical indicators was not enough to achieve the comprehensive quality control of TCM formulations. It was necessary to further introduce the detection of pharmacodynamics indicators on the basis of the existing detection methods. However, the detection of anti-inflammatory activity relied on cells and will take at least 3 days. Traditional detection methods will greatly increase the burden on enterprises. Therefore, it was necessary to establish a system that can complete the rapid detection of biological activity and key chemical components simultaneously. 2.3. Model Construction under Traditional State 2.3.1. Sample Sets Division Each dataset was divided into a calibration set with 67 samples and a prediction set with 23 samples by the SPXY algorithm. The reference value ranges for each dataset are listed in Table 1. It was worth noting that the mean value of the prediction sets was close to the calibration set for each dataset, which proved that the properties of the two datasets were similar and the data division was reasonable. At the same time, the ranges of target indexes in the prediction sets were covered within that of the calibration sets, which was beneficial to NIR models. Sample numbers 3, 6, 22, 32, 34, 35, 40, 55, 91, 92, 99 were randomly selected as the external validation set. Among them, 5 were LOS original samples and 6 were artificially diluted samples. It can be observed that the fluctuation range of the validation set of ANTI-NO, epigoitrin, and geniposide all exceeded the calibration set, which was a challenge to the established PLSR model. 2.3.2. Spectral Pretreatment and Variables Selection The performances of different spectral pretreatments models for each indicator are recorded in Table S2. The optimal pretreatment was chosen according to the model parameters of the calibration set. The normalization pretreated spectra obtained high correlation coefficients of calibration (Rc)values and low relative standard error of calibration (RSEC) values for ANTI-NO and baicalin. For epigoitrin and geniposide, meanwhile, the optimal pretreatment was SG smoothing and MSC, respectively. After preprocessing, the baseline drift between different samples had been compressed. The Rc values of the prediction models of ANTI-NO, epigoitrin, geniposide, and baicalin increased from 0.9305, 0.8989, 0.9802, 0.9265 to 0.9491, 0.9119, 0.9803, 0.9203, respectively. The relative standard error of prediction (RSEP) values representing the model prediction errors decreased from 10.6%, 15.1%, 8.8%, and 19.9% to 9.8%, 14.9%, 8.6%, and 18.5%, respectively. The improvements of the performances of the models demonstrated that the noise in the system had been removed. Synergy interval partial least-squares regression (SIPLS), competitive adaptive reweighted sampling (CARS) and random frog (RF) were applied and compared to select characteristic information correlated with target indexes. For each indicator, the results obtained by different variable screening methods were similar. The optimal screening method was also determined based on the model performance parameters. Models constructed on subsets of variables filtered by different methods are listed in Table S3. The wavenumbers selected by the optimal variable screening method for each indicator are shown in Figure 3. After variables selection by RF, the spectra of epigoitrin and ANTI-NO retained 250 and 290 variables, respectively. It can be observed that although the number of variables was compressed to less than 20% of the original spectra, the selected key wavenumber points of ANTI-NO were distributed in the full spectra. As explained in the introduction part, it was widely accepted that the biological activity of TCM was the result of the synergistic action of multiple components. Therefore, the key variables corresponding to ANTI-NO indicators were relatively scattered, which represented most of the information of the spectra can be collected to calculate the anti-inflammatory ability. The C=C information contained in the epigoitrin structure was concentrated in the low wavenumber region. The related information of the unique S element and N element in its structure was concentrated in the spectral information in the high wavenumber region of NIR. As for geniposide, the spectra were divided into 18 regions of equal length by SIPLS. A total of 345 variables in the 7th, 11th, 12th, and 13th sub-intervals were selected and combined as a modelling subset. The chosen range around 6100 cm−1 corresponded to the absorption bands of C-H in C=C in the iridoid structure of geniposide. The continuous absorption section from 8242 cm−1 to 7351cm−1 can be attributed to the second overtone region of C-H. The optimal model for baicalin was obtained by using CARS processed spectra, the model of baicalin actually adopted only 47 variables. The variable compression ratio was the highest among the four indexes, which was because baicalin contained a characteristic benzene ring structure. The selected wavenumbers were concentrated in the region from 5400 cm−1 to 4000 cm−1, which corresponded to the C-H and C-C stretching vibrations in the benzene ring structure. At the same time, the single-strong absorption band at 4065 cm−1 caused by C-H stretching and bending vibration was also included. The remaining key variables were scattered around 7000 cm−1, which can be attributed to the characteristic absorption of multiple phenolic hydroxyl groups in baicalin. For the four indicators, the variables screened by the chemometric method were consistent with the characteristics of the indicators. Compared to the number of 1557 variables in the original spectra, the number of variables that need to be considered for subsequent modelling was compressed to less than 25%. Variable screening greatly reduced the amount of computation required for modelling. At the same time, as shown in Table S3, the prediction accuracy of the local models constructed by the chosen variable subsets were higher than that of the global models. It demonstrated that valid information was preserved when redundant variables were removed. 2.3.3. The Results of PLSR Models The PLSR quantitative models were constructed with the selected variable subsets as the input and the reference values measured by traditional methods as the output. The measured and predicted values of the calibration set and prediction set samples for each indicator are displayed in Figure 4. The prediction set samples were scattered and covered within the calibration set samples and the sample points were evenly distributed around y = x. Besides 36 prediction set samples, the external validation set was applied to verify the accuracy of the PLSR models. In addition to the model parameters, the Wilcoxon rank-sum test was further introduced to test whether there were significant differences between the predicted values and the measured values. The results are displayed in Table 2. p-values of four datasets were above 0.05 and the relative standard error of validation (RSEV) values below 20%. It demonstrated that the prediction error value of the model met the application requirements, and there was no significant difference between the two groups of data. For 11 independent samples, the correlation coefficients of validation (Rv) values of ANTI-NO, geniposide and baicalin were all greater than 0.9. However, the Rv value of the PLSR model of epigoitrin was 0.7766, which still needed further consideration before entering the practical application. Predicting target index at low concentration has always been a challenge for NIR applications [19]. 2.4. Model Construction under Non-Destructive Conditions 2.4.1. Sample Sets Division Same as the modeling steps for spectra acquired in the traditional state, the non-invasive model construction started with data sets division by SPXY. The results of the division of the dataset are shown in Table 3. It can be observed that the division of the four datasets all met the requirements of model as the distance between the spectra and the reference values were calculated to ensure the rationality of the data division. The external validation set consisted of the original 11 samples. Therefore, the concentration ranges of the validation sets for the three indexes were still outside the calibration sets. It was in line with the situation that may be encountered in the application of the NIR model in Chinese medicine formulations. With the change of production batches, there was a possibility that the concentration range of the new samples will exceed the original dataset. 2.4.2. Spectral Pretreatment and Variables Selection The model prediction accuracy of PLSR would be affected by the pre-processed methods. Model performances of PLSR after different preprocessing are shown in Table S4. According to the evaluation parameters of the model, when the preprocessing method was MSC, the optimal PLSR models were obtained for ANTI-NO and baicalin. The normalized spectra were optimal for epigoitrin, while the raw spectra without any processing achieved the best model performance for geniposide. Preprocessed spectra of the calibration set were applied as input for variable screening to select key spectral data. The results of variable screening of the four indexes are shown in Figure 5. The optimal variable selection method for ANTI-NO, epigoitrin, and geniposide were all SIPLS with non-invasive spectra as input. As a wavelength interval selection, SIPLS retained the continuity of the spectra by regarding the interval as a unit, and the continuous arrangement and combination of intervals also made it suitable for rapid detection of complex TCM systems. Compared with the traditional model, the effective variables of ANTI-NO retained 55 more variables. However, different from the previous state of being scattered in the whole spectra, the wavenumbers selected by SIPLS were more concentrated. The band region spanning 8000 cm−1 corresponds to the second overtone region of C-H. C-H was the structural basis of organic compounds, and information on various indicators of interest can be obtained by analyzing this segment. The selected spectral region below 7000 cm−1 was the first overtone region of hydroxyl. This band provided key information for the prediction of anti-inflammatory ability, implying that the biological activity of LOS may be based on hydroxyl-rich substances. The key variables of epigoitrin were concentrated above 7500 cm−1. It proved that SIPLS further screened the information scattered in the full spectrum and finally locked the high wavenumber region related to S and N elements to quantify epigoitrin. For geniposide, the key variables dropped from 345 in the traditional model to 208. The added low wavenumber region was derived from the C-C vibration. The spectral region originally spanning 8000 cm−1 was compressed to the right of 8000 cm−1. However, the spectral region of 6000 cm−1 was retained, which proved that C=C in the iridoid structure had an important indication effect the on the construction of the model of geniposide. Without destroying the integrity of the vial, the best subset of variables for baicalin was still selected by CARS, and the number of variables was compressed to 36. It can be clearly observed from the figure that the important wave points of baicalin were consistent with the results in the traditional state. Variables associated with benzene rings and hydroxyl groups were retained for the modelling step. Comparisons of relevant model parameters are listed in Table S5. Compared with the global models, the complexity of the models constructed by selected variables was significantly reduced, and the prediction accuracy was improved. Improvements in model parameters also indicated that the selected key variables were associated with the four indicators. 2.4.3. The Results of PLSR Models The PLSR models established by the spectrum penetrating the vial are shown in Figure 6. From the figure, it can be found that the predicted values of the four models were well correlated with the measured values. The sample points were around the line y = x. The validation results of the PLSR model for 11 independent samples are listed in Table 4. The results of the Wilcoxon rank-sum test were satisfactory. It can be observed that the Rv values of the four models were all higher than 0.8, while the RSEV values were less than 20%. These model parameters demonstrated that the established PLSR model can achieve predictions on the validation set samples. The optimal models can be applied to predict new samples. 2.5. Comparison of Two System Models The performance of the PLSR model constructed by the spectra collected by the two acquisition systems is shown in Table 5. Judging from the performance of the samples in the calibration set and prediction set, both models had achieved accurate predictions of the samples. The overall performance was satisfactory and met the application requirements. It can be found from the table that compared with the traditional model, the Rc and Rp values of epigoitrin and baicalin’s non-invasive models were close. The R values of ANTI-NO and geniposide increased slightly. The RSEC and RSEP values of the models constructed by the spectra penetrating the vial, which represented the errors, were all decreased to varying degrees. The residual predictive deviation (RPD) values of the four indicators had increased from 3.2, 3.09, 4.95, and 3.92 in the traditional state to 3.49, 3.10, 5.29, and 4.33, respectively. The improvements of these parameters proved that the prediction error of the model constructed under the non-destructive state was lower than that of the model obtained by the standardized special tubes. The state of the target indexes in the sample system can be more accurately characterized. The results of external validation, listed in Table 2 and Table 4, demonstrated that the prediction accuracy of the non-invasive model for independent samples was as satisfactory as the tedious standardized sweep process that requires breaking the vial and pipetting the sample liquid. Even for epigoitrin with low concentration, the Rv value of the optimal model improved from 0.7766 to 0.8069. From the model optimization process shown in the supplementary material, it can be found that for both systems, the optimization effect of the model parameters introduced by the spectral preprocessing was far less obvious than that of the variable screening process. Therefore, it was speculated that the advantage of the non-invasive models compared with the models in the standard state mainly came from the wavenumber select process. From the comparison between Figure 3 and Figure 5, it can be observed that the key variables in the non-invasive state had been further compressed or concentrated. As shown in Figure 1C, there were quality fluctuations among different batches of bottles, whereby spectral information irrelevant to the target indexes was introduced into the non-invasive system. Under the perturbation of the disturbance information from the vials, more irrelevant variables were effectively identified and eliminated. Therefore, the input applied by the PLSR model had stronger correlations with the target indicators, which was beneficial to the accuracy of the prediction. 3. Materials and Methods 3.1. Cell and Reagents LOS samples of 101 production batches were provided by Yangtze River Pharmaceutical Group (Taizhou, China). The relationship between the production batch number and self-numbering of the sample is shown in Table S1. Standards of epigoitrin (purity > 99.0%, catalog no: A0529), geniposide (purity > 99.0%, catalog no: A0178) and baicalin (purity > 98.0%, catalog no: A0016) were purchased from Chengdu Must Bio-Technology Co., Ltd. (Chengdu, China). HPLC-grade methanol, acetonitrile and phosphoric acid were obtained from Merck (Darmstadt, Germany). Deionized water was purified by a Milli-Q purification system (Millipore, Bedford, MA, USA). The murine macrophage RAW 264.7 cell line and ZQ-120 Dulbecco’s modified Eagle medium (DMEM) were purchased from Shanghai Zhong Qiao Xin Zhou Biotechnology Co., Ltd. (Shanghai, China). NO assay kits (S0021) were provided by Beyotime (Nanjing, China). The phosphate-buffered solution was obtained from Labgic Technology Co., Ltd. (Hefei, China). LPS was purchased from Sigma-Aldrich, Inc. (St. Louis, MO, USA). 3.2. Sample Preparing The 35 batches of samples were directly subjected to subsequent spectrum acquisition and reference value acquisition operations without any preparation. The remaining 65 batches of samples were diluted with purified water to extend the range of concerns in the products. The specific dilution schedule is explained in Table S1. 3.3. Spectra Acquisition NIR spectra of prepared samples were collected in a range of 10,000–4000 cm−1 by an ANTARIS II (Thermo Scientific, Waltham, MA, USA) in absorbance mode at room temperature. Each spectrum was the average of 32 scans and the average spectrum of 3 times measurements was adopted. All the samples were obtained with air as references and the resolution was set as 8 cm−1. For non-invasive NIR spectra, brown glass vials containing LOS samples were placed directly into the sampling module of the spectrometer. Therefore, the optical path was the diameter of a glass vial, about 12 mm. Spectra collection was also performed in the same state for the empty bottles without liquid. For standardized spectra, the unsealed drug was transferred into a dedicated scanning tube configured with the instrument. The optical path length of the standardized spectra was 4 mm. 3.4. Pharmacodynamics Experiment 3.4.1. Cell Culture The RAW 264.7 cells were cultured with ZQ-120 DMEM in Forma 3111 CO2 Incubator (Thermo Scientific, Waltham, MA, USA). Cells were maintained at 37 °C under a 5% CO2 atmosphere and relative humidity was controlled at 90%. 3.4.2. Anti-Inflammatory Ability Assay RAW 264.7 were seeded in 96-well plates (6 × 104 mL−1) and cultured at the CO2 Incubator. Drug group wells were incubated with different batches of LOS (dilute with medium to 1/100 after spectrum acquisition) and 10 μg mL−1 LPS after culturing cells for 24 h. At the same time, 100 μL DMEM medium and 10 μg mL−1 LPS were added to the control group and model group wells, respectively. Set up five repetitions per set. The culture medium was collected after 48 h incubation for the NO kits detection. NO kits worked according to the Griess method. Add equal amounts of Griess Reagent I and Griess Reagent II to the obtained culture medium in turn. Then, the absorbance at 450 nm of the sample obtained by the Spark microplate reader (TECAN) was calculated with a standard calibration curve to confirm the concentration of NO in the sample. The ANTI-NO was calculated by the following formula:ANTI-NO = (Cm − Cd)/(Cm − Cc) × 100%,(1) where Cm, Cd, Cc represented the average concentrations of NO in the model group, drug group, and control group samples, respectively. 3.5. HPLC Analysis The samples were diluted 25 times by 40% methanol (v/v) and then filtrated through 0.22 µm filter membrane. Standards were dissolved by 40% methanol (v/v) to the concentrations of the standard calibration curves in Figure S1. High-performance liquid chromatograph Agilent 1290 (including quaternary pump, online degassing device, automatic sampler, column temperature controller, DAD detector, and Chem Station) was applied to determine the contents of epigoitrin, geniposide and baicalin. A Luna®® C18 column (250 mm × 4.6 mm, 5 μm) was employed. The mobile phase consisted of acetonitrile (A) and 0.1% phosphoric acid aqueous solution (B) with gradient elution: 0–12 min, 5–11% B; 12–24 min, 11% B; 24–38 min, 11–20% B; 38–62 min, 20% B; 62–70 min, 20–32% B; 70–75 min, 32–80% B; 75–85 min, 80–100% B [20]. The flow rate was set as 1 mL/min, the injection volume was set as 10 μL, and the wavelength was set at 245 nm. Chromatograms are shown in Figure 7. 3.6. Chemometrics Methods 3.6.1. Division of Samples First, 10% of samples (11 batches) were randomly selected to form an external validation set, which was applied to certify the reliability of the established models. The validation set did not participate in the modelling step, it was only used to verify the accuracy of the quantitative models. In order to ensure maximum representation of sample distribution, the remaining 90 samples were divided into the calibration set and the prediction set by the sample set partitioning based on joint x–y distance (SPXY) algorithm in a ratio of 3:1. The distance between samples was calculated simultaneously using x and y variables by the SPXY algorithm to guarantee the representativeness of the calibration set [21]. 3.6.2. Spectral Pretreatment Due to the unavoidable changes in the external environment and sample state, redundant noise information existed in NIR spectra. Spectral pretreatment can help mitigate the effects of non-target factors [22]. In this study, four spectral preprocessing methods, namely normalization, standard normal variate (SNV) transformation, Savitzky–Golay (SG) smoothing and multiplicative scatter correction (MSC) were employed and compared to optimize the model. 3.6.3. Variable Selection Methods The advantage of information richness of NIR is detrimental to information analysis when the indicators of interest have been specified. An effective variable selection method can eliminate variables irrelevant to the target index while retaining valid information, thereby reducing the difficulty of modeling and improving the accuracy of the model. The following 3 variable selection methods were applied to determine the optimal subset of variables for each index. SIPLS selected the subintervals of spectra corresponding to the minimum root mean square error of cross-validation (RMSECV) value of local model by dividing and permuting spectral regions [23]. SIPLS followed the principle that NIR spectra had continuous features of bands, while wavenumber point selection efficiently screened key variables based on mathematical principles [24]. CARS simulated the principle of survival of the fittest in Darwin’s evolution theory. In this process, Monte Carlo sampling was used to construct the local model. The wavenumbers with small absolute values of regression coefficients in the model were continuously eliminated, and finally, the optimal subset of variables was selected according to the RMSECV value [25]. RF calculates the importance of each variable by the probability that each variable is selected in the model space [26]. 3.6.4. PLSR PLSR is the most extensive quantitative regression method of NIRs rapid detection system [27]. The performance of the model was affected by the setting of the number of LVs. In this study, LVs was determined according to RMSECV by leave-one-out cross-validation. 3.6.5. Evaluation Criteria of Models Performance of constructed PLSR model was evaluated by 8 indexes, namely: Rc, Rp, RSEC, RSEP, RMSEC, RMSEP, RPD and RMSECV. The results of the external test are mainly judged by correlation coefficients of Rv, RSEV, and RMSEV. At the same time, the Wilcoxon rank-sum test was introduced to further compare the reference value and the model prediction value of the validation set samples to prove the prediction accuracy of the obtained model. In general, the value of correlation coefficients should be close to 1. The relative standard error values of the 3 sample sets were expected to be small and close to each other. Moreover, the RPD value greater than 3 was the requirement of the optimal model [28]. 3.7. Software For NIRs data acquisition, TQ Analyst 8.0 were applied. The data processing and graphic drawing were performed by MATLAB software (version 2018b, Mathworks, Natick, MA, USA). 4. Conclusions In this study, high-performance and non-invasive quantitative models of anti-inflammatory bioactivity and three key chemical indicators including epigoitrin, geniposide, and baicalin were developed based on NIRs and chemometrics methods. The Rc values of the non-destructive system were greater than 0.94 and the RSEC values were lower than 15% for four indexes. Compared with the standard system, the constructed model through vials achieved higher prediction accuracy. The obtained models can be applied to the quality detection of LOS products instead of traditional analytical methods to improve production efficiency. This study was the first to verify the anti-inflammatory ability of LOS at the cellular level and realized its non-destructive and rapid detection. Due to the complexity of the efficacy of TCM, it was difficult to comprehensively characterize the quality of drugs by purely chemical indicators. Therefore, the introduction of anti-inflammatory indicators as testing objects can improve the understanding of the quality of TCM formulations. The multi-indicator advantage of the NIRs can realize the comprehensive detection of TCM, and its fast and non-destructive characteristics provide feasibility for high-throughput analysis. The NIR non-destructive detection system was conducive to controlling the quality of LOS formulations, ensuring the effectiveness and safety of drugs, and can provide a reference for the quality control of other TCM formulations. Acknowledgments The authors would like to acknowledge everyone who provided helpful guidance and would also like to thank the anonymous reviewers for their useful comments. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules27092955/s1, Table S1: Sample production batches and dilution arrangements; Table S2: Performance of different spectral pretreatments models for 4 indexes under standard conditions; Table S3 Performance of different variable selection models for 4 indexes under standard conditions; Table S4: Performance of different spectral pretreatments models for 4 indexes under non-destructive conditions.; Table S5: Performance of different variable selection models for 4 indexes under non-destructive conditions; Figure S1: Concentrations, and standard calibration curves. Click here for additional data file. Author Contributions Conceptualization, Y.W. and H.M.; data curation, L.X.; software, D.X.; writing—original draft preparation, Y.G.; funding acquisition, X.L. and Y.C. All authors have read and agreed to the published version of the manuscript. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Sample Availability Samples of the compounds are available from the authors. Figure 1 The raw NIR absorbance spectra: (a) traditional spectra; (b) spectra that penetrates the vial; (c) spectra of the vial; (d) average spectra. Figure 2 Heat map of 4 chosen targets. Figure 3 Crucial variables selected of 4 indexes of interest under traditional state. Figure 4 The scatter plot of reference measurements and NIR predictions using the optimal traditional PLSR model: (a) ANTI-NO; (b) epigoitrin; (c) geniposide; (d) baicalin. Figure 5 Crucial variables selected of 4 indexes of interest without destroying vial. Figure 6 Scatter plot of reference measurements and NIR predictions using the optimal non-invasive PLSR model.: (a) ANTI-NO; (b) epigoitrin; (c) geniposide; (d) baicalin. Figure 7 HPLC chromatograms of (A) standard solution (1. epigoitrin, 2. geniposide, 3. baicalin) and (B) LOS sample. molecules-27-02955-t001_Table 1 Table 1 Reference values for 4 indexes in the data sets. Data Sets. Sample Number Minimum Concentration (% or mg/mL) Maximum Concentration (% or mg/mL) Mean (% or mg/mL) Std ANTI-NO Calibration set 67 33.49 87.12 62.87 0.1626 Prediction set 23 41.13 86.33 61.93 0.1620 Validation set 11 44.26 88.97 69.77 0.1644 Epigoitrin Calibration set 67 0.0156 0.0633 0.0354 0.0138 Prediction set 23 0.0186 0.0626 0.0334 0.0142 Validation set 11 0.0185 0.0702 0.0471 0.0151 Geniposide Calibration set 67 1.537 7.413 3.609 1.539 Prediction set 23 1.668 5.840 3.424 1.609 Validation set 11 1.421 7.032 4.602 1.842 Baicalin Calibration set 67 0.4739 3.131 1.493 0.6037 Prediction set 23 0.4742 2.323 1.354 0.6001 Validation set 11 0.5729 2.820 1.746 0.7158 molecules-27-02955-t002_Table 2 Table 2 The results of external validation of traditional models. Sample No. ANTI-NO Epigoitrin Geniposide Baicalin Reference Value Predicted Value Reference Value Predicted Value Reference Value Predicted Value Reference Value Predicted Value 1 0.4426 0.4917 0.0425 0.02952 2.396 2.632 0.7825 0.8637 2 0.8427 0.8541 0.0702 0.07235 5.63 5.692 2.421 2.171 3 0.6185 0.5988 0.0381 0.02531 4.221 2.897 1.303 1.421 4 0.5161 0.5743 0.0305 0.02882 2.631 2.094 1.295 1.172 5 0.6956 0.6698 0.0452 0.03126 4.054 3.738 1.706 1.451 6 0.4594 0.4362 0.0185 0.01739 1.421 1.239 0.5729 0.6174 7 0.7785 0.8132 0.0468 0.05402 5.027 5.152 1.608 1.712 8 0.8735 0.8326 0.0616 0.06644 6.525 6.937 2.82 2.366 9 0.7952 0.9022 0.0466 0.04715 6.437 6.279 2.413 2.449 10 0.8897 0.7868 0.0648 0.04770 5.248 4.650 2.014 2.020 11 0.7633 0.7989 0.053 0.05547 7.032 6.399 2.269 2.652 Rv 0.9356 0.7766 0.9516 0.9468 RMSEV 1 0.055 0.009 0.540 0.220 RSEV 7.7% 18.5% 11.0% 11.7% p 0.8955 0.7928 0.7427 1 1 RMSEV: the root mean square error of validation. molecules-27-02955-t003_Table 3 Table 3 Reference values for 4 indexes in the data sets. Data Sets Sample Number Minimum Concentration (% or mg/mL) Maximum Concentration (% or mg/mL) Mean (% or mg/mL) Std ANTI-NO Calibration set 67 33.49 87.12 64.38 0.1642 Prediction set 23 41.76 84.10 57.51 0.1453 Validation set 11 44.26 88.97 69.77 0.1644 Epigoitrin Calibration set 67 0.0156 0.0633 0.0368 0.0140 Prediction set 23 0.0167 0.0605 0.0293 0.0119 Validation set 11 0.0185 0.0702 0.0471 0.0151 Geniposide Calibration set 67 1.537 7.413 3.799 1.537 Prediction set 23 1.558 5.600 2.871 1.401 Validation set 11 1.421 7.032 4.602 1.842 Baicalin Calibration set 67 0.4739 3.131 1.462 0.6193 Prediction set 23 0.7137 2.286 1.443 0.5637 Validation set 11 0.5729 2.820 1.746 0.7158 molecules-27-02955-t004_Table 4 Table 4 The results of external validation of traditional models. Sample No. ANTI-NO Epigoitrin Geniposide Baicalin Reference Value Predicted Value Reference Value Predicted Value Reference Value Predicted Value Reference Value Predicted Value 1 0.4426 0.4728 0.0425 0.02919 2.396 2.056 0.7825 0.979 2 0.8427 0.9373 0.0702 0.07578 5.63 6.118 2.421 2.518 3 0.6185 0.6515 0.0381 0.03811 4.221 3.496 1.303 1.700 4 0.5161 0.5683 0.0305 0.03752 2.631 2.755 1.295 1.557 5 0.6956 0.6996 0.0452 0.04223 4.054 3.847 1.706 1.796 6 0.4594 0.4520 0.0185 0.01870 1.421 1.466 0.5729 0.629 7 0.7785 0.7173 0.0468 0.03451 5.027 5.126 1.608 1.585 8 0.8735 0.9306 0.0616 0.07149 6.525 6.323 2.82 2.623 9 0.7952 0.8521 0.0466 0.05203 6.437 5.420 2.413 2.427 10 0.8897 0.8075 0.0648 0.04884 5.248 4.977 2.014 2.099 11 0.7633 0.8206 0.053 0.05317 7.032 5.813 2.269 2.286 Rv 0.9349 0.8069 0.9457 0.9670 RMSEV 0.056 0.008 0.571 0.174 RSEV 7.8% 17.3% 11.6% 9.3% p 0.6936 0.7928 0.6458 0.6936 molecules-27-02955-t005_Table 5 Table 5 The optimal PLSR model of 4 indicators. Analytes M. T. 1 P. M. 2 V. S. M. 3 V. N. 4 LVs 5 Rc RMSEC 6 RSEC Rp 7 RMSEP 8 RSEP RPD ANTI-NO T 9 Normalization RF 290 9 0.9526 0.049 7.6% 0.9296 0.058 9.1% 3.20 N 10 MSC SIPLS 345 11 0.9658 0.042 6.4% 0.9524 0.043 7.3% 3.49 Epigoitrin T 9 SG smoothing RF 250 8 0.9434 0.005 12.0% 0.9439 0.005 12.7% 3.09 N 10 Normalization SIPLS 388 11 0.9409 0.005 12.0% 0.9437 0.004 12.2% 3.10 Geniposide T 9 MSC SIPLS 345 13 0.9820 0.289 7.4% 0.9791 0.320 8.5% 4.95 N 10 Raw SIPLS 208 8 0.9885 0.231 5.6% 0.9814 0.263 8.3% 5.29 Baicalin T 9 Normalization CARS 47 10 0.9680 0.150 9.4% 0.9669 0.150 10.1% 3.92 N 10 MSC CARS 36 7 0.9735 0.141 8.9% 0.9652 0.144 9.3% 4.3 1 M.T.: model type. 2 P.M.: pretreatment methods. 3 V. S. M.: variables selection methods. 4 V. N.: variable numbers. 5 LVs: latent variables. 6 RMSEC: root mean square error of calibration. 7 Rp: correlation coefficients of prediction. 8 RMSEP: the root mean square error of prediction. 9 T.: traditional model. 10 N.: non-invasive model. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093564 sensors-22-03564 Article RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model https://orcid.org/0000-0002-4433-5422 Kim Yoon Ji 1 Ju Woong 2 Nam Kye Hyun 3 Kim Soo Nyung 4 Kim Young Jae 1 https://orcid.org/0000-0001-9714-6038 Kim Kwang Gi 15* Huang Shih-Chia Academic Editor 1 Department of Biomedical Engineering, Gil Medical Center, College of Medicine, Gachon University, 21 Namdong-daero 774 Beon-gil, Namdong-gu, Incheon 21565, Korea; younji524@gachon.ac.kr (Y.J.K.); youngjae@gachon.ac.kr (Y.J.K.) 2 Department of Obstetrics & Gynecology, Seoul Hospital, Ewha Womans University, Seoul 07804, Korea; goodmorning@ewha.ac.kr 3 Department of Obstetrics & Gynecology, Bucheon Hospital, Soonchunhyang University, Bucheon-si 14584, Korea; khnam@schmc.ac.kr 4 R & D Center, NTL Medical Institute, Seongnam-si 13449, Korea; snkim@chollian.net 5 Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21565, Korea * Correspondence: kimkg@gachon.ac.kr; Tel.: +82-32-458-2770 07 5 2022 5 2022 22 9 356409 4 2022 05 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Cervical cancer is one of the main causes of death from cancer in women. However, it can be treated successfully at an early stage. This study aims to propose an image processing algorithm based on acetowhite, which is an important criterion for diagnosing cervical cancer, to increase the accuracy of the deep learning classification model. Then, we mainly compared the performance of the model, the original image without image processing, a mask image made with acetowhite as the region of interest, and an image using the proposed algorithm. In conclusion, the deep learning classification model based on images with the proposed algorithm achieved an accuracy of 81.31%, which is approximately 9% higher than the model with original images and approximately 4% higher than the model with acetowhite mask images. Our study suggests that the proposed algorithm based on acetowhite could have a better performance than other image processing algorithms for classifying stages of cervical images. cervical cancer acetowhite RGB channel superposition deep learning ResNet ITRC (Information Technology Research Center)IITP-2022-2017-0-01630 GRRC program of Gyeonggi provinceGRRC-Gachon2020(B01) Gachon University Gil Medical CenterFRD2019-08 This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2017-0-01630) supervised by the IITP (Institute for Information & Communications Technology Promotion), and by the GRRC program of Gyeonggi province. [GRRC-Gachon2020(B01), AI-based Medical Image Analysis, and by the Gachon University Gil Medical Center (FRD2019-08). ==== Body pmc1. Introduction Cervical cancer is the fourth most common cancer in women worldwide. In 2018, approximately 570,000 women were diagnosed with cervical cancer, and an estimated 311,000 women, representing 7.5% of all female cancer deaths, died due to cervical cancer worldwide [1]. Cervical cancer is divided into two large groups, namely, atypical (A) and positive (P), which is the precancerous stage. A is graded into atypical 1 (A1) and atypical 2 (A2), and P, for human papillomavirus-infected tissue in which cervical cancer progresses, is classified as positive 1A (P1A), positive 1 B (P1B), and positive 2 (P2) depending on the state of lesions [2]. Cervical cancer is one of the cancers that can be treated successfully, as long as it is detected early and managed effectively [3,4,5]. Therefore, early diagnosis through regular screening is important. It is important to distinguish between A and P because treatment is required when the patient is in case P, which is the initial state of the lesion. Furthermore, when analyzing actual cases, A1 shows an overwhelming number of atypical cases, and P1B shows the largest number of dysplasia cases [6]. Therefore, a clear distinction between A1 and P1B cases is necessary. Tests for the early diagnosis of cervical cancer include cervical Pap smear, colposcopy, and cervicography. A Pap smear test, which is one of the existing methods for diagnosing cervical cancer, is based on cytology and requires electricity for a microscope, consumables for examination, and experts for the interpretation of results, making it difficult to be implemented in an environment with insufficient resources [7]. In addition, the sensitivity estimate for the detection of invasive cancer is low, so repeated screening tests are required [8]. To compensate for the high false-negative rate of the Pap smear test, colposcopy was performed to diagnose lesions by directly observing the changed cervix after applying 3–5% acetic acid. Colposcopy shows a high accuracy, but depending on the experience of the specialist performing the examination, small lesions may not be detected and may be omitted, resulting in different results [9,10]. Cervicography is a test that diagnoses the lesion by enlarging the image after taking a picture of the cervix coated with acetic acid. This test has the advantage of being simple and maintaining objectivity [11]. However, technical defects may occur due to obstruction of the visual field, and a relatively high false-positive rate due to metaplasia, etc. [12,13]. When it is difficult to distinguish between normal and lesions in all cervical cancer diagnostic tests, one of the important abnormal findings to pay attention to in lesion diagnosis is the appearance of white spots on the cervix after acetic acid application in cervicography, that is, the expression of acetowhite [14,15,16,17]. The density of acetowhite areas generally increases with lesion severity. Clear and thin acetowhite areas are most likely due to immature metaplasia or inflammation, and thin but opaque areas of acetowhite are more likely to be asymptomatic for papillomavirus infection (SPI) or CIN1. Distinct opaque acetowhite areas after acetic acid application suggest high-grade lesions (HSILs). In addition, if the edge of the acetowhite region is unclear or angled, then it can be determined as a metaplasia, SPI, or CIN1, and the more regular the boundary of the acetowhite region, the more likely the HSIL is [18]. Because acetowhite is an important criterion for diagnosing lesions according to its color and shape, it is gaining attention as a tool to overcome the limitations of the accuracy and diagnostic efficiency of existing colposcopy [19]. In 2015, Sheng et al. showed an average of 0.7765 DSC by eliminating unbalanced labels through class-averaging graph-based transduction based on superpixels made with the k-means clustering algorithm [20]. In addition, in 2021, Yue et al. performed acetowhite segmentation with a 0.835 dice coefficient and 93.40% precision using a deep attention network for an image to which the specular reflection removal algorithm was applied [21]. In 2021, Liu et al. extracted the cervical region using the k-means clustering algorithm and then segmented acetowhite with an accuracy of 90.36% using a pre-trained ResNet101-based DeepLab V3+ network [22]. To date, deep learning has been used as a tool for the automatic segmentation of acetowhite [23,24,25]. However, to the best of our knowledge, no classification model based on acetowhite has been reported. Therefore, in this paper, we compared the performance of the model for the original image without image processing, the mask image with acetowhite as the region of interest, and the RGB channel superposition image using the original image and mask image. In conclusion, we propose an image processing algorithm that accurately classifies images of the A1 and P1B states, which is a boundary between normal and abnormal, through RGB channel superposition using the acetowhite mask image. This method made it possible to intensively learn about the characteristics of acetowhite according to the state of each lesion. 2. Methods 2.1. Data The data in this study consisted of simple atypical A1 and dysmorphic P1B images, and 438 A1 and 477 P1B images were used, respectively. Of the cervical images, 670 were obtained using the Dr. Cervicam+ camera (https://ntlhealthcare.com (accessed on 17 March 2021), Seongnamsi, Korea), and 245 were obtained using the Dr. Cervicam camera (https://ntlhealthcare.com (accessed on 17 March 2021), Seongnamsi, Korea). As for the age composition of patients, 190 of 915 patients were sampled and surveyed, and 46.32% were in their 20s or younger, 28.95% in their 30s, 17.37% in their 40s, and 7.37% in their 50s or older. The training set used for training the deep learning model and the test set used for evaluating the deep learning model consisted of a ratio of 8:2. The training set comprised 350 A1 and 382 P1B, and the test set comprised 108 A1 and 95 P1B. 2.2. Image Preprocessing For the collected cervical images, an expert directly annotated the acetowhite as a rectangular type using the NTL AI Data Manager system (https://ntlhealthcare.com (accessed on 23 May 2021), Seongnamsi, Korea). Except for the annotated area, the background was treated with black to create a mask image of the acetowhite area. The captured cervical images were all constant at 1504 × 1000 pixels. Except for the external os located in the center of the image, the vaginal wall and colposcopy were on both sides of the image, so unnecessary parts for learning were included. Accordingly, both sides were cropped based on the center such that the aspect ratio of all images was the same, and the size was converted to 256 × 256 pixels. To generate an RGB channel superposition image, the acetowhite mask image and original image were prepared by separating each RGB channel image. 2.3. RGB Channel Superposition The RGB channel superposition image clearly shows the acetowhite region of interest, while also learning the pixel values of the acetowhite periphery. To create an RGB channel superposition image, the original image was divided into R, G, and B channels to have one channel and defined as OR, OG, and OB, respectively. In addition, the acetowhite mask image created through image preprocessing was divided into images with one channel and defined as MR, MG, and MB, respectively. The RGB channel superposition image was created by selecting two channels from the three channels of the original image, selecting one channel from the three channels of the mask image, and placing each image into the three RGB channels and merging them. Figure 1 shows a schematic of the RGB channel superposition process. For example, if MR is selected as one of the three channels of the mask image and OG and OB are selected as two of the three channels of the original image, the acetowhite part has a purple-red color, and in the other areas, a blue-colored image is created. Because one image has three RGB channels, if multiple cases are created by selecting one of the three channels of the mask image and two of the three channels of the original image in the same way, a total of nine cases will be made. Figure 2 shows each of the nine cases created by the RGB channel superposition. Each RGB channel superposition image consisted of 438 A1 and 477 P1B images identical to the original image and was used for the training and testing of each model. The OpenCV library (version 4.5.0) was used to superpose the RGB channels of the original image and acetowhite mask image. 2.4. Classification Deep Learning Model The image classification deep learning model used in this study is the ResNet. Based on the VGGNet, a shortcut is placed between the convolutional layers, and the input value x was added to the output value F(x) after the training layer to determine the minimum value of F(x) + x and use it for the next input value. Thus, by learning the optimal F(x) + x, the classification performance increases as the layers become deeper, and the error rate is lower than that of VGGNet or GoogLeNet [26]. ResNet 50 is a model with 50 convolutional layers in the ResNet structure. Figure 3 shows the ResNet 50 model structure with shortcuts connected every three layers. A learning process that requires a large amount of data and time is essential for a deep-learning model to achieve a high level of performance. Therefore, transfer learning was used, which enables high performance with a small amount of data by learning and training the prepared data based on the weights of the pretrained model [27,28]. In this study, a ResNet 50 model based on ImageNet was trained using the Adam optimizer; the batch size was 40, the epoch was 200, and the learning rate was set to 0.0001. 2.5. Evaluation of the Deep Learning Model Performance In this study, to evaluate the performance of the deep learning classification model, the precision and recall, F1-score and accuracy, and area under curve (AUC) score were calculated by comparing the ground truth of the data and the deep learning classification results. True negative (TN) designates cases when a normal cervical image was classified as normal, whereas true positive (TP) represents cases when a lesioned cervical image was classified as an abnormal. A case in which a normal cervical image was classified as abnormal is defined as a false positive (FP), and a case in which the abnormal cervical image was classified as normal is defined as a false negative (FN). The four indicators used to evaluate the performance of the deep learning classification model were calculated using Equations (1)–(4). (1) Precision =TPTP+FP×100 (2) Recall =TPTP+FN×100 (3) F1−score =Precision × RecallPrecision + Recall×2 (4) Accuracy =TP+TNTN+TP+FP+FN×100 In addition, a receiver operating characteristic (ROC) curve for the performance of each model was drawn, and AUC, which is the area under the ROC curve, was calculated. The closer the AUC is to 1, the better the model’s performance. 2.6. Statistical Analysis Statistical analysis was performed to confirm the statistical significance between the study results using MedCalc (version 8.2.1.0, MedCalc Software, Ostend, Belgium). The precision, recall, F1-score, accuracy, and AUC of the original image model, acetowhite mask model, and RGB channel superposition model were compared and analyzed using the Friedman–Nemenyi test. A p-value less than 0.05 is considered statistically significant. For the RGB channel superposition model, the model showing the highest performance among the nine models was selected, and the statistical significance of the results was checked. We further confirmed the statistical significance using the critical difference diagram which shows that the mean ranks of each model under 5 different deep learning model performance evaluation methods. The lower the rank, further to the left, the better the performance of a model compared to the others [29]. 3. Results The precision, recall, F1-score, accuracy, and AUC were calculated to evaluate the classification performance of each model, to which the original images, acetowhite mask images, and RGB channel superposition images were trained. To prevent overfitting and increase the reliability of the deep learning model performance evaluation, the entire dataset was divided into five and evaluated through five cross-validations using each as a test set once. Table 1 shows the average deep learning model performance evaluation score of each RGB channel superposition case calculated through five cross-validations. The original image model showed a precision of 84.73%, recall of 57.45%, F1-score of 68.25%, and accuracy of 72.46%. In the model trained on the image made with the mask using acetowhite as the region of interest, the precision was 84.70%, recall rate was 66.45%, the F1-score was 74.41%, and the accuracy was 76.28%. In the model trained with the original image and acetowhite mask image, to which the RGB channel superposition algorithm was applied, the model with the highest performance had a precision of 90.05%, a recall rate of 72.55%, an F1-score of 79.94%, and an accuracy of 81.31%. Table 2 shows the deep learning performance evaluation score of each model depending on the applied algorithm. We compared the precision, recall, F1-score, accuracy, and AUC of the original image model, acetowhite mask model, and RGB channel superposition model using the Friedman–Nemenyi test. The result from the test shows 0.0388 of p-value. Figure 4 shows the critical difference diagram of the models. The ROC graph and AUC of the original image model, acetowhite mask model, and RGB channel superposition model are shown in Figure 5. The AUC values were 0.731 in the original image model, 0.767 in the acetowhite mask image model, and 0.817 in the RGB channel superposition model. 4. Conclusions In this study, we compared the performance of a deep learning classification model for cervical cancer, the original image without image processing, a mask image made with acetowhite as the region of interest, and an RGB channel superposition model, which was created by selecting the channel in the original image and acetowhite mask image. We aim to propose an image processing algorithm for improving the classification performance. Based on the evaluation results of the deep learning classification performance, the original image showed 72.46% accuracy and 0.731 AUC, and the acetowhite mask image showed 76.28% accuracy and 0.767 AUC. The acetowhite mask image model showed an improvement of approximately 4% compared with the original image model. The model with the highest performance among the nine cases of RGB channel superposition is the model with the R channel of the acetowhite mask and the R and B channels of the original image. This model showed an accuracy of 81.31% and an AUC of 0.817, which is approximately 9% higher than those of the original image model and approximately 5% higher than those of the acetowhite mask image model. As a result of the Friedman–Nemenyi test, which can verify the statistical significance, it shows 0.0388 of p-value, meaning a statistically significant difference. In addition, the critical difference diagram shows that the leftmost of the three models, the RGB channel superposition model, has the best performance compared to others. The model trained with the acetowhite mask image had a better performance than the original model because the characteristic of acetowhite, a white spot that appears on the cervix after acetic acid treatment, is an important criterion for diagnosing the stage of the lesion [30]. The mask image made with acetowhite as the region of interest reduces the influence of additional elements, such as the vaginal wall and colposcopy, except for the acetowhite part, and enables a deep learning model to efficiently train the features of acetowhite. RGB channel superposition is an algorithm that creates an image by taking one channel from the acetowhite mask image and two channels from the original image. The performance of this RGB channel superposition was superior to that of the original or acetowhite mask image. For the acetowhite region of interest, the pixels of all three channels were trained, and for parts other than the region of interest, the pixels of the two channels were trained. It is thought that this is because it uses less data on parts other than the region of interest and uses more information about the acetowhite region of interest for training. Among the nine models combined with the RGB channel superposition algorithm, the model that showed the highest performance was the one combining the R channel of the acetowhite mask and the R and B channels of the original image. When the histogram of the cervical image was analyzed, the number of pixels in the image was largest in the order of the R, B, and G channels. As a result, this model, which combines the R channel of the mask and the R and B channels of the original image, trained the image with the highest number of pixels among the nine models created by superposing the RGB channels. Accordingly, it had the highest performance among the nine models as it obtained the largest amount of pixel information for acetowhite and peripheral pixel information from the cervical image. Various methods were proposed to utilize the acetowhite region of interest for deep learning through a systematic comparison of each model. However, a more advanced deep learning classification model can be developed through further research. In this study, a mask was created using the acetowhite region of interest as a rectangular region. However, if a polygonal region mask that can show a clear boundary for acetowhite is created, then the characteristics of the acetowhite boundary can be trained more clearly. It is expected that the impact of areas not necessarily included in the rectangular region can be reduced. In addition, in this study, acetowhite ROI data manually annotated by specialists and experts were used. Therefore, the cervical data are insufficient for training. The classification performance of the deep learning model will be further improved using a sufficient amount of cervical data annotated with acetowhite or data augmentation to satisfy the amount of data required for training. According to the results of this study, if the RGB channel superposition algorithm is applied to a cervical classification image, the performance of the deep learning model of cervical cancer can be improved by training the acetowhite region with more pixel information than the peripheral part. Therefore, the diagnostic efficiency and accuracy of professional personnel in cervical cancer screening and diagnosis are expected to increase in the future. In addition, it is expected to help develop a CAD system for diagnosing cervical cancer by providing various evaluation indicators for the use of acetowhite in deep learning. Author Contributions Conceptualization, Y.J.K. (Young Jae Kim) and K.G.K.; Resources, W.J., K.H.N. and S.N.K.; Supervision, Y.J.K. (Young Jae Kim) and K.G.K.; Writing—original draft, Y.J.K. (Yoon Ji Kim). All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 A schematic diagram of the RGB channel superposition process. (a) Acetowhite mask image (b) Original image (c) RGB channel superposition image. Figure 2 Nine cases of cervical images made through RGB channel superposition. Figure 3 Diagram of the ResNet 50 model architecture. Figure 4 Critical Difference Diagram of the Friedman–Nemenyi test for deep learning model performance comparison. The number shows the lank of three models. The lower the rank, the better the performance of a model. Figure 5 ROC graph and AUC of the original, acetowhite mask, and RGB channel superposition models. sensors-22-03564-t001_Table 1 Table 1 Deep learning model performance evaluation score of each RGB channel superposition case. Precision (%) Recall (%) F1-Score (%) Accuracy (%) MR + OG+ OB 90.18 68.51 77.51 79.56 MR + OG + OR 89.60 69.59 77.96 79.89 MR + OB + OR 90.05 72.55 79.94 81.31 MG + OG + OB 89.18 70.46 78.61 80.22 MG + OG + OR 89.75 70.03 78.37 80.22 MG + OB + OR 89.86 68.96 77.85 79.67 MB + OG + OB 91.19 68.53 77.58 79.89 MB + OG + OR 87.88 70.65 78.10 79.67 MB + OB + OR 88.77 68.35 77.26 78.80 sensors-22-03564-t002_Table 2 Table 2 Performance evaluation score of each deep learning model according to the applied algorithm. Precision (%) Recall (%) F1-Score (%) Accuracy (%) Original image 84.73 57.45 68.25 72.46 Acetowhite mask 84.70 66.45 74.41 76.28 RGB channel superposition 90.05 72.55 79.94 81.31 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Stelzle D. Tanaka L.F. Lee K.K. Ibrahim Khalil A. Baussano I. Shah A.S.V. McAllister D.A. Gottlieb S.L. Klug S.J. Winkler A.S. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095264 ijerph-19-05264 Article Applicability of Novel Urinary Biomarkers for the Assessment of Renal Injury in Selected Occupational Groups in Sri Lanka: A Comparative Study with Conventional Markers Ekanayake E. M. D. V. 1 https://orcid.org/0000-0002-3095-6164 Gunasekara T. D. K. S. C. 2 https://orcid.org/0000-0002-1927-6512 De Silva P. Mangala C. S. 2* Jayasinghe Sudheera 3 Chandana E. P. S. 4 https://orcid.org/0000-0003-2485-6893 Jayasundara Nishad 5 Andreoli Roberta Academic Editor 1 Department of Biomedical Sciences, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, ND 58203, USA; emdvekanayake@gmail.com 2 Department of Zoology, Faculty of Science, University of Ruhuna, Matara 81000, Sri Lanka; sameera.ac@live.com 3 Department of Pharmacology, Faculty of Medicine, University of Ruhuna, Galle 80000, Sri Lanka; sudheerasj@yahoo.com 4 Department of Biosystems Technology, Faculty of Technology, University of Ruhuna, Matara 81000, Sri Lanka; epschandana@zoo.ruh.ac.lk 5 The Nicholas School of the Environment, Duke University, Durham, NC 27708, USA; nj58@duke.edu * Correspondence: chathura@zoo.ruh.ac.lk; Tel.: +94-41-2222682 (ext. 4702) 26 4 2022 5 2022 19 9 526414 3 2022 20 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Screening approaches with more robust biomarkers, are of the utmost importance in the characterization of renal injuries, particularly among communities with high burdens of chronic kidney disease of uncertain etiology (CKDu). The present study aimed to assess the utility of two emerging biomarkers: kidney injury molecule (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) in predicting renal injury in different occupational groups in Sri Lanka. A cross-sectional study was conducted with six occupational groups (n = 188): fisherfolk (FF), paddy farmers (PF), sugarcane farmers (SF), factory workers (FW) and plantation workers (PW) to assess the predictive performance of KIM-1 and NGAL against a CKDu patient (PT) group (n = 40). The median KIM-1 levels of the study groups; FF, PF, SF, FW, PW and PT were 0.67, 0.59, 0.49, 1.62, 0.67 and 5.24 ng/mgCr, respectively, while the median NGAL levels were 1.16, 2.52, 1.42, 1.71, 1.06 and 22.41 ng/mgCr respectively. In ROC analysis to predict CKDu susceptibility, the area under the curve for KIM-1 ranged from 0.88 to 0.99 for the study groups, and in overall analysis, the sensitivity and specificity were 100% and 96%, respectively, for a cutoff value of 2.76 ng/mgCr. Similarly, for NGAL the range of AUC was 0.78–0.94, and a cutoff value of 3.12 ng/mgCr produced 88% sensitivity and 82% specificity. Compared with conventional markers, KIM-1 was the best biomarker for the characterization of renal injury in the participants of the occupational groups. With further validations, KIM-1 may be adopted as a prognostic marker to identify early renal injury and CKDu susceptibilities in community screening. kidney diseases biomarkers early diagnosis occupational groups mass screening ==== Body pmc1. Introduction Over the last few decades, a peculiar form of chronic kidney disease (CKD) has continued to emerge, predominantly in association with agricultural communities in hot humid tropics across the globe. This neophropathy is known as CKD of uncertain etiology (CKDu), and has become predominant in several global hotspots, including some countries in Central America: El Salvador, Nicaragua, Guatemala, Mexico, Panama, and Costa Rica [1], Sri Lanka [2], Andhra Pradesh in India, and the El-Minia Governorate in Egypt [3]. The Etiology behind CKDu in both Sri Lanka and Central America is debated, and the exact cause of this unique form of CKD is yet unknown [4]. A consistent histopathological and biochemical characteristics associated with the disease in its global hotspots, suggest a potentially common etiology [5]. According to epidemiological evidence, CKDu is predominant in rural agricultural communities and disproportionately affects young to middle-aged male farmers [6,7]. In the current clinical settings, community screening for the compromised renal function is mainly based on conventional markers such as dipstick proteinuria, urinary albumin to creatinine ratio (ACR) and serum creatinine (SCr) [2,8]. In a recent study on the prevalence of CKDu in Sri Lanka, conducted by the World Health Organization (WHO), ACR has been used as the leading diagnostic tool to identify susceptible cases [8]. Moreover, estimated Glomerular Filtration Rate (eGFR) is used in combination with ACR for defining CKD stages, and assessment of CKD risk. However, histopathological findings suggest that CKDu is a tubulointerstitial disease, hence the use of proteinuria and SCr is rather questionable in detecting early renal damage [9,10]. Particularly, the disease is relatively asymptotic in the early stages, with no detectable changes in urinary albumin excretion, SCr, or eGFR. A number of studies have rendered the limitations of sensitivity and specificity of the conventional markers in the diagnosis of CKDu in the early stages, and the reliance of renal screening on the conventional markers, may underestimate the prevalence of CKDu within communities [11,12,13]. Several emerging urinary biomarkers with enhanced sensitivity and specificity, such as Kidney Injury Molecule-1 (KIM-1), Neutrophil Gelatinase-Associated Lipocalin (NGAL), N-acetyl beta glucosaminidase (NAG), Interleukin 18 (IL-18), Insulin-like Growth Factor-Binding Protein 7 (IGFBP7) and Tissue Inhibitor of Metalloproteinase-2 (TIMP-2) are preferably used for the detection of Acute Kidney Injury (AKI) [14]. The utility of such biomarkers in the characterization of nephropathies associated with CKDs is evident in communities impacted by Mesoamerican nephropathy [15,16]. Notably, while KIM-1 and NGAL remain more widely used in higher income countries, clinical use of these markers remain novel in the clinical settings of low-middle income countries. Recently, a few comparative studies in Sri Lanka have demonstrated the applicability of several novel biomarkers along with conventional markers in CKDu screening [11,12,17]. To date a comprehensive assessment of novel biomarkers in communities with diverse occupational and socioeconomic strata in different climatic zones has not been performed in Sri Lanka. Hence, the applicability and usefulness of these novel biomarkers or biomarker combinations have to be further validated through community studies to implement these biomarkers in the clinical practice. The main objective of the study was to perform a comparative assessment on the applicability of two novel urinary biomarkers, KIM-1 and NGAL, in predicting renal injury in different occupational communities in Sri Lanka. Furthermore, we aimed to compare the performance of these biomarkers in terms of sensitivity and specificity against the conventional markers used for the assessment of renal function in the current clinical practice. 2. Materials and Methods 2.1. Study Design and Participants A cross-sectional study was performed with five different occupational groups including fisherfolk (Mannar, Northern Province), sugarcane farmers and sugar factory workers (Buttala, Uva Province), paddy farmers (Wasgamuwa, North Central Province) and tea plantation workers (Nuwara Eliya, Central Province) along with a patient group diagnosed with CKDu (Anuradhapura, North Central Province) in Sri Lanka (Figure 1). Here, we considered CKDu patient group as the positive control as they had been clinically diagnosed with CKDu, and the subjects were in CKD stages 3, 4, and 5. For the recruitment of participants, the minimum sample size was determined using the formula n = [(z2) P (1 − P)]/d2. The standard normal variate (z2) was taken as 1.96 at 5% type 1 error (p < 0.01) and the absolute error (d) was assumed to be 5% (d = 0.05). The prevalence of CKD (P) was taken as 2.35% for CKDu endemic regions in North Central Province, based on the most recent study by Ranasinghe et al. [7]. As per the calculations from the above formula, the minimum sample size was 36 for the regions with the highest burden of CKDu. The same number of participants were recruited for each occupational group. In the study, individuals involved in the selected occupations, for a consecutive period of 10 years, and clinically confirmed CKDu patients at CKD stages 3, 4 and 5, were eligible for participation. The selection of participants was performed based on systematic random sampling using the electoral register in each Grama Niladhari (GN) division. The eligible individuals in all occupational groups and the patient group were ranked according to their age with a sequential number assigned for each individual. Using a computer-generated random number table, 33–40 individuals from each study group were selected for the assessment of renal biomarkers. 2.2. Data and Sample Collection An interviewer-administered pre-tested questionnaire was used for collecting demographic and socioeconomic data and details on health status, lifestyle and exposure to potential risk factors from the participants. An onsite medical examination was conducted, and resting blood pressure, height and weight of each participant were measured, in addition to the inspection of medical records. An early morning first void non-fasting urine sample and a non-fasting blood sample were obtained from each individual. Urine samples were collected into sterile containers (50 mL), while blood samples were collected into sterile serum separator tubes. Qualified medical professionals were involved in the medical examination and blood sampling. 2.3. Sample Preparation and Analysis Blood samples were kept standing for 30 min for coagulation. Following centrifugation at 1000× g for 15 min at 37 °C, serum was transferred into plain vacutainer tubes. Serum and urine samples were temporarily stored at 2–4 °C, and transported to the laboratory within 24 h. Processed samples were stored at −80 °C until analysis, and all the analyses were completed within five days. Quantitative assessment of SCr, serum Cystatin-C (SCys-C), blood urea nitrogen (BUN), serum uric acid (SUA), urinary creatinine (UCr) and urinary microalbumin (UmALB) was performed with an automated biochemistry analyzer (Humasatr100, Human mbH, Wiesbaden, Germany) in the biochemistry laboratory of the Department of Zoology, Faculty of Science, University of Ruhuna, Sri Lanka. The instrument was calibrated for the mentioned biochemical assays using the standards and calibrators from the manufacturer before sample analysis along with quality control. Urine samples were centrifuged at 1000× g for 10 min and the supernatant was taken for the analysis. Quantitative analysis of KIM-1 and NGAL in urine samples was performed with Enzyme-linked immunosorbent assay (ELISA) using the assay kits (CUSABIO, Wuhan, China) according to the manufacturer’s assay protocol. A microplate spectrophotometer (Epoch 2; Biotek Instruments, Winooski, VT, USA) was used for absorbance measurements. The detection ranges for KIM-1 and NGAL were 0.312–20 ng/mL and 15.6–1000 pg/mL respectively. 2.4. Diagnostic Criteria For the assessment of renal function, SCr, SCys-C, BUN, SUA, ACR and eGFR were used as the diagnostic criteria. Obesity was defined in terms of body mass index (BMI) ≥27 kg/m2 [18] and hypertension was defined as either systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg, as confirmed with repeated measures [19]. Albuminuria was defined as ACR ≥30 mg/g. Based on the CKD-EPI creatinine-cystatin C equation (2021), eGFR was calculated [20]. 2.5. Statistical Analysis All the categorical variables were represented as numbers and percentages. Urinary KIM-1 and NGAL concentrations were adjusted to urinary creatinine before the data analysis. A Shapiro–Wilk test was used to assess the normality of clinical data of the study groups. Clinical data showed significant deviations from normality; hence a non-parametric approach was adopted for statistical analysis. Kruskal–Wallis one-way analysis followed by Dunn’s multiple comparison test was used for the comparison of clinical parameters among the study groups. Receiver-operator characteristic (ROC) analysis was performed to assess the diagnostic performance of the biomarkers. To construct ROC curves, the presence of CKDu was considered as the outcome variable. Each occupational group (negative outcome) was compared with the CKDu patient group (positive outcome). SCr, SCys-C, BUN, AUA, ACR, eGFR and creatinine-adjusted urinary KIM-1 and NGAL concentrations were used for the ROC analysis with no normalization or transformation. Multiple linear regression analysis was performed to characterize potential associations of KIM-1, NGAL, ACR and eGFR with several predictor variables: age, gender, body mass index (BMI), hypertension, diabetes mellitus, occupation, quality of drinking water source, agrochemical exposure, habits of smoking and liquor consumption. Statistical analyses and representations were conducted using GraphPad Prism 9.3 (GraphPad software LLC, San Diego, CA, USA), IBM SPSS Statistics 26.0 (IBM INC., New York, NY, USA) and NCSS 2021 (NCSS, LLC., Kaysville, UT, USA). 2.6. Ethical Considerations The study was conducted in accordance with the declaration of Helsinki, under the approval of the Ethics Review Committee of the Faculty of Medicine, University of Ruhuna, Matara, Sri Lanka. (Reference No: 09.03.2016:3.2). Informed written consent was obtained from each participant before enrollment for the study. The participants granted consent for the provision of data and samples and the use of results for publications. 3. Results 3.1. Characteristics of the Study Participants Table 1 presents the demography, health, and lifestyle data of the participants in the five occupational groups and the CKDu patient group. Diabetes mellitus and hypertension were the most common comorbidities observed among the participants, and the prevalence of these comorbidities was significantly higher among CKDu patients, compared to the other occupational groups. According to the medical history, at the time of diagnosis for CKDu, the patients had shown no evidence of diabetes mellitus or hypertension. 3.2. Novel Urinary Biomarkers Table 2 presents the creatinine-adjusted urinary biomarkers KIM-1 and NGAL in the study participants. The highest expression of both KIM-1 and NGAL was observed in CKDu patients. The expression of both biomarkers showed substantial variation among the occupational groups (Figure 2). Among the occupational groups, the highest urinary KIM-1 level was observed from sugar factory workers, and this was significantly higher than those of paddy and sugarcane farmers. On the contrary, paddy farmers showed the highest NGAL level, which was significantly higher than those of the fisherfolk and plantation workers. According to the interpretations of the clinical studies in the regions with no records of CKDu prevalence, and with least exposure to potential risk factors of CKDu according to observational studies in Sri Lanka, the mean (range) KIM-1 and NGAL levels of healthy individuals are known as 0.17 (0.0–3.4) ng/mg Cr and 0.47 (0.0–1.63), respectively [12]. The biomarker levels reported in all the study groups in our study are higher than those values of the healthy individuals with least exposure. 3.3. Conventional Markers The group of the patients showed the highest levels of SCr, SCys-C, BUN and SUA along with the lowest eGFR (Table 3). The incidence of albuminuria (ACR ≥ 30 mg/g) and declined eGFR (eGFR < 60 mL/min/1.73 m2) was at its highest among the sugarcane farmers, compared to the other occupational groups. The distributions of eGFR and ACR in the study groups are shown in Figure 3. Both clinical markers of renal function, eGFR and ACR, were significantly different (p < 0.0001) in the occupational groups compared to the patient group (Figure 3). Sugar factory workers reported the highest eGFR that was significantly different compared to those of the fisherfolk and sugarcane farmers. However, ACR showed no substantial differences among the occupational groups. Furthermore, the other four clinical markers: SCr, SCys-C, BUN and SUA also showed substantial differences among the study groups (Figure 4). 3.4. Assessment of Biomarker Performance ROC curves for the assessment of biomarker performance in the occupational groups in comparison to the patients are shown in Figure 5. In the overall analysis, KIM-1, SCr, ACR and SCys-C produced excellent classifier models with area under the curve (AUC) values above 0.9. Based on the sensitivity and specificity of the biomarkers in the classifier models, KIM-1 served as the most robust biomarker in the characterization of renal injury (Table 4). However, in comparison to all biomarkers, KIM-1 produced a cut-off value of 2.76 ng/mgCr with 100% sensitivity and 96% specificity in predicting the susceptibility of CKDu among the studied communities. Multiple linear regression analysis revealed significant associations of several variables with the urinary expression of KIM-1 and NGAL, and the two conventional markers, ACR and eGFR (Table 5). Among the predictor variables, occupation showed significant associations with the four markers, while the effect of diabetes mellitus was significant only for KIM-1 and NGAL. 4. Discussion The expression of both novel biomarkers, KIM-1 and NGAL, was significantly higher in CKDu patients, as expected. The declined renal function of the patients was further evidenced by significantly elevated ACR and significantly declined eGFR, compared to the other occupational groups. The distributions of biomarkers, except ACR showed significant variation among the occupational groups to varying degrees. In comparison to the other occupational groups, the proportions of albuminuria (ACR ≥ 30 mg/g) and declined eGFR (eGFR < 60 mL/min/1.73 m2) were at the highest among sugarcane farmers, with significant differences compared to the other occupational groups. Furthermore, the highest median levels of SUA, SCys-C, and the lowest median of eGFR were observed among sugarcane farmers with substantial differences compared to the other study groups. However, there were no significant differences in ACR among the study groups. ROC analysis of biomarkers demonstrated KIM-1, SCr, ACR, and SCys-C as potential biomarkers in predicting renal injury, with AUC values above 0.9. In terms of sensitivity and specificity, KIM-1 performed better in the prediction. In the classifier model for KIM-1, AUC was 0.988 while sensitivity and specificity were 100% and 96%, respectively, for a cutoff value of 2.76 ng/mgCr. Although a novel biomarker, the predictive power of NGAL was not as significant as KIM-1 in the study communities. Emerging evidence from recent research suggests certain novel biomarkers including KIM-1 and NGAL as more robust indicators of renal injury in comparison to the conventional markers, particularly in community screening in CKDu-affected regions. The applicability of these novel biomarkers in the settings of Mesoamerican nephropathy is much more evident [6,21]. However, the utility of these biomarkers, particularly in the characterization of early renal injury in CKDu-affected communities in Sri Lanka has not been well studied. Hence, the main focus of the present study was to assess the applicability of KIM-1 and NGAL for the early diagnosis of renal injury in selected occupational groups in Sri Lanka, along with a patient group diagnosed with CKDu. According to the interpretations on albuminuria and declined eGFR, there may be a high incidence of potential renal damage among sugarcane farmers. However, this observation is not always supported by other biomarkers, as they do not exhibit distinct differences compared to all other occupational groups. Furthermore, the two novel biomarkers, KIM-1 and NGAL, did not exhibit profound differences among the occupational groups. Hence, within the context of these findings, it is difficult to reliably identify a particular occupational group with a high risk of compromised renal function, mainly due to the low sample size. Importantly, our main focus was to assess the applicability of the novel biomarkers, KIM-1 and NGAL, against the conventional markers for the identification of renal damage in different occupational groups. In that context, we identified KIM-1 as the best marker to indicate renal injury over the other biomarkers, based on ROC analysis. A recent analysis of biomarker studies rendered the utility of several novel biomarkers, including KIM-1 and NGAL, to characterize early renal injury in adult and pediatric communities in Mesoamerica and Sri Lanka [4]. In 2016, De Silva et al. [12] demonstrated the potential utility of urinary KIM-1 and NGAL for detecting early renal damage among farming communities in Sri Lanka, in the absence of albuminuria. This study indicated that creatinine-adjusted urinary KIM-1 has some potential to diagnose early CKDu cases over the other conventional and novel biomarkers. This was further confirmed with the high sensitivity and specificity of KIM-1 in three ROC models, including sugarcane workers and paddy farmers in regions with emerging evidence of CKDu [12]. On the contrary, Wanigasuriya et al. (2018) demonstrated that specificities of Cys-C, KIM-1 and clusterin were very low for given sensitivities when comparing healthy individuals even with diagnosed CKDu patients at stage 3 or above. Hence, the researchers did not identify KIM-1 as an effective biomarker for screening tests [11]. However, consistent with our findings, the utility of KIM-1 in combination with two other biomarkers, alpha-1-microglobulin (α1M) and retinol-binding protein-4 (RBP-4), to differentiate CKD cases from healthy individuals was shown by Fernando et al. [21], with an overall sensitivity of ≥0.867 and specificity ≥0.765. Furthermore, a combination of KIM-1 with osteopontin and RBP-4 was shown to be capable of distinguishing CKDu patients from CKD patients with both a sensitivity and specificity of ≥0.93. The role of KIM-1 and NGAL as indicators of potential renal injury has also been studied in association with Mesoamerican communities. Multiple studies have shown that significantly elevated urinary expression of KIM-1 and NGAL in sugarcane field workers along with elevated levels of SCr and BUN and reduced eGFR during the work shifts [4]. Furthermore, elevated expression of KIM-1 and NGAL has been observed in children and adolescents from regions with a high burden of CKDu in Nicaragua [22] and El Salvador [23]. These studies provide evidence that proves the potential utility of KIM-1 and NGAL as prognostic indicators of renal injury and susceptibility of CKDs. However, an assessment of the diagnostic performance of these biomarkers in terms of sensitivity and specificity has not been performed in a Mesoamerican context. There are several noteworthy strengths in the present study. In clinical practice, community screening for declined kidney function, and the identification of susceptible cases is mainly based on dipstick proteinuria, ACR, and SCr [2,8]. However, the accuracy and reliability of such conventional markers are often questioned. A few comparative studies using conventional markers and novel markers in CKDu screening have been recently reported [11,12,17]. However, a detailed comparison of different occupational groups and inhabitants in different climatic zones does not exist. Furthermore, the effect of currently known risk factors of CKDu, such as agrochemical exposure, heat exposure, contaminated drinking water, climatic factors, and food and lifestyle habits, on individuals varies depending on the occupation and residential climatic zones. Therefore, this study was focused on several occupational groups (fishermen, paddy farmers, sugarcane factory workers, sugarcane farmers and Plantation workers) from the three climatic zones (arid zone, dry zone and wet zone) in Sri Lanka, and it aimed to find the most specific and sensitive marker for early CKDu diagnosis. According to the findings, KIM-1 appeared to be the best marker to identify compromised renal function in the study groups. In this study, CKDu patient group was considered as the positive control where all the individuals were confirmed CKDu subjects at CKD stages 3, 4, and 5. It is known that CKDu is different from CKD, and this nephropathy is not associated with diabetes, glomerulonephritis, hypertensive renal diseases, and polycystic kidneys [2,16,24,25,26]. However, here we found 52.5% of patients with diabetes and 30% of patients with hypertension in the CKDu patient group. This could be expected in the selected individuals were with end-stage kidney disease. Importantly, past medical history records of all patients in the CKDu patient group confirmed the absence of diabetes or hypertension at the time of diagnosis. Moreover, a significant association of occupation with KIM-1, NGAL, ACR, and eGFR was observed in regression analysis. This may indicate certain effects of the nature of occupation on renal function. However, several factors, such as environmental toxins, heat stress and dehydration tendencies, level of physical activity, and lifestyle, potentially affect renal health while the characterization of such associations with detailed studies is an important measure to ensure occupational renal health. In addition, the present study presents several limitations. The main limitation is the low sample size, although we have met the required minimum sample size for the study as calculated by a power analysis. The lack of a negative control group also appears to be a limitation of our study. Depending on the occupation lifestyle, contamination of food and water with potential nephrotoxic agents, communities are likely exposed to potential environmental risk factors. For instance, fishermen are more exposed to heat compared to the farmers and, on the contrary, farmers are more exposed to agrochemicals in farming fields while fishermen do not experience agrochemical exposure. Furthermore, the climate is also different from region to region. Studies indicate that several environmental risk factors such, as heavy metals, agrochemicals, fluoride and hardness in drinking water, heat exposure and dehydration tendencies, and environmental toxins, serve as the main risk factors for renal diseases including CKDu [6], but their role may vary across communities in the country. Hence, the selection of an ideal negative control group is challenging. However, a recent clinical study in communities from the regions with no evidence of CKDu incidence and known to have the lowest level of exposure to the potential risk factors reported medial KIM-1 and NGAL levels comparatively lower than those of the occupational groups participated in the present study. In our study, the main focus was not to assess the prevalence of CKDu susceptibility but to assess the performance of KIM-1 and NGAL in predicting renal damage and abnormal renal function in diverse occupational groups in Sri Lanka. Here, we have shown a better performance of KIM-1 over the other biomarkers in predicting abnormal renal function. However, as the sample size is the main limitation of our study, the prognostic utility of KIM-1 requires further validation with detailed studies. Furthermore, there are no established cutoff values for KIM-1 and NGAL to distinguish CKD/CKDu cases from healthy individuals in Sri Lanka. The sensitivity and specificity of KIM-1 and NGAL over conventional markers to predict early renal injury has been shown in many studies. The establishment of cutoff values is a critical milestone in the journey of these novel biomarkers to the level of clinical biomarkers of renal injury, from the experimental level. Hence these biomarkers should be further validated, and we recommend in-depth studies in association with different age and occupational groups in different clinical settings. 5. Conclusions Common conventional markers such as SCr, SCysC, and ACR had low specificity and sensitivity, and hence were not suitable for the early diagnosis of renal injury. According to our analysis, creatinine adjusted novel urinary biomarker KIM-1 had the highest sensitivity and specificity for detecting early renal injury. However, here we have produced preliminary findings, and it requires further validation with detailed studies with an increased sample size. Moreover, with further validations, KIM-1 may be developed as a robust clinical tool to diagnose abnormal renal function and disease susceptibilities in mass screening. Acknowledgments The authors would like to thank all the participants, authorities and government officials in the study areas for their valuable support. Author Contributions Conceptualization: P.M.C.S.D.S. and N.J.; data curation: P.M.C.S.D.S., E.M.D.V.E. and T.D.K.S.C.G.; formal analysis: P.M.C.S.D.S., E.M.D.V.E. and T.D.K.S.C.G.; funding acquisition: P.M.C.S.D.S. and N.J.; investigation: P.M.C.S.D.S., E.M.D.V.E. and T.D.K.S.C.G.; methodology: P.M.C.S.D.S.; project administration: P.M.C.S.D.S. and N.J.; resources: P.M.C.S.D.S., E.P.S.C., S.J. and N.J.; software: E.M.D.V.E. and T.D.K.S.C.G.; supervision: P.M.C.S.D.S., S.J., E.P.S.C. and N.J.; validation: P.M.C.S.D.S., S.J., E.P.S.C. and N.J.; visualization: E.M.D.V.E. and T.D.K.S.C.G.; writing—original draft: E.M.D.V.E. and T.D.K.S.C.G.; writing—review & editing: P.M.C.S.D.S., S.J., E.P.S.C., N.J. and T.D.K.S.C.G. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the University of Ruhuna research grants [Grant number: RU/PG-R/16/04] under the funds of the University Grants Commission, Sri Lanka, The Nicolas School of the Environment, and Duke Global Health Institute Pilot Grant. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Review committee of the Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka (Reference No. 09.03.2016:3.2). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper. Data Availability Statement The data presented in this study are not publicly available due to the restrictions under ethical liabilities with the participants. Conflicts of Interest The authors declare no conflict of interest. Figure 1 CKDu burden in Sri Lanka and the study locations. The prevalence of CKD/CKDu is given as the number of reported cases at the Divisional Secretariat level (Adapted from [7]). Figure 2 Biomarkers of renal injury: (a) kidney injury molecule (KIM-1); (b) neutrophil gelatinase-associated lipocalin (NGAL) in the study participants. The plots represent biomarker distribution with median and interquartile ranges. Inter-group statistical significance is shown with p values as implied by the Kruskal- Wallis test followed by Dunn’s multiple comparison test. Occupational groups: FF: fisherfolk, PF: paddy farmers, SF: sugarcane farmers, FW: factory workers, PW: plantation workers and PT: CKDu patients. Figure 3 Parameters of renal function: (a) estimated glomerular filtration rate (eGFR) according to CKD-EPI creatinine-Cystatin-C equation 2021; (b) albumin creatinine ratio (ACR). The plots represent distribution with median and interquartile ranges. Inter-group statistical significance is shown with p values as implied by the Kruskal-Wallis test followed by Dunn’s multiple comparison test. Occupational groups: FF: fisherfolk, PF: paddy farmers, SF: sugarcane farmers, FW: sugar factory workers, PW: plantation workers, and PT: CKDu patients. Figure 4 Parameters of renal function: (a) serum creatinine (SCr), (b) serum cystatin C (SCys-C), (c) blood urea nitrogen (BUN), and (d) serum uric acid (SUA). The plots represent distribution with median and interquartile ranges. Inter-group statistical significance is shown with p values as implied by the Kruskal-Wallis test followed by Dunn’s multiple comparison test. Occupational groups: FF: fisherfolk, PF: paddy farmers, SF: sugarcane farmers, FW: sugar factory workers, PW: plantation workers, and PT: CKDu patients. Figure 5 Receiver Operator Characteristic (ROC) curves of conventional and novel urinary biomarkers. SCr: serum creatinine, SCys-C: serum cystatin C, ACR: albumin creatinine ratio, BUN: blood urea nitrogen, SUA: serum uric acid, KIM-1: kidney injury molecule, NGAL: neutrophil gelatinase-associated lipocalin. ijerph-19-05264-t001_Table 1 Table 1 Demographic and clinical characteristics of the study participants. Characteristics Fisherfolk (n = 40) Paddy Farmers (n = 40) Sugarcane Farmers (n = 40) Factory Workers (n = 33) Plantation Workers (n = 35) CKDu Patients (n = 40) Demographic characteristics Age/years † 47 (37–54) 46 (32–55) 50 (36–62) 37 (29–46) 47 (39–56) 53 (47–65) Gender Male Female 27 (67.5) 13 (32.5) 12 (30.0) 19 (70.0) 24 (60.0) 16 (40.0) 21 (63.6) 12 (36.4) 16 (45.7) 19 (54.3) 26 (65.0) 14 (35.0) Clinical characteristics BMI/kgm−2 † 23.7 (22.1–26.7) 21.7 (19.4–25.6) 20.2 (17.3–22.9) 19.9 (18.6–22.6) 20.8 (18.4–24.6) 23.5 (22.0–26.0) Obesity ⁑ 5 (12.5) 5 (12.5) 1 (2.5) 1 (3.0) 2 (5.7) 1 (2.5) Diabetes mellitus ⁑ 5 (12.5) * 3 (7.5) * 0 * 0 * 2 (5.7) * 21 (52.5) Hypertension ⁑ 6 (15.0) * 3 (7.5) * 5 (12.5) * 0 * 4 (11.4) * 12 (30.0) Lifestyle-related characteristics Agrochemical Exposure ⁑ 0 40 (100.0) 32 (80.0) 0 22 (62.9) 28 (70.0) Drinking water quality ⁑ Low Medium High 0 29 (72.5) 11 (27.5) 20 (50.0) 19 (47.5) 21 (52.5) 6 (15.0) 34 (85.0) 6 (15.0) 2 (6.0) 22 (66.7) 11 (33.3) 4 (11.4) 31 (88.6) 0 0 28 (70.0) 12 (30.0) Smoking ⁑ 6 (15.0) 2 (5.0) 18 (45.0) 17 (51.2) 15 (42.9) 1 (2.5) Alcohol consumption ⁑ 19 (47.5) 10 (25.0) 15 (37.5) 17 (51.5) 22 (42.9) 3 (2.5) † Values are expressed as median (interquartile distance). * Denotes statistical significance compared to the patient group. ⁑ The incidence is given as the number of cases (percentage) for each study group and the statistical significance is expressed as implied by the Chi-squared test. Obesity is expressed in terms of body mass index (BMI) ≥27 kg/m2. Drinking water quality, Low: untreated water from shallow surface wells, Medium: disinfected water from water treatment plants, High: bottled water, filtered water from domestic filters or reverse osmosis (RO) filters. Lifestyle characteristics are based on self-reported data. ijerph-19-05264-t002_Table 2 Table 2 Creatinine-adjusted biomarker levels. Study Group KIM-1 (ng/mg Cr) NGAL (ng/mg Cr) Fisherfolk 0.665 (0.428–0.945) 1.155 (0.653–1.715) Paddy farmers 0.585 (0.373–0.910) 2.515 (1.498–4.380) Sugarcane farmers 0.490 (0.383–0.703) 1.420 (0.998–1.910) factory workers 1.625 (0.500–2.418) 1.710 (0.733–5.470) Plantation workers 0.67 (0.490–0.840) 1.060 (0.690–1.600) CKDu patients 5.242 (4.298–8.800) 22.41 (4.630–154.300) Urinary biomarker levels are given as median (inter quartile distance). KIM-1: Kidney injury molecule; NGAL: Neutrophil gelatinase-associated lipocalin. ijerph-19-05264-t003_Table 3 Table 3 Creatinine-adjusted biomarker levels. Clinical Parameters Fisherfolk (n = 40) Paddy Farmers (n = 40) Sugarcane Farmers (n = 40) Factory Workers (n = 33) Plantation Workers (n = 35) CKDu Patients (n = 40) ACR (mg/g) 4.455 (2.885–7.645) 3.945 (2.755–7.110) 3.640 (2.415–18.88) 4.600 (2.323–7.613) 3.350 (2.430–6.350) 1105.00 (227–2458) eGFR (mL/min/1.73 m2) 100.7 (89.1–105.3) 106.0 (88.5–122.7) 86.7 (59.0–118.2) 115.1 (108.4–131.3) 107.7 (90.3–123.5) 20.0 (9.9–34.8) SCr (mg/dL) 1.130 (1.02–1.228) 1.010 (0.670–1.263) 0.725 (0.590–1.243) 0.950 (0.880–1.030) 0.760 (0.670–0.960) 3.990 (2.150–9.035) SCys-C (mg/L) 0.810 (0.693–0.903) 0.770 (0.578–0.855) 1.110 (0.760–1.440) 0.710 (0.590–0.828) 0.830 (0.710–1.090) 2.510 (1.715–3.443) BUN (mg/dL) 21.00 (17.95–24.10) 10.00 (8.40–12.70) 23.70 (16.95–29.45) 23.90 (20.48–32.15) 26.30 (22.50–34.30) 46.65 (26.28–73.73) SUA (mg/dL) 5.385 (4.925–6.850) 4.445 (3.185–5.288) 5.545 (4.090–6.753) 3.850 (3.353–4.745) 5.060 (4.140–6.410) 6.295 (5.258–7.593) ACR ≥ 30 mg/g 3 (7.5) †,* 2 (5.0) †,* 9 (22.5) † 1 (3.0) †,* 1 (2.9) †,* 40 (100) eGFR < 60 mL/min/1.73 m2 2 (5.0) †,* 3 (7.5) †,* 10 (25.0) † 1 (3.0) †,* 5 (14.3) † 40 (100) Estimated Glomerular Filtration Rate (eGFR) is given according to the CKD-EPI creatinine-cystatin C equation (2021). Clinical parameters are expressed as median (interquartile distance) for each group. The incidence of elevated ACR and declined eGFR are given as the number of cases along with its proportion of the total size of the respective occupational group and the statistical significance is expressed as implied by the Chi-squared test. † Significant compared to the patient group (p < 0.0001); * significant compared to the sugarcane farmers (p < 0.05). ACR: albumin creatinine ratio, SCr: serum creatinine, SCys-C: serum cystatin C, BUN: blood urea nitrogen, SUA: serum uric acid. ijerph-19-05264-t004_Table 4 Table 4 Diagnostic performance of biomarkers. Parameter ROC Parameters FF vs. PT SW vs. PT SF vs. PT PF vs. PT EW vs. PT Overall SCr (mg/dL) AUC (95% CI) 0.942 (0.88–1.00) 0.977 (0.94–1.01) 0.891 (0.82–0.97) 0.947 (0.89–0.99) 0.978 (0.95–1.00) 0.944 (0.90–0.98) Cutoff 1.41 1.30 1.38 1.65 1.14 1.21 Sensitivity 0.92 0.93 0.92 0.90 0.95 0.93 Specificity 0.95 1.00 0.80 0.93 0.94 0.81 SCys-C (mg/L) AUC (95% CI) 0.955 (0.91–1.00) 0.985 (0.97–1.00) 0.807 (0.70–0.91) 0.95 (0.90–0.99) 0.935 (0.88–0.99) 0.920 (0.88–0.96) Cutoff 0.98 1.09 1.42 0.97 1.11 1.09 Sensitivity 0.95 0.93 0.88 0.95 0.92 0.93 Specificity 0.88 0.97 0.75 0.88 0.80 80.7 ACR (mg/g) AUC (95% CI) 0.954 (0.90–1.00) 0.963 (0.92–1.07) 0.926 (0.87–0.98) 0.959 (0.91–1.00) 0.96 (0.91–1.01) 0.949 (0.90–0.99) Cutoff 8.64 12.71 28.21 13.28 10.60 8.55 Sensitivity 0.92 0.92 0.90 0.92 0.92 0.93 Specificity 0.83 0.88 0.78 0.93 0.80 0.77 BUN (mg/dL) AUC (95% CI) 0.807 (0.69–0.92) 0.757 (0.64–0.87) 0.707 (0.59–0.83) 0.942 (0.89–0.99) 0.731 (0.61–0.85) 0.789 (0.69–0.89) Cutoff 20.85 37.25 37.00 13.55 38 24.5 Sensitivity 0.80 0.62 0.62 0.92 0.62 0.8 Specificity 0.48 0.88 0.83 0.80 0.91 0.69 SUA (mg/dL) AUC (95% CI) 0.639 (0.52–0.76) 0.904 (0.84–0.97) 0.637 (0.52–0.76) 0.851 (0.77–0.93) 0.688 (0.57–0.81) 0.738 (0.66–0.82) Cutoff 6.94 5.37 7.00 5.79 6.41 4.76 Sensitivity 0.38 0.72 0.38 0.62 0.50 0.90 Specificity 0.78 0.91 0.78 0.88 0.80 0.45 KIM-1 (ng/mgCr) AUC (95% CI) 0.998 (0.99–1.00) 0.910 (0.84–0.98) 0.875 (0.79–0.97) 0.975 (0.93–1.02) 0.999 (0.99–1.00) 0.988 (0.98–1.00) Cutoff 2.608 3.352 2.984 2.928 2.291 2.76 Sensitivity 1.00 0.92 1.00 1.00 1.00 1.00 Specificity 0.98 0.76 0.78 0.95 0.97 0.96 NGAL (ng/mgCr) AUC (95% CI) 0.936 (0.87–1.00) 0.777 (0.67–0.88) 0.923 (0.86–0.99) 0.819 (0.72–0.92) 0.863 (0.78–0.95) 0.893 (0.82–0.96) Cutoff 1.54 10.88 1.59 7.58 7.36 3.12 Sensitivity 0.95 0.12 0.90 0.68 0.70 0.88 Specificity 0.75 0.88 0.75 0.93 0.86 0.82 SCr: serum creatinine, SCys-C: serum cystatin C, ACR: albumin creatinine ratio, BUN: blood urea nitrogen, SUA: serum uric acid, KIM-1: kidney injury molecule, NGAL: neutrophil gelatinase-associated lipocalin. Occupational groups; FF: fisherfolk, PF: paddy farmers, SF: sugarcane farmers, FW: sugar factory workers, PW: plantation workers, and PT: CKDu patients. ijerph-19-05264-t005_Table 5 Table 5 Novel and conventional markers of renal function and effect of variables. Predictor Variable KIM-1 NGAL ACR eGFR β p β p β p β p Age −0.048 0.354 −0.094 0.174 −0.069 0.255 −0.163 0.002 Gender 0.06 0.264 −0.003 0.967 −0.32 0.612 −0.04 0.491 BMI 0.032 0.535 −0.05 0.462 0.041 0.494 0.012 0.829 Occupation 0.415 <0.0001 0.207 0.002 0.292 <0.0001 −2.08 <0.0001 Hypertension 0.259 <0.0001 0.093 0.272 0.368 <0.0001 −0.487 <0.0001 Diabetes mellitus 0.182 0.001 0.233 <0.0001 0.085 0.226 0.014 0.827 Quality of Drinking water −0.05 0.385 −0.029 0.707 0.008 0.905 0.039 0.535 Agrochemical exposure −0.09 0.881 −0.078 0.339 0.007 0.922 0.079 0.224 Smoking −0.073 0.163 −0.06 0.392 −0.137 0.015 0.045 0.43 Alcohol consumption 0.157 0.001 0.091 0.216 −0.064 0.324 0.097 0.099 Predictor variables with significant association with biomarkers are given in bold. β: standard coefficient; p: probability; BMI: body mass index. ACR: albumin creatinine ratio, KIM-1: kidney injury molecule, NGAL: neutrophil gelatinase-associated lipocalin, and eGFR: estimated glomerular filtration rate. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094611 ijms-23-04611 Article Isolation of Potato Endophytes and Screening of Chaetomium globosum Antimicrobial Genes Zhang Jiaxin https://orcid.org/0000-0003-1120-1863 Islam Md. Samiul Wang Jieyu Zhao Yang https://orcid.org/0000-0001-8513-8751 Dong Wubei * Skriver Karen Academic Editor Garrido José Manuel García Academic Editor Department of Plant Pathology, College of Plant Science and Technology and the Key Lab of Crop Disease Monitoring & Safety Control in Hubei Province, Huazhong Agricultural University, Wuhan 430070, China; 2019301110162@webmail.hzau.edu.cn (J.Z.); samiulislam@webmail.hzau.edu.cn (M.S.I.); 2019301120134@webmail.hzau.edu.cn (J.W.); yzhao@webmail.hzau.edu.cn (Y.Z.) * Correspondence: dwb@mail.hzau.edu.cn; Tel.: +86-150-0710-9436 21 4 2022 5 2022 23 9 461116 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Antimicrobial peptides (AMPs) have natural antibacterial activities that pathogens find difficult to overcome. As a result of this occurrence, AMPs can act as an important substitute against the microbial resistance. In this study, we used plate confrontation tests to screen out 20 potential endophytes from potato tubers. Among them, endophyte F5 was found to significantly inhibit the growth of five different pathogenic fungi. Following that, phylogenetic analysis revealed that the internal transcribed spacer (ITS) sequences were 99% identical to Chaetomium globosum corresponding sequences. Thereafter, the Bacillus subtilis expression system was used to create a C. globosum cDNA library in order to isolate the resistance genes. Using this approach, the resistance gene screening technology in the indicator bacteria built-in library was used to identify two antimicrobial peptides, CgR2150 and CgR3101, with broad-spectrum antibacterial activities. Furthermore, the results showed that CgR2150 and CgR3101 have excellent UV, thermal, and enzyme stabilities. Also, these two peptides can significantly inhibit the growth of various bacteria (Xanthomonas oryzae pv. oryzae, Xanthomonas oryzae pv. oryzicola, Clavibacter michiganensis, and Clavibacter fangii) and fungi (Fusarium graminearum, Rhizoctonia solani, and Botrytis cinerea). Scanning electron microscopy (SEM) observations revealed that CgR2150 and CgR3101 peptides act against bacteria by disrupting bacterial cell membranes. Moreover, hemolytic activity assay showed that neither of the two peptides exhibited significant hemolytic activity. To conclude, the antimicrobial peptides CgR2150 and CgR3101 are promising in the development of a new antibacterial agent and for application in plant production. endophyte potato Chaetomium globosum antimicrobial peptide Bacillus subtilis ==== Body pmc1. Introduction Microorganisms, including bacteria, fungi, and viruses, have always posed a threat to human, animal, and plant health. Antibiotics have effectively treated a wide range of diseases to lessen the damage. However, the overuse of antibiotics has resulted in the development of drug resistance in many pathogenic microbes, which will have disastrous consequences for humans and the biosphere [1,2]. To solve this problem, researchers have been looking for novel antibacterial compounds. Currently, antimicrobial peptides (AMPs) are a powerful tool against the microbial resistant pattern because they have several advantages, such as high activity, low drug resistance, and multiple modes of action, and they can resist infection by external pathogenic microorganisms by affecting the host’s immune response or directly inhibiting or killing some pathogenic bacteria, fungi, parasites, and viruses [3,4]. AMPs are naturally occurring active molecules produced by the organism. They are normally made up of 12 to 100 amino acids and have a helix or sheet structure. Besides, their structures are generally cyclic, hydrophobic, and include unique moieties such as D-amino acids (AA), which is an important prerequisite for the use as a bioactive candidate [5]. AMPs can be found in a wide range of organisms, including bacteria, fungi, higher plants, insects and other invertebrates, amphibians, fish, mammals, and even humans [6,7,8,9]. Furthermore, these peptides have antibacterial, antifungal, antiviral, and anti-parasitic features, as well as anti-inflammatory and anti-tumor properties, which makes them useful in agriculture, animal husbandry, aquaculture, and medicine [10,11,12,13,14]. In addition, AMPs have a variety of mechanisms of action. The most common action involves membrane ion channel formation and disruption via electrostatic attraction, resulting in intracellular substance leakage and cell death. Hence, these phenomena make it difficult for pathogens to develop drug resistance [15,16]. The prokaryotic expression system is one of the most frequently utilized genetic engineering approaches for the expression and secretion of exogenous target genes [17]. Bacillus subtilis has been one of the most extensively utilized prokaryotic expression systems in recent years due to its rapid growth, absence of endotoxin, and minimal damage to organism [18]. Moreover, B. subtilis possesses a robust secretion and expression capability, which enables it to directly secrete recombinant proteins into the extracellular space, thereby contributing significantly to the large-scale development of antibacterial genes [19,20,21]. Using B. subtilis as a bioengineering strain to generate functional protein products has the potential to reduce costs and add economic values [22,23]. Potato is the fourth largest food crop in the world after corn, rice, and wheat. It is primarily propagated via tubers, thereby the endophytes can be transmitted directly. In recent years, secondary metabolites such as alkaloids, lignins, terpenoids, and anthraquinones have been isolated from a variety of endophytic fungi, with the majority of them having antibacterial activity [24,25,26]. Chaetomium globosum is a widespread endophytic fungus found in Buxus L, Holly, Ginkgo, and Longan trees according to the reports [27]. C. globosum is a type of biological control agent with application potential that can prevent and treat a variety of plant diseases [28,29]. It can also produce a wide range of secondary metabolites with biological activity, including chaetocin, ergosterol, erpenes, cellulase, and so on. These secondary metabolites not only have good inhibitory activity against a range of microbes such as Rhizoctonia solani, Pyricularia oryzae, Anthracnose, and Phytophthora parasitica, but also have anti-tumor, anti-malarial, and anti-inflammatory properties [30,31,32]. In our laboratory, we developed a method for screening potential antimicrobial genes using a B. subtilis expression system. Using this technology, resistance genes have been successfully isolated from rice, pinellia, maize, and potatoes [23,33,34,35]. In this study, C. globosum was isolated and screened from potato tubers as an endophytic fungus with a significant antagonistic effect on pathogenic fungi. The B. subtilis expression system was used to screen out genes of antimicrobial peptides from C. globosum with broad-spectrum antibacterial activities. Then, the physicochemical properties and bacteriostatic capability of the obtained peptides were investigated, establishing the foundation for future research into the function and application of C. globosum resistance genes. 2. Results 2.1. Isolation, Purification, and Screening of Potato Endophytes A total of 20 endophytic fungi were isolated from potato tubers and named as F1-F20. Through the plate confrontation experiment, the endophytic fungus antagonistic effect was screened against Fusarium oxysporum, Rhizoctonia solani, Fusarium graminearum, Verticillium dahliae, and Botrytis cinerea. B. cinerea had the highest inhibition rate of 78.82 percent, followed by other fungi (the inhibition rate against R. solani is 61.91%; F. graminearum, 54.48%; and V. dahliae, 48.68%) (Figure 1). These findings suggest that the endophyte F5 can suppress the growth of several pathogenic fungi, suggesting that further research is necessary. 2.2. Identification of Endophytic Fungus F5 The morphological features of F5 were obtained with the following characteristics: yellowish-brown with a wave-shaped edge of the colony, with olive-brown or gray-white aerial hyphae, often with olive-colored exudates; the ascus shells are superficial, spherical or nearly spherical, with dense appendages in outer layers; the asci are clustered, stalked, cudgel-shaped, with eight ascospores inside; the ascospores are lemon-shaped or oval (Figure 2). In addition, the 5.8S rRNA sequence fragment of endophytic fungus F5 was amplified using the universal primers ITS1 and ITS4, and the target size was obtained about 500 bp (Figure S1). The F5 isolates’ sequences were submitted to the GenBank database, where they were assigned the accession number OM929184.1 (Table S1). Following an evolutionary analysis of the ITS sequences, the phylogenetic analysis revealed that this new isolate had the highest similarity to Chaetomium globosum TNAU Chg4 from the GenBank database (99%) (GenBank accession number MK791715) (Figure 3). As a result of the morphological and molecular analyses, we confirmed that our isolated endophyte F5 was C. globosum. 2.3. Screening of Antibacterial Genes from C. globosum cDNA Library by B. subtilis System There were 4280 transformants in the C. globosum cDNA library constructed using the B. subtilis expression system. Through detection, it was found that the recombination rate of the library was 92.4%, indicating that the quality of the library was good (Figure S3). From 4280 transformants, we screened 10 monoclonal strains with abnormal phenotype, which showed obvious autolysis (Figure 4). The electron microscopy results indicated that the control B. subtilis SCK6-e strain had normal cell morphology. However, when CgR2150 and CgR3101 genes were introduced into cells, the cell membrane was damaged, resulting in obvious shrinkage, deformation, and lysis. These findings reveal that when the exogenous resistance gene of C. globosum is introduced into the host cell, the expressed and secreted product has a toxic effect on the host cell, causing severe damage and autolysis. By performing blastp alignment analysis, we discovered that CgR2150 and CgR3101 have no homologs in the NCBI database, indicating that these two peptides are novel. On this basis, CgR2150 and CgR3101 genes were selected for further research. 2.4. Antibacterial Activities of Antimicrobial Genes against Pathogenic Bacteria and Fungi Extracellular proteins from autolytic strains were extracted by ammonium sulfate precipitation method for antibacterial studies (Table S3). Compared with the control B. subtilis SCK6-e strain, the peptides CgR2150 and CgR3101 can significantly inhibit the growth of Clavibacter fangii, Clavibacter michiganensis, Xanthomonas oryzae pv. oryzae, Xanthomonas oryzae pv. oryzicola, and Xanthomonas campestris pv. Holcicola (Figure 5A). In addition, by measuring the inhibitory effects of peptides CgR2150 and CgR3101 on pathogenic fungi, it was found that CgR2150 and CgR3101 could significantly inhibit the mycelia growth of R. solani, B. cinerea, and F. graminearum (Figure 5B–D). The inhibitory activities of peptides CgR2150 and CgR3101 on tomato plant-infected diseases were determined under in vitro conditions at 7 d (Figure S4). The results showed that the incidence of tomato plants inoculated with C. michiganensis was significantly reduced after treatment with the two peptides (Table 1), indicating that the peptides CgR2150 and CgR3101 could effectively control the occurrence of C. michiganensis. From the above findings, it can be concluded that these two peptides had strong antibacterial and antifungal activities. 2.5. Effects of UV, Temperature, and Enzymes on Antibacterial Activities of Antimicrobial Peptides The two peptides were exposed to UV light for 0.5 h, 1 h, 1.5 h, and 2 h, respectively, to determine the stability of their antibacterial activity. The results showed that CgR2150 and CgR3101 exhibited stable bacteriostatic activity against Gram-positive and Gram-negative bacteria (Figure 6). For thermal stability testing, the antimicrobial peptides were heated at 25 °C, 50 °C, 75 °C, and 100 °C for 30 min. The results showed that CgR2150 and CgR3101 exhibited obvious antibacterial activity in the range of 4–75 °C; at 100 °C, both peptides lost their activity (Figure 7). In addition, by measuring the sensitivity of antimicrobial peptides to enzymes, it can be seen that CgR2150 and CgR3101 still have significant activities after treatment with trypsin and lipase (Figure 8). To conclude, the antimicrobial peptides CgR2150 and CgR3101 exhibit excellent UV, thermal, and enzymatic stability, laying the foundation for antimicrobial peptide application. 2.6. SDS-PAGE Analysis and Western Blot Reveal the Expression of Antimicrobial Peptides The His-tagged and TEV fusion CgR2150 and CgR3101 peptides were expressed in B. subtilis SCK6-e. To observe antimicrobial gene expression in host cells, we purified extracellular peptides using nickel columns and then detected their expression using SDS-PAGE and western blotting (Figure 9). The molecular weight of CgR2150 (approximately 12.5 kDa) can be observed from the SDS-PAGE gel and polyvinylidene fluoride (PVDF) membrane. The observed band is comparable in size to the predicted result from the website, indicating that the target protein was successfully expressed. We firstly measured the amino acid sequences of CgR2150 and CgR3101 peptides, and then predicted their molecular weight, secondary structure, and isoelectric point (Table 2 and Table 3). The results showed that the CgR2150 peptide contained an α-helix structure. In addition, both CgR2150 and CgR3101 had higher isoelectric points, so we speculated that the antibacterial activity of the antimicrobial peptides might be related to the high electric point. 2.7. Minimum Inhibitory Concentration (MIC) Assay for Peptides CgR2150 and CgR3101 To observe the antibacterial activity of the peptides CgR2150 and CgR310, the minimum inhibitory concentration (MIC) values of the purified two peptides against Gram-negative bacteria and Gram-positive bacteria were determined. According to the findings, the peptides CgR2150 (10–25 µg/mL) and CgR3101 (10–24 µg/mL) had broad-spectrum antibacterial activity, considerably suppressed the pathogenic bacteria growth at lower concentrations, and had robust antibacterial activity against both Gram-negative and Gram-positive bacteria (Table 4). Besides, in order to further observe the killing effect of CgR2150 and CgR3101 on bacterial cells at MICs, we performed time-kill curve determination. The results showed that the two peptides had strong bactericidal effects on the four tested strains, and the bacteria hardly grew after 12 h of peptides treatment (Figure 10). 2.8. Hemolytic Activity Assay of CgR2150 and CgR3101 on Mammalian Cells To determine the hemolysis of antimicrobial peptides on mammalian cells, triton X-100 was used as a positive control (100%) and PBS buffer as a negative control (0%) (Figure 11). Then, the porcine erythrocytes were treated with CgR2150 and CgR3101, respectively. The results showed that CgR2150 and CgR3101 had minimal hemolytic activity in mammals, even at 5× MIC concentration, with hemolytic activities of 0.32% and 0.46%, respectively. Therefore, we can conclude that the peptides CgR2150 and CgR3101 are basically non-toxic to mammals. 2.9. Destructive Effects of CgR2150 and CgR3101 on X. oryzae pv. oryzae and C. michiganensis To see how these two peptides destroyed the pathogenic bacteria’s cell membrane, we examined cell samples of X. oryzae pv. oryzae and C. michiganensis treated with CgR2150 and CgR3101 under a scanning electron microscope. The results showed that the surface of the cells in the control group was smooth, complete, and normal in shape. In contrast, after treatment with peptides CgR2150 and CgR3101, the cell membrane surface of X. oryzae pv. oryzae was significantly shrunken and morphologically abnormal; while the cell membrane surface of C. michiganensis was severely damaged, with obvious gaps and ruptures (Figure 12). These results indicated that the peptides CgR2150 and CgR3101 had certain damaging effects on the cell membranes of X. oryzae pv. oryzae and C. michiganensis. 3. Discussion Endophytes are a large group of microorganisms that live in plant tissues but do not cause significant damage in plants [36,37,38]. The distribution of endophytes is universal, and a large number of endophytes can be isolated from different plant parts, including plant roots, stems, leaves, flowers, fruits, tubers, seeds, and nodules [39,40,41,42,43]. Plant endophytes can produce many secondary metabolites, such as flavonoids, terpenoids and so on, which are beneficial to the prevention and control of plant pests and human diseases, so they have broad application prospects [44,45,46,47,48]. A C. globosum strain isolated from potato tubers in this experiment is a common endophytic fungus. It has been reported in literature that it can effectively inhibit R. solani, F. oxysporum, and B. cinerea [29,49]. In our study, it was also demonstrated that the C. globosum significantly inhibited the mycelial growth of V. dahliae, F. oxysporum, R. solani, F. graminearum, and B. cinerea, a species of biological control agent with application potential. In recent years, the scope of antibiotic usage has expanded, resulting in an increasing number of drug-resistant bacteria, which poses a serious threat to human health [50]. Antimicrobial peptides are considered one of the possible therapeutics to overcome these resistant patterns because of their significant antimicrobial characteristics [51,52]. In addition, some studies have shown that antimicrobial peptides can be used in combination with existing antibiotics, which not only exerts synergistic effects, but also prevents the generation of drug resistance [53,54]. Since B. subtilis has the ability to secrete and express efficiently, it is easier to obtain the secreted and expressed products of exogenous genes. However, the accumulation of exogenous gene expression and secretion products to a certain extent may have toxic effects on the host cells, leading to the rupture of the host cells, and finally the phenotype of the recombinant strains will change significantly, resulting in autolysis [55,56]. Using the B. subtilis expression system, we have developed a novel, high-throughput technique for the isolation of novel AMPs [20,21,22,23]. In this study, we screened 10 candidate genes from 4280 clones of C. globosum cDNA library, and finally we selected two antimicrobial peptide genes CgR2150 and CgR3101 with strong antifungal and antibacterial activities for further study. Some AMPs have been reported to show strong antibacterial activity against a variety of microorganisms and have stable physicochemical properties, so they have been used in clinical trials, including hLF-1-11, pexiganan, omiganan, and so on [57,58,59]. Since the structure, isoelectric point, and hydrophilicity of antimicrobial peptides can affect the bacteriostatic activity of AMPs [60,61], we first translated the nucleotide sequences of AMPs into amino acid sequences, and performed biological information prediction for CgR2150 and CgR3101 (Table 2 and Table 3). It was found that both peptides have amphiphilicity and a high isoelectric point (IP), and CgR2150 also has an α-helical structure, which is similar to the structure of reported peptides with broad-spectrum antibacterial activity. Furthermore, we used the ExPASy and PEP-FOLD3 online servers to predict functional peptides. For identifying antimicrobial functional domains, we used the antiBP server to narrow down sequences of the full-length gene [62]. Based on this analysis, we will perform further investigations to characterize the core sequence responsible for the antimicrobial activity. Unlike traditional antibiotics, which have specific target sites, the mechanism of action of antimicrobial peptides involves multiple targets rather than a specific high-affinity target [63,64,65]. Antimicrobial peptides generally act on the cell membrane of bacteria, destroying its integrity, causing the cell contents to spill out of the cell and killing the bacteria [66,67]. In our study, observation by scanning electron microscopy revealed severe cell surface damage (shrinkage, rupture, and perforation) of bacteria treated with CgR2150 and CgR3101. Therefore, we speculate that CgR2150 and CgR3101 kill pathogens by disrupting their cell membranes. Although AMPs have broad-spectrum antibacterial activities, some studies have found that some AMPs have certain toxic effects on mammalian cells [68]. As a result, we performed a hemolytic assessment of CgR2150 and CgR3101 and showed that both peptides have very low hemolytic activity on porcine erythrocytes even at higher peptide concentrations (Figure 11). Based on our findings, we speculate that these two peptides obtained from C. globosum are relatively safe for animal cells. However, because our experiment is currently focused on the hemolysis of CgR2150 and CgR3101 peptides in porcine erythrocytes, we will further investigate the toxic effects of antimicrobial peptides on animal and plant cells to ensure that peptides are safe for organisms. In our study, we observed that both peptides significantly reduced the disease index and morbidity of tomato plants treated with them (Table 1), indicating that both peptides are effective at controlling C. michiganensis and have promising biocontrol potential. As a result, we believe that these peptides can be developed into biocontrol agents for the prevention of plant disease in the future. 4. Materials and Methods 4.1. Isolation, Purification, and Screening of Endophytic Fungi Healthy potato tubers were firstly cut into little pieces and rinsed with a 75% ethanol and 1% sodium hypochlorite solution. The tubers were then placed on PDA medium with streptomycin and cultivated for 3–7 days at 28 °C. The hyphae that had developed around the tuber’s edge were selected for separation and purification. We inoculated the endophytic and pathogenic fungi at two sites of 2 cm from the center of the PDA plate, respectively, and kept them at 28 °C for 2–5 days using the plate confrontation culture method. Then, calculating the inhibition rate, we observed the antagonism between the strains. The formula for calculating the inhibition rate (%) is as follows: Inhibition rate (%) = [(control pathogenic fungus colony radius − treated pathogenic fungal colony radius)/control pathogenic fungal colony radius–radius of pathogen disk] × 100. 4.2. Identification of Endophytic Fungi The morphological identification technique was carried out by observing the fungal colony’s appearance features. To do so, we placed a small amount of lactic phenol cotton blue staining solution in the center of a clean glass slide, picked the endophytic fungus’ mycelium with a toothpick, and placed it on the glass slide, then covered it. The microscopic properties of the specimens were examined under a light microscope. To determine the isolates’ 5.8S rRNA gene sequence, we extracted the total genomic DNA using the CTAB technique with minor modifications [69]. The internal transcribes spacers (ITS1) (3′-TCCGTAGGTGAACCTGCGG-5′) and ITS4 (3′-TCCTCCGCTTATTGATATGC-5′) universal primers were used for polymerase chain reaction (PCR) amplification. For this analysis, a total amount of 25 μL of reaction mixture was employed. The PCR results of the 5.8S rRNA genes were sequenced by Wuhan Tianyi Huiyuan Biological Technology Co., Ltd. in Wuhan, Hubei, China. Nucleotide sequence homology enquiries were conducted via GenBank online search engine blastp (http://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 15 November 2020). The CLUSTALW was used to make multiple sequence alignments and comparisons with the reference strain for each of the genes, and the Neighbor-joining method was employed to build the phylogenetic tree topologies by performing bootstrap values of 1000 data sets using MEGA7.0 tools [70]. In Supplementary Table S1, the relevant sequencing accession numbers were included for constructing phylogenetic tree analysis. 4.3. Activation of C. globosum First, the PDA plate was covered in sterilized circular cellophane, and then C. globosum was inoculated at a spot 2 cm from the center. After two or three days of growth, another point was inoculated with B. cinerea 2 cm from the center of the PDA plate (the two points were on the same straight line). On the third day following inoculation with B. cinerea, total RNA from C. globosum was extracted. 4.4. Construction of C. globosum cDNA Library The basic steps for constructing a cDNA library are found in Supplementary Method S1. Total RNA, mRNA, and cDNA images are shown in Supplementary Figure S2 and the primers used to create the C. globosum cDNA library and purify the peptides are listed in Supplementary Table S2. 4.5. Screening of Candidate Resistance Genes We took 1 μL of bacterial solution from all the clones in the cDNA library, spotted them on the LB plate containing kanamycin (10 mg/L), and observed the morphology of the colonies every 12 h. The experiment was repeated 3 times to determine whether the autolysis phenomenon is stable. 4.6. Expression of Extracellular Peptides Extracellular peptides were extracted using ammonium sulfate precipitation [22], and then their inhibitory activities against some Gram-positive bacteria (C. michiganensis and C. fangii), Gram-negative bacteria (X. oryzae pv. oryzae, X. oryzae pv. oryzicola, and X. campestris pv. Holcicola), and pathogenic fungi (R. solani, F. graminearum, and B. cinerea) were determined. B. subtilis was grown in 200 mL liquid LB medium containing kanamycin (10 mg/L) and incubated at 180 r/min, 37 °C for 72 h. The supernatant was then collected by centrifugation at 10,000× g for 20 min at 4 °C. The saturated ammonium sulfate solution was added dropwise with a 10 mL syringe to the supernatant (placed on ice), agitated slowly until the clear solution became hazy, and then stored at 4 °C overnight. The next day, the solution was placed at 10,000× g, centrifuged at 4 °C for 20 min, and the supernatant was discarded. The peptides were dissolved in PBS buffer (pH 7.0) and dialyzed for 48 h at 4 °C in the same PBS buffer. 4.7. Determination of the Inhibitory Effect of Antimicrobial Peptides on Pathogenic Bacteria and Fungi To determine the effect of extracellular peptides on bacteria, the agar diffusion test (filter paper method) [71,72] was used. The detailed procedure is available in Supplementary Method S2. After the fungal hyphae had grown for 10 mm, 200 μL of extracellular peptide was pipetted into the well. Finally, it was placed in a 28 °C incubator for 2–4 days, and the growth diameter of the fungal hyphae was observed and measured. 4.8. Stability Determination of Antimicrobial Peptides The AMPs were treated under different UV, temperature, and enzymes to verify their stability. The AMPs were treated with UV light at a wavelength of 265 nm for 0.5 h, 1 h, 1.5 h, and 2 h, respectively, and the effect of UV on the antibacterial activity of the AMPs was determined by agar diffusion test. In addition, the AMPs were treated at 25 °C, 50 °C, 75 °C, and 100 °C for 30 min, respectively, for thermal stability tests. Finally, AMPs were treated with 100 μg/mL lipase (30 °C), α-amylase (55 °C), pepsin, and trypsin (37 °C) for 1 h to determine the effect of different biological enzymes on them. Agarose diffusion experiments were performed on two Gram-positive (C. michiganensis and C. fangii) and Gram-negative bacteria (X. oryzae pv. oryzae and X. oryzae pv. oryzicola). 4.9. Purification of His-Tag Fusion Peptides To purify the CgR2150 and CgR3101 peptides, we firstly inserted a 6× His tag and a protease (TEV) sequence between the signal peptide of the pBE-S vector and the target fragment, and then transformed it into B. subtilis SCK6-e cells [23]. Then, CgR2150 and CgR3101 were purified by nickel column affinity chromatography. The detailed purification procedure is available in Supplementary Method S3. 4.10. Tris-Tricine SDS-PAGE and Western Blotting In order to detect the purified AMPs by western blot, we firstly used the Tricine-SDS-PAGE kit to set up the gel, and then loaded the processed samples for electrophoresis. The overall SDS-PAGE analysis procedure can be found in Supplementary Method S4. After the electrophoresis, the stacking gel and the interlayer gel were removed, and the membrane was transferred first. After the transfer, the PVDF membrane was placed in 5% nonfat milk powder to seal for 2.5–3 h. Finally, immunoblotting was performed with anti-His-tag monoclonal antibody, HRP, and ECL detection reagents. The molecular weights of AMPs were predicted by comparison with color pre-dyed low peptide molecular weight markers. 4.11. Determination of the Minimum Inhibitory Concentration (MIC) The indicator bacteria were inoculated in LB liquid medium (OD600 ≈ 0.8–1.0) and then diluted with the indicator bacteria (OD600 ≈ 0.02–0.05). After that, we added 40 μL of diluted indicator bacteria solution to 96-well titer plates. The AMPs were diluted through a series of concentration gradients, and then an equal volume of peptides was added to the above 96-well plate and incubated at 28 °C for 12 h. The MIC value is the lowest concentration of peptide that significantly inhibits bacterial growth. To determine the time-kill assay, the purified antimicrobial peptide concentration was adjusted to 2× MIC concentration, and then added to a 96-well plate in equal volume with the indicator bacteria, with at least 3 wells for each concentration. After mixing the liquid evenly, it was incubated at 28 °C for 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, and 24 h, and then the OD value was obtained at 600 nm. 4.12. Hemolytic Assay Fresh porcine erythrocyte cells were centrifuged at 4 °C for 10 min (1500× g), then the cells were washed three times with pre-cooled PBS solution, and the pellet was collected and then diluted with PBS buffer. Equal volumes of diluted red blood cells and antimicrobial peptide solution (1× MIC, 2× MIC, 3× MIC, 4× MIC, and 5× MIC) were mixed and incubated at 37 °C for 1 h, centrifuged at 4000× g for 10 min. The supernatant was added to a 96-well plate, and the absorbance at 385 nm was measured using an ELISA plate reader. The erythrocyte suspension treated with 0.1% Triton X-100 was used as a positive control, and the suspension incubated with PBS buffer was used as a negative control. The calculation formula of the percentage of hemolytic activity is as follows: hemolysis (%) = [(Apeptide − APBS)/(ATriton − APBS)] × 100. 4.13. Scanning Electron Microscope Analysis X. oryzae pv. oryzae and C. michiganensis were grown at 28 °C to logarithmic growth phase. Antimicrobial peptides at 2× MIC concentration were co-incubated with bacteria for 2 h at 28 °C, and the autolysed strains (CgR2150 and CgR3101) and B. subtilis empty vector strain SCK6-e were shaken at 37 °C for 60 h, and then centrifuged at 1000× g for 5 min. After washing the cells 3 times with pre-cooled PBS (pH 7.4), they were centrifuged at 5000× g for 5 min and we then discarded the supernatant. The cell pellets were resuspended in 2.5% glutaraldehyde for 2–4 h to fix the cells, washed twice with PBS, and then washed with different gradients of ethanol (30%, 50%, 70%, and 90%). Finally, the samples were washed with 100% ethanol for 10 min, dried in a freeze dryer overnight, and detected by a HITACHI S-4800 SEM. 4.14. Pathogenicity Assay C. michiganensis was cultured to the logarithmic growth phase, then the pathogenic bacteria were mixed with an equal volume of antimicrobial peptides at 2× MIC, and finally tomato plants were inoculated using the stem acupuncture method. The positive control was mixed with an equal volume of polymyxin B and pathogenic bacteria, and the negative control was mixed with an equal volume of PBS buffer and pathogenic bacteria. After inoculation, the tomato plants were cultured under greenhouse conditions, and the disease incidence was observed and recorded. The equation of disease grading criteria, incidence rate, and disease index are found in Supplementary Method S5. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23094611/s1. Click here for additional data file. Author Contributions Conceptualization, J.Z., M.S.I. and W.D.; methodology, J.Z., M.S.I., J.W., and Y.Z.; validation, J.Z., M.S.I., J.W., Y.Z. and W.D.; formal analysis, J.Z. and W.D.; investigation, J.Z., M.S.I., J.W. and Y.Z.; writing—original draft preparation, J.Z., M.S.I. and W.D.; writing—review and editing, J.Z., M.S.I., J.W., Y.Z. and W.D.; supervision, J.W. and W.D.; project administration, W.D.; funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the National Major Project for Transgenic Organism Breeding (2016ZX08003-001). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Endophyte F5 inhibits the growth of five pathogenic fungi’s mycelia. Inhibitory effect on (A) V. dahlia, (B) F. graminearum, (C) B. cinerea, (D) F. oxysporum, (E) R. solani. Figure 2 Microscopic observation of endophyte F5 under light microscope. (A) perithecium; (B) ascus; (C) ascospores, and (D) appendage. Bars: A = 80 μm, B = 60 μm, C = 20 μm, and D = 40 μm. Figure 3 Phylogenetic tree of Chaetomium globosum based on 5.8S rRNA gene sequencing showing similarity with Chaetomium spp. The Neighbor-Joining (NJ) approach was used in MEGA7, with bootstrap values (n = 1000) greater than 50% apparent at the tree’s internodes. As the outer group, Achaetomium globosum CBS 332.67 strain was included. The purple symbol indicates the position of our isolated endophyte strain (F5). Figure 4 Injury effect of antimicrobial genes of C. globosum on B. subtilis cells. B. subtilis strains (SCK6-e) containing the empty vector and autolytic strains (CgR2150 and CgR3101) were placed on Luria-Bertani (LB) plates and incubated at 37 °C. (A-I) B. subtilis SCK6-e strain, (B-I) CgR2150 and (C-I) CgR3101 after 12 h of incubation, and (A-II) B. subtilis SCK6-e strain, (B-II) CgR2150, and (C-II) CgR3101 after 48 h of incubation, while (A-III) B. subtilis SCK6-e strain, (B-III) CgR2150, and (C-III) CgR3101 after 192 h of incubation. (A-IV) B. subtilis SCK6-e strain, (B-IV) CgR2150, and (C-IV) CgR3101 were observed under a scanning electron microscope. Figure 5 Determination of antimicrobial activities of two resistant genes against pathogenic bacteria and fungi. The B. subtilis SCK6-e strain was used as a control to determine the inhibitory effects of the peptides on pathogenic bacteria. Phosphate buffered saline (PBS) was used as a negative control, and geneticin (G418) was used as a positive control to detect the inhibitory activities of the peptides against pathogenic fungi. (A) Inhibitory effects of peptides CgR2150 and CgR3101 on five tested strains compared with SCK6-e. (B) Inhibitory activities of peptides CgR2150 and CgR3101 on R. solani hyphae compared to PBS buffer. (C) Inhibitory effects of peptides CgR2150 and CgR3101 on B. cinerea hyphae. (D) Inhibitory activities of peptides CgR2150 and CgR3101 on F. graminearum hyphae. The data are the mean values obtained after three independent experiments, and the vertical bars are the standard deviations (SD). Significance analysis was performed by t-test; * p < 0.05, ** p < 0.01. Figure 6 UV stability tests of CgR2150 and CgR3101. Antimicrobial peptides without UV treatment were used as controls. (A) C. fangii, (B) X. oryzae pv. oryzicola, (C) X. oryzae pv. oryzae, and (D) C. michiganensis are the antibacterial activity of the two peptides under UV treatment at different times (0.5 h, 1 h, 1.5 h, and 2 h). The data are the mean values obtained after three independent experiments, and the vertical bars are the standard deviations (SD). Significance analysis was performed by t-test. Figure 7 Thermal stability tests of CgR2150 and CgR3101. Antimicrobial peptides without temperature treatment at 4 °C were used as controls. (A) C. fangii, (B) X. oryzae pv. oryzicola, (C) X. oryzae pv. oryzae, and (D) C. michiganensis are the bacteriostatic diameter of the two peptides at different temperatures (25 °C, 50 °C, 75 °C, and 100 °C). The data are the mean values obtained after three independent experiments, and the vertical bars are the standard deviations (SD). Significance analysis was performed by t-test; ** p < 0.01. Figure 8 Enzyme stability assay of CgR2150 and CgR3101. Using the antimicrobial peptide without protease treatment as a control, the changes in the antimicrobial activity of the two peptides were observed through different biological enzyme treatments. (A) C. fangii, (B) X. oryzae pv. oryzicola, (C) X. oryzae pv. oryzae, and (D) C. michiganensis are the bacteriostatic diameter of two peptides treated with four different proteases, including lipase, trypsin, pepsin, and α-amylase. The data are the mean values obtained after three independent experiments, and the vertical bars are the standard deviations (SD). Significance analysis was performed by t-test; ** p < 0.01. Figure 9 SDS-PAGE and western blotting analysis of fusion peptide CgR2150. (A) SDS-PAGE electrophoresis, (B) western blotting analysis. Lane M indicates pre-stained ultra-low molecular weight marker (1.7–40 kDa). Lanes 1 and 2 indicate the molecular weight of peptide CgR2150. Figure 10 Time-kill curve of CgR2150 and CgR3101. The inhibitory effects of CgR2150 and CgR3101 on bacteria were demonstrated by bacterial growth curves. Among them, PBS buffer treatment was used as a negative control, and polymyxin B treatment was used as a positive control. (A) C. fangii, (B) X. oryzae pv. oryzicola, (C) X. oryzae pv. oryzae, and (D) C. michiganensis are time-kill kinetics by CgR2150 and CgR3101 at their MICs. The data are the mean values obtained after three independent experiments, and the vertical bars are the standard deviations (SD). Figure 11 Hemolytic activities of the peptides on mammalian porcine erythrocytes. Figure 12 Effects of antimicrobial peptides CgR2150 and CgR3101 on cell membrane damage of X. oryzae pv. oryzae and C. michiganensis after treatment at MIC concentrations. (A-I) PBS, (A-II) CgR2150, and (A-III) CgR3101 acting on the cell membrane of X. oryzae pv. oryzae. (B-I) PBS, (B-II) CgR2150, and (B-III) CgR3101 acting on the cell membrane of C. michiganensis. ijms-23-04611-t001_Table 1 Table 1 Control effects of peptides CgR2150 and CgR3101 on C. michiganensis. Treatment Group Disease Index Incidence (%) Control Efficacy (%) PBS Buffer 78.33 a 86.67 a - Polymyxin B 3.33 c 10.00 d 95.75 a CgR2150 5.83 bc 13.33 c 92.56 b CgR3101 7.50 b 16.67 b 90.43 c Note. Different letters denote differences at the p < 0.05. ijms-23-04611-t002_Table 2 Table 2 Amino acid sequences of CgR2150 and CgR3101 peptides. Peptides Amino Acid Sequence Accession Number CgR2150 LAVHHLHSIRGRHHSCTIAQTQANHRHNSYQPNNSSCLAKETDLPTTRSTPATTSSTAPARKSPPASPAPTRQRLCPSPPRGPVSSRHPRRRGQARGRTRAAGVRRTKGYGGKCV XP_001222255.1 CgR3101 LAVPNFEPPTRTTCPAPTSPGNPCGPPPPASQTRRPPFHPTSPLRR XP_001225253.1 ijms-23-04611-t003_Table 3 Table 3 Bioinformatics prediction of antimicrobial peptides CgR2150 and CgR3101. Antimicrobial Peptides Amino Acid Number Second Structure PI MV CgR2150 115 α-helix Random coil 11.96 12,469.01 CgR3101 46 Random coil 11.40 4899.59 Note, PI: theoretical pI, MV: molecular weight. ijms-23-04611-t004_Table 4 Table 4 MIC values (µg/mL) of CgR2150 and CgR3101 against different bacteria. Strain MIC (µg/mL) CgR2150 CgR3101 X. oryzae pv. oryzae 10 12 X. oryzae pv. oryzicola 10 12 C. fangii 15 15 X. campestris pv. Holcicola 25 24 C. michiganensis 18 18 Note. Gram-positive bacteria: C. michiganensis and C. fangii. Gram-negative bacteria: X. oryzae pv. oryzicola, X. oryzae pv. oryzae, and X. campestris pv. Holcicola. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Dzhioev Y.P. Zlobin V.I. Salovarova V.P. Stepanenko L.A. Bukinet Y.S. Analysis of the “superbacteria” issue and contemporary approaches to its solution Proc. Univ. Appl. Chem. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091788 polymers-14-01788 Article Comparative X-ray Shielding Properties of Single-Layered and Multi-Layered Bi2O3/NR Composites: Simulation and Numerical Studies Thumwong Arkarapol 1 Darachai Jitsuna 2 https://orcid.org/0000-0001-7401-3654 Saenboonruang Kiadtisak 3456* Arteiro Albertino Academic Editor Arjmand Mohammad Academic Editor 1 Department of Materials Science, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand; arkarapol.th@ku.th 2 Department of Engineering Physics, Tsinghua University, Beijing 100084, China; jitsuna.note@gmail.com 3 Department of Applied Radiation and Isotopes, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand 4 Kasetsart Research and Development Institute, Kasetsart University, Bangkok 10900, Thailand 5 Specialized Center of Rubber and Polymer Materials in Agriculture and Industry (RPM), Faculty of Science, Kasetsart University, Bangkok 10900, Thailand 6 Special Research Unit of Radiation Technology for Advanced Materials, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand * Correspondence: kiadtisak.s@ku.th; Tel.: +66-2562-5555 (ext. 646219) 27 4 2022 5 2022 14 9 178825 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This work theoretically compared the X-ray attenuation capabilities in natural rubber (NR) composites containing bismuth oxide (Bi2O3) by determining the effects of multi-layered structures on the shielding properties of the composites using two different software packages (XCOM and PHITS). The shielding properties of the single-layered and multi-layered Bi2O3/NR composites investigated consisted of the transmission factor (I/I0), effective linear attenuation coefficient (µeff), effective mass attenuation coefficient (µm,eff), and effective half-value layer (HVLeff). The results, with good agreement between those obtained from XCOM and PHITS (with less than 5% differences), indicated that the three-layered NR composites (sample#4), with the layer arrangement of pristine NR (layer#1)-Bi2O3/NR (layer#2)-pristine NR (layer#3), had relatively higher X-ray shielding properties than either a single-layer or the other multi-layered structures for all X-ray energies investigated (50, 100, 150, and 200 keV) due to its relatively larger effective percentage by weight of Bi2O3 in the composites. Furthermore, by varying the Bi2O3 contents in the middle layer (layer#2) of sample#4 from 10 to 90 wt.%, the results revealed that the overall X-ray shielding properties of the NR composites were further enhanced with additional filler, as evidenced by the highest values of µeff and µm,eff and the lowest values of I/I0 and HVLeff observed in the 90 wt.% Bi2O3/NR composites. In addition, the recommended Bi2O3 contents for the actual production of three-layered Bi2O3/NR composites (the same layer structure as sample#4) were determined by finding the least Bi2O3 content that enabled the sample to attenuate incident X-rays with equal efficiency to that of a 0.5-mm lead sheet (with an effective lead equivalence of 0.5 mmPb). The results suggested that the recommended Bi2O3 contents in layer#2 were 82, 72, and 64 wt.% for the combined 6 mm, 9 mm, and 12 mm samples, respectively. natural rubber Bi2O3 X-ray shielding simulation multi-layered structure Kasetsart University Research and Development Institute (KURDI)FF (KU)25.64 This research was funded by the Kasetsart University Research and Development Institute (KURDI), Bangkok, Thailand, by grant number FF (KU)25.64. ==== Body pmc1. Introduction Since the discovery of X-rays in 1895 by Wilhelm Roentgen, various applications have relied heavily on the utilization of X-ray technologies, especially X-ray imaging and X-ray irradiation in medicine, industry, material characterization, security, the arts, foods, and agriculture [1,2,3,4,5,6]. Despite their great potential and usefulness, excessive exposure to X-rays could harmfully affect the health of users and the public, with various symptoms, including nausea, skin burn, diarrhea, permanent disability, cancer, and death, depending on the exposure dose and duration as well as the sex, health condition, and age of those exposed [7,8]. Hence, to reduce and/or prevent the risks of excessive exposure to X-rays, a radiation safety principle, namely “As Low As Reasonably Achievable” or “ALARA”, must be strictly followed in all nuclear facilities to ensure the safety of all users and the public [9]. One of the three safety measures in ALARA is the utilization of sufficient and appropriate shielding equipment; for which different applications may require different types and specific properties from the materials [10]. For example, X-ray shielding materials based on polyethylene (PE), including Gd2O3/HDPE and nano-ZnO/HDPE composites, are suitable for applications that require exceptional strength and rigidity, such as those involving products for use as movable panels, walls, and construction parts in nuclear facilities [11,12]. On the other hand, shielding equipment, such as personal protective equipment (PPE) and covers for transporting casks, requiring exceptional flexibility, high strength, and a large amount of elongation from the materials, relies on natural and synthetic rubber composites. For example, Bi2O3/NR, Bi2O3/EPDM, BaSO4/EPDM, and W/SR composites were among recently developed X-ray shielding rubber materials that offered not only effective X-ray attenuation abilities but also sufficient mechanical strength and flexibility to the users [13,14,15,16]. Notably, these mentioned examples of X-ray shielding materials are lead-free, which is presently sought-after in materials, as they could substantially reduce the risks to users from exposure to highly toxic lead (Pb) elements and compounds that are common protective fillers used for the manufacturing of X-ray and gamma shielding materials due to their economical accessibility and excellent attenuation capability [17,18]. Generally, the addition of heavy metals, including Bi2O3, to the main matrix is a common method to enhance the X-ray attenuation abilities of the composites, mainly due to the relatively high atomic number (Z) and density (ρ) of Bi2O3 that enhance the interaction probabilities between the incident X-rays and the materials, subsequently increasing the ability to attenuate the incident X-rays of the composites [19]. Some examples showing the effects of Bi2O3 on improving the shielding capabilities of the composites have been reported by Intom et al., who showed that the mass attenuation coefficients (µm) of Bi2O3/NR composites increased from 0.1324 to 0.3847 and then to 0.4779 cm2/g when the Bi2O3 contents in the NR composites increased from 0 to 80 and then to 150 parts per hundred parts of rubber by weight (phr), respectively (determined at an energy level of 223 keV) [20]. Similarly, the report from Toyen et al. suggested that increases in the Bi2O3 contents from 0 to 300 and then to 500 phr increased the linear attenuation coefficients (µ) of NR composites from 2.1 to 14.7 and then to 20.4 m−1, respectively (determined at an energy level of 662 keV) [13]. Nonetheless, despite the positive relationship between the contents of Bi2O3 and the shielding properties of the composites, increases in Bi2O3 contents may lead to undesirable reductions in the mechanical properties, such as decreased values of the tensile strength and elongation at the break of Bi2O3/NR composites from 14 to 7 MPa and from 630% to 500%, respectively, when the Bi2O3 contents increase from 100 to 500 phr [13]. This behavior was observed mainly due to particle agglomerations caused by filler–filler interactions and phase separation at higher filler contents [14,21]. To alleviate or limit such drawbacks by adding high filler contents to the composites, one possible method is to prepare the materials with multi-layered structures, which would enable the pristine NR layers to better support and transfer external forces exerted on the Bi2O3/NR layers, consequently limiting the reduction in the overall strength of the materials [22,23]. As aforementioned, due to the competing roles of Bi2O3 in the enhancement of X-ray-shielding properties and the reductions in mechanical properties, this work investigated appropriate multi-layered structures of Bi2O3/NR composites by theoretically comparing X-ray shielding parameters, consisting of the transmission factor (I/I0), the effective mass attenuation coefficient (µeff), the effective linear attenuation coefficient (µm,eff), the effective half-value layer (HVLeff), and the effective lead equivalence (Pbeq,eff), from 11 distinct multi-layered structures using XCOM and PHITS. In addition, the recommended Bi2O3 contents for the multi-layered structure that produced the highest shielding properties were also determined by finding the least Bi2O3 contents that, when being added to the NR composites, produced the required Pbeq,eff value of 0.5 mmPb. The outcomes of this work would not only provide comparative X-ray shielding properties of multi-layered products but also present promising methods to preserve the mechanical properties of shielding materials containing high contents of fillers. 2. Determination of X-ray Shielding Properties Using XCOM and PHITS 2.1. Multi-Layered Structures of Bi2O3/NR Composites The details and schemes of 11 distinct multi-layered structures for Bi2O3/NR composites with varying numbers (1–5) of layers and varying Bi2O3 contents for each layer are shown in Table 1 and Figure 1, respectively. In order to simplify the setups for the determination of X-ray shielding properties, all samples would have the same average weight contents per thickness, i.e., ΣCixi/Σxi where Ci and xi are Bi2O3 content and thickness of the ith layer, respectively. Notably, for Figure 1, the left surface of each design was the side that faced the incident X-rays. 2.2. Determination of X-ray Shielding Properties Using XCOM The X-ray shielding properties of all 11 multi-layered structures at the X-ray energies of 50, 100, 150, and 200 keV were numerically determined using the web-based XCOM software, provided by the National Institute of Standards and Technology (NIST) (Gaithersburg, MD, USA) [24,25]. The NIST standard reference database 8 (XGAM), released in 2010, was used as the photon cross-section database in this work and the X-ray shielding parameters were calculated from the total attenuation with the inclusion of coherent scattering [26]. In order to obtain the final transmission factor (I/I0) for each design, the mass attenuation coefficient (µm) for the Bi2O3/NR composites containing varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.% were determined using XCOM. The details of the procedure to input material parameters and contents have been described elsewhere [24]. Then, the linear attenuation coefficients (µ) for each corresponding Bi2O3 content were determined using the obtained µm, following Equation (1):(1) μ=μm×ρ where ρ is the density of the Bi2O3/NR composites containing varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.%, theoretically calculated using Equation (2):(2) ρ=100CNRρNR+CBi2O3ρBi2O3 where ρNR (ρBi2O3) is the density of NR (Bi2O3), which is 0.92 g/cm3 (8.90 g/cm3), and CNR (CBi2O3) is the weight content of NR (Bi2O3) in the composites. Notably, CNR + CBi2O3 = 100 wt.%. The value of (I/I0)i for the ith layer was calculated from its corresponding µ using Equation (3):(3) (II0)i=e−μxi where xi is the thickness of the ith layer for each design shown in Table 1. Then, the final I/I0 value for each sample was calculated by multiplying individual (I/I0)i values from each layer, according to Equation (4):(4) II0=∏1n(II0)i  where n is the number of layers in the sample and i is 1, 2, …, n. Lastly, the effective linear attenuation coefficient (µeff), the effective mass attenuation coefficient (µm,eff), and the effective half-value layer (HVLeff), which represented the overall X-ray shielding properties for each design, were determined using Equations (5)–(7), respectively:(5) μeff=−ln(II0)ixi (6) µm,eff=μeffρeff (7) HVLeff=ln(2)μeff where ρeff is the effective density of the sample, calculated using Equation (8):(8) ρeff=∑1nρixi∑1nxi where ρi and xi are the density and the thickness of the ith layer, respectively. Notably, for further determination, the values of µ for a pure Pb sheet at X-ray energies of 50, 100, 150, and 200 keV were also determined using XCOM. Furthermore, the effective percentage by weight (Ceff,Bi2O3) of Bi2O3 in different multi-layered samples (sample#2–sample#11) was also determined using Equation (9), which was derived from Equation (2):(9) ρeff=100100−Ceff,Bi2O3ρNR+Ceff,Bi2O3ρBi2O3 2.3. Determination of X-ray Shielding Properties Using PHITS In order to verify the X-ray shielding properties obtained using XCOM, the final I/I0 values were also determined for all multi-layered structures using PHITS by setting up the incident X-ray beam with a diameter of 1 mm pointing directly to the center of each sample, having a surface area of 20 cm × 20 cm and a combined thickness of 6 mm. This setup would minimize the possible overestimation of the final I/I0 value caused by build-up effects [27]. In addition, the detector with a 100% detection efficiency was set up to capture all primary transmitted X-rays. Further details of the PHITS setup are provided elsewhere [10,11]. The percentages of difference (%Difference) between the final I/I0 values obtained from XCOM and those from PHITS were determined, following Equation (10):(10) %Difference=|(II0)XCOM−(II0)PHITS|(II0)XCOM×100% where (I/I0)XCOM and (I/I0)PHITS are the effective transmission factors of the Bi2O3/NR composites obtained from XCOM and PHITS, respectively. 2.4. Determination of Effective Lead Equivalence and Recommended Contents of Bi2O3 The values of effective lead equivalence (Pbeq,eff) at X-ray energies of 50, 100, 150, and 200 keV for the multi-layered Bi2O3/NR composites offering the highest final I/I0 values among all 11 designs were calculated, following Equation (11):(11) μPbPbeq,eff=µNR,effxNR where µPb is the linear attenuation coefficient of a pure Pb sheet, µNR,eff is the effective linear attenuation coefficient of multi-layered Bi2O3/NR composites, and xNR is the combined thickness of the multi-layered Bi2O3/NR composites, which varied from 6 to 9 to 12 mm. Notably, the Bi2O3 contents for the determination of Pbeq,eff were varied up to the maximum content of 90 wt.% and the µPb values were 90.9, 62.7, 22.8, and 1.13 cm−1 at X-ray energies of 50, 100, 150, and 200 keV, respectively, determined using XCOM. To determine the recommended Bi2O3 contents, the values of Pbeq,eff for all conditions obtained from the previous steps were plotted against their corresponding Bi2O3 contents. Then, a horizontal straight line with a Pbeq value of 0.5 mmPb (the common requirement for X-ray shielding equipment in general nuclear facilities) was plotted and the points of intersection were noted for each thickness (6, 9, and 12 mm), which represented the least Bi2O3 contents providing the composites with a Pbeq value of 0.5 mmPb, and could be regarded as the recommended Bi2O3 contents for the actual production. 3. Results and Discussion 3.1. Values of µm, µ, and ρ for Bi2O3/NR Composites The values of the numerically determined µm, ρ, and µ for the single-layered Bi2O3/NR composites with varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, or 30 wt.% at X-ray energies of 50, 100, 150, and 200 keV are shown in Table 2, Table 3 and Table 4, respectively. The results shown in Table 2 indicated that the values of µm tended to increase with increasing Bi2O3 content but decreased with increasing X-ray energy. The positive relationship between µm and filler contents was mainly due to the high atomic number (Z) of Bi and the much higher density (ρ) of Bi2O3 compared to those of NR, resulting in substantially enhanced interaction probabilities between the incident X-rays and the materials through the very effective and dominant X-ray interaction, namely photoelectric absorption, which subsequently improved the overall X-ray shielding properties of the composites with the addition of Bi2O3. The behavior could be mathematically explained by considering the relationship between the photoelectric cross-section (σpe), atomic numbers (Z) of elements in the composites, and the frequencies (ν) of incident X-rays, following Equation (12):(12) σpe=Zn(hν)3 where h is Planck’s constant [11]. Notably, ν and the X-ray energy (E) are directly proportional to each other as shown in Equation (13):(13) E=hν Equations (12) and (13) also depict that the interaction probabilities between the incident X-rays and the materials are inversely proportional to ν3 or E3; for which the results in Table 2 clearly illustrate this effect, as evidenced by the lowest µm values being observed at the X-ray energy of 200 keV [28]. Table 3, which shows the calculated densities (ρ) of a single-layered Bi2O3/NR composite with varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.% that were used for the determination of the linear attenuation coefficient (µ), suggested that the density of the NR composites increased with increasing Bi2O3 contents, which is mainly due to the much higher ρ value of Bi2O3 (ρBi2O3 = 8.90 g/cm3) than for NR (ρNR = 0.92 g/cm3). Using the results shown in Table 2 and Table 3, and Equation (1), the values of µ for all the single-layered Bi2O3/NR composites with varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.% were determined, and the results are shown in Table 4, which indicates similar behavior as for µm (Table 2). However, more pronounced effects of Bi2O3 on the enhancement of µ were observed compared to those for µm due to the simultaneous roles of Bi2O3 in increasing both the µm and ρ values of the composites, which further amplified the values of µ at higher Bi2O3 contents (Equation (1)). 3.2. Final I/I0 of Multi-Layered Bi2O3/NR Composites Table 5, Table 6, Table 7 and Table 8 show the transmission factors (I/I0) for each layer as well as the final I/I0 values of the 11 multi-layered Bi2O3/NR composites at X-ray energies of 50, 100, 150, and 200 keV, respectively, and Figure 2 shows the schematic representation of relative X-ray intensities for each layer of some designs at the X-ray energy of 50 keV. All the results suggested that the NR layers containing Bi2O3 could attenuate X-rays with higher efficiencies than those without Bi2O3 due to the much higher µ values of Bi2O3/NR composites (Table 4), especially those with higher Bi2O3 contents, that better interacted and attenuated incident X-rays. Furthermore, the results revealed that the final I/I0 values for the composites had larger transmitted X-ray intensities at higher X-ray energies (for the same sample#). This behavior could be explained using Equation (12), which suggested that the interaction probabilities, as well as their X-ray attenuation capabilities, decreased with increasing X-ray energies, resulting in more X-rays being able to escape the materials. Among the 11 multi-layered designs, sample#4, which has a three-layered structure, had the lowest final I/I0 values of 0.5064, 0.6178, 0.7995, and 0.8645 at X-ray energies of 50, 100, 150, and 200 keV, respectively, while sample#1, a single-layered structure, had the highest final I/I0 values of 0.5663, 0.6671, 0.8220, and 0.8765 at X-ray energies of 50, 100, 150, and 200 keV, respectively. Based on the results from these two designs, the multi-layered structure exhibited higher X-ray shielding capabilities by as much as 10.5, 8.7, 4.0, and 2.1% compared to a single-layered structure, determined at X-ray energies of 50, 100, 150, and 200 keV, respectively. Specifically, for sample#4, its highest X-ray attenuation capability was due to its highest effective density and effective percentage by weight of Bi2O3 contained in the sample, determined using Equations (8) and (9); for which the results of both parameters for all designs are shown in Table 9. The larger values of both quantities in multi-layered structures were mainly due to the much higher density of Bi2O3 particles in comparison with that of the NR matrix (for instance, adding 20 wt.% of Bi2O3 to layer#2 in sample#4 would require much less volume than removing 20 wt.% of NR, resulting in a considerable reduction in the total volume and subsequently the increase in the density of the sample). These effects then enabled sample#4 to have more Bi atoms to interact with incoming X-rays through the photoelectric absorption than that of sample#1. In addition, Equation (3) could be modified for the calculation of I/I0 as Equation (14):(14) II0=e−∑iNμixi where µi is the linear attenuation coefficient of the ith layer, xi is the thickness of the ith layer, and N is the total number of layers in the composites [29], which depicted that the values of ∑iNμixi for the multi-layered structures (using information from Table 1 and Table 4) were larger than that of the single-layered sample. For instance, sample#4 had the value of ∑iNμixi of 0.6804, while sample#1 had the value of 0.5687, leading to a lower I/I0 and better X-ray shielding capabilities in sample#4. Furthermore, the results showed that rearranging layers of the samples having the same Bi2O3 contents and numbers of layers did not have effects on X-ray shielding capabilities. For instance, sample #2 and sample #3, as well as samples #6–#9, had the same values of I/I0, regardless of how the layers were arranged. This was due to the values of ∑iNμixi being the same for all of them. Table 10 shows the final I/I0 values of all 11 multi-layered structures using XCOM and PHITS, as well as their corresponding %Difference values for these two methods. The comparisons indicated that the results obtained from both methods were in good agreement, with the largest %Difference value being 4.78% and the average %Difference being 2.24%. Consequently, the values obtained from XCOM and PHITS could be further used for the determination of other parameters, including µm,eff, µeff, HVLeff, and Pbeq,eff. Another interesting outcome from Table 9 was that the final I/I0 values from PHITS seemed to be slightly higher than those from XCOM. This could have been due to factors, such as backscattering and the rescattering of X-rays inside the materials, resulting in an increase in the transmitted X-rays and a subsequent underestimation of the theoretical or ideal X-ray attenuation capabilities of the composites in the results obtained from PHITS [30]. 3.3. Values for µeff, µm,eff, and HVLeff of Multi-Layered Bi2O3/NR Composites Table 11 shows the values of µeff, µm,eff, and HVLeff for the 11 multi-layered Bi2O3/NR composites at X-ray energies of 50, 100, 150, and 200 keV, determined using Equations (5)–(7) and the effective densities (ρeff) of the samples shown in Table 9. The results indicated that similar to those of the final I/I0 (Table 10), sample#4 had the most efficient X-ray shielding properties as well as ρeff, as evidenced by its highest values of µeff, µm,eff, and HVLeff compared to the other designs. 3.4. X-rays Shielding Properties and Recommended Bi2O3 Contents of Three-Layered Bi2O3/NR Composites (Sample#4) Figure 3 shows the values of the final I/I0, µeff, µm,eff, and HVLeff of the three-layered Bi2O3/NR composites (sample#4, which provided higher X-ray shielding properties compared to the other designs), with varying Bi2O3 contents in layer#2 (middle layer) from 10 to 90 wt.% in 10 wt.% increments and a fixed combined thickness of 6 mm, determined at X-ray energies of 50, 100, 150, and 200 keV. The results indicated that the ability to attenuate incident X-rays greatly improved with increasing Bi2O3 contents, as evidenced by the decreases in the values of I/I0 and HVLeff and the increases in µeff and µm,eff with increasing contents. On the other hand, the overall shielding properties of the composites tended to decrease with increasing X-ray energy, as the lowest (highest) values of µeff and µm,eff (I/I0 and HVLeff) were observed at an X-ray energy of 200 keV. These two sets of behavior could be explained using Equation (12), which states that the photoelectric cross-section (σpe) (the ability to attenuate X-rays) is directly proportional to Zn while being inversely proportional to ν3 (E3), resulting in enhanced (lower) shielding properties at higher filler contents (X-ray energies). The Pbeq,eff values of the three-layered Bi2O3/NR composites (sample#4) with varying Bi2O3 contents in layer#2 (middle layer) from 10 to 90 wt.% in 10 wt.% increments and varying combined thicknesses of 6, 9, and 12 mm, are shown in Figure 4. The results indicated that the least Bi2O3 contents in layer#2, which could be regarded as the recommended Bi2O3 contents, that provided the three-layered NR composites with the required Pbeq of 0.5 mmPb, were 82, 72, and 64 wt.% for the combined thicknesses of 6, 9, and 12 mm, respectively. The decreases in the recommended Bi2O3 contents with thicker samples were due to more Bi atoms being available in thicker materials (with the same filler content) to interact with incident X-rays, subsequently reducing the required Bi2O3 contents in layer#2. Notably, while it is possible to prepare NR composites with a 90 wt.% of fillers, as reported by Gwaily et al. who prepared Pb/NR composites for gamma shielding with the Pb contents up to 2000 phr (~95 wt.%) [31], difficulties in the sample preparation process, as well as possible substantial reductions in mechanical properties, could limit the processibility of multi-layered composites with very high filler contents. Consequently, for applications that allow space for thicker materials, lower recommended Bi2O3 fillers, such as those in 9 mm and 12 mm samples, should be considered to ease the difficulty and preserve the mechanical properties and product flexibility. In order to understand how the developed multi-layered structure (sample#4) performed with respect to previously reported materials, the results revealed that sample#4 in this work with the Bi2O3 content of 90 wt.% in layer#2 (middle layer) exhibited the µ value of 7.51 cm−1 (at 100 keV), while the dimensionally-enhanced wood/Bi2O3/NR composites and Gd2O3/NR composites with a total Bi2O3 content of 50 phr (approximately equal Bi2O3 content in the sample as those in sample#4) but with a single-layer structure, had the µ values of 2–3 and 2.6 cm−1 (at 100 keV), respectively [11,32]. These comparisons clearly indicate that the use of a multi-layered structure had great potential to substantially improve the X-ray shielding properties of the products. 4. Conclusions This work theoretically compared the X-ray shielding properties of single-layered and multi-layered Bi2O3/NR composites by determining various shielding parameters (µeff, µm,eff, HVLeff, and Pbeq,eff). In total, 11 different single-layered and multi-layered designs were used to investigate the X-ray attenuation capabilities at X-ray energies of 50, 100, 150, and 200 keV. The results indicated that the layers with higher Bi2O3 contents had better shielding abilities than those with lower contents and the three-layered structure (sample#4), with the layer arrangement of pristine NR (layer#1)-Bi2O3/NR (layer#2)-pristine NR (layer#3), had the highest overall X-ray shielding properties among the designs investigated, due to its highest effective Bi2O3 content, offering enhanced X-ray shielding properties of 10.5, 8.7, 4.0, and 2.1% compared to those of a single-layered structure (sample#1). Additionally, further investigation by varying the Bi2O3 contents in layer#2 (the middle layer) of sample#4 from 10 to 90 wt.% in 10 wt.% increments revealed that the X-ray shielding properties could be further enhanced by increasing the Bi2O3 contents; for which the recommended filler contents for actual production, determined from the common required Pbeq value of 0.5 mm Pb, were 82, 72, and 64 wt.% for sample combined thicknesses of 6, 9, and 12 mm, respectively. The overall outcomes of this work reported not only the comparison of X-ray shielding properties of single-layered and multi-layered Bi2O3/NR composites but also presented potential methods to limit the reduction in mechanical properties and flexibility of the composites containing high filler contents. Acknowledgments The Kasetsart University Research and Development Institute (KURDI) and the Specialized Center of Rubber and Polymer Materials in Agriculture and Industry (RPM) provided publication support. Author Contributions Conceptualization, K.S.; formal analysis, A.T., J.D. and K.S.; funding acquisition, K.S.; investigation, A.T., J.D. and K.S.; methodology, A.T., J.D. and K.S.; supervision, K.S.; validation, A.T., J.D. and K.S.; visualization, K.S.; writing—original draft, K.S.; writing—review and editing, A.T., J.D. and K.S. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 1 Schemes showing single-layered and multi-layered structures and Bi2O3 contents for 11 distinct designs for determination of X-ray shielding properties in Bi2O3/NR composites, where thicknesses are in millimeters and the numbers enclosed in circles represent the sample#. Figure 2 Schemes showing relative X-ray intensities for each layer of sample#1, sample#2, sample#4, sample#5, sample#7, sample#9, sample#10, and sample#11, at the X-ray energy of 50 keV. The numbers enclosed in circles represent sample#. Figure 3 (a) Final values of I/I0, (b) µeff, (c) µm,eff, and (d) HVLeff of three-layered Bi2O3/NR composites (sample#4) containing varying Bi2O3 contents from 10 to 90 wt.% in layer#2 (middle layer) and a fixed combined thickness of 6 mm, determined at X-ray energies of 50, 100, 150, and 200 keV. Figure 4 Effective Pbeq of three-layered Bi2O3/NR composites (sample#4) with varying Bi2O3 contents from 10 to 90 wt.% in layer#2 (middle layer) and varying combined thicknesses of 6, 9, and 12 mm, determined at X-ray energies of (a) 50, (b) 100, (c) 150, and (d) 200 keV. The green dotted lines represent the common requirement of 0.5 mmPb used as a benchmark for this work and the blue, red, and black dotted lines represent the least Bi2O3 contents providing the composites with the Pbeq of the required 0.5 mmPb for varying thicknesses. polymers-14-01788-t001_Table 1 Table 1 Sample codes with details of the number of layers, thickness of each layer, and Bi2O3 content in each layer for determination of X-ray shielding properties in Bi2O3/NR composites (Sample# and Layer# denote Sample Number and Layer Number, respectively). Sample# Number of Layers Thickness of Each Layer (mm) Bi2O3 Contents in Layer# (wt.%) 1 2 3 4 5 1 1 6.0 10 - - - - 2 2 3.0 0 20 - - - 3 2 3.0 20 0 - - - 4 3 2.0 0 30 0 - - 5 3 2.0 15 0 15 - - 6 4 1.5 20 0 20 0 - 7 4 1.5 0 20 0 20 - 8 4 1.5 0 20 20 0 - 9 4 1.5 20 0 0 20 - 10 5 1.2 16.7 0 16.7 0 16.7 11 5 1.2 0 25 0 25 0 polymers-14-01788-t002_Table 2 Table 2 Mass attenuation coefficients (µm; cm2/g) of Bi2O3/NR composites with varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.% at the X-ray energies of 50, 100, 150, and 200 keV. X-ray Energy (keV) Bi2O3 Content (wt.%) 0 10 15 16.7 20 25 30 50 0.2047 0.9379 1.3050 1.4290 1.6710 2.0380 2.4040 100 0.1683 0.6677 0.9174 1.0020 1.1670 1.4170 1.6670 150 0.1501 0.3233 0.4099 0.4393 0.4965 0.5831 0.6696 200 0.1371 0.2174 0.2575 0.2712 0.2976 0.3378 0.3779 polymers-14-01788-t003_Table 3 Table 3 Calculated densities (ρ) of Bi2O3/NR composites with varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.%. Bi2O3 Content (wt.%) Density (g/cm3) 0 0.920 10 1.011 15 1.063 16.7 1.082 20 1.121 25 1.186 30 1.259 polymers-14-01788-t004_Table 4 Table 4 Linear attenuation coefficients (µ; cm−1) of single-layered Bi2O3/NR composites with varying Bi2O3 contents of 0, 10, 15, 16.7, 20, 25, and 30 wt.% at X-ray energies of 50, 100, 150, and 200 keV. X-ray Energy (keV) Bi2O3 Content (wt.%) 0 10 15 16.7 20 25 30 50 0.1883 0.9478 1.3872 1.5457 1.8732 2.4167 3.0255 100 0.1548 0.6747 0.9751 1.0838 1.3082 1.6803 2.0979 150 0.1380 0.3267 0.4357 0.4751 0.5565 0.6914 0.8427 200 0.1261 0.2197 0.2737 0.2933 0.3336 0.4005 0.4755 polymers-14-01788-t005_Table 5 Table 5 Relative X-ray intensities for each layer of multi-layered Bi2O3/NR composites at an X-ray energy of 50 keV (Sample# and Layer# denote Sample Number and Layer Number, respectively). Sample# Number of Layers Thickness of Each Layer (mm) Relative X-ray Intensities for Layer# 1 2 3 4 5 1 1 6.0 0.5663 - - - - 2 2 3.0 0.9451 0.5388 - - - 3 2 3.0 0.5701 0.5388 - - - 4 3 2.0 0.9630 0.5258 0.5064 - - 5 3 2.0 0.7577 0.7298 0.5529 - - 6 4 1.5 0.7550 0.7340 0.5542 0.5388 - 7 4 1.5 0.9722 0.7340 0.7136 0.5388 - 8 4 1.5 0.9722 0.7340 0.5542 0.5388 - 9 4 1.5 0.7550 0.7340 0.7136 0.5388 - 10 5 1.2 0.8307 0.8121 0.6746 0.6596 0.5479 11 5 1.2 0.9777 0.7315 0.7152 0.5352 0.5232 polymers-14-01788-t006_Table 6 Table 6 Relative X-ray intensities for each layer of multi-layered Bi2O3/NR composites at an X-ray energy of 100 keV (Sample# and Layer# denote Sample Number and Layer Number, respectively). Sample# Number of Layers Thickness of Each Layer (mm) Relative X-ray Intensities for Layer# 1 2 3 4 5 1 1 6.0 0.6671 - - - - 2 2 3.0 0.9546 0.6447 - - - 3 2 3.0 0.6754 0.6447 - - - 4 3 2.0 0.9695 0.6373 0.6178 - - 5 3 2.0 0.8228 0.7977 0.6564 - - 6 4 1.5 0.8218 0.8030 0.6599 0.6447 - 7 4 1.5 0.9770 0.8030 0.7845 0.6447 - 8 4 1.5 0.9770 0.8030 0.6599 0.6447 - 9 4 1.5 0.8218 0.8030 0.7845 0.6447 - 10 5 1.2 0.8780 0.8619 0.7568 0.7428 0.6522 11 5 1.2 0.9816 0.8023 0.7876 0.6438 0.6319 polymers-14-01788-t007_Table 7 Table 7 Relative X-ray intensities for each layer of multi-layered Bi2O3/NR composites at an X-ray energy of 150 keV (Sample# and Layer# denote Sample Number and Layer Number, respectively). Sample# Number of Layers Thickness of Each Layer (mm) Relative X-ray Intensities for Layer# 1 2 3 4 5 1 1 6.0 0.8220 - - - - 2 2 3.0 0.9594 0.8119 - - - 3 2 3.0 0.8462 0.8119 - - - 4 3 2.0 0.9728 0.8219 0.7995 - - 5 3 2.0 0.9165 0.8916 0.8172 - - 6 4 1.5 0.9199 0.9010 0.8289 0.8119 - 7 4 1.5 0.9795 0.9010 0.8826 0.8119 - 8 4 1.5 0.9795 0.9010 0.8289 0.8119 - 9 4 1.5 0.9199 0.9010 0.8826 0.8119 - 10 5 1.2 0.9446 0.9291 0.8776 0.8631 0.8153 11 5 1.2 0.9836 0.9052 0.8904 0.8195 0.8060 polymers-14-01788-t008_Table 8 Table 8 Relative X-ray intensities for each layer of multi-layered Bi2O3/NR composites at an X-ray energy of 200 keV (Sample# and Layer# denote Sample Number and Layer Number, respectively). Sample# Number of Layers Thickness of Each Layer (mm) Relative X-ray intensities for Layer# 1 2 3 4 5 1 1 6.0 0.8765 - - - - 2 2 3.0 0.9629 0.8712 - - - 3 2 3.0 0.9048 0.8712 - - - 4 3 2.0 0.9751 0.8866 0.8645 - - 5 3 2.0 0.9467 0.9231 0.8740 - - 6 4 1.5 0.9512 0.9334 0.8878 0.8712 - 7 4 1.5 0.9813 0.9334 0.9159 0.8712 - 8 4 1.5 0.9813 0.9334 0.8878 0.8712 - 9 4 1.5 0.9512 0.9334 0.9159 0.8712 - 10 5 1.2 0.9654 0.9509 0.9180 0.9042 0.8729 11 5 1.2 0.9850 0.9388 0.9247 0.8813 0.8680 polymers-14-01788-t009_Table 9 Table 9 Effective densities and effective percentages by weight of Bi2O3 for all 11 multi-layered Bi2O3/NR composites (Sample# denotes Sample Number). Sample# Effective Density (g/cm3) Effective Percentage by Weight (wt.%) 1 1.011 10.00 2 1.021 10.98 3 1.021 10.98 4 1.033 12.19 5 1.015 10.47 6 1.021 10.98 7 1.021 10.98 8 1.021 10.98 9 1.021 10.98 10 1.017 10.64 11 1.026 11.55 polymers-14-01788-t010_Table 10 Table 10 Comparative final transmission factors (I/I0) of 11 multi-layered structures of Bi2O3/NR composites at X-ray energies of 50, 100, 150, and 200 keV using XCOM and PHITS and their corresponding percentage differences (Sample# denotes Sample Number). Sample# XCOM PHITS %Difference Final Transmission Factor (I/I0) at X-ray Energy (keV) 50 100 150 200 50 100 150 200 50 100 150 200 1 0.5663 0.6671 0.8220 0.8765 0.5732 0.6681 0.8258 0.8785 1.22 0.15 0.46 0.23 2 0.5388 0.6447 0.8119 0.8712 0.5518 0.6483 0.8498 0.8964 2.42 0.55 4.67 2.89 3 0.5388 0.6447 0.8119 0.8712 0.5578 0.6756 0.8196 0.8946 3.54 4.78 0.95 2.69 4 0.5064 0.6178 0.7995 0.8645 0.5289 0.6371 0.8255 0.8865 4.44 3.11 3.25 2.54 5 0.5529 0.6564 0.8172 0.8740 0.5766 0.6589 0.8443 0.8871 4.27 0.38 3.32 1.50 6 0.5388 0.6447 0.8119 0.8712 0.5428 0.6480 0.8226 0.8735 0.75 0.50 1.32 0.27 7 0.5388 0.6447 0.8119 0.8712 0.5494 0.6547 0.8432 0.8898 1.97 1.54 3.85 2.14 8 0.5388 0.6447 0.8119 0.8712 0.5471 0.6452 0.8461 0.8917 1.55 0.08 4.22 2.36 9 0.5388 0.6447 0.8119 0.8712 0.5596 0.6522 0.8480 0.8789 3.87 1.15 4.45 0.89 10 0.5479 0.6522 0.8153 0.8729 0.5550 0.6723 0.8523 0.8861 1.30 3.08 4.54 1.50 11 0.5232 0.6319 0.8060 0.8680 0.5297 0.6384 0.8343 0.9039 1.24 1.03 3.51 4.13 polymers-14-01788-t011_Table 11 Table 11 Values for µeff, µm,eff, and HVLeff of 11 multi-layered Bi2O3/NR composites at X-ray energies of 50, 100, 150, and 200 keV (Sample# denotes Sample Number). Sample# µeff (cm−1) µm,eff (cm2/g) HVLeff (cm) 50 keV 100 keV 150 keV 200 keV 50 keV 100 keV 150 keV 200 keV 50 keV 100 keV 150 keV 200 keV 1 0.9479 0.6748 0.3267 0.2197 0.9379 0.7195 0.4541 0.4838 0.7313 1.0272 2.1215 3.1549 2 1.0308 0.7315 0.3473 0.2299 1.0101 0.7243 0.4796 0.4793 0.6724 0.9475 1.9956 3.0153 3 1.0308 0.7315 0.3473 0.2299 1.0101 0.7243 0.4796 0.4793 0.6724 0.9475 1.9956 3.0153 4 1.1341 0.8025 0.3730 0.2426 1.0980 0.7309 0.5103 0.4755 0.6112 0.8637 1.8585 2.8569 5 0.9876 0.7017 0.3365 0.2245 0.9727 0.7214 0.4664 0.4814 0.7019 0.9878 2.0599 3.0873 6 1.0308 0.7315 0.3473 0.2299 1.0101 0.7243 0.4796 0.4793 0.6724 0.9475 1.9956 3.0153 7 1.0308 0.7315 0.3473 0.2299 1.0101 0.7243 0.4796 0.4793 0.6724 0.9475 1.9956 3.0153 8 1.0308 0.7315 0.3473 0.2299 1.0101 0.7243 0.4796 0.4793 0.6724 0.9475 1.9956 3.0153 9 1.0308 0.7315 0.3473 0.2299 1.0101 0.7243 0.4796 0.4793 0.6724 0.9475 1.9956 3.0153 10 1.0028 0.7122 0.3403 0.2265 0.9860 0.7224 0.4712 0.4807 0.6912 0.9732 2.0366 3.0608 11 1.0797 0.7650 0.3594 0.2359 1.0520 0.7272 0.4943 0.4773 0.6420 0.9061 1.9284 2.9382 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092257 cancers-14-02257 Review EGFR-Mutant Non-Small-Cell Lung Cancer at Surgical Stages: What Is the Place for Tyrosine Kinase Inhibitors? Cansouline Xavier 12 Lipan Béatrice 1 Sizaret Damien 3 Tallet Anne 4 https://orcid.org/0000-0003-2559-0660 Vandier Christophe 2 Carmier Delphine 5 https://orcid.org/0000-0002-5271-6946 Legras Antoine 12* Alfieri Roberta Academic Editor 1 Department of Thoracic Surgery, Tours University Hospital, 37170 Chambray-Lès-Tours, France; xavier.cansouline@etu.univ-tours.fr (X.C.); b.lipan@chu-tours.fr (B.L.) 2 Nutrition, Croissance et Cancer, INSERM UMR 1069, University of Tours, 37000 Tours, France; christophe.vandier@univ-tours.fr 3 Department of Pathology, Tours University Hospital, 37170 Chambray-Lès-Tours, France; d.sizaret@chu-tours.fr 4 Platform of Solid Tumor Molecular Genetics, Tours University, 37000 Tours, France; a.tallet@chu-tours.fr 5 Department of Pneumology, Tours University Hospital, 37000 Tours, France; d.carmier@chu-tours.fr * Correspondence: alegras@univ-tours.fr; Tel.: +33-2474-746-36 30 4 2022 5 2022 14 9 225707 4 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary Tyrosine kinase inhibitors are drugs targeting the epidermal growth factor receptor. In lung cancer, they are used to treat advanced EGFR-mutant diseases, and more recently, one has been approved for adjuvant therapy. Even though publications on the topic are numerous, conclusions are difficult to interpret and are sometimes contradictory. We therefore reviewed the literature in order to present an overview of up-to-date data regarding the adjuvant and neoadjuvant use of tyrosine kinase inhibitors, with particular attention given to their benefits, proven or expected, as well as what challenges could be faced when entering them as protocols in standard care. Abstract The ADAURA trial has been significant for the perception of EGFR tyrosine kinase inhibitors (TKIs) as a tool for early stage non-small-cell lung cancer (NSCLC). It produced such great insight that the main TKI, Osimertinib, was rapidly integrated into international guidelines for adjuvant use. However, EGFR-mutant NSCLC is a complex entity and has various targeting drugs, and the benefits for patients might not be as clear as they seem. We reviewed trials and meta-analyses considering TKI adjuvant and neoadjuvant use. We also explored the influence of mutation variability and financial evaluations. We found that TKIs often show disease-free survival (DFS) benefits, yet studies have struggled to improve the overall survival (OS); however, the results from the literature might be confusing because of variability in the stages and mutations. The safety profiles and adverse events are acceptable, but costs remain high and accessibility might not be optimal. TKIs are promising drugs that could allow for tailored treatment designs. EGFR NSCLC adjuvant neoadjuvant targeted therapy resected lung cancer early stages tyrosine kinase inhibitors ADAURA chemotherapy Association Thorax à ToursThis research received no external funding. Publication Charges were supported by Association Thorax à Tours. ==== Body pmc1. Introduction In the last decade, the prognosis and treatment of non-small-cell lung cancer (NSCLC) have improved as a result of the discovery of epidermal growth factor receptor (EGFR) mutations, which are predictor markers for the response to tyrosine kinase inhibitors (TKIs). Nowadays, at advanced stages of the disease, TKIs are part of the treatment tools, but their place in the early stages remains unclear. Recent studies, such as the ADAURA trial [1], have shown improvements in disease-free survival (DFS) for resected NSCLC in TKI groups, but the impact on the overall survival (OS) remains undefined. Osimertinib is already being introduced into international guidelines. In this review, we aimed to assess the possible place for TKIs in standard care for EGFR-mutated NSCLC at the surgical stages in the upcoming years. 2. Lung Cancer and EGFR Mutations Lung cancer accounts for 11.4% of all cancer worldwide and was responsible for 1.8 million deaths in 2020 (WHO). Among these, NSCLC is the most common histology [2], and almost one out of three cases are localized upon diagnosis. For these cases, the gold-standard treatment is surgical resection [3]—anatomical resection through lobectomy or sub-lobectomy, or segmentectomy in the very early stages [4], combined with mediastinal lymphadenectomy [5]. Surgery is associated with peri-operative chemotherapy in cases of nodal involvement or with a tumor size of >4 cm [6]. Chemotherapy slightly improves OS (absolute benefits of 3.9% and 5.4% after 3 and 5 years, respectively) and DFS (absolute benefits of 5.8% and 5.8% after 3 and 5 years, respectively) outcomes, but the recurrence rate remains high—up to 50% after 5 years [7]. At an advanced stage, lung cancer is associated with a poor prognosis, and standard care is represented by platinum-based chemotherapy, with small improvements in OS and progression-free survival (PFS) [8] and high toxicity profiles [9]. The discovery of oncogenic driver mutations allowed for the development of targeted therapies, which rapidly became the standard treatment for eligible patients as a result of their major improvement outcomes [10,11]. These mutations are numerous; however, EGFR mutations are the most represented, and are present in 15% of lung adenocarcinomas in the USA and up to 60% in Asian females [2]. The epidermal growth factor receptor is a transmembrane receptor from the tyrosine kinases family HER/erbB; it is involved in proliferation, angiogenesis, and apoptosis inhibition. Its gene is located on the short arm of chromosome 7 (7p11.2), and 93% of mutations of interest are found within exons 18–21, which code for the tyrosine kinase domain responsible for abnormal activation in cancerous cells [12]. Of these mutations, 90% are represented by exon 19 deletions (Ex19del) and exon 21 L858R mutations [13], which have been proven to be sensitive to TKI drugs [14]. On the contrary, the exon 20 T790M mutation produces resistance to targeted therapy, and is mainly an acquired mutation after clonal selection as a result of drug administration [11]. While exon 20 T790M confers resistance to first- and second-generation EGFR TKIs, it can be overcome by third-generation TKIs (i.e., Osimertinb) [15]. Another frequent (around 5% of all EGFR mutations) [16] TKI-resistant mutation family is found in exon 20 insertions. Although these mutations currently confer de novo resistance for most available TKIs [17], Osimertinib may provide a moderate response [18], and Mobocertinib, a new drug, is in the early stages of evaluation for this particular indication [19]. Less common mutations are also found on exons 18, 20, and 21, and a small amount of patients have been found to have complex mutations, associated with two or more mutations [20]. EGFR-mutant (EGFRm) NSCLC mainly occurs in nonsmoking Asian females with adenocarcinoma [21]. The development of three generations of EGFR inhibitors has shown increased outcomes [22] and acceptable security profiles, providing them a place as a first-line treatment for advanced EFGRm NSCLC [23]. These improvements have provided insight so as to enhance the prognosis of patients with localized stages in adjuvant and neoadjuvant therapy. 3. TKIs as Neoadjuvant Treatment TKIs might be able to be used as neoadjuvant treatment; however, the topic has been poorly explored. Some phase II trials have found potential improvements with Erlotinib; the main results from these trials are summarized in Table 1. Chen et al. [24] compared Erlotinib versus Pemetrexed−Cisplatin chemotherapy in a randomized trial with 86 patients and found an improved objective response rate (ORR)/risk ratio (RR) of 1.53 (95% CI: 1.03–2.27) and operation rate (OpR)/RR of 1.08 (95% CI: 0.9–1.27). Zhong et al. [26] also compared Erlotinib to chemotherapy in a 24-patient non-randomized trial; their results contradicted other studies because the OS was in favor of chemotherapy, with a hazard ratio (HR) = 1.79 (95% CI: 0.73–4.39). Xiong et al. [27] performed a trial with 31 patients, and compared Erlotinib treatment for EGFRm patients versus chemotherapy treatment for EGFR-wild-type (EGFRw) patients. Their findings found an improved ORR (67% vs. 19%), OpR (12/15 vs. 8/16), pathological response rate (67% vs. 38%), and OS (51 vs. 20.9 months) in the Erlotinib group. They also found an improved tumor diameter reduction (35% vs. 16%) with Erlotinib. Recently, Zhong et al. [25] published EMERGING CTONG, a controlled trial that included 72 patients receiving Erlotinib or chemotherapy; it showed an encouraging ORR (primary outcome) of 54.1% (95% CI: 37.2–70.9%) versus 34.3% (95% CI: 17.7–50.8%), odds ratio (OR) of 2.26 (95% CI: 0.87–5.84; p = 0.092), and node down staging (secondary outcome, 10.8% vs. 2.9%, p = 0.185), without reaching statistical significance. The PFS (secondary outcome) was significantly better: 21.5 months (95% CI: 16.7–26.3 months) versus 11.4 months (95% CI: 7.3–15.5 months), and HR of 0.39 (95% CI: 0.23–0.67; p = 0.001). The OS (secondary outcome) was comparable in both groups: 45.8 versus 39.2 months (HR = 0.77, 95% CI: 0.41–1.45; p = 0.417). These results might have been attenuated by the sample size, slow rate of accrual, and short time of exposure. The safety profile was good with no grade 3/4 toxicities in the Erlotinib group. The most common adverse events (AEs) were rash, diarrhea, and cough, as described before. These results warrant more investigations with larger cohorts in order to give stronger evidence of the improvement provided by TKI in neoadjuvant therapy. In 2021, Chen et al. [29] published a meta-analysis collecting data from the five trials (three RCT [25,28,29] and two non-RCT [26,27]) available comparing Erlotinib to chemotherapy as neoadjuvant treatments for EGFRm stage IIIA NSCLC patients; they found a trend in favor of Erlotinib in OS (HR = 0.74, 95% CI: 0.43–1.27) and PFS (HR = 081, 95% CI: 0.27–2.44). The ORR (RR = 1.70, 95% CI: 1.35–2.15), progression rate (RR = 0.64, 95% CI: 0.34–1.19), and OpR (RR = 1.13, 95% CI: 1.01–1.26) were in favor of TKIs in all of the studies included, resulting in significant improvements when using Erlotinib. The effect on OpR was lost when excluding the data from non-RCT trials. The toxicity was greater in the Erlotinib group (RR = 0.50, 95% CI: 0.34–0.75), mainly presenting as skin rash, but not significantly different when excluding the non-RCT data (RR = 0.53, 95% CI: 0.26–1.08). Gefitinib has not been evaluated as much. Rivzi et al. [30] enrolled 50 patients with stages I–II NSCLC in a single-arm trial; patients from a population enriched for EGFR mutations received TKI for 21 days before surgery. The authors reported a radiographic response (>25% reduction in bidimensional measurement) for 21 patients (42%), in which 17 were EGFRm. Nine patients were upstaged and seven were downstaged, but it was not detailed from which group they were. DFS did not significantly differ when stratified according to EGFR status or adjuvant TKI usage. The OS data were incomplete and, to our knowledge, were never reported. No grade IV AEs were reported and only one grade III rash occurred during the pre-operative period. Zhang et al. [31] published a trial of 35 patients with stages II−IIIA EGFRm NSCLC who received Gefitinib 42 days before surgery. They found an ORR of 54.5% (95% CI: 37.7–70.7) and a rate of major pathologic response (MPR) of 24.2% (95% CI: 11.9–40.4) using the RECIST criteria. The median DFS was 33.5 months (95% CI: 19.7–47.3) and the median OS was not achieved. They also found that patients with MPR had a significantly longer median DFS (68 vs. 25.3 months, p = 0.019) and a trend for a longer OS (p = 0.134). NEOADAURA [32], a phase III trial, is currently ongoing, comparing Osimertinib with or without chemotherapy versus chemotherapy alone in resectable stages II–IIIB N2 EGFRm (Ex19del or L858R) NSCLC. The trial is ongoing and might not end before 2024 (estimated final data collection date for primary outcome measure). Of note, nine single-arm phase II trials are currently ongoing to explore new schemes [33,34,35,36,37,38,39,40,41]. These studies are summarized in Table 2. 4. TKI as Adjuvant Treatment TKI was first described as an adjuvant therapy by Tsuboi et al. [42] in 2005 in a 38-patient phase III trial comparing Gefitinib to a placebo. They provided safety and feasibility data, but no survival data because of early trial termination due to a controversy involving a rising rate of interstitial lung disease (ILD) in patients treated with Gefitinib [43] (see safety profile below). Since this publication, a dozen clinical comparisons [44,45] have been conducted, with four phase II [46,47,48,49] and seven phase III [1,42,50,51,52,53,54] randomized trials. When using TKI, most of these trials showed improved DFS compared with a placebo, with an HR ranging from 0.2 to 0.61, with statistical significance for all of the trials (Table 3), except for Feng et al. [48], where the statistical power may have been limited by the sample size (39 patients) and follow-up period (24 months, median DFS not reached). In 2015, Kelly et al. [50] published the RADIANT trial, a randomized, double-blind, placebo-controlled phase III international and multi-center trial that included 973 patients. They compared Erlotinib with a placebo in patients with EGFR-expressing tumors, but not necessarily EGFR-mutated tumors (n = 161). For the EGFRm patients, DFS (as a secondary endpoint) was better (HR = 0.61, 95% CI: 0.38–0.98) with Erlotinib, but the primary outcome was not reached (DFS in the ITT population, HR = 0.90; 95% CI: 0.74–1.10; p = 0.324). It is not clear yet if Gefitinib should be used as an adjuvant therapy. The findings of Goss et al. [52] (BR19 trial, Phase III, 2013) on DFS were not in favor of Gefitinib (HR = 1.84, 95% CI: 0.44–7.73), but the study was stopped early because of a lack of supportive evidence regarding the efficacy of this drug in trials for advanced stages [55]. Zhong et al., in CTONG 1104/ADJUVANT [53] (2018), compared Gefitinib versus Vinorelbine plus Cisplatin (VP), and showed an improved median DFS in the TKI group of 30.8 months (95% CI: 26.7–36.6) versus 19.8 months (95% CI: 15.4–23.0), with an HR of 0.51 (95% CI: 0.36–0.72, p< 0.001). The long-term data did not show any benefit, with 3-year DFS rates of 39.6% (TKI) and 32.5% (VP) (p = 0.316) and 5-year DFS rates of 22.6% (TKI) and 23.2% (VP) (p = 0.928). The Kaplan−Meier curves crossed at 60 months, but the risk at this point represented only 10% of the original contingent, exposing this result to a power insufficiency [56]. Tada et al. [57] recently published IMPACT WJOG6410L, a phase III randomized trial with 234 patients with stages II–III, EGFRm NSCLC. The patients either received 2 years of adjuvant Gefitinib or four cycles of Cisplatin−Vinorelbine adjuvant chemotherapy. Neither DFS nor OS were different between the two arms (DFS HR = 0.92; 95% CI: 0.67–1.28; p = 0.63) (OS HR = 1.03; 95% CI: 0.65–1.65; p = 0.89). In 2021, He et al. [54] published the EVIDENCE trial—a phase III randomized trial comparing Icotinib (highly selective first-generation EGFR TKI) to chemotherapy in 322 patients. Their findings were consistent with previous trials, with an improved DFS of 47 months (95% CI: 36.4–not reached) with Icotinib versus 22.1 months (95% CI: 16.8–30.4) with chemotherapy (HR = 0.36; 95% CI: 0.24–0.55; p < 0.0001). The 3-year DFS was also better (63.9% (95% CI: 51.8–73.7) vs. 32.5% (95% CI: 21.3–44.2)), but the OS data were incomplete and not published. To date, it is the largest trial on Icotinib in an adjuvant setting. The ADAURA [1] study, a phase III trial, compared 3-year adjuvant Osimertinib versus a placebo in stages IB−IIIA (seventh TNM classification) EGFRm (Ex19del or L858R) NSCLC. It is currently the largest cohort to date, with 682 patients enrolled. Adjuvant chemotherapy before targeted therapy was allowed, but not mandatory. The primary endpoint, DFS at 24 months for stages II−IIIA, was shown to be dramatically better for TKI (HR = 0.17; 99.06% CI: 0.11–0.26), with an 80% reduction of risk of recurrence after 2 years. In the overall population (stages IB−IIIA, with stage IB accounting for 32% of the cohort), the same effect was shown (HR = 0.20; 99.12% CI: 0.14–0.30; p< 0.001). In the subgroup analysis, the effect was consistent and progressive according to disease stage, with an HR of 0.39 (95% CI: 0.18–0.76) in stage IB, HR of 0.17 (95% CI: 0.08–0.31) in stage II, and HR of 0.12 (95% CI: 0.07–0.2) in stage IIIA. The use of precedent adjuvant chemotherapy also slightly enhanced DFS (HR = 0.16 with chemo vs. 0.23 without, 95% CI: 0.10–0.26 and 0.13–0.40, respectively). They also reported an 82% reduction in the risk of death or central nervous system (CNS) recurrence (HR = 0.18; 95% CI: 0.10–0.33), but the median CNS DFS was not achieved, and this result should be taken with caution. According to these results, the independent data monitoring committee decided to unblind the trial early. At publication, the OS data were incomplete, and the follow-up is still ongoing as well as the quality-of-life evaluation. Although these results are impressive, this study might be biased in its design. Firstly, a PET−CT scan was not mandatory at baseline staging. Secondly, brain imaging was not updated before TKI introduction, even though the delay between surgery and randomization was allowed to be up to 26 weeks when adjuvant chemotherapy was administered (10 weeks without chemotherapy). Furthermore, there are no data for assessing the quality of surgical resection (lymphadenectomy extend and lymph node capsular effraction, complete or incomplete resection, operative approach). The duration of treatment was 3 years, which is longer than in other trials; however, it is not clear if longer therapy would improve the outcomes [58]. These limitations might have resulted in an underestimation of the staging at randomization, lowering the control of this possible confounding factor. Nevertheless, ADAURA largely contributed to the inclusion of Osimertinib in North American guidelines as an adjuvant treatment for NSCLC with EGFR Ex19del or L858R mutations [59], and in Europe for stages IB−IIIA with the same mutations [60,61]. Of note, ADAURA used the seventh TNM classification with stage IB with tumors of at least 4 cm, which are now classed stage II in the eighth TNM [62]. However, these guidelines would be applied to new stage IB tumors, even though this patient group was not evaluated. Concerning OS, only the EVAN [49] phase II trial was able to prove a statistical benefit of Erlotinib over chemotherapy as a secondary endpoint (HR = 0.165, 95% CI: 0.047–0.579), but this result might be immature because the median OS was not reached for both groups at the data cutoff and there were fewer death events compared with the study discontinuation events. In other trials, the results were heterogeneous from one study to another (HR ranging from 0.37 to 3.16, p > 0.05). Soon, the French Intergroup of Thoracic Cancerology (IFCT) will conduct the ROSIE trial, which aims to identify clinico-pathological and molecular descriptions associated with the probability of relapse after Osimertinib adjuvant exposure in completely resected stages pIIA–IIIA (TNM staging 8th) EGFRm NSCLC, and to describe the clinical, pathological, and molecular characteristics upon relapse during or after Osimertinib treatment. The protocol will include plasmatic circulating tumor DNA (ctDNA) level monitoring, as well as tumor genomic sequencing at baseline and at relapse. Regarding future publications, we considered the ALCHEMIST [63] trial, a nationwide study evaluating Erlotinib versus a placebo in stages IB (>4 cm)−IIIA EGFRm NSCLC. The primary outcome is OS and investigators plan to include 450 patients, with a complete follow-up of 6 years after ending treatment. For more details regarding the ongoing comparative trials, see Table 4. Ten meta-analyses [45,67,68,69,70,71,72,73,74,75] assessed that DFS was better with TKI than without [69] (HR ranging from 0.38 to 0.86 with statistical significance in all ten publications). However, only two of five found an improvement in OS—Tang et al. [73] in 2019 with an OR of 0.63 (95% CI: 0.46–0.86) and Yin et al. [75] in 2021 with an HR of 0.62 (95% CI: 0.45–0.86), but they included non-randomized trials [76,77,78]. One of the latter trials highlighted the heterogeneity of the stage and mutation repartition from one trial to another [67], which could be confounding factors. We found that the patient pool from stages IB to IIIA regrouped a very wide variety of cancers with an inconstant prognosis, and that mutation variability could also interfere with the drug response (see below—Impact of EGFR Mutation). Thus, entering Osimertinib into routine protocol will surely provide data, but its administration should be monitored to define who should receive it and who should not. Of note, unsurprisingly, TKI showed no benefits when used on EGFR-wild-type NSCLC. 5. Impact of EGFR Mutation and Co-Mutations Although Ex19del and L858R are the most frequent mutations and markers for TKI sensitivity, they might not be equal. In advanced cancers, Lee et al. [79] revealed in a meta-analysis that the PFS for tumors with the Ex19del mutation could be up to 50% greater (HR = 0.24, 95% CI: 0.20–0.29) than for tumors with L858R (HR = 0.48, 95% CI: 0.39–0.58). In adjuvant therapy, the subgroups analysis data from ADAURA, EVAN, ADJUVANT, and EVIDENCE were consistent with these results, with a trend towards a better DFS in patients with Ex19del. There are also dozens of rare mutations that could produce the opposite reactions from one or another drug [80], highlighting the challenge of precise genomic sequencing in order to allow for personalized treatment plans. Furthermore, as TKI could promote the emergence of a resistant clone under selection pressure, tumor re-sequencing upon recurrence might be an important step in adaptive therapy. Co-mutations may also be predictive for drug response. Liu et al. [81] reviewed the genomic profiles from the ADJUVANT trial and found five predictive biomarkers (TP53 exon4/5 mutations; RB1 alterations; and copy number gains of MYC, NKX2-1, and CDK4). They were able to define three subtypes of patients with very different responses to Gefitinib in each group—sometimes opposite. These data show the complexity of genomic impact and the uncertainty of the drug response from one patient to another. In the future, as TKIs are becoming part of standard treatment, very careful routine patient data collection will be needed to further document this variability and to design better tailored treatment paths. 6. Safety As TKIs struggle to show an impact on OS, safety, AEs, and quality of life (QoL) appear to be key points for patients’ and clinicians’ acceptability. At the beginning of its use, Gefitinib was shown to cause interstitial lung disease, which could severely injure the lung capacity [43]. Investigations led to the conclusion that this AE, which can occur for any EGFR TKI, is rare and is more likely to occur in Japanese populations [82]. ILD is usually reversible after drug discontinuation, and steroids and reliable drugs can replace TKIs [83]. Dacomitinib and Afatinib have a higher toxicity than Icotinib, Osimertinib, and Gefitinib, but Osimertinib seems to provide the highest risk for ILD [84]. Most trials and meta-analyses have provided safety data—TKIs are more likely to cause AEs than a placebo, but much less than adjuvant chemotherapy [68]. Grade III−IV AEs are extremely rare; the most common AEs are diarrhea, paronychia, mucocutaneous symptoms (skin rash, dry skin, pruritus, mouth ulceration, etc.), and cough. Treatment discontinuation and dose reductions due to AEs are less than in the chemotherapy groups in the ADJUVANT, EVIDENCE, and EVAN trials. ADAURA’s QoL was reported and showed no difference between Osimertinib and a placebo [85]. Finally, as TKIs are oral drugs with a single daily dose, adhesion and acceptation will be better than for intravenous treatments. 7. Cost Effectiveness and Accessibility With every new drug class being routinely used, the financial aspect for health organizations is considered. Lemmon et al. [86] performed a cost-effectiveness model using the ADAURA data and calculated an incremental cost-effectiveness ratio (ICER) for Osimertinib of about USD 317,000 per quality-adjusted life year (QALY) gained (E.D: “One quality-adjusted life year (QALY) is equal to 1 year of life in perfect health”, NICE Glossary). Choi et al. [87] did the same by detailing cost according to stage. They found a better incremental benefit in the stage II group than in the IB and IIIA groups, but with higher a incremental cost−utility ratio (ICUR) than Lemmon, with around USD 1.2 million (Groups IB–IIIA) and USD 636,000, per life year gained. These models are limited by the incompleteness of the OS data, and their outcomes (QALY gain vs. life year gain) may not be comparable. The implementation of adjuvant therapy in standard care will have substantial costs that will warrant more accurate cost-effectiveness evaluations. These costs, associated with the lack of evidence of the benefit on OS, might limit their use to wealthier countries and patients. 8. Discussion TKIs are promising drugs, yet there are still some crucial points to explore. First is the question of an optimal treatment duration [54]. It has been highlighted that most recurrence in TKI arms occurs after treatment discontinuation, with DFS perhaps being dependent on the exposition time. Then, it could be hypothesized that TKIs will not cure the disease, but only keep cancerous cells in a dormant state, while slowly selecting resistant clones, resulting in a delayed but inexorable recurrence [88,89]. This could explain why the benefits of OS are so tricky to find. To date, the duration of treatment is arbitrary and relies on evidence from shorter DFS with shorter treatment times (i.e., ADJUVANT vs. ADAURA); however, more investigations are warranted to develop and define this concept. Indeed, the merits of adjuvant TKIs may also be considered for very early stages—in ADAURA, the stage IB DFS at 24 months was 88% and 71% with the TKIs and a placebo, respectively. Therefore, we do not know how the recurrence was rechallenged, especially in the TKI group, where the clonal selection could make it untreatable with other TKIs. Ultimately, we cannot know if these patients really benefited from Osimertinib. One might wonder if active monitoring and TKI challenging at relapse would not be a more efficient strategy, with a delayed use of chemotherapy (and thus preserved QoL) and wiser money spending. Second, while TKIs are introduced as an adjuvant treatment more and more frequently by national and international guidelines, evidence of their survival improvements needs to be more underpinned. With a wide range of mutations, stages, and prognoses, it is necessary to more accurately explore which groups could benefit more from TKI adjuvant therapy. Their use needs to be managed by genomic detection of appropriate mutations and co-mutations. This detection, associated with a high cost of treatment, may slow down their inclusion into standard care and accessibility for all. With good safety profiles and few AEs, TKIs could enter adjuvant therapy panels with a good patient adhesion expected. Even without evidence of the benefit of survival over chemotherapy, this point may be important in patients for whom an adjuvant treatment is formally indicated, but who are unfit to receive chemotherapy. In these cases, TKIs could be an alternative to chemotherapy, with better outcomes than abstention, while maintaining their QoL. Eventually, these drugs might also have a place as neoadjuvant treatments, but this statement needs to be supported by more accurate data and well-conducted trials. We aimed to highlight the challenges regarding what could be the future of oncologic practice: individualized therapy design, with clinical, pathological, and genomic profiles very precisely defined at baseline and at relapse. There is still a lot to understand about EGFRm NSCLC, and as the amount of data needed will be huge, we can only place our hopes in the usefulness and completeness of national and international databases to help investigators find ways to relentlessly fight this disease [90,91]. We believe that the success of this new era will be dependent on the meticulous collection and sharing of patient data. 9. Conclusions EGFR TKIs in adjuvant therapy clearly improve DFS, but not, to date, OS. The optimal duration of adjuvant treatment and its real place among other therapeutic tools should be defined. In neoadjuvant therapy, EGFR TKIs may improve ORR and operation rates. Their safety and toxicity are good, but the high cost of treatment may restrain accessibility. The understanding the effects of the numerous mutations must be improved for individualized drug assignment and tailored treatment plans. Author Contributions Conceptualization, D.C. and A.L.; methodology, X.C. and C.V.; validation, X.C., C.V. and A.L.; investigation, X.C., B.L., D.C. and A.L.; resources, X.C., C.V. and A.L.; writing—original draft preparation, X.C. and A.L.; writing—review and editing, X.C., B.L., D.S., A.T., C.V., D.C. and A.L.; supervision, A.L.; project administration, A.L.; funding acquisition, D.C., B.L., A.T., D.S. and A.L. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest The authors declare no conflict of interest. cancers-14-02257-t001_Table 1 Table 1 Results of trials considering neoadjuvant therapy. Name, Author Year Design Patients Stage HR (p) N+ Down-Staging (p) ORR OpR PRR PFS OS Chen 2018 [24] Phase II Erlotinib vs. chemo (P + C) EGFRm (86) IIIA 1.53 (<0.05) 1.08 (>0.05) 1.55 (<0.05) NA NA NA CTONG 1103, Zhong 2019 [25] Phase II Erlotinib vs. chemo (G + C) EGFRm (72) IIIA N2 2.26 (0.092) NA, 83.8% vs. 68.6% (0.129) † NA, 9.7% vs. 0% † 0.39 (<0.001) 0.77 (0.417) 10.8% vs. 2.9% (0.185) † Zhong 2015 [26] Phase II Erlotinib vs. chemo (G + C) * EGFRm (12) EGFRw (12) IIIA N2 NA, 58.3% vs. 25% (0.18) † NA, 6/12 vs. 7/12 † NA 2.26 (0.071) 1.79 (0.201) 25% vs. 25% (1) † Xiong 2019 [27] Phase II Erlotinib vs. chemo * EGFRm (15) EGFRw (16) IIIA NA, 67% vs. 19% † NA, 12/15 vs. 8/16 † NA, 67% vs. 38% † NA, 12.1 vs. 11 †,‡ NA, 51 vs. 20.9 (0.12) †,‡ NA Ning 2019 [28] Erlotinib vs. chemo (P + C) * EGFRm (53) EGFRw (53) IIIA NA, 35/53 vs. 22/53 † NA, 46/53 vs. 43/53 † NA NA NA NA * Chemotherapy in the EGFRw group, Erlotinib in the EGFRm group; † data from the tyrosine kinase inhibitors groups vs. data from the chemotherapy groups; ‡ Values in months; HR (p): ORR, OpR, PRR, PFS, and OS are expressed as hazard ratio, p: p-value; ORR: objective response rate; OpR: operation rate; PRR: pathological response rate; PFS: progression-free survival; OS: overall survival; Chemo: chemotherapy; P: Pemetrexed; C: Cisplatin; G: Gemcitabine; EGFRm: EGFR mutant; EGFRw: EGFR-wild-type; NA: not available. cancers-14-02257-t002_Table 2 Table 2 Ongoing phase II trials considering neoadjuvant therapy. All these data are available on https://www.clinicaltrials.gov/ (accessed on 30 March 2022). Name, Trial Number Tested Drug Treatment Plan Adjuvant TKI Primary Outcome Stage n NCT04685070 [33] Almonertinib 8–16 weeks up to 40 weeks ORR Stage III 56 NCT04201756 [34] Afatinib 8–16 weeks 1 year ORR Stage III 47 NCT03749213 [35] Icotinib 8 weeks 2 years ORR IIIA N2 36 NCT03349203 [36] Icotinib 8 weeks 2 years ORR IIIB or oligometastasis 60 Neolpower, NCT05104788 [37] Icotinib + chemo 12 weeks NA MPR IIA−IIIB 27 NCT02820116 [38] Icotinib 8 weeks 2 years Complete resection rate IIIA−IIIB 67 NOCE01, NCT05011487 [39] Osimertinib + chemo 60 days NA Complete lymph node clearance rate * IIIA−IIIB N2 30 NCT03433469 [40] Osimertinib 4–10 weeks No MPR I−IIIA 27 ASCENT, NCT01553942 [41] Afatinib + radio + chemo 8 weeks 2 years Response rate IIIA 30 * The ratio of ypN0 percentage after resection. Chemo: chemotherapy; Radio: radiotherapy; ORR: objective response rate; MPR: major pathologic response; DFS: disease-free survival; OS: overall survival; n: expected patient number; NA: not available. cancers-14-02257-t003_Table 3 Table 3 Results of the main trials considering adjuvant therapy. Name, Author Year Design EGFR Status (n) Stages HR DFS (p) HR OS (p) Tsuboi 2005 [42] Phase III Gefitinib vs. placebo UP (38) IB−IIIA NA NA BR19, Goss 2013 [52] Phase III Gefitinib vs. placebo UP (503) EGFRm (15) IB−IIIA 1.84 (0.395) 3.16 (0.15) Li 2014 [46] Phase II Chemo (P + C) + Gefitinib vs. chemo alone EGFRm (60) IIIA (N2) (5th TNM) 0.37 (0.014) 0.37 (0.076) Feng 2015 [48] Phase II chemo + Icotinib vs. chemo alone EGFRm (39) IB−IIIA NA, 21 vs. 16 (0.122) NA RADIANT, Kelly 2015 [50] Phase III Erlotinib vs. placebo EGFRexp (973) EGFRm (161) IB−IIA (6th TNM) 0.61 1.09 (0.815) EVAN, Yue 2018 [49] Phase II Erlotinib vs. chemo (V + P) EFGRm (102) IIIA 0.268 (<0.0001) 0.165 (0.0013) ADJUVANT, Zhong 2018 [53] Phase III Gefitinib vs. chemo (V + P) EGFRm (222) II−IIIA (N1-2) 0.51 (0.001) 0.92 (0.674) ADAURA, Wu 2020 [1] Phase III Osimertinib vs. placebo EGFRm (682) IB−IIIA (7th TNM) 0.2 (<0.001) NA IMPACT, Tada 2021 [51] Phase III Gefitinib vs. chemo (V + P) EGFRm (234) II−IIIA 0.92 (0.63) 1.03 (0.89) EVIDENCE, He 2021 [54] Phase III Icotinib vs. chemo (V + P) EGFRm (322) II−IIIA (7th TNM) 0.36 (<0.0001) 0.75 (>0.05) * SELECT, Pennel 2019 [47] Phase II Erltonib after chemo (comparison to historical data) EGFRm (100) IA−IIIA (7th TNM) NA, 88% vs. 76% (0.047) † NA * All of the presented studies are RCT, except for SELECT, which is a single-arm trial. † Data in tyrosine kinase inhibitors groups vs. data in chemotherapy groups. Chemo: chemotherapy; V + P: Vinorelbine plus Cisplatin; P + C: Pemetrexed plus Carboplatin; UP: unselected patients; EGFRexp: EGFR expressing; EGFRm: EGFR mutant; NA: not available. cancers-14-02257-t004_Table 4 Table 4 List of ongoing comparative trials. All these data are available on https://www.clinicaltrials.gov/ (accessed on 30 March 2022). Name, Trial Number Design Setting Treatment Arms Primary Outcome Stages n NeoADAURA, NCT04351555 [32] Phase III, randomized, controlled, multi-center, 3-arm trial Neo- adjuvant Osimertinib + chemo vs. placebo + chemo vs. Osimertinib MPR II−IIIB N2 328 ADAURA2, NCT05120349 [64] Phase III, randomized, controlled, multi-center, international, 2-arm trial Adjuvant Osimertinib vs. placebo DFS IA2, IA3 380 ICTAN, NCT01996098 [65] Phase III, randomized, open label, multi-center, 3-arm trial Adjuvant, after chemo Icotinib 6 months vs. Icotinib 12 months vs. no treatment DFS IIA −IIIA 318 APEX, NCT04762459 [66] Phase III, randomized, open label, multi-center, 3-arm trial Adjuvant Almonertinib vs. Almonertinib + chemo vs. chemo DFS II −IIIA 606 ALCHEMIST, NCT02193282 [63] Phase III, randomized, controlled, nationwide, multi-center, 4-arm trial Adjuvant Erlotinib (blinded) vs. placebo (blinded) vs. Erlotinib (unblinded) vs. placebo (unblinded) OS IB (≥4 cm) −IIIA 450 Chemo: chemotherapy; MPR: major pathologic response; DFS: disease-free survival; OS: overall survival; n: expected patient number. 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==== Front Foods Foods foods Foods 2304-8158 MDPI 10.3390/foods11091325 foods-11-01325 Article Metabolite Profiling of Chestnut (Castanea crenata) According to Origin and Harvest Time Using 1H NMR Spectroscopy Yu Ja Myung † Nam Miso † Kim Min-Sun * Tenore Gian Carlo Academic Editor Food Analysis Research Center, Korea Food Research Institute, Wanju 55365, Korea; j.m.yu@kfri.re.kr (J.M.Y.); msnam@kfri.re.kr (M.N.) * Correspondence: mskim@kfri.re.kr † These authors contributed equally to this work. 02 5 2022 5 2022 11 9 132505 4 2022 28 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Chestnuts are an important food crop commonly used as a food ingredient due to their nutritional properties and potential health benefits. In Korea, chestnuts have been crossbred to develop cultivars with insect resistance and high productivity, producing multiple chestnut varieties. This study classified 17 Castanea crenata cultivars produced in Korea according to origin and harvest time and determined the metabolites in chestnut kernels using 1H nuclear magnetic resonance spectroscopy. The 17 C. crenata cultivars were divided into four groups based on their geographic origin: Korean native, Korean hybrid, Japanese native, and Japanese hybrid. The cultivars were also divided into three groups depending on their harvest period: early-ripening cultivar, mid-ripening cultivar, and late-ripening cultivar. The partial least squares-discriminant analysis score plot revealed differences among the groups. Identified metabolites, including amino acids, organic acids, and sugars, contributed to discriminating the origin and harvest time of the C. crenata chestnut kernels. Significant differences were observed, mainly in amino acids, which suggests that the composition of amino acids is one factor influenced by both the origin and harvest time of C. crenata. These results are useful to both growers and breeders because they identify the nutritional and metabolic characteristics of each C. crenata cultivar. chestnut (Castanea crenata) metabolites profiling 1H nuclear magnetic resonance (NMR) origin harvest time R&D Program for Forest Science Technology2020207A00-2222-BA01 This study was carried out with the support of ‘R&D Program for Forest Science Technology (Project No. 2020207A00-2222-BA01)’ provided by Korea Forest Service (Korea Forestry Promotion Institute). ==== Body pmc1. Introduction The chestnut, belonging to the Fagaceae family, is cultivated globally, including the Korean and Japanese chestnut (Castanea crenata Siebold & Zucc.), Chinese chestnut (Castanea mollissima Blume), European chestnut (Castanea sativa Miller), and North American chestnut (Castanea dentate (Marshall) Borkh.) [1,2,3]. According to the Food and Agriculture Organization, chestnut production continuously increased in Asia until 2014, and Korea is the second-largest producer of chestnuts in Asia after China [4]. In Korea, more than 50,000 tons of ripe chestnuts were produced from September to October every year until 2020 [5]. Chestnut is popular in Korea for its rich taste and high nutritive value [6]. Compared to other fruits, chestnut has a higher starch content and a lower moisture content, and it also contains proteins, amino acids, dietary fiber, vitamins, and minerals [7]. It is mainly prepared by roasting in winter or it consumed as a canned food and is used in traditional customs, such as wedding ceremonies and other ancestral rites. The inner skins are used as cosmetic ingredients [8,9]. In 1958, the chestnut gall wasp (Drycocosmus kuriphylus Yasumatsu), which damages and kills chestnut trees, appeared in Korea and spread across the country. As a result, many native Korean chestnut trees were harmed. Since then, large-scale crossbreeding of chestnut trees has been carried out to develop cultivars with resistance to insects and cold, high productivity, and large nut size [10]. The cultivars commonly grown in Korea are related to the Japanese chestnut and are derived from intra-hybridization or individual selection [11,12]. Currently, cultivars originating from Japan or Korea, such as Daebo, Okkwang, Ginyose, Tanzawa, Tsukuba, and Riheiguri, are widely cultivated in Korea. However, little information is available regarding the metabolic characteristics of these varieties [13,14]. Food qualities, such as fragrance, taste, appearance, shelf-life, and nutritional content, are determined by biochemical compositions and are reflected in the metabolite profiles [15,16]. Metabolites represent the intermediates or final products of cellular regulatory mechanisms, and they can be considered as the response of the biological system to environmental changes [17]. Because plant materials vary chemically, the same cultivars can have different chemical components due to genotype variations, environmental factors, time of harvesting, and post-harvesting factors [18]. The investigation of differences in metabolite profiling across cultivars may provide useful information for the comprehensive evaluation of the nutritional value of chestnuts, contributing to the improvement of new varieties or the selection of chestnut varieties for specific uses. Metabolomics has proven to be a powerful tool for the extensive analysis and identification of plant and food compositions [19]. Nuclear magnetic resonance (NMR) spectroscopy gives excellent metabolite profiles by taking advantage of this technique’s high reproducibility, non-selective analysis, and complete analysis of the molecular components of a complex matrix [20]. Recently, metabolomics studies using 1H NMR spectroscopy or mass spectrometry have also been applied to food science [17,21]. There are not many metabolomics studies on chestnuts, and most of the previous studies have been on chestnut shells [22,23]. No metabolite profiles of C. crenata chestnut kernel extracts using NMR spectroscopy have been reported to date. In this study, we performed the metabolic profiling of 17 C. crenata cultivar kernels using 1H NMR spectroscopic analysis to measure the metabolite level of several metabolite classes. In addition, 17 C. crenata cultivars were classified according to their origin and harvest time, and metabolic profiling results were submitted for statistical multivariate analyses to determine the metabolic characteristics according each category. This research provides information on the features of chestnut cultivars according to their origin and harvest time that is useful to both growers and breeders. 2. Materials and Methods 2.1. Standards and Reagents A 99.8% methanol-d4 and 99.9% deuterium oxide were purchased from Cambridge Isotope Laboratories, Inc. (Andover, MA, USA), 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid (TSP), and hydrochloric acid were obtained from Sigma-Aldrich (St. Louis, MO, USA), and sodium hydroxide was obtained from Junsei Chemical (Tokyo, Japan). All chemicals were of analytical grade. 2.2. Sample Collection C. crenata chestnuts were commercially purchased, approximately 4 kg per sample, from local markets in South Korea. A total of seventeen C. crenata chestnuts were collected between September and November 2020. All chestnut varieties were produced in Buyeo-gun or Gongju-si, Chungcheongnam-do, South Korea. The sample information of C. crenata are shown in Table 1. C. crenata cultivars were classified into four groups depending on the C. crenata chestnut cultivar origin: Korean native or crossbred between Korean native (KN), Korean hybrid crossbred with other origins (KH), Japanese native or crossbred between Japanese native (JN), and Japanese hybrid crossbred with other origins (JH). Also, the chestnut varieties were divided into three types according to their optimal ripening time: early-ripening cultivar (ERC), mid-ripening cultivar (MRC), and late-ripening cultivar (LRC). To prepare the kernels, the outer shell and inner shell from the chestnuts were separated automatically using a chestnut skin removal machine. They were stored at −80 °C before freeze-drying. They were put in a freeze dryer (FDCF-12003, Operon, Korea) and freeze-dried for 72 h under conditions with a condenser temperature of −50 °C and a pressure of 10 mTorr. Dried kernels were ground to a fine powder and stored at −80 °C until the analysis. 2.3. Sample Preparation The metabolite extraction step was performed following a method described by Jung et al. [24] but was slightly modified. Approximately 50 mg of dried sample was extracted in 500 μL of methanol-d4, 400 μL of a 0.2 M sodium phosphate buffer in D2O (pH 7.0), and 100 μL of 6 mM TSP. D2O was used as a field frequency lock signal and TSP was an internal standard with a chemical shift reference (δ) of 0.0 ppm. The mixtures were vortexed and sonicated for 20 min, and then adjusted to pH 7.0 ± 0.1 using 0.2 M NaOH and 0.2 M HCl solutions. Afterwards, they were centrifuged at 12,000 rpm at room temperature for 20 min. The upper layer was transferred into 5 mm diameter NMR tubes and stored at 4 °C until analysis. 2.4. 1H NMR Analysis 1H NMR spectra were recorded on a Bruker AbanceHD 800 MHz FT-NMR Spectrometer (Bruker BioSpin Co., Billerica, MA, USA), operating at 298 K, using a 5 mm triple resonance inverse cryoprobe with Z-gradients. The NOESY pulse sequence was applied to suppress the residual water signal. For each sample, 64 transients were collected in to 64,000 data points using a spectral width of 16,393.4 Hz with a relaxation delay of 2.0 s, and an acquisition time of 2.0 s. A line-broadening function of 0.5 Hz was applied to all spectra for Fourier transformation. All NMR spectra were phased and underwent baseline correction. Signal assignments for representative samples were facilitated by using two-dimensional (2D) total correlation spectroscopy (TOCSY), 800 MHz NMR database of Chenomx NMR Suite Version 8.6 (Edmonton, AB, Canada), and spiking experiments [25]. In addition, the Chenomx NMR Suite Version 8.6 software was used to quantify the metabolites in C. crenata. This program compares the concentration of a known reference signal (TSP) with signals derived from a library of compounds to determine the concentrations of individual metabolites [26]. 2.5. Statistical Analysis Partial least squares-discriminant analysis (PLS-DA) were performed with a unit variance scale using SIMCA-P+, version 16.0 (Umetrics, Umeå, Sweden). PLS-DA was performed to maximize the separation between samples. The PLS-DA was described as a regression extension of the principal component analysis (PCA) that provides the maximum covariance between the measured data (X) and the response variable (Y) [27]. R2 and Q2 values describe the quality of the models. R2 represents the goodness of fit and is defined as a proportion of the variance in the data described by the models. Q2 represents the predictability and is defined as the proportion of variance in the predictable data [27]. Also, to verify the significant differences in the quantified metabolites between more than two groups, the nonparametric Kruskal–Wallis test followed by Dunn’s post hoc test was performed using GraphPad Prism, version 5 (San Diego, CA, USA). 3. Results and Discussion 3.1. Metabolite Profiling of C. crenata by 1H NMR Spectroscopy We analyzed the kernels of 17 C. crenata chestnut cultivars produced in Buyeo-gun or Gongju-si, Chungcheongnam-do, using 1H NMR spectroscopy to determine metabolite differences. To minimize the impact of the cultivation environment, we selected C. crenata chestnuts produced in Buyeo-gun or Gongju-si, which are geographically close and have similar environments, in the Chungcheongnam-do Province. Figure 1 shows representative 800 MHz 1H NMR spectra of Okkwang (KN and MRC), Ginyosi (JN and LRC), and Moriwase (JH, which crossbreeds easily, and ERC), which are three cultivars of the C. crenata chestnut. The most dominant part of each 1H NMR spectrum was in the carbohydrate region (3.2–5.4 ppm), whereas the aromatic regions (6.9–8.5 ppm) had very low intensities. The spectral resonances of metabolites were assigned according to the 800 MHz library from Chenomx NMR Suite (version 8.6), 2D TOCSY NMR spectra, and spiking experiments. The analysis of the C. crenata chestnut kernel extracts by 1H NMR spectroscopy detected the essential primary metabolites. The chemical shifts of the identified metabolites are listed in Table 2. A total of 30 metabolites were quantified, including 15 amino acids (alanine, arginine, asparagine, aspartate, glutamate, glutamine, histidine, isoleucine, leucine, phenylalanine, threonine, trigonelline, tryptophan, tyrosine, and valine), six organic acids (citric acid, formic acid, fumaric acid, malic acid, malonic acid, and succinic acid), three sugars (fructose, glucose, and sucrose), and six other compounds (4-aminobutyrate, betaine, choline, ethanol, ethanolamine, and myo-inositol). 3.2. Metabolic Characterization Depending on C. crenata Chestnut Cultivar Origin As most chestnut cultivars cultivated in Korea originated in Japan or Korea, differences in metabolites across the varieties were first examined according to their origin. Although the morphological characteristics of the Korean and Japanese varieties are similar, a previous study suggested that the Japanese C. crenata and the Korean C. crenata might have separate evolutionary histories, according to a phenogram constructed based on polymorphisms [11]. These genetic differences may affect their metabolite contents. We classified the C. crenata cultivars into four groups: KN, KH, JN, and JH. The average concentrations of all metabolites identified in C. crenata chestnut kernels for each group are presented in Table S1. PLS-DA was performed on the concentrations of the identified metabolites to discriminate the four groups of C. crenata chestnuts, as shown in Figure 2. Good discrimination between Korean and Japanese cultivars was achieved according to the second component (PLS-2), which had a goodness of fit of R2Y = 0.516 and predictability of Q2 = 0.356 (Figure 2A). No significant differences were observed between the Korean cultivars categorized as KN and KH, but distinct differences were identified between the Japanese cultivars categorized as JN and JH. In particular, the JH group was distinct from the other three groups based on the first component (PLS-1) of the PLS-DA score plot, indicating that the JH group features different metabolic patterns from the other groups. Because the KH group displayed no significant difference from the KN group, and PLS-DA was performed for three groups, except the KH group, to detect more distinct metabolite patterns among the three groups. The PLS-DA score plot showed good separation among the three groups with a goodness of fit of R2Y = 0.609 and predictability of Q2 = 0.521 (Figure 2B). Unlike the other three groups, the JH group was hybridized with the Chinese chestnut (Castanea mollissima Blume) or the Pyongyang chestnut (Castanea bungeana Blume), which are different species from C. crenata. These results show that the cultivars in the JH group, which are produced by crossbreeding with other species, have a large difference in metabolite content compared with the other cultivars cultivated in Korea, and the origins of hybrid species could affect the metabolite levels in C. crenata chestnut kernels. As a result of the univariate analysis for assessing the differences in metabolite levels among the groups, 19 of the 30 identified metabolites showed significant concentration differences between KN, JN, and JH, which is evident in the PLS-DA plots (Figure 3). Of the 19 metabolites with significant differences, 13 were amino acids, which indicates that the amino acid profile is one of the main factors that can be used to determine the origins of C. crenata. Amino acids play an important role in cellular metabolism [28], and high levels of glutamic acid and aspartic acid in food influences taste and flavor [29]. The most abundant amino acids identified in C. crenata chestnut kernels were asparagine and glutamate, but most of the essential amino acids were present. In particular, the JH group was characterized as having reduced amino acid levels compared with the other two groups, except for trigonelline. The amino acids that showed significant differences between the KN and JN groups were glutamine, arginine, and aspartate, whereas the concentrations of other amino acids were similar between these two groups. Sugars, including fructose, glucose, and sucrose, were identified in C. crenata chestnut kernels. The sugar composition alters the sensory characteristics of chestnuts by making them sweeter; therefore, the sugar profile represents a commercially important quality indicator [30,31]. Sugar variance has also been used for the inter-cultivar discrimination of chestnuts [32]. Sucrose was the most abundant compound among the metabolites identified in C. crenata chestnut kernels, with average concentrations between 38,943 and 42,280 µM in each group. This finding was similar to previous analyses reporting a high sucrose content in chestnuts [30] but no significant difference in sucrose concentrations was identified between the groups. The lowest fructose and glucose levels were detected in the JN group, which were significantly different from those in the KN group but not those in the JH group. Similar to our results, previous studies also reported the highest fructose contents for the Pyeonggi (KN group) and Riheiguri (JH group) cultivars [13]. Our results showed that fructose and glucose could be used as discriminatory factors to distinguish between the KN and JN groups. Citric acid showed a significant difference in concentration between the KN and JH groups, and its concentration was the lowest in the JH group. Malic, citric, and ascorbic acids were the major organic acids found in the chestnut kernels, and some differences were observed among the organic acid compositions of the chestnut varieties [33]. The properties and levels of organic acids are key factors influencing the flavors of fruits and vegetables. In addition, organic acids can protect against various diseases because of their antioxidant activities [34]. Choline and betaine had significantly lower levels in the JH group compared with the JN group. Choline, which is abundant in plants, is an important nutrient that acts as a biosynthesis precursor of phospholipids and the neurotransmitter acetylcholine [35]. Betaine tends to accumulate in the cytoplasm and intercellular fluids, where it exerts protective functions on the structures of proteins, nucleic acids, and cell membranes, in response to abiotic plant stresses such as the reduced availability of water, high soil salinity, hypoxia, cold, and freezing [35]. The 4-aminobutyrate (GABA) level was the lowest in the KN group, and the difference was significant compared to the JH group. GABA can affect fruit storage or seed germination by changing the metabolism of nitrogen and carbon [36] and contributes to plant development and stress adaptation [37,38,39]. In addition, GABA modulates anion flux in plants through its role in signal transduction, which regulates plant physiology [40]. 3.3. Metabolic Characterization Depending on Harvest Time of C. crenata Chestnut Cultivars The chestnut varieties were divided into three types according to the optimal ripening time: ERC, MRC, and LRC. The optimal ripening time for ERCs ranges from the end of August to early September, the optimal ripening time for MRCs is mid-September, and the optimal ripening time for LRCs ranges from the end of September to early October. The optimal ripening time for each chestnut variety determines the harvest time, which affects metabolite concentrations. The average contents of all identified metabolites in C. crenata chestnut kernels for each group are shown in Table S2. In Figure 4, the PLS-DA score plot shows the separation among the three groups using three components and has R2Y and Q2 values of 0.417 and 0.324, respectively. In particular, the ERC group was the most strongly discriminated from the other groups using PLS-1. These results showed that the harvest time affects the concentrations of metabolites in C. crenata chestnut kernels. A Kruskal–Wallis test with Dunn’s multiple comparison post hoc test was used to compare the three groups. Of the 30 metabolites quantified, 13 showed significant differences in levels between the ERC, MRC, and LRC groups (Figure 5). In particular, nine metabolites with significant differences between the three groups had the highest concentrations in the LRC group. Of the 13 metabolites with significant differences, nine were amino acids, suggesting that the harvest time of C. crenata is one of the main factors affecting amino acid levels. A previous study has shown that the levels of free amino acids and sugars in peanuts decline after the early harvest time, plateau during the optimum harvest season, and increase during the late harvest time [41]. The analysis of free amino acids in the stems of Acanthopanax koreanum Nakai, harvested in May, July, and September, showed the highest concentrations in those harvested in September [42]. Our results showed that most amino acid levels were high in the LRC group, except those of trigonelline and aspartate. Betaine, ethanol, GABA, and myo-inositol had significant differences in concentrations between the groups. The level of GABA was significantly higher in the LRC group than in the MRC group, and betaine and ethanol levels were significantly higher in the LRC group than in the ERC group. In contrast, the concentration of myo-inositol was higher in the ERC group than in the LRC group. Myo-inositol oxidizes to d-glucuronic acid and plays a role in the biogenesis of plant cell walls and related structures [43,44]. The isomerization and methylation of myo-inositol forms O-methyl inositol, which participates in stress-related responses and seed product storage [45,46,47,48,49]. 4. Conclusions In this study, we determined the metabolite profiles of various cultivars of C. crenata to investigate the effects of different origins and harvest times. Metabolite profiling using 1H NMR spectroscopy revealed that the levels of primary metabolites in various groups of C. crenata were significantly different according to their origin and harvest time. The KN, JN, and JH (origin) groups could be distinguished according to their metabolite profiles, especially JH, which was the most strongly separated group. The significantly different metabolites across the three groups (KN, JN, and JH) included 13 amino acids, two sugars, one organic acid, and three additional compounds. The significantly different metabolites across the three harvest time groups (ERC, MRC, and LRC) included nine amino acids and four other compounds. The concentrations of significantly different metabolites were higher in the LRC group, and myo-inositol was detected at high levels in the ERC group. In particular, significant differences in amino acid concentrations were observed, suggesting that the origin and harvest time of C. crenata affect the composition of amino acids. This is the first report to describe different metabolite compositions in C. crenata chestnut kernels with different origins and harvest times using 1H NMR spectral analysis. Since there have been few metabolomics studies on chestnut kernels before, this study provides information on the metabolites of chestnut kernels. Our results provide information regarding the differences in metabolite profiles associated with various C. crenata cultivars and can be used to improve the nutritional or metabolic benefits of C. crenata chestnut kernels. These results can help growers and consumers intentionally select chestnut cultivars that satisfy their nutritional and taste needs. This information could also be useful for breeding programs to develop superior C. crenata varieties. In addition, although we have only focused on metabolite differences between on cultivars, further study on the genetic and metabolite differences of chestnut cultivars together will provide good information for producing better chestnut cultivars. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods11091325/s1, Table S1: Metabolite concentrations quantified by 1H NMR in C. crenata according to geographical origin; Table S2: Metabolite concentrations quantified by 1H NMR in C. crenata according to harvest time. Click here for additional data file. Author Contributions Conceptualization, M.-S.K.; Methodology, J.M.Y. and M.N.; Software, J.M.Y. and M.N.; Validation, J.M.Y. and M.N.; Formal Analysis, J.M.Y. and M.N.; Investigation, J.M.Y. and M.N.; Resources, M.-S.K.; Data Curation, M.-S.K.; Writing—Original Draft Preparation, J.M.Y. and M.N.; Writing—Review & Editing, M.-S.K.; Visualization, J.M.Y. and M.N.; Supervision, M.-S.K.; Project Administration, M.-S.K.; Funding Acquisition, M.-S.K. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available within the manuscript and the Supplementary Materials. Conflicts of Interest The authors declare that there is no conflict of interest regarding the publication of this article. Figure 1 800 MHz 1H NMR spectra of pulp of Castanea crenata harvested in Chungcheongnam-do. (A) Okkwang (Korean native and mid-ripening cultivar), (B) Ginyosi (Japanese native and late-ripening cultivar), (C) Moriwase (Japan hybrid which crossbreeds with other origins and early-ripening cultivar).(1, alanine; 2, arginine; 3, asparagine; 4, aspartate; 5, glutamate; 6, glutamine; 7, histidine; 8, isoleucine; 9, leucine; 10, phenylalanine; 11, threonine; 12, trigonelline; 13, tryptophan; 14, tyrosine; 15, valine; 16, citric acid; 17, formic acid; 18, fumaric acid; 19, malic acid; 20, malonic acid; 21, succinic acid; 22, fructose; 23, glucose; 24, sucrose; 25, 4-aminobutyrate; 26, betaine; 27, choline; 28, ethanol; 29, ethanolamine; 30, myo-inositol). Figure 2 (A) PLS-DA score plot (R2X = 0.441, R2Y = 0.516, Q2 = 0.356) of quantified metabolites in KN, JN, KH, and JH C. crenata chestnut show the pattern in which KH is included in KN. (B) PLS-DA score plot (R2X = 0.339, R2Y = 0.609, Q2 = 0.521) of quantified metabolites shows the separation among KN, JN, and JH C. crenata chestnut. Letters on the legend represent: KN, Korean native or crossbred between Korean native; KH, Korean hybrid which crossbreeds with other origins; JN, Japanese native or crossbred between Japanese native; JH, Japanese hybrid which crossbreeds with other origins. Figure 3 Quantification of metabolites in KN, JN, and JH of C. crenata chestnut. Kruskal–Wallis tests yielded significant differences determined by Dunn’s multiple comparison post hoc tests using GraphPad Prism 5.0. Significant differences in Kruskal–Wallis tests are represented as # p < 0.05, ## p < 0.01, and ### p < 0.001 and by Dunn’s multiple comparison post hoc tests are represented as * p < 0.05, ** p < 0.01, and *** p < 0.001. Error bars indicate means ± standard deviation. Letters on the horizontal axis represent: KN, Korean native or crossbred between Korean native; KH, Korean hybrid which crossbreeds with other origins; JN, Japanese native or crossbred between Japanese native; JH, Japanese hybrid which crossbreeds with other origins. Figure 4 PLS-DA score plot (R2X = 0.357, R2Y = 0.417, Q2 = 0.324) of quantified metabolites in ERC, MRC, and LRC of C. crenata chestnut. Letters on the legend represent: ERC, early-ripening cultivar; MRC, mid-ripening cultivar; LRC, late-ripening cultivar. Figure 5 Levels of metabolites in early-ripening cultivars, mid-ripening cultivars and late-ripening cultivars of C. crenata chestnut. Kruskal–Wallis tests yielded significant differences determined by Dunn’s multiple comparison post hoc tests using GraphPad Prism 5.0. Significant differences by Kruskal–Wallis tests are represented as # p < 0.05, ## p < 0.01, and ### p < 0.001, and by Dunn’s multiple comparison post hoc tests are represented as * p < 0.05, ** p < 0.01, and *** p < 0.001. Error bars indicate means ± standard deviation. Letters on the horizontal axis represent: ERC, early-ripening cultivar; MRC, mid-ripening cultivar; LRC, late-ripening cultivar. foods-11-01325-t001_Table 1 Table 1 Sample list of Castanea crenata chestnut used in this study. Region Number of Samples Cultivar Name Origin Crossbreeding Origin Group Harvest Time Chungchengnam-do (latitude 36° N, Longitude 127° E) 5 Okkwang Korea crenata a KN Mid ripening 5 Jahong Korea crenata a KN Mid ripening 5 Mipung Korea crenata a KN Late ripening 5 Juok Korea crenata hybrid (Gwangjujoyul a × Okkwang a) KN Mid ripening 3 Pyeonggi Korea crenata hybrid (Riheiguri × Ginyosi b) KH Mid ripening 4 Daebo Korea crenata hybrid (Sangmyeon 1 a × Riheiguri) KH Mid ripening 4 Idea Korea crenata hybrid (Ibuki × Sandae a) KH Late ripening 5 Otanba Japan crenata b JN Mid ripening 5 Ginyosi Japan crenata b JN Late ripening 5 Tanzawa Japan crenata hybrid (Otomune b × Taishouwase b) JN Early ripening 5 Tsukuba Japan crenata hybrid (Ganne b × Hayadama b) JN Mid ripening 4 Ishizuuchi Japan crenata hybrid (Ganne b × Kasaharawase b) JN Late ripening 5 Porotan Japan (crenata × bungeana) × crenata hybrid (550-40 × Tanzawa b) JH Early ripening 3 Moriwase Japan crenata × bungeana hybrid (Pyungyangyul × Toyotamawase b) JH Early ripening 4 Riheiguri Japan crenata × mollissima hybrid JH Mid ripening 4 Hyogo57 Japan crenata × mollissima hybrid JH Mid ripening 5 Banseki c Japan - - Late ripening a The origin of cultivars was Korea only. b The origin of cultivars was Japan only. c Since there was no information on crossbreeding, the Banseki cultivar was excluded from the groups classified according to origin. foods-11-01325-t002_Table 2 Table 2 Metabolites and 1H chemical shifts identified by 800 MHz 1H NMR a. No. Metabolites 1H Chemical Shifts b Formula Amino acids 1 Alanine 1.5 (d), 3.8 (q) C3H7NO2 2 Arginine 1.7 (m), 1.9 (m), 3.2 (t), 3.8 (t) C6H14N4O2 3 Asparagine 2.8 (q), 2.9 (dd), 3.9 (q), 6.9 (s) C4H8N2O3 4 Aspartate 2.6 (q), 2.8 (dd), 3.8 (dd) C4H7NO4 5 Glutamate 2.0 (m), 2.1 (m), 2.4 (m), 3.8 (q) C5H9NO4 6 Glutamine 2.44 (m), 2.12 (m) C5H10N2O3 7 Histidine 3.2 (m), 4.0 (t), 7.1 (s), 7.9 (s) C6H9N3O2 8 Isoleucine 0.9 (t), 1.0 (d), 1.2 (m), 1.5 (m), 2.0 (m), 3.7 (d) C6H13NO2 9 Leucine 0.9(d), 1.0 (d), 1.7 (m), 1.7 (m), 1.7 (m), 3.7 (q) C6H13NO2 10 Phenylalanine 3.1 (q), 3.3 (q), 4.0 (q), 7.3 (d), 7.4 (m) C9H11NO2 11 Threonine 1.3 (d), 3.6 (d), 4.3 (q) C4H9NO3 12 Trigonelline 8.0 (t), 8.8 (dd), 9.1 (s) C7H7NO2 13 Tryptophan 7.1 (t), 7.2 (t), 7.3 (s), 7.5 (d), 7.7 (d) C11H12N2O2 14 Tyrosine 3.1 (q), 3.2 (q), 3.9 (q), 6.8 (d), 7.2 (d) C9H11NO3 15 Valine 1.0 (d), 2.3 (m), 3.6 (d) C5H11NO2 Organic acids 16 Citric acid 2.5 (d), 2.7 (d) C6H8O7; 17 Formic acid 8.4 (s) CH2O2 18 Fumaric acid 6.5 (s) C4H4O4 19 Malic acid 2.4 (q), 2.7 (dd), 4.3 (d) C4H6O5 20 Malonic acid 3.1 (s) C3H4O4 21 Succinic acid 2.3 (s) C4H6O4 Sugars 22 Fructose 3.5–4.1 (m) C6H12O6 23 Glucose 3.2 (q), 3.4 (m), 3.5 (q), 3.7 (m), 4.6 (d), 5.2 (d) C6H12O6 24 Sucrose 3.4 (t), 3.5 (dd), 3.6 (dd), 3.7 (t), 3.8 (m), 4.0 (t), 4.2 (d), 5.4 (d) C12H22O11 Others 25 4-Aminobutyrate 1.9 (m), 2.3 (t), 3.0 (t) C4H9NO2 26 Betaine 3.3 (s), 3.9 (s) (CH3)3N+ CH2COO− 27 Choline 3.2 (s), 3.5 (m), 4.0 (m) C5H14NO 28 Ethanol 1.18 (t), 3.6 (q) C2H5OH 29 Ethanolamine 3.1 (t), 3.8 (t) C2H7NO 30 Myo-inositol 3.2 (t), 3.5 (dd), 3.6 (t), 4.0 (t) C6H12O6 a The chemical shifts were determined at pH 7.0 and expressed as relative values to those of TSP at 0 ppm. b Letters in parentheses indicate the peak multiplicities: s, singlet; d, doublet; t, triplet; dd, doublet of doublet; tt, triplet of triplet; q, quartet; and m, multiplet. 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==== Front J Clin Med J Clin Med jcm Journal of Clinical Medicine 2077-0383 MDPI 10.3390/jcm11092514 jcm-11-02514 Article Determinants of Higher Mortality at Six Months in Patients with Hip Fracture: A Retrospective Study González-Marcos Enrique 1 González-García Enrique 2 Rodríguez-Fernández Paula 3* Sánchez-González Esteban 4 https://orcid.org/0000-0002-7298-9060 González-Bernal Jerónimo J. 3* González-Santos Josefa 3 Grützner Paul Alfred Academic Editor Gómez-Barrena Enrique Academic Editor 1 RACA 11 Artillery Regiment, Cid Campeador Military Base, 09193 Burgos, Spain; enriquegojs@gmail.com 2 Traumatology and Orthopedic Surgery Service, Burgos University Hospital, 09006 Burgos, Spain; enriqueglezgar@yahoo.es 3 Department of Health Sciences, University of Burgos, 09001 Burgos, Spain; mjgonzalez@ubu.es 4 Department of Health Sciences, University of Jan Kochanowski, 25-369 Kielce, Poland; estebansg2001@gmail.com * Correspondence: prfernandez@ubu.es (P.R.-F.); jejavier@ubu.es (J.J.G.-B.) 29 4 2022 5 2022 11 9 251420 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). (1) Background: Hip fracture is a pathology with high mortality, but the lack of a universal adaptation of the factors associated with death makes it difficult to predict risk and implement prevention in this group. This study aimed to identify the factors that determine a higher mortality at six months following hip fracture. (2) Methods: A retrospective longitudinal study, whose study population consisted of patients over 65 years of age. The main variable was mortality at 6 months of fracture. Relevant data related to sociodemographic and clinical variables for subsequent bivariate (χ2) and multivariate analysis were obtained. (3) Results: In all, 665 people participated in the study, 128 of whom died within 6 months of the fracture. The multivariate adjusted analysis demonstrated significant relationships between the main variable and aspects such as institutionalization at discharge (Odds Ratio (OR) = 2.501), a worse overall functional capacity (OR = 2.453) and cognitive capacity (OR = 3.040) at admission, and complications such as heart failure (OR = 5.767) or respiratory infection (OR = 5.308), in addition to the taking of certain drugs and the presence of a greater number of comorbidities. (4) Conclusions: There are certain factors related to higher mortality at six months in patients with hip fracture who are aged 65 years or older. hip fracture mortality associated factors elderly This research received no external funding. ==== Body pmc1. Introduction Hip fracture is a pathology with a high mortality and incidence, which has increased by 1.5% every year during the last decade in Spain, currently producing 104 cases per 100,000 inhabitants, which means about 50,000 fractures per year [1]. A recent study by Blanco-Rubio [2] estimated an early mortality due to hip fracture in Spain of 22%. Globally, the previous knowledge on mortality associated with hip fracture is well established. Hip fracture is a cause of mortality in the elderly, and between 2% and 7% of the world population with this pathology die on admission and almost 30% die a year after suffering it [3], with the male gender and a higher age being added risk factors. The relative risk of death of people with hip fracture is two to three times higher than that of the population of the same age [4], especially in the first six months [5]. Males have demonstrated higher mortality, especially during admission, which has been linked to the so-called “Fragility Index” (FI), which represents the proportion of deficits present in an individual over the total number of age-related health variables considered [6,7,8]. Hip fracture, and fractures in general, is accepted as a fragility fracture when it occurs as the result of a low-energy mechanism, such as a fall at the same level [9]. In Spain, several studies have been performed to know the factors associated with hip fracture. The factors found in recent studies have been many and varied, but most research considers variables such as age, patient dependence [10], and comorbidities [11]. Likewise, mortality in people with hip fracture, even at two years, has been shown to be significantly related to factors such as respiratory infection, decompensated heart diseases, or dementia [12]; as well as with male gender and complications during admission such as chronic renal failure, anemia, delirium, renal functional decompensation, and waiting for surgery more than 2 days [13]. For his part, Blanco-Rubio [2] with an excellent prospective design and a multivariate analysis, related mortality during the first six months with the presence of a heart disease when admitted for hip fracture. Excess mortality in people with hip fracture derives from the presence of certain factors, which differ markedly from each other in the literature reviewed. This means that there is no universal adaptation of them that facilitates the prediction of risk, complications, functional impairment, or even mortality in people with this problem. Therefore, the main objective of this research was to identify the factors that determine a higher mortality at six months following hip fracture in a sample of patients over 65 years of age who attend the University Hospital of Burgos (HUBU), Spain. 2. Materials and Methods 2.1. Study Design—Participants A retrospective longitudinal study was designed with the following inclusion criteria: − Patients aged 65 years or older; − Who suffered a hip fracture by a low-energy mechanism; − In the biennium 14 March 2019–14 March 2021, all patients admitted to the HUBU with these characteristics were included in the study and followed after discharge from the outpatient clinics of the Orthopedic Surgery and Traumatology Service of the same hospital through face-to-face and non-face-to-face consultations. The exclusion criteria were as follows: − Patients with peri-prosthetic fractures; − Peri-synthesis fractures; − Pathological fractures, that is, on bones affected by primary tumor or metastasis, were excluded from the study; − Likewise, patients who were referred to other hospitals without completing the treatment or follow-up period for any cause, except death were also excluded. 2.2. Sample Size The sample size was estimated following the procedure for finite populations, using the formula n=N∗Zα=1.962∗p∗qδ2∗N−1+1.962∗p∗q. This calculation took into account the known population reported by the National Institute of Statistics [14] and the results of a similar study [15], establishing a proportion of hip fractures in the population of 0.389% (p = 0.000398, and its complementary q = 0.99602) and assuming a sampling error of 1% (δ2 = 01 in the formula). Based on this, it was concluded that the sample should consist of 152 patients with hip fractures under care by the HUBU. 2.3. Main Outcomes—Instruments The head of the Traumatology Section of the Orthopedic Surgery and Traumatology Service was responsible for collecting the data for subsequent analysis, obtained through the electronic clinical history of each participant. Information related to sociodemographic and general clinical characteristics was considered by variables such as sex (female or male); age; original place of residence and at discharge (home or residence); type of treatment (conservative or surgical); type of fracture (intracapsular or extracapsular); surgical risk (assessed according to the American Society of Anesthesiologists’ physical status classification (ASA)) [16]; surgical delay (understood as the days elapsed from admission to intervention); length of hospital stay or days between admission and discharge; cognitive impairment before and after admission (assessed using the Pfeiffer Scale (PS)) [17]; functional capacity (assessed by the Barthel Index (BI)) [18]; and the capacity for standing, sedation, and walking prior, during, and after admission. Comorbidities were also obtained (active oncological process, chronic anemia, atrial fibrillation, heart failure, valvular diseases, ischemic heart disease, chronic obstructive pulmonary disease (COPD), and chronic renal failure), as were relevant drugs (anti-hypertensives, antiplatelet agents, anticoagulants, Sintrom, neuroleptics, bronchodilators, oxygen at home, and protein supplements and thickeners) and complications of admission such as the need for a transfusion, constipation, “delirium”, deterioration of kidney function, or ulcers. The evolution of the patients and/or their death were recorded during the 6 months after the fracture in the face-to-face or telematic check-ups. 2.4. Statistical Analysis To characterize the sample, absolute frequencies and percentages were used, if the variables were categorical, or mean and standard deviation (SD) were employed in the case of continuous variables. The categorical variables of more than two categories were dichotomized based on previous studies and the continuous variables based on the average score in order to obtain two groups as homogeneous as possible. Bivariate analyses were performed to study the relationship between dichotomous variables and death at 6 months using the Pearson independence test (χ2) as well as the likelihood ratio. In the comparisons of significant dichotomous variables, the ratio of advantages or odds ratio (OR) was also obtained. To quantify the magnitude of these relationships and identify possible predictive factors of mortality at 6 months based on the independent variables, a binary logistic regression was performed taking the fact of dying or surviving the sixth month of the hip fracture as a dichotomous dependent variable. The multivariate analysis was adjusted for age (≥85 years), sex (male), surgical delay (≥3 days), and length of hospital stay (≥11 days). All variables with a value of p < 0.05 in the bivariate analysis were included as independent variables in the multivariate analysis, obtaining a statistic (χ2 Wald), a p value, and a risk measured in adjusted OR for each of them. Statistical analysis was performed with SPSS software version 25 (IBM-Inc., Chicago, IL, USA). For the analysis of statistical significance, a p-value < 0.05 was established. 3. Results The study sample consisted of a total of 665 people, 128 of whom died during the 6 months after the hip fracture. The age of the participants was between 65 and 102 years, with a mean of 86.2 years with 76.7% women (n = 510) and 23.3% men (n = 155). Males demonstrated a significantly higher mortality at six months compared to women, although the advantage ratio was only moderately higher (OR = 1.59). The survival of people aged 85 and over was significantly lower, with a risk of dying at 6 months that was 4426 times higher than that for younger people. Other sociodemographic factors such as pre- and post-fracture institutionalization also appear to be a risk factor for death at 6 months following hip fracture (Table 1). Regarding the type of intervention and fracture, there was a significant relationship between surgery or conservative treatment, patients who received conservative treatment reported high mortality (OR = 8.985) compared to those who received surgical treatment (OR = 0.111), but no statistically significant differences were found in the mortality at 6 months of patients with intracapsular and extracapsular hip fractures (Table 1). Likewise, patients with a higher surgical risk demonstrated more than twice the chances (OR = 2.308) of dying at six months after fracture than those classified as ASA I or II. A surgical delay of three or more days (the average was 4.58 ± 3.79 days) tripled (OR = 3.352) the significant risk of dying in the sixth month compared with those operated on during the first 48 h of admission. The same happened with the hospital stay (10.46 ± 5.44), with patients with a stay equal to or greater than eleven days were associated with a possibility of dying at the sixth month that was 2.438 greater than that for those who were hospitalized for less than eleven days. Regarding the clinical characteristics related to the ability to stand and sit and the gait, statistically significant relationships were found in all the variables studied, especially in the results during admission, since those patients who were not able to stand or walk demonstrated more than ten times more chances of dying in the sixth month of having fractured their hip than those who did manage to stand and walk, and those who did not manage to sit during admission showed 60.59 times more chances of dying than those who did sit (Table 1). Table 2 shows variables related to comorbidities and drugs prior to admission, as well as some new prescriptions after discharge. All the previous comorbidities recorded in the clinical history demonstrated a significant relationship with mortality at six months after hip fracture, highlighting suffering from active oncological process, chronic anemia, and heart failure, with a probability ratio of OR = 3.109, OR = 3.457, and OR = 3.510, respectively. Likewise, a strong association was found between mortality at six months and the presence of three or more of the previously mentioned comorbidities, since having three or more pathologies prior to admission quintupled the possibility of death at the sixth month after the hip fracture (OR = 5.034), and according to Kaplan–Meier’s analysis, the survival of these patients seemed to be very decreased (log Rank = 20.62, p < 0.001). Regarding the drugs prior to admission, no mortality relationship was found with the previous taking of antiplatelet agents, but there was an association with the rest of the drugs studied. In particular, the use of bronchodilators (OR = 2.429) and home oxygen (OR = 4.959) prior to admission were associated with a higher chance of dying at the sixth month following hip fracture in patients who used it compared to those who did not (Table 2). When the 30-day mortality was extracted from the series, the drugs with a significant relationship with the increase in mortality were antihypertensives and home oxygen prior to admission, which meant an increase in the chances of early death (OR = 2.154 and OR = 0.269, respectively). Patients prescribed protein supplements (OR = 12.019) and thickeners (OR = 8.429) “de novo” at discharge also demonstrated significantly higher mortality at six months following hip fracture compared to those who did not receive such a prescription (Table 2). Regarding the complications presented during hospital admission in patients with hip fracture, all those studied were related to a higher mortality in the sixth month. Among all the complications, the infection of the surgical wound stands out, since its presence implies that the possibility of death at the sixth month is almost eight times greater (OR = 7.790), followed by respiratory infection (OR = 9.550) and acute heart failure (OR = 10.350). In addition, patients with impaired renal function and/or pressure ulcers on admission showed an almost a five times greater chance of dying within six months of the fracture (OR = 4.925 and OR = 4.955, respectively). The “delirium” on admission of the elderly patient with a fractured hip, which is a very frequent event, was also shown to double the possibility of death at the sixth month (OR = 2.689) (Table 3). Below are the results of binary logistic regression to estimate the relationship between mortality at the sixth month following hip fracture and relevant sociodemographic and clinical characteristics (Table 4). Furthermore, some drugs such as Sintrom and antiplatelet agents and comorbidities such as valvular diseases, ischemic heart disease, and atrial fibrillation were not related to mortality at the sixth month in patients with hip fracture (Table 5). Table 6 shows the multivariate analysis adjusted for age, sex, surgical delay, and hospital stay to estimate mortality at the sixth month following hip fracture and characteristics and complications at admission. It was found that the only characteristics and complications in admission related to mortality at the sixth month were acute heart failure, respiratory infection, and deterioration of renal function (OR = 5.767, OR = 5.308, and OR = 3.622, respectively). Anemia, delirium, and transfusion during admission were also significant variables in logistic regression. 4. Discussion The results of this research show that there are factors related to higher mortality at the sixth month following hip fracture. Sociodemographic characteristics such as sex, age, length of hospital stay, or place of residence before and after discharge have been shown to be significantly related to the main variable. Almost all of the literature reviewed states that age is the factor that is always related to the highest mortality in patients with hip fractures [13,15,19,20,21]. Moreover, the male sex is described to have a lower significance on the relationship [13,15,19,20,21,22,23,24,25], since the comorbidities presented tend to influence mortality to a greater extent [26]. Rapp et al. [27] found excess mortality in the institutionalized elderly population with hip fracture regardless of sex during the three and six months after the episode, which decreases from the sixth month on. In this sense, our study points out that institutionalization at hospital discharge has an important relationship with mortality at six months, especially in the adjusted analysis, which coincides with the findings of Cree et al. [25], who in prospective work to study factors related to mortality at three months and institutionalization after hip fracture found that age, male sex, and cognitive impairment were the factors that determined a greater need for institutionalization after discharge and also higher mortality. General clinical factors such as cognitive impairment, functional characteristics (BI), and surgical risk (ASA) have also shown a relevant role in mortality at the sixth month of patients with hip fracture studied in this research. The literature relates cognitive impairment to high mortality in people with hip fracture [13,23,25,28,29,30,31,32] and is usually correlated with lower postoperative ambulatory capacity [33] and a consequent higher mortality [33,34]. These results coincide with the findings of this research, since a worse capacity for global functionality, gait, and sedation was related to a higher mortality at the sixth month of the hip fracture. In this line, Uriz-Otano et al. [23] found a relationship between the mean BI value at admission and 3-year mortality, and Folbert et al. [24] and Duaso et al. [13] found in their studies that hip fracture patients who died one year after the episode had the lowest BI values at admission and discharge. According to Aranguren et al. [10], mortality at one and two years in patients with a BI at admission ≤60 is significantly higher, as shown by the results of this research at six months. It has also been shown that survival in nonagenarian patients with hip fracture who are unable to walk is significantly lower than that of those who do manage to walk [35], and in line with our results, Heinonen et al. [36] described that the inability to sit, stand, or walk in the first two weeks after hip fracture is the main factor related to an increase of mortality per year in people with hip fractures aged 65 years or older. In terms of treatment, not operating has a very close relationship with the mortality of these patients [37]. Like our results, the literature associates surgical-anesthetic risk (ASA) with mortality [28,38,39,40], and one study considers it the most important factor determining mortality at two years, although lower than age and cognitive decline [31]. In the case of surgical treatment, a delay of more than 48 h has been shown to be a determinant of mortality with high scientific evidence [41,42,43,44,45], which coincides with the findings of the present research. It is worth mentioning a study carried out by Uzoigwe et al. [46], where the time limit was set to relate surgical delay to mortality not at 48 h, but at 36. As in our results, mortality is not usually associated with the type of fracture [47], but there are studies that show a higher mortality of extracapsular fractures compared to intracapsular fractures even after adjusting for the variables age and comorbidities [48,49]. The latter influence the mortality of patients with hip fracture [50,51,52], and the relationship between plurimorbidity and mortality from this pathology is generally established [53,54]. In this research, mortality was significantly related to the presence of three or more pathologies before the fracture occurs. As for drugs that are prescribed before admission, a recent study demonstrates the relationship with 30-day mortality with the use of antihypertensives and psychotropic drugs [55], and Uriz-Otano et al. [23] found a significant relationship with previous taking of benzodiazepines and neuroleptics and mortality at 3 years. In this line, our results show a significant relationship between mortality at the sixth month following hip fracture and previous drugs, except for antiplatelet drugs. In his work, Wordsworth et al. [56] also found no significant relationship between mortality and taking antiplatelet drugs. Likewise, there is evidence that systematically giving oral protein supplements to all elderly hip operated patients reduces complications and mortality at 1 year [57]. In our research, on the other hand, patients taking protein supplements or thickeners at discharge demonstrated a significantly higher probability of dying within six months of the fracture, which may be due to the low representativeness of the sample since these supplements were prescribed in less than 20 patients. Finally, a more significant relationship of mortality with complications at admission than with previous comorbidities has been found, as in the studies of other authors [58]. This fact is underlined because mortality at six months after hip fracture has been shown to be six times higher in patients suffering complications at admission [59,60]. In agreement with our results, acute renal failure and the need for transfusion [61], acute heart failure [62,63], respiratory infection [64], and agitation and disorientation syndrome [65] are complications of admission significantly related to the highest mortality at six months; although a recent Dutch study denies that “delirium” is associated with higher mortality [66]. Additionally, in agreement with our results, the study conducted by Bielza et al. [67] found that, although the complications that appeared in patients with hip fracture are very numerous, delirium, acute urinary retention, acute heart failure, acute respiratory infection, and deterioration of renal function acquire special relevance. A recent Spanish study identified pneumonia, cardiocirculatory disorders, and delirium as the main determinants of death at two years following hip fracture, a risk that increased with age and male sex as in this research [12]. This study should be considered in the context of its limitations. Despite having collected a wide variety of clinical and sociodemographic data, there were no complicating events that could influence the mortality of patients with hip fracture beyond hospital discharge. The exact cause of death of the deceased patients was also not recorded, which could have allowed us to better adjust the influence of fracture morbidity. No objectiv mortality or comorbidity assessment scale was applied, such as the usually used Charlson scale [52], nor were other, also objective scales, such as the “Geriatric Comorbidity Index” [68], the “Cumulative Illness Rating Scale” [69], or the “Index of Coexisting Diseases” [70] or scales of gait assessment such as the so-called “Functional Ambulation Classification (FAC)” used [71]. However, although they have a wide use in the literature, their use in it is heterogeneous and not completely validated to the specific case of the elderly person affected by hip fracture in their environment. The strengths of this publication are the great representativeness of the sample in the health area studied, where the vast majority of elderly patients with hip fracture are treated at the HUBU, covering both the rural and urban populations. In addition, practically all the risk factors that can influence hip fracture mortality in the elderly were collected, taking into account the previously reviewed literature. It should also be noted that the temporal evolution to six months, together with the multivariate study, allows us to affirm an etiopathogenic relationship of the factors studied and the death at the sixth month in patients with hip fracture. 5. Conclusions There are certain factors related to higher mortality at six months in patients with hip fracture aged 65 years or older. The main characteristics related to death at the sixth month were having a worse global functional and cognitive capacity at admission and discharge, an increased surgical risk, institutionalization at discharge, a greater number of comorbidities, and the appearance of complications during admission. Author Contributions Conceptualization, E.G.-M., E.G.-G. and P.R.-F.; methodology, E.G.-M., E.G.-G. and J.J.G.-B.; software, E.G.-M., E.G.-G. and P.R.-F.; validation, E.G.-M., E.G.-G., P.R.-F. and J.J.G.-B.; formal analysis, E.G.-M., E.G.-G. and P.R.-F.; investigation, E.G.-M. and E.G.-G.; resources, E.G.-M., E.G.-G., P.R.-F. and J.J.G.-B.; data curation, E.G.-M., E.G.-G., P.R.-F. and J.J.G.-B.; writing—original draft preparation, E.G.-G. and P.R.-F.; writing—review and editing, E.G.-G., P.R.-F. and E.S.-G.; visualization, E.G.-M., E.G.-G., P.R.-F., E.S.-G., J.J.G.-B. and J.G.-S.; supervision, P.R.-F. and J.J.G.-B.; project administration, E.G.-M., E.G.-G. and J.J.G.-B. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee for Drug Research of the Health Area of Burgos and Soria (CEIm 2537, approved on 27 April 2021). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Conflicts of Interest The authors declare no conflict of interest. jcm-11-02514-t001_Table 1 Table 1 Chi2 test results between mortality at sixth month and relevant sociodemographic and clinical characteristics. Sociodemographic and General Clinical Characteristics Death at 6th Month Chi2 Test OR Yes No χ2 χ RV2 p-Value Rho Lower Limit Upper Limit Sex Female (n = 510) 89 (17.5%) 421 (82.5%) 4.06 0.044 0.629 0.410 0.965 Male (n = 155) 39 (25.2%) 116 (74.8%) 4.34 0.037 1.590 1.036 2.442 Age ≥85 years (n = 441) 112 (25.4%) 329 (74.6%) 30.68 <0.001 4.426 2.549 7.685 65 to 84 years (n = 224) 16 (7.1%) 208 (92.9%) 36.37 <0.001 0.226 0.130 0.392 Previous place of residence Institutionalized (n = 197) 51 (25.9%) 146 (74.1%) 7.35 0.007 1.774 1.187 2.651 At home (n = 468) 77 (16.5%) 391 (83.5%) 7.62 0.006 0.564 0.377 0.842 Place of residence upon discharge Institutionalized (n = 294) 65 (22.1%) 229 (77.9%) 23.64 <0.001 3.238 2.003 5.234 At home (n = 335) 27 (8.1%) 308 (91.9%) 25.17 <0.001 0.309 0.191 0.499 ASA surgical risk ASA I + II (n = 296) 31 (10.5%) 265 (89.5%) 12.67 <0.001 0.433 0.275 0.683 ASA III + IV (n = 334) 71 (21.3%) 263 (78.7%) 13.83 <0.001 2.308 1.464 3.638 Type of treatment Conservative (n = 26) 17 (65.4%) 9 (34.6%) 34.03 <0.001 8.985 3.904 20.677 Surgical (n = 639) 111 (17.4%) 528 (82.6%) 27.82 <0.001 0.111 0.048 0.256 Surgical delay ≤2 days (n = 151) 10 (6.6%) 141 (93.4%) 12.49 <0.001 0.298 0.151 0.589 ≥3 days (n = 479) 92 (19.2%) 387 (80.8%) 15.67 <0.001 3.352 1.697 6.620 Length of hospital stay ≤11 days (n = 402) 58 (14.4%) 344 (85.6%) 14.42 <0.001 0.465 0.315 0.687 >11 days (n = 263) 70 (26.6%) 193 (73.4%) 14.90 <0.001 2.151 1.456 3.178 Type of fracture Intracapsular (n = 274) 49 (17.9%) 225 (82.1%) 274 0.42 p > 0.05 Extracapsular (n = 391) 79 (20.2%) 312 (79.8%) 391 0.56 PS on entry No impairment or mild (n = 500) 86 (17.2%) 414 (82.8%) 4.92 0.027 0.608 0.400 0.926 Moderate to severe (n = 165) 42 (25.5%) 123 (74.5%) 5.19 0.023 1.644 1.080 2.503 BI on entry ≤60 (n = 113) 42 (37.2%) 71 (62.8%) 26.75 <0.001 3.205 2.053 5.005 >60 (n = 552) 86 (15.6%) 466 (84.4%) 24.68 <0.001 0.312 0.200 0.487 Ambulation at admission Capablen (n = 587) 92 (15.9%) 486 (84.1%) 29.92 <0.001 0.268 6.524 16.184 Incapable or with difficulty (n = 87) 36 (41.4%) 51 (58.6%) 26.76 <0.001 3.729 0.062 0.153 Standing and walking during admission Capable (n = 114) 65 (57%) 49 (43%) 123.36 <0.001 0.097 0.062 0.153 Incapable (n = 511) 63 (11.4%) 488 (88.6%) 103.90 <0.001 10.28 6.524 16.184 Seating during admission No (n = 14) 13 (92.9%) 1 (7.1%) 45.13 <0.001 60.59 7.848 467.80 Yes (n = 651) 115 (17.7%) 536 (82.3%) 37.14 <0.001 0.017 0.002 0.127 Ambulation at discharge Capable (n = 364) 22 (6%) 342 (94%) 49.35 <0.001 0.179 0.108 0.299 Unable (n = 265) 70 (26.4%) 195 (73.6%) 51.44 <0.001 5.580 3.350 9.296 PS at discharge No impairment or mild (n = 468) 57 (12.2%) 411 (87.8%) 8.02 0.005 0.499 0.313 0.795 Moderate to severe (n = 161) 35 (21.7%) 126 (78.3%) 8.18 0.004 2.003 1.257 3.191 OR: odds ratio; ASA: American Society of Anaesthesiologists’ physical status classification; PS: Pfeiffer Scale; BI: Barthel Index. jcm-11-02514-t002_Table 2 Table 2 Results of the Chi2 test between mortality at the sixth month and comorbidities and drugs at admission. Comorbidities and Drugs on Admission Death at 6th Month Chi2 Test OR Yes No χ2 χ RV2 p-Value Rho Lower Limit Upper Limit Active oncological process Yes (n = 84) 32 (38.1%) 52 (61.9%) 20.61 <0.001 3.109 1.901 5.084 No (n = 581) 96 (16.5%) 485 (83.5%) 18.93 <0.001 0.322 0.197 0.526 Chronic anemia Yes (n = 134) 50 (37.3%) 84 (62.7%) 33.80 <0.001 3.457 2.261 5.286 No (n = 531) 78 (14.7%) 453 (85.3%) 31.24 <0.001 0.289 0.189 0.442 Atrial fibrillation Yes (n = 160) 45 (28.1%) 115 (71.9%) 9.94 0.002 1.990 1.311 3.020 No (n = 505) 83 (16.4%) 422 (83.6%) 10.02 0.002 0.503 0.331 0.763 Heart failure Yes (n = 187) 65 (34.8%) 122 (65.2%) 38.89 <0.001 3.510 2.350 5.241 No (n = 478) 63 (13.2%) 415 (86.8%) 37.21 <0.001 0.285 0.191 0.426 Valvular heart disease Yes (n = 66) 20 (30). 3%) 46 (69.7%) 5.00 0.025 1.977 1.124 3.477 No (n = 599) 108 (18%) 491 (82%) 5.19 0.023 0.506 0.288 0.890 Ischemic heart disease Yes (n = 59) 19 (32.2%) 40 (67.8%) 6.11 0.013 2.166 1.208 3.884 No (n = 606) 109 (18%) 497 (82%) 6.20 0.013 0.462 0.257 0.828 COPD Yes (n = 94) 31 (33%) 63 (67%) 12.27 <0.001 2.405 1.484 3.895 No (n = 571) 97 (17%) 474 (83%) 11.84 0.001 0.416 0.257 0.674 Chronic renal failure Yes (n = 148) 47 (31.8%) 101 (68.2%) 18.14 <0.001 2.505 1.647 3.810 No (n = 571) 81 (15.7%) 436 (84.3%) 17.56 <0.001 0.399 0.262 0.607 Antihypertensive Yes (n = 400) 90 (22.5%) 310 (77.5%) 6.31 0.012 1.734 1.144 2.629 No (n = 265) 38 (14.3%) 227 (85.7%) 7.03 0.008 0.577 0.380 0.874 Antiplatelet agents Yes (n = 104) 21 (20.2%) 83 (79.8%) 0.02 0.896 p > 0.05 No (n = 561) 107 (19.1%) 454 (80.9%) 0.07 0.791 Anticoagulants Yes (n = 150) 41 (27.3%) 109 (72.7%) 7.49 0.006 1.850 1.208 2.835 No (n = 515) 87 (16.9%) 428 (83.1%) 7.66 0.006 0.540 0.353 0.828 Sintrom Yes (n = 80) 26 (32.5%) 54 (67.5%) 9.33 0.002 2.280 1.363 3.813 No (n = 585) 102 (17.4%) 483 (82.6%) 9.15 0.002 0.439 0.262 0.734 Neuroleptics Yes (n = 115) 30 (26.1%) 85 (73.9%) 3.67 0.055 1.628 1.017 2.605 No (n = 550) 98 (17.8%) 452 (82.2%) 3.94 0.047 0.614 0.384 0.983 Bronchodilators Yes (n = 77) 26 (33.8%) 51 (66.2%) 10.78 0.001 2.429 1.447 4.079 No (n = 588) 102 (17.3%) 486 (82.7%) 10.41 0.001 0.412 0.245 0.691 O2 at the previous address Yes (n = 29) 15 (51.7%) 14 (48.3%) 18.45 <0.001 4.959 2.328 10.563 No (n = 636) 113 (17.8%) 523 (82.2%) 16.17 <0.001 0.202 0.095 0.430 Protein supplements “de novo” Yes (n = 17) 11 (64.7%) 6 (35.3%) 31.09 <0.001 12.019 4.326 33.391 No (n = 612) 81 (13.2%) 531 (86.8%) 23.09 <0.001 0.083 0.030 0.231 Thickeners “de novo” Yes (n = 14) 8 (57.1%) 6 (42.9%) 17.39 <0.001 8.429 2.853 24.900 No (n = 615) 84 (13.7%) 531 (86.3%) 14.00 <0.001 0.119 0.040 0.350 OR: odds ratio; COPD: chronic obstructive pulmonary disease. jcm-11-02514-t003_Table 3 Table 3 Results of the Chi2 test between mortality at the sixth month and characteristics and complications at admission. Characteristics and Complications at Admission Death at 6th Month Chi2 Test OR Yes No χ2 χ RV2 p-Value Rho Lower Limit Upper Limit Significant anemia Yes (n = 468) 102 (21.8%) 366 (78.2%) 6.05 6.98 0.014 0.008 1.833 1.149 2.925 No (n = 197) 26 (13.2%) 171 (86.8%) 0.546 0.342 0.871 Transfusion Yes (n = 330) 76 (23%) 254 (77%) 6.06 0.014 1.628 1.101 2.408 No (n = 335) 52 (15.5%) 283 (84.5%) 5.56 0.018 0.614 0.415 0.908 Delirium Yes (n = 241) 71 (29.5%) 170 (70.5%) 24.34 <0.001 2.689 1.815 3.984 No (n = 424) 57 (13.4%) 367 (86.6%) 24.50 <0.001 0.372 0.251 0.551 Constipation Yes (n = 294) 68 (23.1%) 226 (76.9%) 4.67 0.031 1.560 1.059 2.297 No (n = 371) 60 (16.2%) 311 (83.8%) 5.08 0.024 0.641 0.435 0.944 Impaired kidney function Yes (n = 203) 77 (37.9%) 126 (62.1%) 63.90 <0.001 4.925 3.281 7.393 No (n = 462) 51 (11%) 411 (89%) 61.03 <0.001 0.203 0.135 0.305 Acute heart failure Yes (n = 121) 68 (56.2%) 53 (43.8%) 127.04 <0.001 10.35 6.611 16.203 No (n = 544) 60 (11%) 484 (89%) 107.88 <0.001 0.097 0.062 0.151 Respiratory infection (pneumonia) Yes (n = 90) 53 (58.9%) 37 (41.1%) 102.30 <0.001 9.550 5.880 15.510 No (n = 575) 75 (13%) 500 (87%) 84.23 <0.001 0.105 0.064 0.170 Acute urinary retention Yes (n = 75) 22 (29.3%) 53 (70.7%) 4.82 0.028 1.895 1.105 3.251 No (n = 590) 106 (18%) 484 (82%) 5.03 0.025 0.528 0.308 0.905 Surgical wound infection Yes (n = 5) 3 (60%) 2 (40%) 4.25 0.039 7.970 1.315 48.312 No (n = 625) 99 (15.8%) 526 (84.2%) 4.96 0.026 0.125 0.021 0.761 Surgical wound seroma Yes (n = 47) 13 (27.7%) 34 (72.3%) 4.05 0.044 2.122 1.078 4.180 No (n = 583) 89 (15.3%) 494 (84.7%) 4.29 0.038 0.471 0.239 0.928 Pressure ulcers Yes (n = 21) 11 (52.4%) 10 (47.6%) 13.19 <0.001 4.955 2.056 11.939 No (n = 644) 117 (18.2%) 527 (81.8%) 11.95 0.001 0.202 0.084 0.486 OR: odds ratio. jcm-11-02514-t004_Table 4 Table 4 Results of binary logistic regression to estimate the relationship between mortality at the sixth month and relevant sociodemographic and clinical characteristics. Sociodemographic and Clinical Characteristics χ2 Wald p-Value Rho BI at admission: ≤60, severe or total dependence 10.661 0.001 2.453 PS on admission: moderate to severe impairment 8.310 0.003 3.040 ASA surgical risk: ASA III + IV 3.835 0.050 1.618 Discharge PS: moderate to severe impairment 16.256 <0.001 1.203 Ambulation at admission: unable 11.811 <0.001 2.803 Standing and walking during admission: unable to cope 2.107 0.146 1.510 Previous place of residence: institutionalized 3.493 0.061 1.577 Place of subsequent residence: institutionalized 11.590 <0.001 2.501 ASA: American Society of Anesthesiologists’ physical status classification; PS: Pfeiffer Scale; BI: Barthel Index. Covariates: age, sex, delay ≥3 days, and stay ≥11 days. jcm-11-02514-t005_Table 5 Table 5 Results of binary logistic regression to estimate the relationship between mortality at sixth month and comorbidities and drugs at admission. Comorbilities and Drugs on Admission χ2 Wald p-Value Rho De novo protein supplements: YES 15.744 <0.001 10.222 Thickeners “de novo”: YES 10.743 0.001 8.303 O2 at the previous address: YES 15.640 <0.001 6.186 Active oncological process: YES 16.201 <0.001 3.273 Chronic anemia: YES 18.407 <0.001 2.895 Heart failure: YES 13.222 <0.001 2.360 COPD: YES 6.073 0.013 2.036 Bronchodilators: YES 4.879 0.027 2.004 Anticoagulants: YES 4.879 0.027 2.004 Neuroleptics: YES 5.890 0.015 1.924 Chronic renal failure: YES 6.986 0.008 1.914 Sintrom: YES 3.259 0.071 1.718 Valvular heart disease: YES 1.810 0.178 1.570 Ischemic heart disease: YES 0.743 0.388 1.371 Atrial fibrillation: YES 0.843 0.358 1.259 COPD: chronic obstructive pulmonary disease. Covariates: age, sex, delay ≥3 days, and stay ≥11 days. jcm-11-02514-t006_Table 6 Table 6 Results of binary logistic regression to estimate the relationship between mortality at the sixth month and characteristics and complications at admission. Characteristics and Complications at Admission χ2 Wald p-Value RHO Surgical wound infection: YES 3.204 0.073 6.124 Acute heart failure: YES 41.915 <0.001 5.767 Respiratory infection: YES 33.931 <0.001 5.308 Impaired kidney function: YES 27.437 <0.001 3.622 Pressure ulcers: YES 1.930 0.164 2.201 Significant anemia: YES 5.818 0.015 2.139 Delirium: YES 9.845 0.001 2.090 Surgical wound seroma: YES 2.273 0.136 1.749 Transfusion: YES 4.595 0.032 1.691 Acute urinary retention: YES 2.684 0.101 1.670 Constipation: YES 1.217 0.269 1.298 Covariates: age, sex, delay ≥3 days, and stay ≥11 days. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Sáez-López P. Ojeda-Thies C. Alarcón T. Muñoz-Pascual A. Mora-Fernández J. González de Villaumbrosia C. Molina-Hernández M.J. Montero-Fernández N. Cancio-Trujillo J.M. Díez-Pérez A. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094603 ijms-23-04603 Article Insights into the Molecular Regulation of Lignin Content in Triploid Poplar Leaves https://orcid.org/0000-0002-5662-4694 Xu Tingting 12 Zhang Shuwen 12 https://orcid.org/0000-0002-3040-4175 Du Kang 12 https://orcid.org/0000-0001-5190-135X Yang Jun 12* https://orcid.org/0000-0002-2557-4356 Kang Xiangyang 12* Fajkus Jiří Academic Editor 1 National Engineering Research Center of Tree Breeding and Ecological Remediation, Beijing Forestry University, Beijing 100083, China; tingtingxu0411@163.com (T.X.); shuwenzhang@bjfu.edu.cn (S.Z.); dukang@bjfu.edu.cn (K.D.) 2 Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China * Correspondence: yang_jun@bjfu.edu.cn (J.Y.); xykang@163.com (X.K.); Tel.: +86-010-6233-6168 (J.Y.); +86-010-6233-6168 (X.K.) 21 4 2022 5 2022 23 9 460310 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). After polyploidization, plants usually undergo some morphological and physiological changes, including the lignin content of polyploids usually becoming lower than that of diploids. However, the regulatory mechanism of the variation of lignin content in polyploid plants remains unclear. Therefore, in this research, we used full-sib poplar triploids and diploids to explore the molecular regulatory basis of lignin content in poplar triploid leaves through the determination of lignin content, the observation of xylem cells, and transcriptome sequencing. The results showed that the lignin content of triploid leaves was significantly lower than that of diploid leaves. The xylem cells of triploid leaves were significantly larger than those of diploids. Transcriptome sequencing data show that most lignin biosynthesis genes were significantly downregulated, and genes related to cell growth were mostly upregulated in triploid leaves compared with diploid leaves. In addition, co-expression network analysis showed that several transcription factors might be involved in the regulation of lignin biosynthesis. Consequently, the altered expression of genes related to lignin might lead to the reduced lignin content in triploids. These results provide a theoretical basis for further exploring the molecular mechanism of the variation of polyploid lignin content and the utilization of polyploid lignocellulosic resources. triploid lignin content gene expression regulatory mechanism ==== Body pmc1. Introduction Lignin is a major component of the secondary cell wall that is deposited when cell differentiation is completed and the secondary cell wall starts thickening [1]. Lignin is also the second most abundant plant lignocellulosic material in nature [2]. Lignin plays an important role in mechanical support, water transportation, and stress defense during plant growth and development [3]. However, it interweaves with cellulose and hemicellulose in the secondary cell wall, which seriously affects the depolymerization and utilization of plant lignocellulosic resources [4]. The high lignin content in plants is the main restrictive factor for lignocellulosic bioenergy production, pulp and paper making, and forage digestion [4,5,6]. Therefore, plants with properly low lignin content can better meet the needs for human social production practices. Plant polyploidy, accompanied by an increase in chromosome numbers, usually brings about some morphological and physiological changes through affecting the various physiological activities and metabolic processes of plants [7,8]. For example, plant polyploidy usually improves the growth rate, wood quality, and stress resistance [4,9,10,11]. Studies have shown that the cell-wall components of polyploid plants tend to be altered. In Populus tomentosa, compared with diploids, triploids were characterized by longer and wider fiber cells, lower lignin content, which decreased by 17.9%, and higher cellulose content; therefore, they were easier to cook and soften in pulp and paper industrial processes, and energy consumption was reduced [12]. In triploid shrub willows, the lignin content significantly decreased, and the lignin monomer composition also changed [13]. Compared with Arabidopsis diploids, the lignin contents of tetraploids, hexaploids, and octaploids were decreased by 20%, 50%, and 55%, respectively [14]. A similar phenomenon was found for polyploid crops such as rice and potatoes; the lignin content of polyploids was significantly lower than that of diploids [15,16]. With the development of high-throughput sequencing technology, an increasing number of studies have led to in-depth discussions about the gene expression and regulatory mechanisms of polyploid plants. For example, in poplar triploids, the expression of genes related to vegetative growth has a dose effect, which enhances the capacities for photosynthesis, carbon fixation, and sucrose and starch metabolism, accompanied by a higher chlorophyll content and lower chlorophyll degradation, delaying leaf senescence [17]. Due to the gene dosage effect of polyploidy, some plant physiological and biochemical processes are strengthened along with more vigorous metabolism; consequently, the contents of biochemical components are also increased. However, in contrast to the higher photosynthesis, carbon fixation, and sucrose and starch metabolism, lignin content was lower in poplar triploids than in diploids. Why does the lignin content decrease in triploids? The mechanism behind the variation remains unclear. The analysis of the regulation in leaf lignin biosynthesis is beneficial to the development and utilization of polyploid-based leaf bioenergy. The existing studies on stems could not completely characterize the mechanism of lignin biosynthesis in leaves. The development and utilization of leaf-based biomass energy or important secondary products in polyploids still requires a more accurate regulatory mechanism model. Therefore, in this study, we used full-sib poplar triploids and diploids as study materials to explore the molecular mechanism of the regulation of lignin content in poplar triploids by determining the lignin content, observing xylem cells and performing transcription sequencing analysis. These results provide a theoretical basis for further exploring the molecular mechanism of lignin trait variation in other polyploid plants or tissues, e.g., woody tissues, and the utilization of polyploid lignocellulosic resources. Moreover, it can also enrich the theories on the molecular mechanisms behind the formation of advantageous traits of poplar triploids. 2. Results 2.1. Analysis of Lignin Content in Triploid and Diploid Leaves The fifth leaf was in the growth stage, with its net photosynthetic rate and leaf area increasing. However, the net photosynthetic rate and leaf area peaked at the tenth leaf, meaning that the cell growth and leaf function tended to be stable [18]. In this study, juvenile and mature leaves represented the fifth and tenth leaves, respectively. In order to compare the lignin contents of different ploidy plants, the fifth leaves of triploids and diploids were used to determine the lignin content. The results show that the lignin content in triploid leaves was significantly lower than that in diploid leaves (Figure 1A). Lignin is mainly deposited in specific vascular tissue cells. Thus, we further observed the xylem cell microstructure of the main veins by histochemical staining. The results show that the xylem vessels of triploid leaves were significantly larger than those of diploid leaves (Figure 1B,C). 2.2. Analysis of Transcriptome Sequencing Data To analyze the gene expression differences between triploid and diploid poplar leaves, transcriptome sequencing was performed on juvenile and mature leaves of different ploidy plants. A total of 12 cDNA libraries were constructed, including three biological replicates. In total, 90.73%, 90.43%, 87.50%, and 89.27% of the average reads were matched to genomic locations, and uniquely mapped reads accounted for 84.90%, 84.13%, 82.13%, and 84.07% in the Dip_JL, Tri_JL, Dip_ML, and Tri_ML libraries, respectively (Table S2). To assess the reliability of the tested samples, principal component analysis (PCA) was performed (Figure 2A). The results show that there was a high degree of similarity among the biological replicates of each sample, and that different ploidy plants and different leaves had different gene expression patterns, indicating that the sequencing data were relatively reliable and suitable for further analysis. In this study, the screening threshold for differentially expressed genes (DEGs) was |FC| ≥ 2 and FDR < 0.05. Compared with diploids, there were 743 DEGs, including 320 upregulated and 423 downregulated genes in juvenile leaves (Figure 2B). A total of 1886 DEGs were identified in mature leaves; 1012 were upregulated, and 874 were downregulated (Figure 2B). 2.3. Functional Enrichment Analysis of DEGs To characterize the biological roles of DEGs, Gene Ontology (GO) enrichment analysis was performed at three levels: biological process (BP), molecular function (MF), and cellular component (CC) (Figure 3A,B; Table S3). The major GO items are shown in Figure 3A,B (FDR < 0.05). In juvenile leaves, the DEGs were significantly enriched for 18 BP items, mainly including “metabolic process”, “oxidation–reduction process”, and “flavonoid biosynthesis process”. The DEGs were significantly enriched for 45 MF items, the most significant being “transferase activity”, “oxidation–reduction enzyme activity”, and “catalytic activity”. Four items were enriched for CC items, among which “extracellular region” and “cell wall” were the most significant. In mature leaves, the DEGs were significantly enriched for 53 BP items, primarily including “metabolic process”, “defense stress response”, “heat response”, and “protein folding”. A total of 15 MF items were enriched, the most significant being “transferase activity”. To further analyze the specific metabolic pathways of the DEGs involved, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (Figure 3C–F; Table S3). All the significantly enriched KEGG pathways (p < 0.05) are shown in Figure 3C–F. In juvenile leaves, the upregulated genes were mainly enriched for “biosynthesis of secondary metabolites”, “flavonoid biosynthesis”, and “circadian rhythm—plant” (Figure 3C). The downregulated genes were mainly enriched for “the biosynthesis of secondary metabolites”, “protein processing in endoplasmic reticulum”, and “phenylpropanoid biosynthesis” (Figure 3D). In mature leaves, the pathways of “protein processing in endoplasmic reticulum”, “glutathione metabolism”, and “plant–pathogen interaction” were mainly enriched in upregulated genes (Figure 3E). The pathways of “biosynthesis of secondary metabolites”, “sesquiterpenoid and triterpenoid biosynthesis”, and “phenylpropanoid biosynthesis” were mainly enriched in differentially downregulated genes (Figure 3F). Noteworthily, the downregulated genes were all significantly enriched in phenylpropanoid biosynthesis pathways in both leaves. In conclusion, the enrichment results show that the DEGs in juvenile triploid leaves were mainly involved in the processes of cell growth, such as metabolite synthesis, physiological enzyme activities, and cell-wall activities. However, mature leaves gradually participated in the processes of various environmental interactions such as stimulus responses and defense stresses. 2.4. Profiling of Differentially Expressed Lignin Biosynthesis Genes Lignin is synthesized through the phenylpropanoid biosynthesis pathway, with the participation of many enzymes and genes. The lignin biosynthesis genes are mainly expressed during secondary cell-wall biosynthesis and eventually lead to lignin deposition in the cell wall [1]. In our current study, cell growth was relatively stable in mature leaves, while cell growth, including cell-wall activities, was ongoing in juvenile leaves. Compared with diploids, a total of eight and 11 DEGs related to lignin biosynthesis were observed in triploid juvenile and mature leaves, respectively (Figure 4; Table S4). Among them, there were five common genes differentially expressed in both leaves. PAL (phenylalanine ammonia lyase) is the first rate-limiting enzyme in this pathway. Two PAL1 genes were upregulated in mature triploid leaves compared with diploid leaves. A key enzyme that catalyzes the formation of CoA esters is 4CL (4-coumaroyl: CoA ligase). Two 4CL2 genes were downregulated in both triploid leaves compared with diploid leaves. HCT (hydroxycinnamoyl-CoA shikimate) is also a key enzyme in lignin biosynthesis. In triploids, there was one HCT gene downregulated in juvenile leaves and two HCTs upregulated in mature leaves compared with diploids. F5H (ferulic acid 5-hydroxylase) is necessary for lignin monomer synthesis. FAH1 was downregulated in both triploid leaves compared with diploids. In addition, PODs (peroxidases) play a role in the oxidative polymerization of lignin monomers at the end of this pathway. Compared with diploids, there were four PODs differentially expressed, three of which were downregulated in juvenile triploid leaves. There were also four PODs differentially expressed in mature triploid leaves, two of which were downregulated. A shared POD (Potri.006G129900) was downregulated in both triploid leaves. These results indicate that most lignin biosynthesis genes were downregulated in triploids, especially in juvenile leaves, which might be related to the decrease in lignin content. 2.5. Profiling of DEGs Related to Cell Growth In our research, we found that the xylem cells in triploid leaves were significantly larger than those in diploid leaves. Therefore, we further analyzed the expression pattern of genes related to cell growth, and some DEGs between triploid and diploid leaves were found (Figure 5A; Table S4). XTHs (xyloglucan endotransglucosylase/hydrolases) are key genes related to cell-wall remodeling. In this study, a total of five XTHs were significantly differentially expressed in triploids compared with diploids, most of which were upregulated. Two XTH9 genes were upregulated in juvenile leaves, whereas XTH15 and XTH30 were upregulated in mature leaves. Expansins are also important regulators involved in cell expansion. In this study, most genes encoding expansins were not differentially expressed, but EXPA15 was differentially downregulated in mature triploid leaves compared with diploid. Phytohormones are also important for cell expansion during plant growth and development. In this study, we found that some auxin-related genes were differentially expressed between triploids and diploids. Genes associated with auxin transportation were mostly upregulated in triploid leaves compared with diploid leaves, such as PISL, LAX2, and PDR9. Three auxin signal transduction inhibitors AUX2–11 were downregulated in both triploid leaves compared with diploid leaves. Genes related to auxin responses, such as ARF2, IAA11, and ILL6, also showed higher expression in mature triploid leaves than in diploid leaves. In addition, as the other main cell-wall components, cellulose- and hemicellulose-related genes were also analyzed in this study. The results show that only SUS6 (sucrose synthase 6) was differentially downregulated in the cellulose biosynthesis of triploid leaves compared with diploids, while the CESAs (cellulose synthase), which were crucial for cellulose biosynthesis, were not differentially expressed. Only DUF57 and CSCL6 (cellulose synthase-like C6) were differentially downregulated in the hemicellulose biosynthesis of triploid mature leaves compared with diploid, while IRXs (irregular xylem), which are important for xylan synthesis, were not differentially expressed. Consequently, XTHs and auxin-related genes might be the major regulators to promote the cell growth in triploid leaves compared with diploids. 2.6. Profiling of DEGs Related to Plant Stress Resistance Plant polyploidy usually results in stronger resistance to various stresses compared to diploidy. Lignin accumulation is an effective pathway for plant stress resistance. However, in our study, triploids had a lower lignin content than diploids; thus, we speculated that there may be some other pathways for plant stress resistance in triploids. We found that a few antioxidant enzyme genes associated with stress resistance were differentially expressed (Figure 5B; Table S4). APX2 is a gene encoding ascorbate peroxidase, which was upregulated in triploids compared with diploids. There were eight GSTs related to glutathione metabolism that were differentially expressed in juvenile triploid leaves, and half of them were upregulated. In mature triploid leaves, 20 GSTs were differentially expressed, and 18 GSTs were upregulated, indicating that mature leaves were more active in response to stress defense than juvenile leaves. In addition, we also found that some DEGs involved in flavonoid biosynthesis shared the phenylpropanoid metabolic pathway with lignin biosynthesis, and most of them had higher expression in triploids than in diploids (Figure 4; Table S4). Flavonoids play an important role in defending against stresses and promoting growth and development in plants. CHS (chalcone synthase) is a key enzyme involved in the biosynthesis of flavonoids. In this study, there were three CHSs and one CHS gene that were upregulated in juvenile and mature leaves, respectively. F3H encodes flavanone 3-hydroxylase and regulates flavonoid biosynthesis. There were three F3Hs gene differentially expressed in triploids compared with diploids, one of which was upregulated in juvenile leaves and two of which were upregulated in mature leaves. F3′H encodes flavonoid 3′ hydroxylase, related to flavonoid biosynthesis. Compared with diploids, seven F3′H and two F3′H genes were differentially expressed in juvenile and mature triploid leaves, respectively, and most of the F3′Hs were downregulated in juvenile triploid leaves. FLS1 encodes a flavonol synthase involved in the synthesis of flavonols, which was differentially upregulated in both triploid leaves. LDOX encodes a leucoanthocyanidin dioxygenase involved in proanthocyanin biosynthesis, which was upregulated in both triploid leaves. DFR encodes dihydroflavonol reductase, involved in the biosynthesis of anthocyanins. There were three DFRs differentially expressed in mature triploid leaves, two of which were upregulated. These results show that some other DEGs related to stress resistance were mostly upregulated in triploids compared with diploids, indicating that triploids may gain stronger resistance through other pathways. 2.7. Analysis of Differentially Expressed Transcription Factors Transcription factors (TFs) play crucial roles in regulating lignin biosynthesis. In this study, 38 differentially expressed TFs were identified in juvenile leaves, composed of 17 TF families. A total of 122 differentially expressed TFs involving 28 TF families were identified in mature leaves. The overall distribution patterns of the TFs are shown in Figure 6, most of which belonged to TF families such as MYB, ERF, NAC, and bHLH (Figure 6A; Table S5). To identify TFs significantly associated with lignin synthesis, we constructed a co-expression network using DEGs related to lignin including 15 lignin biosynthesis structural genes, nine genes related to cell growth, 11 genes related to stress resistance, and all the differentially expressed TFs (Figure 6B; Table S5). The screening threshold for high correlation was r > 0.85 and p < 0.05. The larger nodes have stronger connectivity degrees, indicating that the genes may be more important. In this co-expression network, we identified 162 pairs correlated between 14 lignin structural genes and 53 TFs. Among the lignin structural genes, HCT (Potri.001G042900), 4CL2 (Potri.003G188500), FAH1 (Potri.007G016400), and POD (Potri.006G129900) directly connected with the greatest numbers of TFs, all downregulated in triploids compared with diploids. We further analyzed TFs directly linked to lignin structural genes in the network, and the results show that 50 TFs came from 20 families, among which the MYB family had the largest number, with 14 TFs. In addition, the network showed that TFs with high degrees of connectivity were mainly involved in the processes of phenylpropanoid biosynthesis, cell growth, leaf development, and stress response, and they were mainly composed of MYB and bHLH family members (Table S5). Among them, MYB63 is a transcriptional activator of lignin biosynthesis. MYB63 connected with the largest numbers of lignin structural genes, and its expression was lower in mature triploid leaves than in diploid leaves. We also found that MYB115, associated with proanthocyanidin biosynthesis, was upregulated in both juvenile and mature triploid leaves compared with diploid leaves, which may play an important role in the regulation of flavonoids synthesis. MYC4, a bHLH transcription factor, could participate in the regulation of secondary cell-wall synthesis induced by blue light [19]. It was differentially upregulated in triploids with low expression and may work under the induction of blue light. MYB4, a repressor involved in the phenylpropanoid pathway, was differentially downregulated in mature triploid leaves compared with diploids. Other TFs have not yet been reported to be involved in lignin synthesis; whether they can directly or indirectly participate in lignin synthesis remains to be studied in the future. 2.8. Expression Analysis by qRT-PCR To verify the reliability of the transcriptome sequencing data, 10 DEGs related to lignin were chosen for real-time fluorescence quantification (Figure 7 and Table S1). These selected genes included structural genes and TFs. The results of the qRT-PCR show that the expression patterns of these genes were similar to the transcriptome sequencing data, indicating that the transcriptome sequencing results were reliable. 3. Discussion Lignin biosynthesis is a very complex process, which begins with phenylalanine entering the phenylpropanoid metabolic pathway, followed by the lignin-specific synthesis pathway. Finally, three lignin monomers are oxidatively polymerized to three types of lignin by peroxidases in the cell wall [2,20]. In this study, the cell growth of mature leaves was relatively stable, while the cells of juvenile leaves were growing and constructing a cell wall. Thus, it is more important to analyze the expression of lignin biosynthesis genes in juvenile leaves. The phenylpropanoid metabolic pathway mainly involves the PAL, C4H, and 4CL genes. PAL is the first rate-limiting enzyme-encoding gene located at the beginning of this pathway and determines the metabolic flow of the entire pathway. As the last key gene, 4CL regulates the formation of coumaroyl-CoA, caffeoyl-CoA, and feruloyl-CoA, which determines the synthesis of lignin precursors. According to the results of this study, two 4CL2 genes were significantly downregulated in both triploid leaves compared with diploid leaves. Two PAL1 genes showed no significant difference in juvenile triploid leaves, although they were significantly upregulated in mature triploid leaves compared with diploid leaves. We know that PAL is upstream of 4CL, and that its expression also responds to various outside stresses and plant hormone regulation [21]. Studies have shown that, when genes regulating lignin synthesis change, some other chemical components can also change correspondingly, aiming to ensure the normal physiological metabolism, growth, and development of plants [22,23]. In this study, we found that some flavonoid synthesis genes such as CHS, F3H, and FLS were significantly upregulated in mature triploid leaves. Therefore, we speculated that, during leaf growth and development, PAL1, an upstream pathway gene, may lead to more precursors flowing to the branch of flavonoid synthesis, compensating for the reduced lignin accumulation, which might enhance the stress resistance and growth of polyploids. As the final regulatory gene, 4CL2 is widely involved in the synthesis of lignin precursors. Its expression was more in line with the actual lignin synthesis in triploid leaves. Thus, the results indicate that the downregulated expression of key genes in the phenylpropanoid metabolic pathway was one of the reasons leading to the decrease in the lignin content in triploid leaves. Genes such as C3H, HCT, F5H, COMT, and POD are the main regulators of the lignin-specific pathway [24,25,26,27]. Among them, HCT is key for the synthesis of G and S monomers. F5H is necessary for S monomer synthesis. POD determines the final oxidative polymerization of monolignols. According to the results of this study, DEGs involved in the lignin-specific pathway were most significantly downregulated in triploids compared with diploids. Among them, HCT was downregulated in juvenile triploid leaves. FAH1 was downregulated in both triploid leaves. A total of four PODs were differentially expressed in juvenile triploid leaves, three of which were significantly downregulated. Consequently, these results also indicate that the downregulation of the expression of key genes in this pathway might result in the reduction in lignin content in triploid leaves during leaf growth and development. Lignin synthesis not only requires the cooperative regulation of structural genes, but also requires the regulation of TFs. Previous studies have shown that a multilevel regulatory network mainly composed of NAC and MYB TFs can regulate lignin biosynthesis [28]. NACs (VND1–7 and NST1–3) are primary switches that regulate secondary cell-wall synthesis. MYB46 and MYB83 are secondary switches that can regulate downstream TFs, regulating the expression of secondary cell-wall synthesis genes [28,29]. Among the downstream TFs, MYB58, MYB63, and MYB85 can specifically bind to and activate the expression of lignin structural genes [30], whereas MYB4, MYB7, and MYB32 are transcriptional inhibitors of lignin synthesis [3]. In this study, the co-expression network between related structural genes and TFs showed that TFs with high degrees of connectivity were mainly MYBs. As an activator of lignin synthesis, MYB63 was differentially downregulated in triploid leaves compared with diploids, which may lead to decrease lignin accumulation. MYB4, a repressor of phenylpropanoid biosynthesis, has been suggested to repress not only the synthesis of monolignols, but also the synthesis of flavonoids [3,31]. In our study, MYB4 was downregulated in the mature leaves, which may decrease the inhibition of phenylpropanoid pathway and possibly participate in flavonoid synthesis. However, during leaf growth and development, most MYBs have no significant differences, MYB115 was significantly upregulated in juvenile triploid leaves compared with diploid leaves. MYB115 shows high homology in amino acid sequence to Arabidopsis MYB5, promoting proanthocyanin biosynthesis [32]. Previous studies have shown that certain MYB transcription factors can regulate both flavonoid metabolism and secondary cell-wall formation, such as MYB5-like transcription factors [33,34,35]. VvMYB5a and VvMYB5b from grapevine positively regulate proanthocyanin and anthocyanin biosynthesis, but negatively affect lignin metabolism [34]. In Populus tomentosa, MYB6, the homolog of MYB5-like transcription factors, can promote proanthocyanin and anthocyanin biosynthesis and inhibit secondary cell-wall biosynthesis by interacting with KNAT7, regulating multiple branches of the phenylpropanoid pathway [35]. In our study, we found that most lignin biosynthesis genes were negatively correlated with flavonoid biosynthesis genes. MYB115, as a positive activator of proanthocyanin synthesis, might also participate in the regulation of lignin synthesis. However, further studies are needed to verify whether MYB115 has a regulatory effect on lignin synthesis. In this study, our results showed that most genes involved in the regulation of lignin biosynthesis were downregulated during leaf growth and development, leading to a decrease in lignin content in triploid leaves. However, what causes the downregulation of the expression of these genes in triploids? It is known that plant polyploidy can result in cellular giganticity, and poplar triploids are no exception [8,17,36,37]. Zhang [38] showed that phytohormones that including auxin can participate in cell growth, modulating the expression of genes related to cell expansion, and promoting the formation of huge cells. In the study, we analyzed genes related to cell growth, such as XTHs, expansins, and plant hormone-related genes, and the results showed that XTHs and auxin-related genes may play a major regulatory role in triploid leaves. Their expressions were mostly upregulated, which may lead to the cell growth in triploid leaves. In addition, from the results of this study, most cellulose and hemicellulose biosynthesis genes had no significant difference between triploid leaves and diploids, unlike lignin biosynthesis genes. Only several related genes were differentially downregulated in triploid leaves, indicating that there may be not significant changes or downregulation of chemical component contents between triploid and diploid leaves. However, this needs to be further studied. From the results of our study, we suppose that polyploids have larger cells, usually accompanied by decreased cell-surface areas per unit volume, which possibly decrease the demand for lignin accumulation. Lignin content is usually reduced in multiple polyploid plants, such as poplar in this study, shrub willow, Arabidopsis, rice, and potato [12,13,14,15,16]. In contrast, the cellulose and hemicellulose contents of polyploids showed different trends in different species. For example, the cellulose content increased in triploid shrub willow [13], while it decreased in polyploid Arabidopsis, tetraploid rice, and tetraploid potato [14,15,16]. These results indicated that triploids may have a certain mechanism of regulating lignin biosynthesis to adapt their own demands, which are usually achieved by decreasing the expression of lignin structural genes and changing the expression of transcription factors, together regulating lignin biosynthesis. With an increase in chromosome numbers, how do triploids regulate the expression of lignin synthesis genes to adapt to the altered lignin demand due to cell enlargement? In the future, more in-depth and comprehensive studies need to be conducted to explore the molecular regulation mechanism of lignin trait variation in polyploids, such as focusing on woody tissues and studying at the post-transcriptional level. This research provides an important theoretical basis for future studies. 4. Materials and Methods 4.1. Plant Materials A synthetic poplar allotriploid (2n = 3x = 57) and diploid (2n = 2x = 38) were used as plant materials in this study, which were full-sib progeny induced by Populus pseudo-simonii × P. nigra ‘Zheyin3#’ and P. × beijingensis hybridization [39]. All the materials were planted under 16 h/8 h (day/night) conditions and a relative humidity of 45–70% in the greenhouse of Beijing Forestry University (Beijing, China). The fifth and tenth leaves from the top were collected at the same time when they were 3 months old. Three clones for each ploidy were randomly selected as three biological replicates. After sampling, the materials were immediately frozen in liquid nitrogen and then stored at −80 °C for transcriptome sequencing and other experiments. 4.2. Determination of Lignin Content The lignin content of leaves was determined by ultraviolet spectrophotometry, according to the instructions of a lignin content determination kit (COMINBIO, Suzhou, China). Samples were collected and dried at 105 °C. The dried samples were ground to a fine powder and used to prepare alcohol insoluble residues (AIRs) according to Foster [40]. The pretreated AIRs were used to determine lignin content according to the instructions. Firstly, 5 mg of AIRs were weighed into test tubes, leaving one tube empty for a blank. Then, 1 mL of acetyl bromide solution (25% v/v acetyl bromide in glacial acetic acid) and 40 µL of perchloric acid were gently added, fully mixed, and heated at 80 °C for 40 min with vortexing every 10 min. After cooling to room temperature, 1 mL of a solution of sodium hydroxide and glacial acetic acid was added to terminate the reaction. Finally, after centrifuging, 40 µL of supernatant was added to 1.96 mL of glacial acetic acid for dilution, and 1 mL of liquid was added to a quartz cuvette to determine absorbance at 280 nm using an ultraviolet spectrophotometer. The lignin content was calculated on the basis of the absorbance at 280 nm, and three biological replicates and three technical replicates were performed. 4.3. Histochemical Staining The leaves, including main veins, were fixed in FAA fixative solution for more than 24 h and used for paraffin sectioning. Firstly, the samples were dehydrated through a gradient of ethanol solutions (70%, 80%, and 100%; 1 h each), followed by ethanol–dimethylbenzene (1:1, v/v, 30 min) and dimethylbenzene solutions (30 min). Secondly, each sample was transferred to mixtures of paraffin, dimethylbenzene (1:1, v/v, 60 °C for 6 h), and pure paraffin three times (60 °C for 2 h each time), after which each sample was embedded in a paper cup. Thirdly, the paraffin block was trimmed to a suitable size and sectioned (8 µm) using a paraffin-slicing machine. Lastly, after dewaxing, the paraffin section was stained using 1% safranin O and 0.1% fast green. All the materials were observed and photographed under an Olympus BX51 microscope. The cell areas were measured using ImageJ, and data statistical analysis was conducted using SPSS 20.0. 4.4. Transcriptome Sequencing and Mapping Total RNA was extracted from the samples using TRIzol Reagent Kits (Invitrogen, Carlsbad, CA, USA), followed by using a NanoDrop 2000 bioanalyzer (Thermo Fisher Scientific Inc., Wilmington, DE, USA) to determine the quality. The RNA integrity was verified using 1.5% agarose gels and then was used to construct cDNA libraries using an Ion Total RNA-Seq kit v2. Transcriptome sequencing was performed on the Ion Proton platform (Life Technologies, Carlsbad, CA, USA) by Shanghai Novelbio Biological Technology Co. Ltd., Shanghai, China. The adapter sequences were removed from the raw data. Low-quality reads and reads shorter than 50 bp were filtered out to obtain clean reads using Fast QC [41]. Then, the clean reads were mapped to the genome of Populus trichocarpa using MapSplice [42]. 4.5. Identification of Differentially Expressed Genes and Functional Analysis The transcript abundance was calculated in reads per kilobase per million mapped reads (RPKM) [43]. Differential expression analysis was performed using DESeq [44]. Genes were deemed to be differentially expressed when they had a fold change (FC) ≥2 and false discovery rate (FDR) <0.05. Differentially expressed genes (DEGs) were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The GO enrichment analysis was performed using agriGO (http://bioinfo.cau.edu.cn/agriGO/) (accessed on 10 April 2022) [45]. GO terms with FDR <0.05 were considered significantly enriched terms. DEGs were mapped to the KEGG database (http://www.kegg.jp/) (accessed on 10 April 2022) to identify signaling and metabolic pathways [46]. Pathways were considered to be significantly enriched when p < 0.05. 4.6. Co-Expression Network Analysis The RPKM of the genes was used for co-expression analysis. The gene correlation was calculated in terms of the Spearman correlation coefficient using the R package. The screening threshold was |r| ≥ 0.85 and p < 0.05. A positive value indicated a positive correlation, and a negative value indicated a negative correlation. The co-expression network diagram was visualized using the software Cytoscape 3.9.0, and the connectivity degree of genes was calculated using the same software. The node size was positively correlated with the degree of the connectivity of the genes. 4.7. Quantitative Real-Time PCR Firstly, the total RNA was extracted using RNeasy Plant Mini Kits (Qiagen China, Shanghai, China). Then, cDNA was synthesized using FastQuant RT Kit (with gDNase) (Tiangen Biotech CO., LTD, Beijing, China). The qRT-PCR was accomplished with a SuperReal PreMix Plus (SYBR Green) kit (Tiangen Biotech) according to the manufacturer’s recommendations using an Applied Biosystems 7500 Fast Instrument (AB Ltd., Lincoln, NE, USA). Three technical replicates and three biological replicates were used for each gene. The relative expression of the selected genes was calculated using the 2−ΔΔCT method. The sequences of the primers were designed using Primer3Plus (http://www.primer3plus.com/) (accessed on 10 April 2022). All the primer sequences used in this study are listed in Table S1. Actin (accession number: EF145577) was chosen as the reference gene. 5. Conclusions In summary, our results show that the lignin content in triploid poplar leaves was significantly lower than that in diploid leaves. In the process of triploid lignin biosynthesis, most DEGs were significantly downregulated, which may lead to a decrease in lignin content. At the same time, transcription factors may play an important regulatory role, together resulting in the reduced lignin content. The change in the expression pattern of lignin-related genes in triploids may be a type of adjustment and adaptation to the decreased demand for lignin accumulation. Acknowledgments We thank to Zhao Liu for his help in the process of paper submission. Supplementary Materials The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ijms23094603/s1. Click here for additional data file. Author Contributions Conceptualization, T.X., J.Y. and X.K.; methodology, T.X. and K.D.; formal analysis, T.X. and S.Z.; visualization, T.X.; writing—original draft, T.X.; writing—review and editing, T.X., J.Y. and X.K. All authors have read and agreed to the published version of the manuscript. Funding This research was supported by the National Key R&D Program of China during the 14th Five-Year Plan Period (2021YFD2200105). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available in Supplementary Materials. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Analysis of lignin content in triploid and diploid leaves. (A) Lignin content in triploid and diploid leaves. (B) Cell area measurement of triploid and diploid xylem vessels. (C) The observation of xylem cells in triploid and diploid leaves. Values are the means ± SE of three independent experiments. The asterisk indicates significant differences (* p < 0.05, ** p < 0.01). Bar = 50/20 µm. Figure 2 Analysis of transcriptome sequencing data. (A) Principal component analysis (PCA) analysis of transcriptome sequencing samples. (B) Numbers of differentially expressed upregulated and downregulated genes in juvenile and mature leaves between triploids and diploids. Dip_JL, juvenile diploid leaves; Dip_ML, mature diploid leaves; Tri_JL, juvenile triploid leaves; Tri_ML, mature triploid leaves. Figure 3 Functional enrichment analysis of differentially expressed genes (DEGs). (A) Gene Ontology (GO) analysis of DEGs between juvenile triploid and diploid leaves. (B) GO analysis of DEGs between mature triploid and diploid leaves. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially upregulated genes between juvenile triploid and diploid leaves. (D) KEGG analysis of differentially downregulated genes between triploid and diploid mature leaves. (E) KEGG analysis of differentially upregulated genes between mature triploid and diploid leaves. (F) KEGG analysis of differentially downregulated genes between mature triploid and diploid leaves. The Y-axis indicates the KEGG pathway; the X-axis indicates the rich factor. The dot size indicates the number of DEGs of the pathway, and the dot color indicates the p-value. Dip_JL, juvenile diploid leaves; Dip_ML, mature diploid leaves; Tri_JL, juvenile triploid leaves; Tri_ML, mature triploid leaves. Figure 4 Expression profiles of DEGs involved in the lignin biosynthesis and flavonoid biosynthesis. The color scale represents the log−transformed RPKM value. PAL, phenylalanine ammonia-lyase; C4H, cinnamate 4−hydroxylase; 4CL, 4−coumarate:CoA ligase; C3H, p−coumarate 3−hydroxylase; HCT, p−hydroxycinnamoyl−CoA:quinate/shikimate p−hydroxycinnamoyltransferase; CSE, caffeoyl shikimate esterase; CCoAOMT, caffeoyl−CoA O−methyltransferase; COMT, caffeic acid O-methyltransferase; F5H, ferulate 5−hydroxylase; CCR, cinnamoyl−CoA reductase; CAD, cinnamyl alcohol dehydrogenase; LAC, laccase; POD, peroxidase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3−hydroxylase; DFR, dihydroflavonol 4−reductase; ANS/LDOX, anthocyanidin synthase/leucoanthocyanidin dioxygenase; F3′H, flavonol synthase; FLS, flavonol synthase; LAR, leucoanthocyanidin reductase; ANR, anthocyanidin reductase; UFGT, UDP glucose−flavonoid 3−o−glycosyltranferase. Dip_JL, juvenile diploid leaves; Dip_ML, mature diploid leaves; Tri_JL, juvenile triploid leaves; Tri_ML, mature triploid leaves. Figure 5 Expression profiles of DEGs. (A) DEGs related to cell growth. (B) DEGs related to antioxidant enzymes. The color scale represents the log−transformed RPKM value. Dip_JL, juvenile diploid leaves; Dip_ML, mature diploid leaves; Tri_JL, juvenile triploid leaves; Tri_ML, mature triploid leaves. Figure 6 Analysis of differentially expressed transcription factors (TFs). (A) The distribution of differentially expressed TFs in juvenile and mature leaves. (B) Co-expression network between structural genes (SGs) and TFs. Green hexagonal nodes represent TFs. Triangular nodes represent SGs, of which blue nodes are lignin structural genes, brown nodes are genes related to stress resistance, and pink nodes are genes related to cell growth. The node size is positively correlated with the gene degree. The width of the connecting line is positively related to the correlation between genes. Dip_JL, juvenile diploid leaves; Dip_ML, mature diploid leaves; Tri_JL, juvenile triploid leaves; Tri_ML, mature triploid leaves. Figure 7 Validation of qRT−PCR. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094918 ijms-23-04918 Article Aqueous-Deficient Dry Eye Exacerbates Signs and Symptoms of Allergic Conjunctivitis in Mice https://orcid.org/0000-0002-5437-7298 Kishimoto Tatsuma https://orcid.org/0000-0002-3217-3059 Ishida Waka Nakajima Isana https://orcid.org/0000-0003-4990-6308 Fukuda Ken * https://orcid.org/0000-0001-9354-8558 Yamashiro Kenji Dogru Murat Academic Editor Kojima Takashi Academic Editor Department of Ophthalmology and Visual Science, Kochi Medical School, Kochi University, Kochi 783-8505, Japan; t.kishimoto@kochi-u.ac.jp (T.K.); wakai@kochi-u.ac.jp (W.I.); jm-i-nakajima@kochi-u.ac.jp (I.N.); yamashk@kochi-u.ac.jp (K.Y.) * Correspondence: k.fukuda@kochi-u.ac.jp; Tel.: +81-88880-2391 28 4 2022 5 2022 23 9 491829 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Dry eye disease (DED) and allergic conjunctivitis affect a large number of patients, and many patients usually have both symptoms. We investigated the interactions between DED and allergic conjunctivitis in mice. Four experimental groups were compared: control, DED, allergy, and allergy with DED. DED was induced by removing the extraorbital lacrimal glands of the mice. Allergic conjunctivitis was induced by intraperitoneal administration of ovalbumin and antigen eye drops. The early phase reaction of the allergy was evaluated using the clinical score, scratching behavior, and vascular permeability in the conjunctiva. Epithelial barrier function was assessed by an LC-biotin assay. Tear fluid volume and corneal fluorescein staining decreased in the DED and allergy with DED groups. LC-biotin penetrated the entire epithelium of both the cornea and conjunctiva in DED mice. The clinical score of the early phase reaction was higher in allergy-induced mice than in non-allergy mice. Edema of the eyelid and conjunctiva were aggravated in mice with DED. The number of scratching episodes and leakage of Evans blue into the conjunctiva were higher in allergy-induced DED mice than in control mice. The presence of aqueous-deficient dry eye caused ocular surface epithelial damage and exacerbated allergic signs and symptoms. dry eye allergic conjunctivitis tear fluid conjunctiva cornea barrier function mouse JSPS KAKENHI21K16896 This work was supported by JSPS KAKENHI (grant number 21K16896). ==== Body pmc1. Introduction Allergic conjunctival disease (ACD) is an allergen-induced inflammatory disease of the conjunctiva. Several types of ACD have been identified: seasonal and perennial allergic conjunctivitis (AC), atopic keratoconjunctivitis, vernal keratoconjunctivitis, and giant papillary conjunctivitis [1]. Allergic diseases are very common worldwide, and their prevalence is increasing. In a recent survey, the prevalence of allergic conjunctival diseases (ACDs) was reported to be 48.7% in Japan [2]. Allergic conjunctivitis is the most common type of ACD and is mediated by IgE-dependent type I hypersensitivity. Atopic keratoconjunctivitis and vernal keratoconjunctivitis, which are more severe forms of ACD, are caused by both IgE-dependent reactions and non-IgE-mediated chronic inflammation. Conjunctival mast cells play critical roles in the conjunctival response in allergic conjunctivitis. Allergens that penetrate the conjunctiva cross-link the antigen-specific IgE on mast cells, causing mast cell degranulation and inducing itching, edema, and hyperemia. Therefore, reduced permeability of the epithelium and clearance of ocular surface antigens may affect signs and symptoms of allergic conjunctivitis. Dry eye disease (DED) is a multifactorial chronic disease characterized by loss of homeostasis in the tear film [3]. The prevalence of DED is also high worldwide, and its frequency is reported to be higher in Asia, including Japan, than in other continents [4]. Large epidemiological studies have shown that the prevalence of DED in Japan is 17.4% among men and 30.3% among women [5]. In an epidemiological survey of office workers, the prevalence of DED, including probable cases, was very high at 65.6% [6]. Using visual display terminals for >4 h is associated with an increased risk of DED [7], and may be related to the high prevalence of dry eye in Japan. Etiologically, DED can be classified as aqueous-deficient dry eye (ADDE) or evaporative dry eye (EDE). Recent reports have proposed that ADDE and EDE are not mutually exclusive and may exist on a continuum [3]. Older patients, or those with inflammation of the lacrimal gland, androgen deficiency, or presence of systemic drugs, can also be predisposed to lacrimal gland dysfunction. Both ADDE and EDE cause epithelial cell loss and damage that results in the disruption of the epithelial barrier. Patients with DED and ACD show similar signs and symptoms. For example, the simultaneous occurrence of itchiness and dryness of the eye has been observed in many patients [8], and one report showed that many subjects with moderate-to-severe ocular itch symptoms also had severe symptoms of DED [9]. These studies suggest that DED and ACD not only occur simultaneously but that they also mutually influence onset and severity. The causes of DED in patients with ACD have been studied in detail. Ocular surface inflammation in ACD reportedly affects tear volume, tear film stability, mucin expression, meibomian gland dysfunction, and epithelial phenotype, resulting in DED [10,11,12,13,14,15,16,17,18]. Whereas many studies have demonstrated the impact of ACD on DED, there are few direct studies of the impact of DED on ACD. Researchers have speculated that altered epithelial barrier, decreased surface clearance, and inflammation may be factors that exacerbate ACD in patients with DED [19]. However, no direct studies have demonstrated that DED worsens the ocular allergic symptoms. The aim of this study was to investigate whether ADDE affects or exacerbates the signs and symptoms of ACD in mice. 2. Results 2.1. Effects of AC on Dry Eye First, we examined the effects of AC on dry eye signs. Before lacrimal gland removal, there was no difference in tear fluid volume between the lacrimal gland removal group and control group (data not shown), but the amount of tear fluid was significantly decreased in mice with excised lacrimal glands regardless of the induction of AC (Figure 1). The number of goblet cells in the conjunctiva tended to increase in DED mice, but there were no significant differences between any of the groups (Figure 2). Corneal fluorescein scores were significantly high in mice with excised lacrimal glands, but no further increase was observed in allergy-induced DED mice (Figure 3). Corneal fluorescein scores were similar before and after the allergy challenge. We also evaluated the barrier function of the ocular surface in DED mice using an LC-biotin assay. Fluorescent immunostaining after the administration of LC-biotin eye drops showed that LC-biotin remained on the superficial epithelial layer of the cornea, and the dye did not penetrate the epithelium of mice without DED. In contrast, the dye infiltrated the entire corneal epithelium layer of DED mice. Similarly, whereas LC-biotin was observed only in the superficial layer of the conjunctival epithelium of mice without DED, dye infiltration was observed in the entire conjunctival epithelium of DED mice (Figure 4). 2.2. Effects of Dry Eye on AC We then examined the effects of DED on AC. Systemic immune responses were not different in mice with or without DED; serum levels of total IgE and IgG1 were significantly increased, and IgG2a was significantly decreased in all sensitized mice, regardless of the presence of DED (Figure 5). The clinical scores of early phase reactions, including lid edema, conjunctival edema (chemosis), and conjunctival hyperemia, were measured 20 min after the last antigen challenge. Lid edema, chemosis, and total scores were higher in allergy-induced mice than in non-allergic control mice. The clinical score further increased in the allergy-induced DED mice than that in the allergy-induced mice (Figure 6). Scratching behavior after antigen challenge was also increased in allergy-induced DED mice compared to that in non-allergic control mice (Figure 7). The changes in vascular permeability, as early phase reactions of the conjunctiva, were evaluated by the leakage of Evans blue in the conjunctiva. The amount of leakage was significantly higher in allergy-induced DED mice than in non-allergic control mice (Figure 8). 3. Discussion In this study, we demonstrated that tear deficiency exaggerates the signs and symptoms of AC in mice. The signs and symptoms of AC, including edema of the eyelid and conjunctiva, eye scratching behavior, and vascular permeability in the conjunctiva after antigen challenge, were significantly augmented in mice that underwent lacrimal gland removal. Decreased tear clearance and epithelial barrier function of the ocular surface may result in longer retention and higher penetration of antigens to the ocular surface, leading to exaggeration of the symptoms of AC. Epithelial barrier dysfunction can lead to pathogenesis of both DED and ACD [19,20]. Many reports have demonstrated that ACD affects the status of tear fluid and ocular surface epithelia, resulting in DED [10,11,12,13,14,15,16,17,18]. However, there are no basic or clinical studies that directly show the effect of DED on the clinical signs and symptoms of ACD. The present study demonstrates, for the first time in an animal study, that lacrimal ADDE may exaggerate signs and symptoms of ACD. Our study may be a first step toward the development of therapeutic agents and the elucidation of the pathophysiology of patients with combined DED and ACD. Various ocular surface diseases manifest as impairment of the epithelial barrier, including DED, ACDs, infection, and chemical injuries [21]. Yokoi et al. demonstrated that the corneal epithelial barrier function had significantly decreased in accordance with the severity of superficial punctate keratopathy in DED patients, as measured by fluorescein uptake [22]. In addition, treatment with hyaluronan eye drops alleviated the decline in the corneal barrier in patients with DED [23]. Furthermore, corneal barrier function reportedly decreased even in patients with DED who did not exhibit visible corneal epithelial damage on slit-lamp examination [24]. These studies suggest that epithelial tight junctions of the ocular surface may be impaired in patients with DED regardless of the presence of visible corneal lesions on slit-lamp examination. In mice, dry eye experimentally induced by low humidity environmental challenge also decreased the levels of tight junction proteins and impaired corneal epithelial barrier function [25,26]. In patients with seasonal AC, the expression of the adherens junction protein E-cadherin in the conjunctival epithelium is reportedly downregulated, regardless of the season [1]. Conjunctival allergen challenge also reportedly decreased the expression of the tight junction proteins zonula occludens-1 and E-cadherin in the conjunctival epithelium in a mouse model of AC [11]. Decreased epithelial barrier function is an important factor for the sensitization and exacerbation of allergic diseases. In this study, we demonstrated that mice with tear deficiency had impaired epithelial barriers in the conjunctiva and cornea, as estimated by LC-biotin penetration. Therefore, it is possible that the reduced epithelial barrier function of the conjunctiva allows antigens to penetrate the conjunctiva easily, possibly exacerbating allergic symptoms. In the present study, because the antigen was systemically sensitized with an adjuvant, a strong Th2-type immune response could be induced; thus, the systemic immune response did not differ between mice with and without dry eye. Fujishima et al. reported that tear volume and clearance were lower in patients with AC who tested negative for serum antigen-specific IgE than in those who tested positive for serum antigen-specific IgE [27]. In a recent study, AC was observed in patients who tested negative for serum antigen-specific IgE [28]. Therefore, in patients with aqueous-deficient dry eye, the antigens may remain on the ocular surface for a long period owing to poor tear clearance, possibly contributing to local sensitization and exacerbation of symptoms. The influence of different types of ACDs on tear function has been extensively investigated. Most studies have reported that in patients with AC, tear volume does not change, but tear stability estimated by tear breakup time (TBUT) is reduced [10,15,18,27]. In patients with atopic keratoconjunctivitis (AKC), the tear volume has been reported to be unchanged [11,12,16,17], while some studies have reported a decrease in the tear volume [13,14]. However, all those studies reported a decrease in the TBUT [11,12,13,14,16,17]. Onguchi et al. revealed that the onset time of AKC affects tear function and ocular surface findings, and tear volume and epithelial damage in childhood-onset adult AKC patients were considerably worse than those in adult-onset adult AKC patients, pediatric patients, and controls. These results suggest that prolonged ocular surface inflammation may be important for the disruption of the healthy ocular surface, including the tear film [16]. In patients with vernal keratoconjunctivitis (VKC), the tear volume is increased, but the TBUT is reportedly decreased [14,29]. Interestingly, decreased tear film stability has been observed even in the quiescent phases of VKC [29]. Conjunctival goblet cells have also been reported to decrease in patients with various ACDs, including AC [18,30], AKC [13,14,16,17,31], and VKC [14,17]. In particular, a decrease in MUC5 AC, a secreted mucin, has been found along with a decrease in goblet cells in patients with AKC [13,14,16]. In contrast, the density of goblet cells and the severity of corneal staining were not affected in mice with AC in the present study, which may change in the future depending on the duration of ocular surface inflammation, as reported by Onguchi [16]. When cells become necrotic, intracellular inflammatory substances called alarmins, such as IL-1α and IL-33, are released outside the cells, resulting in sterile inflammation [32,33]. Recent studies have reported the involvement of alarmins in both ocular allergies and DED. Alarmins released from necrotic corneal epithelial cells act on corneal fibroblasts and epithelial cells, causing enhanced production of chemokines and cytokines, such as eotaxin, and decreased epithelial barrier function [34,35]. IL-33, an epithelial cell-derived alarmin, does not degranulate mast cells by itself but synergistically degranulates mast cells when acting simultaneously with antigens [36]. Alarmin may be involved in the exacerbation of allergic inflammation [37]. The concentration of IL-33 is also reportedly elevated in the tear fluid of DED patients [38,39]. In addition, the tear levels of IL-33 in patients with DED were positively correlated with the tear levels of IL-4 and IL-5 [38]. These results may be related to the fact that many patients have overlapping symptoms of itching and dryness of the eye [8] and that dry eye is severe in patients with itching [9]. Therefore, alarmins released by epithelial injury caused by dry eye may exacerbate allergic symptoms by promoting mast cell degranulation. The role of alarmins in DED and allergies requires further investigation. We evaluated the epithelial barrier function with the use of the LC-biotin assay. The limitation of the present study is the lack of detailed analysis of each component of the epithelial barrier, such as the adhesion proteins and the mucin expression. Further immunohistochemical analysis should be performed in the future to evaluate the ocular surface epithelia and the mucin expression in more detail. In conclusion, the presence of ADDE exacerbated the clinical signs and symptoms of ACD, possibly through ocular surface epithelial barrier disruption and reduced allergen clearance. 4. Materials and Methods 4.1. Ethical Treatment of Animals This study was approved by the Kochi University Animal Care and Use Committee (permit number J-70). BALB/c mice were purchased from Japan SLC Inc. (Hamamatsu, Shizuoka, Japan) and maintained under specific pathogen-free conditions at the animal facility of Kochi Medical School. Eight-week-old specific-pathogen-free female mice were used in the experiments. All the procedures were performed in accordance with the Association for Research in Vision and Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research. 4.2. Experimental Procedure for Lacrimal Grand Excision and Experimental Allergic Conjunctivitis The mice were anesthetized with an intraperitoneal injection of an anesthetic combination of 0.45 µg/g medetomidine (Domitor; Nippon Zenyaku Kogyo, Tokyo, Japan), 6 µg/g midazolam (Sandoz, Yamagata, Japan), and 7.5 µg/g butorphanol (Vetorphale; Meiji Seika Pharma, Tokyo, Japan). The exorbital lacrimal glands of both eyes were exposed through a careful incision near the anterior ear and excised [40]. Subsequently, talibid ophthalmic ointment 0.3% (Santen Pharmaceutical Co., Osaka, Japan) was applied. Experimental AC was induced using a previously described protocol with slight modifications [41] (Figure 9). Briefly, mice were injected intraperitoneally three times with 30 µg ovalbumin (OVA) (Worthington Biochemical Corp, NJ, USA) mixed with 1 mg of Imject Alum (Thermo Fisher Scientific, MA, USA) at intervals of 7 days. Seven days after the third sensitization, both eyes of each mouse were challenged with OVA in PBS (1 mg per 5 µL per eye) from day 21 to day 28. 4.3. Evaluation of Dry Eye To assess dry eye signs, we evaluated corneal fluorescein staining and tear fluid volume as described previously [40]. Fluorescein solution was extracted from one sheet of Fluores Ocular Examination Test Paper 0.7 mg (AYUMI Pharmaceutical Corporation, Tokyo, Japan) with 500 μL of sterile saline. The concentration of the fluorescein sodium salt was approximately 1.4 mg/mL. Then, 1 μL of fluorescein solution was applied to each eye, wiped off, and observed using a portable slit lamp with a blue filter (Kowa Company, Ltd., Tokyo, Japan). Corneal fluorescein scores were classified as 0–3 (0, no fluorescence; 1, sparse spot fluorescence; 2, dense spot fluorescence; and 3, very dense spot fluorescence). Five locations were observed (center, upper left, lower left, upper right, and lower right), and the sum of the scores obtained was calculated. The volume of tear fluid was measured using phenol red threads (Zone Quick, Ayumi Pharmaceutical Co. Tokyo, Japan). The mice were fixed for 15 s with the thread inserted into the lower eyelid under no anesthesia, and the length of the wet thread was measured. 4.4. Evaluation of Clinical Conjunctival Allergic Reaction Clinical signs were assessed on day 21, as previously described [42]. The scratching behavior of the mice was counted for 10 min after the first eye drop, and the number of scratching episodes by the hindlimb was counted. To investigate vascular permeability, Evans blue dye leakage was evaluated as previously described, with slight modifications [43]. The conjunctivas were harvested 30 min after the OVA or PBS challenge. Evans blue was extracted for 48 h in 400 μL of 0.5% Na2 SO4 and acetone (3:7). After centrifugation, absorbance (620 nm) of the supernatant was measured using a spectrophotometer. Twenty-four hours after the last challenge, serum IgE, IgG1, and IgG2a levels were evaluated using ELISA, as described previously [41]. OVA-specific IgE and IgG1 were assayed using an anti-OVA IgE ELISA kit or an anti-OVA IgG1 ELISA kit (Cayman Chemical Company, MI, USA) according to the manufacturer’s protocol. 4.5. Histological Analysis of Periodic Acid-Schiff Staining, and LC-Biotin Assay Paraffin sections were cut to 2 µm slices, deparaffinized, and hydrated. The sections were dipped in 0.5% periodate solution (FUJIFILM WakoPure Chemical Corporation, Osaka, Japan), oxidized for 5 min, and then washed with distilled water, dipped in Cold Schiff’s Reagent (FUJIFILM WakoPure Chemical Corporation) for 15 min, washed in water for 5 min, and then placed in sulfurous acid water (FUJIFILM WakoPure Chemical Corporation) for 3 min three times. After rinsing, the sections were dipped in Mayer’s hematoxylin (Muto Pure Chemicals Co., Ltd., Tokyo, Japan) for 5 min. The sections were washed in water for 5 min, dehydrated, and mounted using a mounting medium. The number of goblet cells in the epithelium of the conjunctiva showing a purple-magenta PAS-positive reaction was counted by two observers in a blinded manner. To assess the epithelial barrier function, we performed an LC-biotin assay. LC-biotin has been reported to cross-link proteins and not penetrate intact tight junctions, and we followed a previously reported method [44]. LC-biotin (Thermo Fisher Scientific, Waltham, MA, USA) in PBS (1 mg/mL; 5 µL) was administered 30 min prior to eye harvest. Frozen sections were cut into 7 µm slices and fixed with 4% paraformaldehyde (Nacalai Tesque, Inc., Kyoto, Japan) for 10 min at room temperature. The cells were then washed with PBS and incubated with streptavidin Alexa Fluor 488 conjugate (Thermo Fisher Scientific) for 1 h at room temperature, washed with PBS, and mounted with the VECTIRSHIELD mounting medium containing DAPI (Vector Laboratories, Inc., Burlingame, CA, USA). 4.6. Statistical Analysis Data were analyzed using Dunnett’s test or the Tukey–Kramer test using Statcel 4 software (OMS, Saitama, Japan). Author Contributions Conceptualization, T.K. and K.F.; methodology, T.K., W.I. and K.F.; investigation, T.K., W.I. and I.N.; writing—original draft preparation, T.K. and K.F.; writing—review and editing, T.K., W.I., K.F. and K.Y.; supervision, K.F. and K.Y.; funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement This study was approved by the Committee for the Care and Use of Laboratory Animals at Kochi University (permit no. J-70) and was performed in strict accordance with the Statement on the Use of Animals in Ophthalmic and Vision Research of the Association for Research in Vision and Ophthalmology. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Tear volume before and after induction of AC in mice with or without dry eye (DED). The amount of tear fluid was measured at day 21 before antigen challenge (A) and at day 28 (B). The length of the wet thread is shown as means ± standard error of the means in each group. Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). ** p < 0.01 (Tukey–Kramer test) versus non-allergy control group. PBS, mice challenged with phosphate-buffered saline (PBS) (non-allergy group); OVA, mice challenged with ovalbumin (OVA) (allergy-induced group); DED, mice with excised extraorbital lacrimal glands. Figure 2 Number of goblet cells in the conjunctiva. The eyes were isolated for histological analysis; the number of goblet cells in the epithelium of the conjunctiva was counted on day 29. Representative photo of conjunctiva and goblet cells showing a purple-magenta periodic acid–Schiff-positive reaction (A). Data are shown as means ± standard error of the means in each group (B). Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). PBS, mice challenged with phosphate-buffered saline (PBS) (non-allergy group); OVA, mice challenged with ovalbumin (OVA) (allergy-induced group); dry eye disease (DED), mice with excised extraorbital lacrimal glands. Bar, 50 μm. Figure 3 Corneal staining score before and after the induction of AC with or without dry eye (DED). Corneal fluorescein score was classified at day 21 before antigen challenge (A) and at day 28 (B). The sum of the scores is shown as means ± standard error of the means in each group. Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). ** p < 0.01 (Tukey–Kramer test) versus non-allergy control group. PBS, mice challenged with phosphate-buffered saline (PBS) (non-allergy group); OVA, mice challenged with ovalbumin (OVA) (allergy-induced group); DED, mice with excised extraorbital lacrimal glands. Figure 4 Epithelial barrier function of ocular surface by LC-biotin assay. Epithelial barrier was evaluated by LC-biotin assay. The eyes were challenged with LC-biotin in phosphate-buffered saline 30 min prior to harvest. Frozen sections of the cornea (A,C) and conjunctiva (B,D) were stained with streptavidin Alexa Fluor 488 conjugate in mice without (A,B) and with dry eye (C,D). Bar, 50 μm. Figure 5 Serum IgE (A), IgG1 (B), and IgG2a (C) levels of mice. Serum IgE and IgG levels were evaluated 24 h after the last antigen challenge. Data are shown as means ± standard errors of the means in each group. Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). * p < 0.05; ** p < 0.01 (Dunnett’s test) versus naïve mice. PBS, mice challenged with phosphate-buffered saline (PBS) (non-allergy group); OVA, mice challenged with ovalbumin (OVA) (allergy-induced group); dry eye disease (DED), mice with extraorbital lacrimal gland excision. Figure 6 Clinical signs were measured at 20 min after first antigen challenge at day 21. Scores are shown as means ± standard errors in each group. Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). ** p < 0.01 (Tukey–Kramer test) versus non-allergy control group; †† p < 0.01 (Tukey–Kramer test) versus allergy-induced group; ‡‡ p < 0.01 (Tukey–Kramer test) versus non-allergy dry eye disease (DED) group. Figure 7 Number of scratching episodes. The scratching behavior of the mice was counted for 10 min after first antigen challenge at day 21, and the number of scratching episodes by the hindlimb was counted. The number of scratching episodes is shown as means ± standard error in each group. Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). ** p < 0.01 (Tukey–Kramer test) versus non-allergy control group. PBS, mice challenged with phosphate-buffered saline (PBS) (non-allergy group); OVA, mice challenged with ovalbumin (OVA) (allergy-induced group); dry eye disease (DED), mice with excised extraorbital lacrimal glands. Figure 8 Evans blue dye leakage in conjunctiva. Dye leakage was evaluated 30 min after antigen challenge at day 28, shown as arbitrary units ± standard error in each group. Four experimental groups were compared: control (n = 6), DED (n = 6), allergy (n = 6), and allergy with DED (n = 5). * p < 0.05 (Tukey–Kramer test) versus non-allergy control group. PBS, mice challenged with phosphate-buffered saline (PBS) (non-allergy group); OVA, mice challenged with ovalbumin (OVA) (allergy-induced group); dry eye disease (DED), mice with excised extraorbital lacrimal glands. Figure 9 Timeline for induction of dry eye and experimental AC. To induce dry eye, the extraorbital lacrimal glands of the mice were removed 1 day before sensitization. To induce experimental AC, a mixture of ovalbumin (OVA) and alum (Al(OH)3) was administered intraperitoneally three times at an interval of 7 days. Seven days after the third sensitization, corneal fluorescein score and the amount of tear fluid were measured; then, both eyes of each mouse were challenged with OVA in phosphate-buffered solution (PBS) from day 21 to day 28. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Miyazaki D. Takamura E. Uchio E. Ebihara N. Ohno S. Ohashi Y. Okamoto S. Satake Y. Shoji J. Namba K. Japanese guidelines for allergic conjunctival diseases 2020 Allergol. Int. 2020 69 346 355 10.1016/j.alit.2020.03.005 33211650 2. Miyazaki D. Fukagawa K. Fukushima A. Fujishima H. Uchio E. Ebihara N. Shoji J. Takamura E. Namba K. Ohashi Y. Air pollution significantly associated with severe ocular allergic inflammatory diseases Sci. 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==== Front Nanomaterials (Basel) Nanomaterials (Basel) nanomaterials Nanomaterials 2079-4991 MDPI 10.3390/nano12091586 nanomaterials-12-01586 Article Structure and Magnetic Properties of ErFexMn12−x (7.0 ≤ x ≤ 9.0, Δx = 0.2) Gao Penglin 12 Xia Yuanhua 2 Gong Jian 2 Ju Xin 1* Bucher Jean-Pierre Academic Editor 1 School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China; gaoyingluo@163.com 2 Key Laboratory of Neutron Physics and Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang 621900, China; xiayuanhua2009@126.com (Y.X.); gongjian@hotmail.com (J.G.) * Correspondence: jux@ustb.edu.cn 07 5 2022 5 2022 12 9 158602 4 2022 05 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The magnetic interactions of iron-rich manganese-based ThMn12 type rare earth metal intermetallic compounds are extremely complex. The antiferromagnetic structure sublattice and the ferromagnetic structure sublattice had coexisted and competed with each other. Previous works are focus on studying magnetic properties of RFexMn12−x (x = 0–9.0, Δx = 0.2). In this work, we obtained a detailed magnetic phase diagram for iron-rich ErFexMn12−x series alloy samples with a fine composition increment (Δx = 0.2), and studied the exchange bias effect and magneto-caloric effect of samples. ErFexMn12−x series (x = 7.0–9.0, Δx = 0.2) alloy samples were synthesized by arc melting, and the pure ThMn12-type phase structure was confirmed by X-ray diffraction (XRD). The neutron diffraction test was used to confirm the Mn atom preferentially occupying the 8i position and to quantify the Mn. The magnetic properties of the materials were characterized by a comprehensive physical property measurement system (PPMS). Accurate magnetic phase diagrams of the samples in the composition range 7.0–9.0 were obtained. Along with temperature decrease, the samples experienced paramagnetic, ferromagnetic changes for samples with x < 7.4 and x > 8.4, and paramagnetic, antiferromagnetic and ferromagnetic or paramagnetic, ferromagnetic and antiferromagnetic changes for samples with 7.4 ≤ x ≤ 8.2. The tunable exchange bias effect was observed for sample with 7.4 ≤ x ≤ 8.2, which resulting from competing magnetic interacting among ferromagnetic and antiferromagnetic sublattices. The maximum magnetic entropy change in an ErFe9.0Mn3.0 specimen reached 1.92 J/kg/K around room temperature when the magnetic field change was 5 T. This study increases our understanding of exchange bias effects and allows us to better control them. neutron diffraction exchange-bias magnetocaloric effect National Natural Science Foundation of China11504348 This research was funded by the National Natural Science Foundation of China, grant number 11504348. ==== Body pmc1. Introduction Manganese (Mn) is the only 3d-series element that forms a stable ThMn12-type structure with rare earth elements [1,2], and it is mainly ferromagnetic and antiferromagnetic [3]. However, the pure ThMn12-type rare earth iron compound RFe12 does not exist. In the early 1980s, Yang et al. [4] found that a stable ternary rare earth iron intermetallic compound R(FexMn1−x)12 could be formed by substitution, thus setting off a surge of research into iron-rich ThMn12-type compounds [5]. Subsequent studies have found that a number of tertiary elements can stabilize the ThMn12 phase; their molecular formulas can be written as RFexM12−x or R(Fe,M)12, where R is a rare earth element and M = Mn, V, Cr, Mo, W, Ti, Si, Al, Nb or Ga [6,7,8,9]. In the RMn12 alloy, the strong antiferromagnetic interaction between manganese atoms prohibits interaction between rare earth atoms and manganese atoms, so the RMn12 alloy has two magnetic ordering temperatures: R-R ferromagnetic ordering temperature, and Mn-Mn antiferromagnetic ordering temperature [10]. Iron (Fe) can replace Mn in large quantities (up to 75%) without changing the crystal structure [11]. Researchers [12,13,14,15,16,17,18,19,20,21,22] have investigated the structure and magnetic transitions of RFexMn12−x-series materials (x = 0–9.0, Δx = 1) using neutron diffraction, magnetic measurements and electrical measurements and have found that magnetic interaction in the alloy is extremely complex. As the proportion of Fe increases, the material undergoes an antiferromagnetic → antiferromagnetic + ferromagnetic → ferromagnetic transition. Among the iron-rich RFexMn12−x-series (x = 6.0–9.0) samples, only materials with integer values of x have been studied. This composition range includes the magnetic transition stage in which antiferromagnetism and ferromagnetism coexist in the material and plane anisotropy and axis anisotropy compete with each other. Therefore, it is necessary to prepare iron-rich RFexMn12−x-series (x = 6.0–9.0) alloy samples with a finer composition change to obtain more detailed and complete magnetic phase diagrams, and thus be able to develop new aspects of applications for the material. We first studied YFexMn12−x-series (x = 6.0–9.0) samples to obtain more complete magnetic phase diagrams for the materials and observed very large exchange bias effects and zero field cooling (ZFC) exchange bias effects in the samples [23]. After the discovery of exchange bias effect in Co/CoO nanoparticles, investigations have been mainly focused on a large number of heterogeneous structures such as magnetic bilayers, core-shell nanoparticles, and ferromagnetic nanoparticles embedded in antiferromagnetic matrix compounds [24,25,26]. So, it is necessary to further study exchange bias for the bulk metallic materials with exchange interactions occurring among the bulk sublattice. Firstly, we study how the magnetic atoms affect the EB effect in ThMn12-type compounds. The second-order Stevens factor αJ for Er atoms is >0, but the second-order crystal field coefficient (A20) of the rare earth sublattice in the ThMn12 structure is negative, so magnetocrystalline anisotropy tends to the easy axis. We prepared ErFexMn12−x-series (7.0 ≤ x ≤ 9.0, Δx = 0.2) alloy specimens have been prepared by arc melting to enable us to investigate the structure and magnetism of the alloy. 2. Experimental Methods ErFexMn12−x-series (7.0 ≤ x ≤ 9.0, Δx = 0.2) alloys were prepared by arc melting. The raw material was melted 4–5 times in an argon gas atmosphere according to the stoichiometric ratio to produce the alloy ingot; 5% more rare earth and 13% more Mn were added to compensate for volatilization in the melting process. A smaller current of 150 A was applied twice for melting, followed by a 200 A current once or twice to control the against excessive Mn volatilization. Specimens from the master alloy ingots were placed in sealed quartz tubes filled with argon and cooled down to room temperature after heat treatment at 1173 K for 2 days. Phase purity was confirmed by a Cu target X-ray powder diffractometer (PANalytical, Almelo, The Netherlands) at room temperature. The high-resolution neutron diffraction spectrometer (λ = 0.18846 nm) of Mianyang Research Reactor (CMRR, Mianyang, China) was used to analyze the crystal structure, in particularly for the positions of Mn atoms. Powdered alloy was bonded into a small cylinder with epoxy resin or the alloy ingot was shattered, so that we could select a small piece of regular shape for magnetic measurement. The ZFC and field cooling (FC) thermomagnetic curves (M−T curves) of the samples were recorded, and the magnetic hysteresis loops (M−H loops) of the samples under different FC and temperature conditions were measured by the comprehensive physical property measurement system (PPMS, Quantum Design (San Diego, CA, USA)). 3. Experimental Results and Analysis A phase of the ThMn12-type structure was formed in the ErFexMn12−x-series (7.0 ≤ x ≤ 9.0) ingots, and some samples contained a small quantity of the Er(Fe, Mn)2 phase. Heterogeneous Er2(Fe, Mn)17 and Er(Fe, Mn)2 phases are formed in ErFexMn12−x-series (7.0 ≤ x ≤ 9.0) alloys after heat treatment above 1273 K, which differentiates them from YFexMn12−x-series (7.0 ≤ x ≤9.0) alloys. Long duration high-temperature heat treatment is therefore not suitable for this series of materials; 1173 K heat treatment for 48 h will produce homogeneous alloy samples with good crystal shapes. The X-ray diffraction (XRD) spectra of the samples were examined before and after heat treatment. FullProf software [27] was used to refine the structure of the samples after heat treatment, and the relationship between the lattice constant and the composition of the samples was determined, as shown in Figure 1. With the increasing proportion of Fe, the lattice constant a decreased linearly and c remained unchanged. The complete neutron diffraction spectra of some heat-treated samples were examined at room temperature, and the structure was refined using FullProf. The fitting spectrum is shown in Figure 2, and the crystal structure parameters are shown in Table 1. The samples formed a pure ThMn12-type phase of space group I4/mmm (139), with rare earth Er atoms occupying the 2a position and Fe and Mn occupying three other unequal positions (8i, 8j, and 8f). Since the coherent neutron scattering lengths of Mn atoms (bMn = −0.39) and Fe atoms (bFe = 0.95) are significantly different, the relative proportions of Fe and Mn in the alloy samples can be obtained by fitting neutron diffraction data; the results are shown in Table 1. The Mn atom occupies the 8i position preferentially. The trend of change in the proportion of Mn in the materials was similar to that of the initial materials, although the proportion of Mn was slightly higher, which indicated that the proportion of compensated Mn in the initial materials was relatively high. The lattice constant a decreased as the proportion of Fe decreased, while the lattice constant c remained basically unchanged. This is because the Mn atom preferentially occupies the 8i position, and 8i–8i lies in the plane ab. Changes in the proportion of Mn therefore greatly influences the lattice constants a and b but has little effect on the lattice constant c. Figure 3 shows the thermomagnetic curves of ErFexMn12−x-series (7.0 ≤ x ≤ 9.0) alloy samples in an external magnetic field of 50 Oe. TC represents the Curie temperature, TN is the Néel temperature, TC and TN is obtained by differentiating the M−T curves under FC. Tf is the temperature corresponding to the bifurcation point in the ZFC and FC magnetization curves. As can be seen from the figure, the ZFC and FC M−T curves of the samples both clearly bifurcated as the temperature decreased. Tf was slightly lower than the paramagnetic–ferromagnetic transition temperature of the samples due to the coexistence of Er-Er and Fe-Fe ferromagnetic exchanges interactions. Er-Fe, Er-Mn, Fe-Mn and Mn-Mn antiferromagnetic exchanges interactions, all interactions compete with each other, leading to spin frustration in the samples at low temperatures. For samples with x > 7.2, the FC M−T curves initially increased to the maximum value and then decreased gradually as the temperature decreased. The curve steepened, and both the speed and amplitude of bending increased as the proportion of Fe decreased; it reached the maximum for x = 7.8 and then began to decrease and disappeared for x = 7.2. The magnetization curves for x > 7.2 samples were typical of ferrimagnetism magnetization curves. This was because light rare earth lattices and metal lattices are ferromagnetically arranged and heavy rare earth lattices and metal lattices are antiferromagnetically arranged in rare earth intermetallic compounds with a ThMn12-type structure. Er is a heavy rare earth atom, so the samples had a ferrimagnetic structure in which the lattice magnetic moments of rare earth and transition metals were inversely arranged. As the temperature decreased, the magnetic moments of rare earth in the lattice increased rapidly and magnetic moments of transition metals increased slowly; the total magnetic moments of the samples initially increased to the maximum value and then decreased rapidly, and even showed a negative magnetic susceptibility. The x = 7.2 and x = 7.0 samples behave like pure ferro- or ferrimagnetic samples where high coercivity has developed already close to TC. This causes the maximum in the ZFC curves very close to TC. In the YFexMn12−x-series (6.0 ≤ x ≤ 8.8) samples, as the proportion of Fe decreased, the TC of the alloy rapidly decreased and the TN slowly increased; the antiferromagnetic exchange magnetic ordering temperature of Mn-Mn was observed [23]. After rare earth Er atoms with magnetic moments replaced Y atoms without magnetic moments, the antiferromagnetic order of Mn-Mn was suppressed; the obvious antiferromagnetic order of Mn-Mn was only observed in the samples with the Fe proportion 7.4 ≤ x ≤ 8.2. The magnetic ordering temperature is shown in Table 2. Similar to YFexMn12−x, the ferromagnetic transition temperature of the alloy materials decreased rapidly as the proportion of Fe decreased. Figure 4 shows the magnetic phase diagram of the ErFexMn12−x-series (7.0 ≤ x ≤ 9.0) alloys. The samples with x < 7.4 or x > 8.4 were mainly ferromagnetic. The samples with 7.4 ≤ x ≤ 8.2 were ferromagnetic and antiferromagnetic, and only the samples in this range of composition showed antiferromagnetic orders between different transition metal lattices. YFexMn12−x-series samples showed a clear exchange bias effect in the region where ferromagnetic interaction and antiferromagnetic interaction compete most intensely [23]. ErFexMn12−x-series samples may therefore similarly display exchange bias effects for 7.4 ≤ x ≤ 8.2. The FC M−H loops of some samples were measured, and the results are shown in Figure 5. The FC M−H loops of ErFe8.2Mn3.8, ErFe7.8Mn4.2 and ErFe7.4Mn4.6 samples all clearly had lateral shifts. The x = 7.4 and x = 7.8 samples had high coercivity, and the M−H loops were not completely closed when the applied field was 5T. The M−H loops were asymmetric, and lateral and vertical shifts occurred simultaneously. This indicates that the samples had very strong magnetocrystalline anisotropy at low temperatures, and that the antiferromagnetic interaction between the rare earth lattice and the transition metal lattice was the source of the anisotropy. When combined with the YFexMn12−x-series experimental results, we see that the exchange bias effect can be controlled by doping different rare earth elements in addition to altering the ratios of Fe and Mn. The ErFe9.0Mn3.0 compound had a Curie temperature of 310 K, and which is near the room temperature. The reverse magnetic moment of Er atom is decrease drastically as temperature increasing, so the samples may have had a considerable magnetocaloric effect near the Curie temperature. The isothermal magnetization curves in the temperature range 270–340 K were created, and are shown in Figure 6. The figure shows that as the temperature increased, magnetization intensity gradually decreased, and ferromagnetism was gradually transformed into paramagnetism. The isothermal magnetization curves were transformed to obtain the Arrott plot, as shown in Figure 7, in order to determine the type of phase transition occurring. There was no S-shaped curve in the Arrott plot, and no negative curve slope was observed, so the phase transition of the materials was also a second-order phase transition. The Maxwell relation was used to calculate the isothermal magnetic entropy change in the samples from the isothermal magnetization curves at different temperatures, as shown in Figure 8. The calculated maximum value of the magnetic entropy changes when an applied field change of 50 kOe reaches 1.92 J/kg/K. The peak of −ΔSM at 312.5 K corresponds to the ferromagnetic to paramagnetic phase transition, because the magnetization changes drastically near the Curie temperature. Although the maximum −ΔSM of ErFe9.0Mn3.0 is not as large as that of some other magnetic refrigerant materials [28], the |ΔSM| vs. T curve of ErFe9.0Mn3.0 is significantly broader compared with other materials, which is favorable for active magnetic refrigeration. Additionally, the magnetocaloric effect was caused by the second-order phase transition near the Curie temperature, and the thermal hysteresis and magnetic hysteresis during phase transition were both very small, which has benefits in the practical application of the material. 4. Conclusions ThMn12-type single phase samples with different Fe/Mn ratios were prepared by arc melting and heat treatment, and the magnetic phase diagrams of ErFexMn12−x-series (7.0 ≤ x ≤ 9.0) samples were obtained by magnetic measurement. At low temperatures, samples with x < 7.4 and x > 8.4 exhibited ferromagnetism, and ferromagnetism and antiferromagnetism coexisted in samples with 7.4 ≤ x ≤ 8.2, with an FC exchange bias effect. The magnetic interaction between transition metal lattices in ThMn12-type structural materials can be changed by substituting non-magnetic Y atoms with rare earth Er atoms with magnetic moments. In this study, Y atoms were completely replaced; in the following study, we will partially replace them to finely modulate the exchange bias effect and the magnetocaloric effect of the materials. Author Contributions Conceptualization, P.G. and Y.X.; methodology, P.G. and Y.X.; validation, P.G. and Y.X.; formal analysis, P.G. and Y.X.; investigation, P.G. and Y.X.; resources, P.G. and Y.X.; data curation, P.G. and Y.X.; writing—original draft preparation, P.G. and Y.X.; writing—review and editing, P.G. and Y.X.; visualization, P.G. and Y.X.; supervision, J.G. and X.J.; funding acquisition, J.G. and X.J. All authors have read and agreed to the published version of the manuscript. Data Availability Statement The data presented in this study are available in this article. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Variation of lattice constants a and c with Fe content of ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys after heat treatment. Figure 2 Refined neutron diffraction pattern of ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys (where red dots are experimental data, black curves are theoretical simulations, blue vertical bars are Bragg diffraction peak positions and the bottom green solid line is the difference curve). Figure 3 M−T curves for ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys under zero field cooling (ZFC) and field cooling (FC) conditions, H = 50 Oe. (The inset shows the M−T curves under FC after differentiation.) Figure 4 The magnetic phase diagram of ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys. Figure 5 M−T curve under field cooling condition (H = 1000 Oe) and M−H curve after 1000 Oe field cooling of ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys. Figure 6 Isothermal magnetization curve of ErFe9.0Mn3.0. Figure 7 Arrott curve of ErFe9.0Mn3.0. Figure 8 Isothermal magnetic entropy change with temperature for ErFe9.0Mn3.0. nanomaterials-12-01586-t001_Table 1 Table 1 Information on crystal structure parameters of ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys. ErFexMn12−x a(Å) c(Å) occ, Fe, 8i occ, Fe, 8j occ, Fe, 8f n, Fe n, Mn Rwp ErFe9.0Mn3.0 8.45469(11) 4.75397(7) 0.476(0) 0.836(4) 0.928(8) 8.96 3.04 5.11 ErFe8.6Mn3.4 8.45777(24) 4.75346(16) 0.412(4) 0.788(12) 0.892(16) 8.368 3.632 4.54 ErFe8.2Mn3.8 8.46289(11) 4.75501(7) 0.344(0) 0.792(4) 0.908(8) 8.176 3.824 4.73 ErFe7.8Mn4.2 8.46758(16) 4.75572(11) 0.300(0) 0.704(8) 0.832(8) 7.344 4.656 3.88 ErFe7.4Mn4.6 8.47191(24) 4.75547(16) 0.284(0) 0.640(8) 0.764(8) 6.752 5.248 3.29 ErFe7.0Mn5.0 8.47767(19) 4.75605(13) 0.232(0) 0.636(4) 0.796(8) 6.656 5.344 7.28 nanomaterials-12-01586-t002_Table 2 Table 2 Magnetic ordering temperature, exchange bias field and coercive force field of ErFexMn12−x (7.0 ≤ x ≤ 9.0) series alloys. ErFexMn12−x TC (K) Tf (K) TN (K) HE (kOe) HC (kOe) Cooling Field 50 Oe 50 Oe 50 Oe 1000 Oe 1000 Oe ErFe9.0Mn3.0 310 306 ErFe8.6Mn3.4 250 248 −0.22 1.28 ErFe8.2Mn3.8 208 203 142 11.73 2.97 ErFe8.0Mn4.0 178 170 163 ErFe7.8Mn4.2 154 160 169 6.615 9.54 ErFe7.4Mn4.6 128 126 176 11.08 4.52 ErFe7.2Mn4.8 22 44 ErFe7.0Mn5.0 22 36 −1.27 28.11 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Florio J.V. Rundle R.E. Snow A.I. Compounds of Thorium with Transition Metals. I. The Thorium-Manganese System Acta Cryt. 1952 5 449 10.1107/S0365110X52001337 2. Deportes J. Givord D. Lemaire R. Nagaï H. Magnetic interactions in the R-Mn12 compounds Physica B+C 1977 86–88 69 70 10.1016/0378-4363(77)90229-7 3. Kirchmayr H.R. Magnetic properties of rare earth—Manganese compounds IEEE Trans. Magn. 1966 2 493 499 10.1109/TMAG.1966.1065871 4. Yang Y.C. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092216 cancers-14-02216 Article Characterizing the Inflammatory Microenvironment in K14-HPV16 Transgenic Mice: Mast Cell Infiltration and MicroRNA Expression Costa Alexandra C. 123† https://orcid.org/0000-0002-6268-524X Santos Joana M. O. 12† Medeiros-Fonseca Beatriz 4 https://orcid.org/0000-0001-9519-4044 Oliveira Paula A. 4 https://orcid.org/0000-0002-8605-0054 Bastos Margarida M. S. M. 56 Brito Haissa O. 7 https://orcid.org/0000-0002-2151-2449 Gil da Costa Rui M. 14567 https://orcid.org/0000-0003-3010-8373 Medeiros Rui 12389* Tchoghandjian Aurélie Academic Editor 1 Molecular Oncology and Viral Pathology Group, Research Center of IPO Porto (CI-IPOP)/RISE@CI-IPOP (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto), Porto Comprehensive Cancer Center (Porto.CCC), 4200-072 Porto, Portugal; alexandracastrocosta@gmail.com (A.C.C.); joana.oliveira.santos@ipoporto.min-saude.pt (J.M.O.S.); rmcosta@fe.up.pt (R.M.G.d.C.) 2 Faculty of Medicine of the University of Porto (FMUP), 4200-319 Porto, Portugal 3 Research Department of the Portuguese League against Cancer—Regional Nucleus of the North (Liga Portuguesa Contra o Cancro—Núcleo Regional do Norte), 4200-177 Porto, Portugal 4 Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Inov4Agro, University of Trás-os-Montes e Alto Douro (UTAD), Quinta de Prados, 5000-801 Vila Real, Portugal; fonsecabeatriz@live.com.pt (B.M.-F.); pamo@utad.pt (P.A.O.) 5 LEPABE—Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal; mbastos@fe.up.pt 6 ALiCE—Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal 7 Postgraduate Programme in Adult Health (PPGSAD), Department of Morphology, Federal University of Maranhão (UFMA), and UFMA University Hospital (HUUFMA), São Luís 65080-805, Brazil; haissa.brito@ufma.br 8 Virology Service, Portuguese Oncology Institute of Porto (IPO Porto), 4200-072 Porto, Portugal 9 Biomedical Research Center (CEBIMED), Faculty of Health Sciences of the Fernando Pessoa University, 4249-004 Porto, Portugal * Correspondence: ruimedei@ipoporto.min-saude.pt; Tel.: +351-22-508-4000; Fax: +351-22-508-4001 † These authors contributed equally to this work and are considered joint first authors. 28 4 2022 5 2022 14 9 221618 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary K14-HPV16 transgenic mice have proved to be a useful model to study the carcinogenic cascade induced by HPV16, the tumor microenvironment and also the epigenetic and genetic factors associated with this type of malignancy. The aim of our study was to evaluate the infiltration of mast cells in two cutaneous regions with different severity of the lesions and to identify potential microRNAs that may regulate mast cell infiltration in this model. We were able to confirm that increased mast cell infiltration is associated with progression of HPV-induced lesions, and that miR-223-3p and miR-125b-5p might be assisting this process via the regulation of mast cell chemotactic proteins. Abstract High-risk human papillomavirus (HPV) is the etiologic agent of several types of cancer. Mast cells’ role as either a driving or opposing force for cancer progression remains controversial. MicroRNAs are dysregulated in several HPV-induced cancers, and can influence mast cell biology. The aim of this study was to evaluate mast cell infiltration and to identify microRNAs potentially regulating this process. Transgenic male mice (K14-HPV16; HPV+) and matched wild-type mice (HPV−) received 7,12-Dimethylbenz[a]anthracene (DMBA) (or vehicle) over 17 weeks. Following euthanasia, chest skin and ear tissue samples were collected. Mast cell infiltration was evaluated by immunohistochemistry. MicroRNAs associated with mast cell infiltration were identified using bioinformatic tools. MicroRNA and mRNA relative expression was evaluated by RT-qPCR. Immunohistochemistry showed increased mast cell infiltration in HPV+ mice (p < 0.001). DMBA did not have any statistically significant influence on this distribution. Ear tissue of HPV+ mice showed increased mast cell infiltration (p < 0.01) when compared with chest skin samples. Additionally, reduced relative expression of miR-125b-5p (p = 0.008, 2−ΔΔCt = 2.09) and miR-223-3p (p = 0.013, 2−ΔΔCt = 4.42) seems to be associated with mast cell infiltration and increased expression of target gene Cxcl10. These results indicate that HPV16 may increase mast cell infiltration by down-regulating miR-223-3p and miR-125b-5p. HPV16 inflammation mast cells K14-HPV16 microRNAs cancer carcinogenesis Portuguese League Against Cancer—Regional Nucleus of the North (Liga Portuguesa Contra o Cancro—Núcleo Regional do Norte)Portuguese Oncology Institute of PortoPI86-CI-IPOP-66-2017 ALiCELA/P/0045/2020 LEPABEUIDB/00511/2020 UIDP/00511/2020 FCT/MCTES (PIDDAC)FCT—Portuguese Foundation for Science and TechnologyUID/AGR/04033/2020 Norte Portugal Regional Operational Programme (NORTE 2020)2SMART (NORTE-01-0145-FEDER-000054) Fundação para a Ciência e Tecnologia (FCT)SFRH/BD/135871/2018 European Social Funds (FSE)national funds of MCTESThis study was funded by the Portuguese League Against Cancer—Regional Nucleus of the North (Liga Portuguesa Contra o Cancro—Núcleo Regional do Norte), by the Research Center of the Portuguese Oncology Institute of Porto (project no. PI86-CI-IPOP-66-2017), by LA/P/0045/2020 (ALiCE), UIDB/00511/2020 and UIDP/00511/2020 (LEPABE), funded by national funds through FCT/MCTES (PIDDAC), and by National Funds from FCT—Portuguese Foundation for Science and Technology, under the project UID/AGR/04033/2020. 2SMART (NORTE-01-0145-FEDER-000054), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Joana M.O. Santos is a Ph.D. scholarship holder (SFRH/BD/135871/2018) supported by Fundação para a Ciência e Tecnologia (FCT), co-financed by European Social Funds (FSE) and national funds of MCTES. ==== Body pmc1. Introduction High-risk human papillomavirus (HPV) is responsible for cancers in several anatomic regions, including the cervix, vulva, vagina, anus, penis, and oropharynx [1]. HPV16 and HPV18 are the high-risk type most commonly found in most of those cancers. In high-risk HPV, the E6, E7 and E5 proteins play an oncogenic role due to their capacity to form specific complexes with tumor suppressor proteins [2]. Importantly, these oncoproteins have the capacity to induce pro-inflammatory signaling pathways [3,4]. Such pro-inflammatory stimuli, along with chronic inflammation resulting from host antiviral response to persistent HPV infection, are essential for tumor development [3,4]. The inflammatory tumor microenvironment is composed by pre-malignant and malignant cells, stromal cells, and innate and adaptative immune cells [5], which interact with each other, modulating cancer development [5]. Mast cells influence other cells, both through soluble mediators and cell-to-cell interactions [6]. The role of mast cells in cancer progression is ambiguous, since they can promote an anti-tumor response or contribute to tumor growth [7,8]. The pro-tumoral functions of mast cells include the promotion of metastasis, the release of pro-angiogenic factors and the participation in immunosuppression and cancer-related inflammation [9]. Additionally, mast cell accumulation has been associated with poor prognosis and reduced survival in several solid tumors [10]. The role of mast cells in high-risk HPV-induced carcinogenesis and the mechanisms that regulate their accumulation in the tumor microenvironment are poorly defined. MicroRNAs (miRNAs/miRs) are small and conserved RNA molecules that play an important role in the regulation of gene expression [11]. The oncoproteins of high-risk HPV are able to alter the expression of cellular miRNAs, contributing to malignant transformation [11]. Further, miRNAs are key players in modulating the tumor microenvironment, including the infiltration and activity of immune cells [12,13]. However, there is not much information regarding the effects of miRNAs in mast cells in the context of cancer development. The HPV oncoproteins may deregulate miRNAs that control mast cell migration, modulating their infiltration into the tumor microenvironment. Therefore, in this study, we sought to evaluate the infiltration of mast cells into HPV16-driven lesions in transgenic mice carrying all of the HPV16 early genes [14,15]. Additionally, we aimed to identify potential microRNAs that may regulate mast cell infiltration in this model. 2. Materials and Methods 2.1. Animals Generation of K14-HPV16 mice on an FVB/n background has been previously reported [16]. These transgenic mice were kindly donated by Dr. Jeffrey Arbeit and Dr. Douglas Hanahan (University of California) through the USA National Cancer Institute Mouse Repository. The animal experiments were approved by the University of Trás-os-Montes and Alto Douro Ethics Committee (approval no. 10/2013) and the Portuguese General Veterinary Directorate (0421/000/000/2014). Animals were housed and bred according to Portuguese (Decreto-Lei 113, 7 August) and European (EU Directive 2010/63/EU) legislation, under controlled temperature (23 ± 2 °C), light-dark cycle (12 h light/12 h dark) and relative humidity (50 ± 10%). Food and water were provided ad libitum. 2.2. Experimental Design and Sample Collection The chest and ear samples and experimental design were previously used by Peixoto da Silva et. al., 2020 [17]. Briefly, 34 male mice were randomly allocated in four groups: Group 1 (n = 9, HPV− mice, without 7,12-Dimethylbenz[a]anthracene (DMBA)), Group 2 (n = 5, HPV− mice, with DMBA), Group 3 (n = 10, HPV+ mice, without DMBA) and Group 4 (n = 10, HPV+ mice, with DMBA). The DMBA (D3254, Sigma-Aldrich, Merck kGAa, Darmstadt, Germany) was dissolved in dimethyl sulfoxide (DMSO) (CARLO ERBA Reagents S.A.S., Val de Reuil, France) and topically administered to the penile mucosa once a week (0.031 mg/animal/week in 4 μL of DMSO), starting at 9–11 weeks during 17 weeks of the experiment. All of the mice were sacrificed at 31–33 weeks of age under ketamine (CLORKETAM 1000, injectable solution, Vétoquinol, Barcarena, Portugal) and xylazine (Rompun® 2%, Bayer Healthcare S.A., Kiel, Germany) anesthesia, by intracardiac puncture and exsanguination, as indicated by the Federation for Laboratory Animal Science Associations (FELASA). The chest skin and ear tissue samples were weighed and collected into TripleXtractor reagent (Grisp®, Porto, Portugal), macerated and kept at −80 °C until further use. Matched chest skin and ear samples were also collected for histological analysis (histological results previously published by [17]). 2.3. Immunohistochemistry Formalin-fixed, paraffin-embedded (FFPE) tissue sections were fixed, deparaffinized in xylene, rehydrated in a graded series of alcohol, and subjected to antigen retrieval through citrate buffer and microwave heating for 15 min and then cooled for 20 min at room temperature. The slides were covered for 5 min with peroxidase blocking reagent (Novolink Polymer Detection System, Novocastra, Leica Biosystems, Germany), followed by washing with PBS 1×-Tween 0.05% (PBS-T), and protein block (Novolink Polymer Detection System, Novocastra) was used for 5 min. Individual slides were incubated for 1 h 15 min with anti-TPSAB-1 (SC68-07; ThermoFisher) antibody diluted in PBS 1× (1:200 dilution), at room temperature. The slides were then washed with PBS-T and incubated with Novolink Polymer (30 min) and washed with PBS-T again. The color reaction was developed in DAB Chromogen (Novolink Polymer Detection System, Novocastra) according to the manufacturer’s instructions. The sections were then counterstained with hematoxylin, dehydrated and mounted. Positive controls consisted of sections of lymph node tissues, while for negative controls the primary antibody was omitted (Figure S1). The staining was assessed and validated by an experienced pathologist (RMGC). To evaluate mast cell infiltration, the number of immunostained cells was determined in at least five high magnification fields (500×) for each animal, randomly chosen from the superficial dermis, representing the dermo-epidermal interface, and counting was performed in ImageJ. The results were presented as the mean and standard deviation of the animals in each group. 2.4. MicroRNA Selection To retrieve the proteins associated with mast cell infiltration we created a protein–protein interaction (PPI) network using Cytoscape 3.7.2. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRINGapp 1.5.1), installed on Cytoscape, was used to search and visualize the proteins associated with mast cell infiltration and mast cell chemotaxis through Pubmed Query. A confidence (score) cutoff of 0.4 and a number of proteins of 100 were selected. The PPI network was further clustered using Clustermaker 2 v.1.3.1, to identify densely connected regions and clusters of proteins that were crucial in the PPI network. The CytoHubba (version 0.1) plugin Cytoscape was also utilized to identify the key proteins within the higher cluster, according to maximal clique centrality (MCC). Finally, for the top 10 proteins, we identified microRNAs that regulate their respective messenger RNAs, using miRNA databases (TargetScan, miRDB, miRmap, miRwalk, miRTarBase and TarBase) [18,19,20,21,22,23]. The four microRNAs with higher scores in those databases and related to the majority of the genes in our network were selected. 2.5. Total RNA Isolation Total RNA extraction from chest skin and ear samples was performed using TripleXtractor reagent (Grisp®, Portugal) followed by a chloroform solution (EMSURE®, Merck kGAa, Darmstadt, Germany). Total RNA was then purified using a commercial kit, GRS total RNA kit (Grisp®, Portugal), according to the manufacturer’s instructions. DNAse treatment was included for all of the samples. RNA concentration and purity were measured using the NanoDrop Lite spectrophotometer (Thermo Scientific®). 2.6. cDNA Synthesis The microRNA samples (50 ng) were used as templates for cDNA synthesis using the TaqMan® MicroRNA Reverse Transcription Kit (Applied Biosystems®) and sequence-specific stem-loop reverse transcription primers for miR-223-3p, miR-466l-3p, miR-466k, miR-125b-5p and snoRNA-202. The conversion was performed in a Biometra® Personal Cycler (Biocompare, USA) with the following conditions: 30 min at 16 °C, 60 min at 42 °C and 10 min at 85 °C. Total RNA samples (300 ng) were also used as templates for cDNA synthesis using the High-Capacity cDNA Reverse Transcription Kit (Applied BiosystemTM). The conversion was performed in a Biometra® Personal Cycler (Biocompare, USA) with the following conditions: 10 min at 25 °C, 120 min at 37 °C, and 5 min at 85 °C. Negative controls lacking RNA were also included in all reactions. 2.7. Relative Quantification of MicroRNAs Each reaction was performed with 5 µL of 2× TaqMan® Fast Advanced Master Mix (Applied Biosystems®) with 0.5 µL of 20× TaqMan® MicroRNA Expression Assays (miR-466l-3p: 002804; miR-466k: 240990_mat; miR-223-3p: 002295; miR-125b-5p: 000449; snoRNA-202: 001232; Applied Biosystems®), 3 µL of nuclease-free water and 1.5 µL of cDNA sample, making a total volume of 10 µL. The quantification was performed in duplicate, and CT standard deviation values superior to 0.5 were excluded. Negative controls lacking cDNA were also included in all reactions. All target and endogenous controls for each sample were amplified in the same plate. The thermal cycling conditions for all assays were the following: 20 s at 95 °C followed by 45 cycles of 1 s at 95 °C and 20 s at 60 °C. The same baseline and threshold were set for each plate using the analysis software for qPCR from the Thermo Fisher Connect platform (Thermo Fisher Scientific, Waltham, MA, USA), in order to generate threshold cycle (Ct) values for all of the miRs/SnoR in each sample. Small nucleolar RNA 202 (snoR-202) was previously tested by our group in this mouse model, was the one that showed the lowest standard deviation values in chest and ear, and therefore, was used as endogenous control [14,15]. 2.8. Relative Quantification of MRNAs Each reaction was performed with 5 µL of 2× TaqMan® Fast Advanced Master Mix (Applied Biosystems®) with 0.5 µL of 20× TaqMan® Gene Expression Assays (Ccr2: Mm00438270_m1; Cxcl10: Mm00445235_m1; β-actin: Mm01205647_g1; Applied Biosystems®), 3.5 µL of nuclease-free water and 1 µL of cDNA sample, making a total volume of 10 µL. The quantification was performed in duplicate, and CT standard deviation values superior to 0.5 were excluded. Negative controls lacking cDNA were also included in all reactions. All target and endogenous controls for each sample were amplified in the same plate. The thermal cycling conditions for all assays were the following: 20 s at 95 °C followed by 45 cycles of 1 s at 95 °C and 20 s at 60 °C. The same baseline and threshold were set for each plate using the analysis software for qPCR from the Thermo Fisher Connect platform (Thermo Fisher Scientific, Waltham, MA, USA), in order to generate Ct values for all of the genes in each sample. β-actin was previously tested by our group in chest skin and ear tissue samples of this mice model, was the one that showed the lowest standard deviation values, and therefore, was used as endogenous control. 2.9. Statistical Analysis Statistical analysis was performed using IBM SPSS Statistics for Windows (Version 27.0). Immune cell infiltration was evaluated using the Mann–Whitney U test and the Kruskal–Wallis test. Additionally, to analyze mast cell distribution in the different histological lesions, a Fisher–Freeman–Halton test was performed. In order to understand which microRNAs were influencing the various degrees of mast cell infiltration, we used the Kruskal–Wallis test. Finally, the presence of statistical differences in microRNA expression was evaluated using the Livak method along with Mann–Whitney U test. All of the graphics were constructed using GraphPad Prism 8 (GraphPad Software). The results were considered statistically significant when the p values were <0.05. 3. Results 3.1. Mast Cell Infiltration To determine if HPV was able to modulate mast cell infiltration in both chest skin and ear tissues of the K14-HPV16 transgenic mice, we evaluated the infiltration of mast cells in FFPE tissue specimens by immunohistochemistry using a specific anti-mast cell tryptase antibody (TPSAB-1). We observed that, in general, mast cell infiltration increased substantially in the transgenic mice (HPV+) when compared with the wild type (HPV) (p < 0.001) (Figure 1 and Figure 2). The presence or absence of DMBA did not have any statistically significant influence on this distribution (p = 0.650 in wild-type mice and p = 0.568 in transgenic mice) (Figure 2). We also evaluated mast cell infiltration in the two organs separately. The mean number of mast cells was higher in ear tissue when compared with chest skin (Figure 3). This alteration was perceptible in both wild-type (HPV−) and transgenic mice (HPV+), although it was much more evident in the HPV+ groups, both without and with DMBA (p < 0.01) (Figure 3). Next, we wanted to understand if mast cell infiltration increased as lesions progressed from normal epidermis to epidermal hyperplasia, dysplasia and squamous cell carcinoma. Analyzing both organs together, we observed important differences between the normal epithelium and the hyperplastic epithelium (p < 0.001), as well as between the normal epithelium and the dysplastic epithelium (p < 0.001) (Figure 4a). No statistically significant difference was observed between the hyperplastic epithelium and the dysplastic epithelium (p = 0.348) (Figure 4a). The evaluation of chest skin alone showed, once again, differences between the normal epithelium when compared with both hyperplastic and dysplastic epithelium (p = 0.04 and p = 0.03, respectively) (Figure 4b). The same occurred when we analyzed only the ear tissue (p < 0.001) (Figure 4c). The single carcinoma sample could not be analyzed statistically, but mast cell infiltration was detected and mast cell counts were similar to those from dysplastic lesions (Figure 4c). In order to determine how mast cells were distributed in the different histological lesions, we ranked mast cell averages in terciles, using the IBM SPSS software platform. A rank of 1 was assigned to a mean of 0 to 2 mast cells per camp and represented a low mast cell count; a rank of 2 was assigned to a mean of 2 to 9 mast cells per camp and represented an intermediate mast cell count; and finally, a rank of 3 was assigned to a mean of 9 to 33 mast cells per camp and represented a high mast cell count. A low mast cell count was mainly observed in normal samples, an intermediate mast cell count was associated with 4 hyperplastic samples and 8 dysplastic samples, and the highest mast cell count was found homogeneously distributed between the hyperplastic and dysplastic lesions (p < 0.001) (Table 1a). By analyzing each organ separately, we were able to confirm that more advanced lesions had an increase in the mast cell count (p = 0.002 and p < 0.001 for Table 1b,c respectively). 3.2. Identification of Proteins and MicroRNAs Potentially Involved in Mast Cell Infiltration To identify proteins related with mast cell infiltration, we used StringApp 1.6.0 in Cytoscape 3.7.2. The top 100 proteins were retrieved with a confidence cut-off of 0.4. Next, using clusterMaker2, we applied the Markov clustering (MCL) to promote the clustering. We obtained clusters of proteins that had more interaction among themselves than with the rest of the network. Then, for the major cluster obtained, the Cytohubba 0.1 app was used to identify the top 10 hub proteins (Table 2). Having found the essential proteins associated with mast cell infiltration (Table 2), we then searched in several miRNA online databases for miRNAs that target the messenger RNA of those top 10 proteins and retrieved the four microRNAs with higher scores, namely miR-466l-3p, miR-466k, miR-223-3p and miR-125b-5p (Table 3). Next, we tried to understand if these four microRNAs could be associated with mast cell infiltration in chest skin and ear tissue of K14-HPV16 transgenic mice. Performing a Kruskal–Wallis test between the relative expression of the four microRNAs and the terciles obtained previously (low, intermediate and high mast cell count), we found that only miR-223-3p and miR-125b-5p had statistically significant results (p = 0.035 and p = 0.024, respectively), meaning that only these two microRNAs were influencing mast cell infiltration (Table 4). For miR-466l-3p and miR-466k, no statistical differences were found, so no further evaluation was performed (Table 4 and Figure 5). 3.3. MiR-223-3p Is Associated with Mast Cell Infiltration To confirm our hypothesis that miR-223-3p could be associated with mast cell infiltration in this transgenic mouse model, we first conducted a joint analysis of both organs, and observed that there were statistically significant differences between the low mast cell count and the high mast cell count (p = 0.013; 2−ΔΔCt = 4.42) and also between the low mast cell count and the intermediate mast cell count (p = 0.040; 2−ΔΔCt = 4.85) (Figure 6a). Since Cxcl10 is one of the essential proteins that is targeted by miR-223-3p and mast cell infiltration, we evaluated its expression and observed that there were statistically significant differences between the low mast cell count and the intermediate and high mast cell counts (p = 0.002; 2−ΔΔCt = 5.37 and p < 0.001; 2−ΔΔCt = 9.30, respectively) and also between the intermediate mast cell count and the high mast cell count (p = 0.023; 2−ΔΔCt = 1.73) (Figure 6b). When we performed a more specific analysis and centered our attention on chest skin, we did not find any statistically significant difference when analyzing the miR-223 relative expression (Figure 6c,e). Nonetheless, when analyzing Cxcl10 expression, we observed a statistically significant difference between the low mast cell count and the intermediate mast cell count (p = 0.008; 2−ΔΔCt = 6.38) (Figure 6d). The same happened when we analyzed the ear tissue separately, where miR-223 did not have any statistically significant differences (Figure 6e), but when we focused on Cxcl10 expression, we observed statistically significant differences between the low mast cell count and high mast cell count (p = 0.009; 2−ΔΔCt = 9.08) and also between the intermediate mast cell count and high mast cell count (p = 0.019; 2−ΔΔCt = 2.81) (Figure 6f). 3.4. MiR-125b-5p Is Associated with Mast Cell Infiltration Next, we performed the same analysis for miR-125b-5p involving both organs and observed statistically significant results between the low mast cell count and the high mast cell count (p = 0.008; 2−ΔΔCt = 2.09) (Figure 7a). When focusing on each organ separately, there was a statistically significant difference between the low mast cell count and the intermediate mast cell count (p = 0.026; 2−ΔΔCt = 2.21) in chest skin, and between the intermediate mast cell count and high mast cell count (p = 0.031; 2−ΔΔCt = 2.27) in ear tissue, suggesting that this miRNA influences the increased mast cell infiltration in both organs (Figure 6e and Figure 7c). Additionally, we also evaluated Ccr2 relative expression, since it is one of the essential proteins related to miR-125b. There were statistically significant differences in Ccr2 expression between the low mast cell count and the intermediate mast cell count (p = 0.035; 2−ΔΔCt = 1.61) and between the intermediate mast cell count and the high mast cell count (p = 0.036; 2−ΔΔCt = 1.55) when we analyzed both organs together (Figure 7b). However, when analyzing chest skin (Figure 7d) and ear (Figure 7f) separately, no statistical differences were found. 4. Discussion 4.1. Mast Cell Infiltration High-risk HPV is considered one of the main etiological factors associated with the development of cancer [28]. The carcinogenic process associated with a persistent HPV infection develops in several locations through a multistep process, that goes from hyperplastic lesions (alteration in the cell number, shape and size, but still maintaining some differentiation), to dysplastic lesions (with cytological aberrations and loss of tissue differentiation), carcinoma in situ (CIS), and subsequently to invasive squamous cell carcinoma (SCC) [29]. There is a critical connection between the inflammatory microenvironment and cancer [30]. Numerous studies have documented this link in several human carcinomas and the influence of local immunity over patient prognosis and response to therapy [31,32,33,34]. In the early stages of tumor development, immunogenic cancer cells are recognized and eliminated by cytotoxic immune cells such as natural killer (NK) cells and CD8+ T cells (process called immunosurveillance) [35]. As the neoplastic tissue evolves into a clinically detectable tumor, there are different inflammatory cells that will impact tumor fate. These so-called tumor-infiltrating immune cells (TIICs) can be trained by tumor cells to favor tumor growth and development. An example of such TIICs are mast cells. Mast cells can secrete a wide range of bioactive molecules (that are contained inside their cytoplasmic granules) that exert both pro- and anti-tumor effects [36]. Several in vivo and in vitro studies showed that mast cells’ pro-tumor activity promotes lymphatic and blood vessel formation, tumor growth and metastasis [37]. With regard to the model that we used in this study, the integration of the early region of HPV16 DNA in these transgenic mice allows the spontaneous development of lesions that are very similar to the ones observed in humans, with analogous multistep development [38]. We were able to observe pre-malignant lesions and squamous cell carcinomas on the epidermis of the ear and chest tissue of these transgenic mice [15,38]. Our team and others also used this mouse strain to develop models for tongue base cancer, cervical cancer and penile cancer [39,40,41]. It is thought that a progression of the lesions associated with a persistent HPV infection is accompanied by increased immune cell infiltration, and that was what we wanted to verify whether this happened with mast cells [15,42]. There are previous reports from HPV16-E7 mice showing that accumulation of mast cells in the ear lesions causes local immune suppression [42]. In K14-HPV16 mice, it was shown that cutaneous mast cells release proteases and reorganize stromal architecture and hyperactivate angiogenesis [43]. However, the comparison between cutaneous regions with different susceptibility to HPV-induced carcinogenesis and the mechanisms that promote mast cell accumulation in this mouse model are poorly defined. The K14-HPV16 mice used in this study had intraepithelial hyperplastic lesions in the chest skin, and dysplastic lesions were restricted to the ear skin [17]. DMBA induced dysplastic lesions in the chest skin and a squamous cell carcinoma in the ear skin [17]. There is evidence that suggests that differences in the histological microenvironment of chest skin and ear tissue of these transgenic mice are correlated with a differential microRNA expression in both of these organs that ultimately affects their susceptibility to cancer [14,15]. Our results were consistent with these previous studies, as we identified not only that HPV presence influenced a higher infiltration of mast cells, but also that this accumulation was more prominent in the ear tissue when compared to the chest skin (Figure 1, Figure 2 and Figure 3). Indeed, this increase in mast cells in ear tissue compared to chest skin was observed in both HPV− and HPV+ mice. Since DMBA presence did not influence the results, mast cell infiltration seems to only be promoted by HPV presence. Since our transgenic animals (HPV+) had different histological lesions, we were also able to evaluate if the infiltration of mast cells was consistent during the entire carcinogenic process or if different lesions possessed distinct levels of mast cell infiltration. We concluded that as lesions progressed, there was also an increase in mast cell infiltration (Figure 4). This supports the hypothesis that increased mast cell counts are associated with lesion progression and the consequent development of cancer, although additional mechanistic studies are required to establish the tumorigenic contributions of mast cells in this model. 4.2. MicroRNAs and Their Putative Roles MiRNAs possess a dual role, being able to act as tumor suppressors or oncogenes, depending on their target gene functions [44]. Some miRNAs have been described as putative biomarkers for the occurrence and development of HPV-induced cancers [45,46]. Numerous miRNAs have been reported to have a crucial role in HPV-induced carcinogenesis [47,48], including in studies by our group using K14-HPV16 transgenic mice [14,15,49,50]. MiRNAs also modulate many aspects of mast cell behavior (mast cell cycle, proliferation and maturation), being able to act as either inducers and/or suppressors of mast cell biological functions by affecting different targets [51]. Despite the importance of this emerging field of research, the available data on the influence of miRNAs in mast cell function, particularly in cancer, remain very limited. 4.2.1. MiR-223-3p Expression Analysis MiR-223 in humans is located within the q12 locus of the X chromosome and is mainly expressed by hematopoietic cells [52]. MiR-223 remained highly conserved during evolution, which suggests that it has an important role in several physiological processes, including monocyte-macrophage differentiation, neutrophil recruitment and pro-inflammatory responses [53]. Additionally, miR-223 can also be transferred to non-myeloid cells through lipoproteins or extracellular vesicles [54]. This microRNA’s expression is also deregulated in many types of cancer, from hematological to solid malignancies [55]. Depending on the clinical context, miR-223 can act either as an oncogene (breast cancer) or tumor suppressor (leukemia and hepatocellular carcinoma) [55]. Additionally, studies suggest that downregulation of miR-223 seems to promote mast cell degranulation, while high levels of miR-223 may promote mast cell apoptosis [56,57]. In our study, miR-223 was chosen because it was associated with two proteins related to mast cell infiltration and chemotaxis, namely C-X-C motif chemokine ligand 10 (Cxcl10) and integrin subunit alpha M (Itgam). From miR-223 targets, we chose to study the expression of Cxcl10, because from the two the targets of miR-223 it was the one on top of the rank (Table 3). Cxcl10 is a chemokine produced by cancer cells that can activate mast cells and promote their recruitment to solid tumors [58]. Furthermore, this chemokine can stimulate mast cells to secrete diverse soluble factors that support invasion, proliferation and survival of tumor cells [59,60]. Our results suggest that miR-223 may interfere with mast cell infiltration in both chest skin and ear tissue of our transgenic mice, as we observed a tendency toward decreased miR-223 expression while mast cell counts increased. However, this tendency was not statistically significant when analyzing chest skin (Figure 6c) and ear (Figure 6e) separately, possibly due to the lack of statistical power due to the small sample size in some of the groups. Future studies should increase the sample size. Regarding Cxcl10, we were able to verify that an increase in Cxcl10 levels was associated with an increase in mast cell infiltration in chest skin and ear tissue lesions. Thus, we hypothesize that a decrease in miR-223 relative expression may lead to an increase in Cxcl10, which will probably increase mast cell infiltration in the tissue. 4.2.2. MiR-125b-5p Expression Analysis MiR-125b has gained special interest in the field of cancer research because it is found aberrantly expressed in a great variety of tumors [61]. This miRNA is the human orthologue of lin-4, one of the first miRNAs to be identified in C. elegans [62]. The function of miR-125b diverges in different cancers depending on the molecular contexts and surrounding tumor microenvironment [61]. It can either act as an oncogene (e.g., nasopharyngeal carcinoma and gastric cancer) or as a tumor suppressor (e.g., breast cancer and colorectal cancer) [63]. Mature miR-125b is generated from two genes (miR-125b-1 and miR-125b-2), both situated in fragile sites commonly deleted in these types of cancer and that imply its loss of function [64]. In general, miR-125b is involved in different cellular processes such as inflammation, cell proliferation and cell cycle regulation. MiR-125b is capable of directly targeting p53, which is known for maintaining genome stability and playing a central role in apoptosis regulation [65]. Furthermore, miR-125b has been shown to interact with multiple mRNAs, including apoptosis regulators such as Bak-1, Bcl-2 and Bcl-w [66,67]. MiR-125b has a central role in productive HPV infection through the regulation of papillomavirus ORF L2 [68]. L2 is associated with the import of HPV DNA to the nucleus, and its inactivation by miR-125b results in loss of viral infectivity [69,70]. Shortly after HPV infection, there is an increased expression of miR-125b that can be explained by interaction with the viral oncoproteins, and that promotes the immune response and inflammation process [71]. As lesions progress, there is a significant decrease in the relative expression of miR-125b [72]. Regarding our bioinformatic analysis, miR-125b was also associated with two proteins related to mast cell infiltration and mast cell chemotaxis, namely C-C motif chemokine receptor 2 (Ccr2) and tumor necrosis factor (Tnf) (Table 3). The results showed a decrease in miR-125b relative expression as the number of mast cells infiltrated in the tumor increased, which we hypothesized could be mediated by increased Ccr2 expression levels. Ccr2 is a chemokine that is thought to promote mast cell degranulation and to stimulate an increase in carcinogenesis-associated inflammation mediated by mast cells [73,74]. However, Ccr2 was not significantly regulated in relation to mast cell infiltration, suggesting that miR-125b may be regulating mast cell infiltration via other downstream effectors. In this context, the expression of Tnf, which was also identified as a major regulated protein in our network, should be explored in future studies. 5. Conclusions With this work and its findings, we conclude that HPV16 promotes the progressive accumulation of mast cells in intraepithelial lesions, potentially promoting carcinogenesis. Additionally, we also came to the conclusion that miR-223-3p and miR-125b-5p may be orchestrating pathways that regulate mast cell infiltration in chest skin and ear tissue. There was increased mast cell infiltration in the microenvironment of ear tissue compared with chest skin in both HPV− and HPV+ mice. This finding may be a possible explanation for the differences in the severity of the lesions in these two cutaneous sites, observed in HPV+ mice. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14092216/s1, Figure S1: Histological analysis of lymph node samples, DAB-hematoxylin. (a). Lymph node with mast cell infiltration observed in mice, 100×. (b). Same lymph node from figure (a). with high magnification 500×. (c). Lymph node without primary antibody, 100×. (d). Same lymph node from figure c. with high magnification 500×. Click here for additional data file. Author Contributions Conceptualization, J.M.O.S., R.M.G.d.C. and R.M.; methodology, A.C.C., J.M.O.S., B.M.-F., P.A.O., H.O.B. and R.M.G.d.C.; software, A.C.C., J.M.O.S. and H.O.B.; validation, A.C.C., J.M.O.S., B.M.-F., P.A.O., H.O.B. and R.M.G.d.C.; formal analysis, A.C.C., J.M.O.S., R.M.G.d.C. and R.M.; investigation, A.C.C., J.M.O.S., R.M.G.d.C. and R.M.; resources, P.A.O., M.M.S.M.B., R.M.G.d.C. and R.M.; data curation, A.C.C., J.M.O.S., R.M.G.d.C. and R.M.; writing—original draft preparation, A.C.C. and J.M.O.S.; writing—review and editing, R.M.G.d.C. and R.M.; supervision, R.M.G.d.C. and R.M.; funding acquisition, P.A.O., M.M.S.M.B., R.M.G.d.C. and R.M. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The animal experiments were approved by the University of Trás-os-Montes and Alto Douro Ethics Committee (approval no. 10/2013) and the Portuguese General Veterinary Directorate (0421/000/000/2014). Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Histological analysis of mice chest skin and ear tissue samples, 500×, DAB-hematoxylin, scale bar = 20 µm. (a) Chest skin with low mast cell infiltration observed in wild-type (HPV−) mice. (b) Normal ear with low mast cell infiltration observed in HPV− mice. (c) Mast cell infiltration observed in chest skin of K14-HPV16 (HPV+) transgenic mice. (d) Mast cell infiltration observed in ear tissue of HPV+ mice. (e) Chest skin of an HPV− mouse with DMBA treatment. (f) Ear tissue of an HPV− mouse with DMBA treatment. (g) Chest skin of HPV+ transgenic mouse with DMBA presence. (h) Ear tissue of HPV+ transgenic mouse with DMBA presence. Figure 2 Mast cell infiltration in chest skin and ear tissue in the different groups. Transgenic mice (HPV+) had a higher infiltration of mast cells when compared to the wild-type (HPV−) (*** p < 0.001). DMBA presence did not alter the predisposition for mast cell infiltration in any of the groups. Figure 3 Mast cell differential infiltration in each group (chest skin and ear tissue together). There was an increase in mast cell infiltration in the ear tissue when compared to the chest skin, in both wild-type and transgenic mice. HPV− had a statistically lower difference between both organs (* p < 0.05), whereas in HPV+ without and with DMBA, there was a higher statistically significant difference (** p < 0.01). Figure 4 Mast cell infiltration in lesion progression in both organs together (a), chest skin (b) and ear tissue (c). (a) Statistically significant differences were found when comparing the normal epithelium versus the hyperplastic epithelium (*** p < 0.001) and between the normal epithelium versus the dysplastic epithelium (*** p < 0.001). (b) In chest skin, comparing the normal epithelium with the hyperplastic and dysplastic epithelium, we observed an increase in mast cell infiltration (** p < 0.01). (c) In ear tissue, we also found statistically significant differences between the normal and hyperplastic epithelium (** p < 0.01) and between the normal and dysplastic epithelium (*** p < 0.001). Figure 5 MiR-466l-3p and MiR-466k relative expression in K14-HPV16 transgenic mice. (a) MiR-466l-3p relative expression in chest skin and ear tissue together. (b) MiR-466k relative expression in chest skin and ear tissue together. L: low mast cell count; I: intermediate mast cell count; H: high mast cell count. Figure 6 MiR-223 and Cxcl10 relative expression in K14-HPV16 transgenic mice. (a) MiR-223 relative expression in chest skin and ear tissue, together. There are statistically significant differences between the low and intermediate mast cell counts (* p < 0.05) and between the low and high mast cell counts (** p < 0.01). (b) Cxcl10 relative expression in chest skin and ear tissue, together. We observed statistically significant results between the low and intermediate mast cell counts (** p < 0.01), between the low and high mast cell counts (*** p < 0.001), and between the intermediate and high mast cell counts (* p < 0.05). (c) Normalized relative expression of miR-223 in chest skin. (d) Normalized relative expression of Cxcl10 in chest skin. Statistically significant differences were found between the low and intermediate mast cell counts (** p < 0.01). (e) MiR-223 relative expression in ear tissue. (f) Cxcl10 relative expression in ear tissue. Statistically significant results were observed between the low and high mast cell counts (** p < 0.01) and between the intermediate and high mast cell counts (* p < 0.05). L: low mast cell count; I: intermediate mast cell count; H: high mast cell count. Figure 7 MiR-125b and Ccr2 relative expression in K14-HPV16 transgenic mice. (a) MiR-125b relative expression in chest skin and ear tissue, together. There are statistically significant differences between the low and high mast cell counts (** p < 0.01). (b) Ccr2 relative expression in chest skin and ear tissue, together. We observed statistically significant results between the low and intermediate mast cell counts (* p < 0.05) and between the intermediate and high mast cell counts (* p < 0.05). (c) Normalized relative expression of miR-125b in chest kin. Statistically significant differences were observed between the low and intermediate mast cell counts (* p < 0.05). (d) Normalized relative expression of Ccr2 in chest skin. (e) MiR-125b relative expression in ear tissue. Statistically significant differences were found between the intermediate and high mast cell counts (* p < 0.05). (f) Ccr2 relative expression in ear tissue. L: low mast cell count; I: intermediate mast cell count; H: high mast cell count. cancers-14-02216-t001_Table 1 Table 1 Differential mast cell count in different histological lesions of both organs together (a), and chest skin (b) and ear tissue (c) separately. Low, intermediate and high mast cell count values were obtained in the IBM SPSS software platform by ranking the mast cell count in terciles; Low represents a mean mast cell per camp of 0 to 2 mast cells, intermediate refers to a mean of 2 to 9 mast cells per camp, and high represents a mean of 9 to 33 mast cells per camp. a Histology Total Normal Hyperplasia Dysplasia Carcinoma Mast cells Average (Chest skin + Ear) Low mast cells 11 3 0 0 14 Intermediate mast cells 3 4 8 0 15 High mast cells 0 7 7 1 15 Total 14 14 15 1 44 b Histology Total Normal Hyperplasia Dysplasia Mast cells Average (Chest skin) Low mast cells 6 3 0 9 Intermediate mast cells 0 4 6 10 High mast cells 0 1 1 2 Total 6 8 7 21 c Histology Total Normal Hyperplasia Dysplasia Carcinoma Mast cells Average (Ear) Low mast cells 5 0 0 0 5 Intermediate mast cells 3 0 2 0 5 High mast cells 0 6 6 1 13 Total 8 6 8 1 23 cancers-14-02216-t002_Table 2 Table 2 Top 10 hub proteins. Rank Name Score Protein 1 ENSMUSP00000132453 3.03 × 1027 CCR2 2 ENSMUSP00000025263 3.03 × 1027 IL4 2 ENSMUSP00000000889 3.03 × 1027 TNF 4 ENSMUSP00000031322 3.03 × 1027 CXCL15 5 ENSMUSP00000031327 3.03 × 1027 CXCL1 6 ENSMUSP00000047646 3.03 × 1027 CXCL10 7 ENSMUSP00000027061 3.03 × 1027 IL13 7 ENSMUSP00000020650 3.03 × 1027 IL17A 9 ENSMUSP00000074885 3.03 × 1027 CXCL2 10 ENSMUSP00000068468 3.03 × 1027 ITGAM cancers-14-02216-t003_Table 3 Table 3 MicroRNAs found in online databases with respective targets and scores (data last accessed in 10 April 2021). TargetScan 7.2 (https://www.targetscan.org/mmu_72/) relies on conservation of binding sites and divides miRNA families into broadly conserved (conserved across most vertebrates, usually to zebrafish), highly conserved (meaning that they are conserved across most mammals, but usually not beyond placenta) and poorly conserved (all others) [24]. miRDB (http://mirdb.org/) prediction scores are between 50 and 100, assigned by new computational target prediction algorithms (>80 considered most likely to be real) [19]. miRmap (http://mirmap.ezlab.org), ranks potential targets according to repression strength (mediated by RISC), from 0 to 100%, with 100% representing the strongest repression [25]. miRwalk (http://mirwalk.umm.uni-heidelberg.de), incorporates putative targets of 13 prediction datasets, with results being expressed in p-values between 0 and 1, representing the probability that a candidate target site is a true target site (high p-values are better) [26]. miRTarBase 8.0 (http://miRTarBase.mbc.nctu.edu.tw/)/TarBase v.8 (http://www.microrna.gr/tarbase) are experimentally validated by low throughput techniques (reporter assays, qPCR, Western blot or enzyme linked immunosorbent assays) or high throughput techniques (microarray or proteomics experiments) [23,27]. HITS-CLIP, high-throughput sequencing of RNAs isolated by crosslinking immunoprecipitation; LRA/WB, luciferase reporter assay/Western blot. MicroRNAs Target Databases (Score) miR-466l-3p IL-4 TargetScan (poorly), miRDB (100), miRmap (93,99) TNF miRDB (100) CXCL1 miRDB (95) IL-13 miRDB (80) IL-17A TargetScan (poorly), miRDB (96) CXCL2 miRDB (98), miRmap (98,64) miR-466k CXCL15 miRTarBase (HITS-CLIP), miRDB (100), miRmap (99,98), miRwalk (0.92) ITGAM TargetScan (poorly), miRDB (100), miRmap (99,95), miRwalk (0.87) miR-223-3p CXCL10 miRDB (90), miRmap (94,46), TarBase (-) * ITGAM miRwalk (1), TarBase (-) * miR-125b-5p TNF miRTarBase (LRA/WB) CCR2 miRwalk (1), Tarbase (-) * * Present in the database but with no score associated. cancers-14-02216-t004_Table 4 Table 4 Kruskal–Wallis test for the normalized relative expression of the four microRNAs along mast cell count. Normalized Relative Expression (−ΔCt) p Value miR-466l-3p 0.408 miR-466k 0.999 miR-223-3p 0.035 miR-125b-5p 0.024 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Kombe A.J.K. Li B. Zahid A. Mengist H.M. Bounda G.A. Zhou Y. Jin T. Epidemiology and Burden of Human Papillomavirus and Related Diseases, Molecular Pathogenesis, and Vaccine Evaluation Front. Public Health 2020 8 552028 10.3389/fpubh.2020.552028 33553082 2. Estevao D. Costa N.R. da Costa R.M.G. Medeiros R. Hallmarks of HPV carcinogenesis: The role of E6, E7 and E5 oncoproteins in cellular malignancy Biochim. Biophys. Acta Gene Regul. Mech. 2019 1862 153 162 10.1016/j.bbagrm.2019.01.001 30707946 3. Boccardo E. Lepique A.P. Villa L.L. The role of inflammation in HPV carcinogenesis Carcinogenesis 2010 31 1905 1912 10.1093/carcin/bgq176 20819779 4. Hemmat N. Baghi H.B. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091715 polymers-14-01715 Article Polymer Coating Integrity, Thrombogenicity and Computational Fluid Dynamics Analysis of Provisional Stenting Technique in the Left Main Bifurcation Setting: Insights from an In-Vitro Model https://orcid.org/0000-0001-5459-9125 Milewski Marek 1 Ng Chen Koon Jaryl 2 https://orcid.org/0000-0001-8564-7257 Gąsior Pawel 1 Lian Shaoliang Shawn 3 Qian Su Xiao 4 https://orcid.org/0000-0003-3365-6383 Lu Shengjie 2 Foin Nicolas 25 Kedhi Elvin 16 https://orcid.org/0000-0002-3681-5207 Wojakowski Wojciech 1 https://orcid.org/0000-0001-6134-5596 Ang Hui Ying 235* Chang Fuh-Yu Academic Editor 1 Division of Cardiology and Structural Heart Diseases, Medical University of Silesia in Katowice, 40-635 Katowice, Poland; marek.milewski92@gmail.com (M.M.); p.m.gasior@gmail.com (P.G.); ekedhi@yahoo.com (E.K.); wojtek.wojakowski@gmail.com (W.W.) 2 National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore 169609, Singapore; jaryl.ng.chen.koon@gmail.com (C.K.J.N.); lu.shengjie@nhcs.com.sg (S.L.); nicolas.foin@gmail.com (N.F.) 3 Department of Biomedical Engineering, National University of Singapore, Singapore 119077, Singapore; lianshaoliang@u.nus.edu 4 Division of Chemical and Biomolecular Engineering, Nanyang Technological University, Singapore 637459, Singapore; suxiaoqian@gmail.com 5 Duke-NUS Medical School, Singapore 169857, Singapore 6 Erasmus Hospital, Université libre de Bruxelles (ULB), 1070 Brussels, Belgium * Correspondence: anghuiyingg@gmail.com; Tel.: +65-6704-2343; Fax: +65-6704-2210 22 4 2022 5 2022 14 9 171529 3 2022 20 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Currently, the provisional stenting technique is the gold standard in revascularization of lesions located in the left main (LM) bifurcation. The benefit of the routine kissing balloon technique (KBI) in bifurcation lesions is still debated, particularly following the single stent treatment. We compared the latest-generation drug-eluting stent (DES) with no side branch (SB) dilatation “keep it open” technique (KIO) vs. KBI technique vs. bifurcation dedicated drug-eluting stent (BD-DES) implantation. In vitro testing was performed under a static condition in bifurcation silicone vessel models. All the devices were implanted in accordance with the manufacturers’ recommendations. As a result, computational fluid dynamics (CFD) analysis demonstrated a statistically higher area of high shear rate in the KIO group when compared to KBI. Likewise, the maximal shear rate was higher in number in the KIO group. Floating strut count based on the OCT imaging was significantly higher in KIO than in KBI and BD-DES. Furthermore, according to OTC analysis, the thrombus area was numerically higher in both KIO and KBI than in the BD-DES. Scanning electron microscopy (SEM) analysis shows the highest degree of strut coating damage in the KBI group. This model demonstrated significant differences in CFD analysis at SB ostia with and without KBI optimization in the LM setting. The adoption of KBI was related to a meaningful reduction of flow disturbances in conventional DES and achieved results similar to BD-DES. polymers damage drug-eluting stent side branch ostia thrombogenicity bifurcation lesions ==== Body pmc1. Introduction Overall, 15–20% of all percutaneous coronary interventions (PCI) take place in bifurcation lesions [1]. They predispose to recurrent, unfavorable complications like in-stent restenosis (ISR) and, in particular, stent thrombosis (ST), which could lead to increased mortality. The clinical data shows that about 23% of all ST occurs in lesions located in bifurcations [2]. Therefore, the optimization of the bifurcation PCI technique is a subject of bench studies and clinical trials. The challenge of performing interventions in bifurcation lesions is associated with their morphological complexity. Furthermore, the diameter differences between the distal and proximal vessel and side branch (SB) require the proper implantation technique to reach the suitable apposition of the drug-eluting stent (DES). It consists of proximal optimization (POT) and SB optimization. Moreover, lesions located in bifurcation are susceptible to lagging the arterial healing after PCI. Better vascular healing occurs in the newest generation of DES, so therefore they should be considered as the preferred stent choice [3]. In first-generation durable-polymer DES (DP-DES), we can observe a lower number of restenosis with a higher amount of ST than in bare-metal stents (BMS) [4]. The introduction of second-generation DP-DES resulted in lower amounts of restenosis and ST [5]. However, very late ST and neoatherosclerosis were still observed; this is probably due to vessel reaction to the presence of durable polymer [6]. To reduce this potential adverse outcome, a biodegradable polymer coated platform (BP-DES) was developed. Due to the hydrolysis of the ester bonds, their coating degrades into lactic or glycolic acids, which–as a result of tricarboxylic acid cycle–slowly degrades into carbon dioxides and water, secreted by the respiratory system [7]. BP-DES were developed to obtain the antiproliferative properties of DP-DES and finally become a BMS. However, the large meta-analysis showed that there were no significant differences in long-term outcomes in DP-DES and BP-DES [8]. Both platforms have similar safety and efficacy [9]. The latest data from large trials demonstrated that in low- and intermediate-risk patients, the results of stenting the left main coronary artery (LMCA) are comparable to coronary artery bypass grafting [10,11,12]. The huge part of the left ventricular myocardium is supplied by the LMCA, so the atherosclerotic stenosis in LMCA is related to meaningful myocardial danger. Furthermore, the suboptimal stenting technique used in the LMCA interventions translates into a high incidence of adverse events [13]. One of the open questions is whether the routine stent strut opening with kissing-balloon inflation (KBI) in SB is necessary for the absence of SB stenosis. Recently published data from the randomized EBC MAIN study demonstrated similar outcomes in patients treated with a provisional stenting technique implementing a single stent usage, compared to patients treated with a more complicated two-stent strategy [14]. Therefore, it seems that provisional stenting should remain as the gold standard in most LMCA true bifurcation procedures. Nevertheless, the ideal treatment strategy for SB protection remains unclear and challenging. The potential benefits of KBI compared with no the SB dilatation “keep it open” method (KIO) in bifurcation lesions is still debated, particularly following the single stent treatment [15,16]. It is also necessary to make out the hemodynamic parameters inside bifurcation because every disruption of physiological flow could increase the inflammation degree and promote plaque progression. The prothrombotic environment occurs as a result of protein adhesion, and blood platelets activation and aggregation close to the vessel wall [17,18,19]. Furthermore, the struts that overhang at the ostium of the SB are probably a relevant point for thrombus formation, and therefore could lead to ST [20]. Benchtop flow loop systems set by various groups showed the advantages and reproducibility of this method to rate different stents and stenting methods. It provides ex vivo data on the impact of different stents design, strut thickness, and implantation techniques on acute thrombogenicity evaluation. Benchtop tests also allow to evaluate the impact of flow disruption following device implantation, and potentially could facilitate development of new stent platforms. In addition, it could theoretically improve the selection of specific implantation techniques in individual lesion subsets in order to reduce the rate of complication following angioplasty [21,22,23,24,25]. In this investigation, we wanted to liken the thrombogenicity, flow disturbances, and polymer coating damages at the ostium of SB after new-generation DES implantation with KBI and KIO approach, versus bifurcation dedicated stent (BD-DES) with the usage of an in-vitro bifurcation model. 2. Materials and Methods 2.1. Device Description In this experiment, we used the latest-generation conventional DES (Xience Sierra, Abbott, Santa Clara, CA, USA) and BD-DES (Bioss LIM C, Balton, Warsaw, Poland). Each tested platform is accessible commercially in countries with CE marks. The newest generation Xience Sierra is an everolimus-eluting stent from the widely used Xience family. Xience Sierra was designed as a cobalt-chromium (Co-Cr) platform, with the strut thickness equal to 81 µm. Everolimus is mixed with undegradable acrylic and fluoropolymers. Xience Sierra belongs to a durable polymer DES group, in which the polymer coating stays permanently on the stent surface after the release of the drug [26]. The polymer coating consists of acrylic and fluoropolymers (vinylidene-fluoridehexafluoropropylene copolymer). Furthermore, the Co-Cr platform is used in the BD-DES, Bioss LIM C, and it has a strut thickness equal to 70 μm. It is a sirolimus-eluting stent, where the drug is eluted from a biodegradable coating made up of glycolic acids copolymer and lactic. Degradation of the polymer lasts eight weeks [27]. The device is delivered on a quick-replacement catheter with a semi-compliant (SC) balloon. The BD-DES is made of two major parts, both with various diameters. To provide the physiological compatibility and correct flow conditions by minimizing the flow disturbances–as all struts are well-apposed (WA) to the wall of the vessel–the ratio between the diameter of proximal and distal part is between 1.15 to 1.3. The platform consists of a central region with two connecting struts, sized 2.0–2.4 mm. For each platform, the polymer coating is applied by manufacturers during the production phase, with the usage of their proprietary technologies. Specifically for this experiment, all platforms were bought from particular manufacturers. 2.2. Deployment of the Stents In this study, we compared BD-DES, Bioss Lim C (4.25–3.50 × 25 mm, n = 5), and Xience Sierra (3.5 × 24 mm) implanted with two techniques: KIO (n = 5) and KBI (n = 5). We implanted all stents into a Y-shaped silicone model of a bifurcation (diameters: proximal segment = 5.5 mm, distal segment = 3.5 mm and SB = 3.5 mm, fabrics: Shore 40A Silicone, the angle between main branch (MB) and SB equals 90°) (Figure 1). According to clinical measurements of the angle between the left anterior descending artery (LAD) and circumflex artery (Cx), the angle between the MB and SB in the model was similar to clinical conditions [28,29]. The angles between branches and the proximal part of the model were set at 135°. Stents were firstly implanted at the pressure specified by the manufacturers to reach intentional stent diameter. Then, to provide proper apposition of the struts in the proximal region of the bifurcation model, the POT was performed with a 5.5 mm SC balloon inflated to 14 atm (5.78 mm according to the compliance chart). Subsequently, in the KBI group, two 3.5 mm non-compliant (NC) balloons were inflated to nominal pressure in both the distal MB and SB followed by a final POT using a 5.5 mm SC balloon inflated to 14 atm. In all groups, OCT pullback (C7x OCT Imaging System, LightLab Imaging Inc., Westford, MA, USA) was obtained just before blood perfusion. The speed of pullback was 10.0 mm/s, with a length of pullback equal 54.0 mm (the equivalent of 540 frames) and a resolution of 15 µm. In Figure 2, there is a flow chart that describes the study plan. 2.3. Flow Perfusion To perfuse fresh porcine blood with the addition of 10% anticoagulant (consists of acid-citrate-dextrose) from the reservoir to implanted stent and back, a special peristaltic pump (Minipuls3, Gibson, USA) was used. The pump provided a continuous flow rate of 200 mL/min for one hour. The perfusion was performed following previous studies, and was similar to the coronary arteries’ blood flow [21]. During perfusion, porcine blood was heated to the physiological temperature of 37 °C. To eliminate remaining blood before performing OCT imaging, all models were rinsed with Tyrode’s dilution after 60 min. Following OCT examination, all stents were gently pulled out from the bifurcation model and examined with SEM imaging analysis. We took extra care during removal to minimalize any contact with in-stent clots and to avoid any loss. 2.4. OCT Analysis Before the blood perfusion, we performed the OCT imaging of the proximal region to measure important dimensions. The ratio between the maximal and minimal diameter of each analyzed OCT frame (Dmax/Dmin at 1 mm intervals, every ten frames with a pullback speed of 10 mm/s), standardized for the length of the stent, gives the elliptical index of the proximal part [30]. To estimate the area of thrombus in the bifurcation region, the OCT cross-section analysis was performed for each frame. All struts in the opening angle of the SB were described as floating struts. The struts that protruded into the lumen of the vessel at a distance greater than the thickness of the strut were described as malapposed (MA) [31]. For the region of bifurcation ostium, the quantity and percentage of WA, MA, and floating struts were calculated. To compute the area of the thrombus for each sample, we selected and averaged three OCT frames with the most extensive thrombus area in the bifurcation region. A qualified operator manually performed all OCT measurements. 2.5. Computational Fluid Dynamics To parse the flow patterns and shear rate, the CFD was performed based on a previously established method [20]. The methodology uses 2D longitudinal images from OCT pullback with boundary conditions based on experimental flow to quantify shear rate within the bifurcation model. It allowed identifying segments of the struts with a greater flow disturbance risk caused by floating struts and MA. Based on OCT images, the 2D longitudinal geometries of stented models were recreated. Subsequently, they were meshed and then, with the usage of fluid computational software (Fluent, ANSYS), simulated with flow conditions similar to experimental conditions. A quadrilateral dominant mesh was assigned to the models with a maximum element size of 0.1 mm, with finer mesh along the boundaries of the vessel wall as well as stent struts to capture more detail on the shear rates near vessel walls and stent strut surfaces. A mesh convergence study was done to ensure that further refinement of element size would not greatly change the centre-line flow velocity of the model, and hence accuracy of the shear rate prediction. There were about 95,000 elements per model. Boundaries of the model were created as inflexible with the no-slip condition. Blood was modeled as Newtonian, incompressible liquid. The density of blood was equal to 1060 kg/m3 and stickiness of 0.0035 Pa.S [32]. At the entry of the bifurcation model, a boundary condition of the flow speed of 0.14 m/s was implemented; this was similar to the experimental flow rate equal to 200 mL/min. The outlets in the model were assigned as zero pressure [33]. We obtained the high shear rate area (>1000 s−1) physiologically; in regular human arteries the flow rate falls within the scope of 100–1000 s−1) [34]. Additionally, we obtained the maximum shear rate from this experiment. 2.6. Drug Coating Integrity All stents, after performing the blood flow perfusion, were softly pulled out from the bifurcation model with the usage of two guide wires. Before that, the models were pressed tenderly at the proximal and distal parts so as to simplify the process and avoid causing any distortion to the main part of the stent. To pull out the stent as delicately as possible, a tweezer was used at one end of the model. The same protocol of removal was used for all stents. In this part of the study, we did not include the proximal and distal ends of the stent. With the usage of SEM, all stents were then examined. All events of strut coating damage were counted and then qualified into four groups in accordance with damage grade. Qualification into each group was performed in accordance with a formerly published study [35]. 2.7. Statistical Analysis SigmaStat software (version 4.0; Systat Software, San Jose, CA, USA) was used to make the statistical analysis. Results are shown as median (interquartile range). Values were assumed to be nonparametric. All values were analyzed with Kruskal-Wallis one-way ANOVA because of a low count of samples. The Dunn’s test that compared KIO, KBI, and BD-DES groups was performed post hoc when the ANOVA was significant. The differences were only considered meaningful if the received p-value was <0.05,. 3. Results 3.1. Optical Coherence Tomography In Table 1 we presented a recapitulation of the OCT analysis. The percent of WA struts was meaningly higher in the KBI and BD-DES than in KIO (respectively: 93.29 (88.06–93.39)% vs. 94.29 (88.46–98.81)% vs. 78.64 (76.07–85.28)%, p = 0.28). The percentage of floating struts based on the OCT imaging was significantly higher in KIO than in KBI and BD-DES (KIO: 15.93 (12.99–17.65) vs. KBI: 3.84 (3.46–5.06) vs. BD-DES: 0 (0–2.86), p = 0.33). Additionally, the percentage of MA struts was significantly lower in the BD-DES. Thrombus area was numerically lower in BD-DES when compared to the KBI and KIO group (KIO: 0.52 (0.17–0.65), KBI: 0.70 (0.15–1.16), BD-DES: 0 (0–0.09), p = 0.15). Representative OCT images, percentage of floating strut, and thrombus quantification are shown in Figure 3. 3.2. Computational Fluid Dynamics The high shear rate (>1000 s−1) area was, by the OTC images, statistically higher in the KIO group when compared to KBI (respectively: 0.12 mm2 (0.09–0.12) vs. 0.02 mm2 (0.01–0.023) p = 0.0133). Furthermore, maximal shear rate was higher in number in the KIO group (KIO: 2293 s−1 (2072–2365) vs. KBI: 1621 s−1 (1299–2230) vs. BD-DES: 1375 s−1 (1348–2068), p = 0.14). Images from CFD analysis and the areas of high and maximum shear rate are shown in Figure 4. 3.3. Drug Coating Integrity A recapitulation of the SEM analysis of drug coating integrity is presented in Table 2. We observed the most significant strut coating damage in the KBI group. BD-DES group displayed the lowest number of second category coating damages when compared to KBI (1 (0–3) vs. 21(18–21) p = 0.0026), third category when compared to KIO and KBI (0 (0–1) vs. 4 (3–12) p = 0.046 vs. 11 (7–12) p = 0.0103), and fourth category when compared to KBI (0 vs. 20 (16–21), p = 0.0024). Representative SEM images of drug coating damage are presented in Figure 5. 4. Discussion In this study, with the usage of the in-vitro bifurcation model, we compared the thrombogenicity, flow disturbances, and polymer coating damages at the ostium of SB after new-generation DES implantation, using KBI and KIO techniques versus BD-DES. The main outcomes of this study are that (1) in KBI and BD-DES groups there was a significantly higher percentage of well-apposed struts than KIO, (2) BD-DES had a significantly lowest percentage of MA struts, (3) in BD-DES there was a numerically lower thrombus area, (4) KBI group have statistically more coating damage, and (5) KIO is associated with the statistically higher area of high shear rate. Interventions in bifurcation lesions are difficult to perform due to their morphological complexity and are related to a higher number of unfavorable clinical events like ST and ISR [36]. The difference between the diameter of the proximal and distal part of the vessel requires stent sizing according to the distal diameter, and optimalization of the proximal part with the usage of the larger balloon to achieve proper expansion. Furthermore, delayed arterial healing occurs in bifurcation lesions [37]. According to recently published data, provisional stenting is recommended as a golden standard in treating LM bifurcations since it brings favorable outcomes when compared to the two-stent technique. A randomized EBC MAIN study shows that in LM bifurcation stenosis–which requires angioplasty–a lower number of major adverse cardiac events occurred in provisional stenting than in dual stent strategy [14]. However, it is vague when it comes to outlining the ideal strategy for protecting SB and the benefits of performing KBI compared with KIO are still debated. In vitro bench testing is necessary to comprehend the hemodynamic parameters inside the bifurcation, which is important to evaluate the potential benefits of the KBI and KIO method compared to BD-DES. The overhanging struts in SB ostium could be the main spot for thrombus formation and could lead to ST [20]. A previously published study with the same bifurcation model showed a correlation between struts protruding into SB ostium and increased thrombogenicity [23]. The study shows that the floating struts have an influence on thrombus formation. Likewise, according to previous publications, most ST events in patients who underwent PCI procedure were associated with morphological abnormalities in OCT imaging, such as strut malposition and underexpansion. Furthermore, MA was the main cause of late and very late ST [38,39]. In our study, the KIO group had a significantly lower percentage of WA struts and a significantly higher percentage of floating struts than KBI and BD-DES, with a meaningly lower percentage of MA struts in BD-DES. This may explain that the area of thrombus was numerically lower in the BD-DES than in the KIO and KBI. Plaque formation and neointimal hyperplasia are promoted by any disturbance of the laminar flow in the vessel [40]. Abnormal flow patterns could lead to ST [41], even with inhibition of neointimal growth by the antiproliferative drug elution from the stent surface. Previous studies showed that high share rate regions related to plaque erosion and ruptures could lead to aggregation of platelets and near the wall of the artery [42]. In the LMCA bifurcation, the proximal part of the stent is often overexpanded [43]. The forces that act on the stent struts could lead to polymer coating damage, and as a result deformation of the platform; probably a smaller thrombus area in the BD-DES relates to the stent architecture that composes of just two connecting struts at the ostium of SB. After balloon inflation, the “self-positioning” of this part occurs so that the side branch can “stay open” [44]. In the BD-DES, two connecting struts reduce a metal-to-artery ratio, but accordingly to a previously published study, it does not impact the outcomes in the LMCA PCI [45]. The presence of struts at SB in bifurcation lesions might disrupt blood flow and increase the high shear rate regions. At least three different mechanisms of platelet aggregation connected to shear rate have been identified [46]: a low shear rate that normally occurs in veins and arteries, a high shear rate that can be found in microcirculation and in moderately narrowed arteries, and the maximal shear rate in severe arterial stenosis. The occurrence of high shear rate areas is related with the matrix metalloproteinase activation, which leads to phenotypic conversion into features of vulnerability of the plaque [47,48]. As a result, it is linked with regression of fibrous and fibrofatty tissues and progression of expansive remodeling, calcium, and the plaque necrotic core. Additionally, in the high shear rate areas in the proximal parts of lesions, the features of a high-risk plaque tended to be localized [49]. According to these observations, recent studies show that the high shear rate area in the proximal part of lesions were related with a higher risk of acute coronary syndromes [50,51]. In this examination, CFD analysis showed a statistically higher area of high shear rate in KIO than in KBI and BD-DES. Furthermore, the maximum shear rate was the highest in number in KIO; this might explain the numerically higher thrombus area in KIO based on IF and OCT analysis. Previous studies using CFD have also drawn associations between larger flow disturbance with increased acute thrombogenicity [19,32], thus corroborating with the results derived from the CFD models and experimental setup in this study. Recently, the European Bifurcation Club consensus recognized the value of in vitro bench testing and computational simulations in bifurcation interventions that help improve knowledge within this field, indicating potential value of this platform to answer other clinically relevant questions using benchtop setup and CFD modelling [52]. Moreover, the drug coating damage or detachment could increase the inflammation with neointimal reaction and lead thrombogenic factor to ST [36,53]. Pieces of polymer peeled from DES surface may cause coronary microembolism and inflammation. In our SEM analysis, we observed the smallest degree of coating damages in BD-DES and the highest in KBI. This could be attributed to the unique BIOSS LIM C architecture, with the proximal part of the stent larger (by 0.75 mm) than in other, conventional DES. This entails the lower overexpansion of the stent and consequently reduces strut coating damage. Furthermore, KBI technique is associated with increased polymer coating damages, especially in the ostial regions of devices due to greater overexpansion and forces exerted on the struts when compared to KIO. This might potentially impact drug elution process in the regions with damaged polymer, and subsequently lead to uneven drug distribution in the arterial wall, which could theoretically increase the risk of ISR [43]. Therefore, in vitro studies are crucial in order to improve the polymer coating technology, which could translate into reduction of device-related adverse events rates 5. Conclusions This study shows that bench testing of bifurcation in vitro model is a useful instrument and could help improve bifurcation angioplasty. This model showed differences in thrombogenicity and mechanical properties at SB ostium when two different ways of protecting SB were used. It suggested the advantages of the BD-DES and KBI method compared with KIO. The adoption of KBI was related to a meaningful reduction of flow disturbances in conventional DES, and achieved results comparable to the BD-DES. This study supports previous publications using this model, which provided an extensive in vitro evaluation of the different stent types and implantation techniques performed in the bifurcation model. In the future, this model can be useful to evaluate different devices, implantation techniques, and various bifurcation geometries [54]. 6. Limitations This study has some limitations typical of static in vitro benchtop experiments. The results of this study were obtained in a non-stenotic vascular model, which does not reflect the atherosclerotic lesion in a clinical setting. Furthermore, the simplified left main model does not completely show the human tissues’ response to stenting and other complexities of bifurcation anatomies. Hence, the data should be interpreted carefully and should be understood as an approximation of the real behavior of the stenting artery response while overstretching [55]. In our model, the perfusion was constant instead of a pulsating flow that occurs physiologically, which could have had an influence on the study results. Nevertheless, available literature shows that usage of constant flow was acceptable as thrombus formation is more affected by the shear generated than flow pulsatility [56]. This benchtop model only shows acute thrombogenicity of stent deployment [21,23], and in the future animal testing should be conducted to understand completely the long-term stent thrombogenicity. Although 2D models are incapable of completely capturing differences between models with various stent construction, it has been shown previously that they demonstrated shear rate trends consistent with experimentally thrombogenicity outcomes [19,57]. Future improvements can include creating 3D CFD models; this could help to reconstruct patient-specific coronary anatomy [58] and complex strut configurations–even for models with more than one stent [59]–for more precise hemodynamic results. Author Contributions Conceptualization, M.M., S.L., P.G., E.K., W.W. and H.Y.A.; Data curation, S.S.L., C.K.J.N. and S.X.Q.; Formal analysis, H.Y.A.; Investigation S.X.Q., C.K.J.N. and W.W.; Methodology, M.M., S.L., P.G., C.K.J.N. and N.F.; Project administration, H.Y.A.; Supervision, E.K. and W.W.; Validation, N.F.; Writing—original draft, M.M., P.G., S.L., H.Y.A. All authors have read and agreed to the published version of the manuscript. Funding The devices were purchased directly from the producers with the usage of the Division of Cardiology and Structural Heart Diseases, Medical University of Silesia in Katowice statutory funds. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Optical images of platforms implanted into a model of bifurcation. Figure 2 Flowchart of benchtop study using in vitro bifurcation model for the three groups. Figure 3 (a) Representative OCT images of thrombus formation on the stents at 60 mins; (b) percentage of floating struts and (c) average thrombus area based on OCT quantification, n = 5. Figure 4 (a) Representative CFD images and (b) area of high shear (>1000 s−1) and maximum shear rate quantification of the three groups (n = 5). Figure 5 Representative SEM images of drug coating damage. The image was taken at ×500 magnification and scale bar = 50 μm. polymers-14-01715-t001_Table 1 Table 1 OCT analysis of stents overexpansion (n = 5). KIO KBI BD-DES p Value OCT Proximal Diameter (mm) Min 5.40 (5.23–5.58) 5.36 (5.12–5.52) 5.33 (5.23–5.37) >0.05 Mean 5.55 (5.42–5.69) 5.52 (5.30–5.66) 5.50 (5.40–5.53) >0.05 Max 5.75 (5.60–5.79) 5.65 (5.41–5.75) 5.59 (5.55–5.64) >0.05 OCT Proximal Lumen Area (mm2) Area 24.22 (23.09–25.4) 23.93 (22.06–25.16) 23.81 (22.92–24.00) >0.05 OCT Proximal Eccentricity Index EI 1.06 (1.05–1.06) 1.05 (1.04–1.06) 1.05 (1.04–1.05) >0.05 OCT Strut Analysis WA (%) 78.6 (76.1–85.3) 93.3 (88.1–93.4) 94.3 (88.5–98.8) >0.05 Floating (%) 15.9 (13.0–17.7) 3.8 (3.5–5.1) 0.0 (0.0–2.7) >0.05 MA (%) 4.9 (4.7–5.3) 3.3 (1.6–6.0) 2.7 (1.2–2.9) >0.05 polymers-14-01715-t002_Table 2 Table 2 SEM analysis of polymer coating damage (n = 5). 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==== Front Plants (Basel) Plants (Basel) plants Plants 2223-7747 MDPI 10.3390/plants11091186 plants-11-01186 Article Native Hyperaccumulator Plants with Differential Phytoremediation Potential in an Artisanal Gold Mine of the Ecuadorian Amazon https://orcid.org/0000-0003-2307-585X Chamba-Eras Irene 1* https://orcid.org/0000-0001-6870-9727 Griffith Daniel M. 2 https://orcid.org/0000-0003-1006-1096 Kalinhoff Carolina 2 https://orcid.org/0000-0002-8839-7457 Ramírez Jorge 1 https://orcid.org/0000-0002-0198-5732 Gázquez Manuel Jesús 3 Gómez Luis Academic Editor Spagnuolo Valeria Academic Editor Capozzi Fiore Academic Editor 1 Departamento de Química, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja 1101608, Ecuador; jyramirez@utpl.edu.ec 2 Departamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja 1101608, Ecuador; dgriffith@utpl.edu.ec (D.M.G.); cgkalinhoff@utpl.edu.ec (C.K.) 3 Departamento de Física Aplicada, Escuela Superior de Ingeniería, Universidad de Cádiz, Campus de Puerto Real avenida, República Saharahui s/n, 11510 Puerto Real, Spain; manueljesus.gazquez@uca.es * Correspondence: idchamba@utpl.edu.ec; Tel.: +593-984832197 28 4 2022 5 2022 11 9 118621 2 2022 21 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In tropical forests of southern Ecuador, artisanal gold mining releases heavy metals that become xenobiotic with indefinite circulation and eventual bioaccumulation. Restoration and rehabilitation of degraded mining sites represent a major ecological, technological and economic issue. In this study, we estimate the capacity of two native woody plants to accumulate cadmium (Cd), lead (Pb), zinc (Zn) and mercury (Hg), with the goal of developing effective strategies for phytoremediation of mining sites. Individuals of Erato polymnioides and Miconia sp., as well as their rhizospheric soils, were sampled from a natural zone (NZ) of montane cloud forest, used as a control, and a polluted zone (PZ) subjected to active gold mining. Concentrations of the four heavy metals were analyzed using atomic absorption spectrophotometry. Cd, Zn and Hg concentrations were higher in soils of PZ than NZ. Bioaccumulation (BCF) and translocation factors (TF) showed that Miconia sp. has potential for Cd and Zn phytostabilization, E. polymnioides has potential for Cd and Zn phytoextraction, and both species have potential for Hg phytoextraction. Despite the low productivity of these species, their adaptability to the edaphoclimatic conditions of the region and the possibility of using amendments to increase their biomass could compensate for the effectiveness of these species in reclaiming soils contaminated by mining. phytoremediation heavy metals Erato polymnioides Miconia sp. bioaccumulation and translocation factors Vice-rectorate of Research of the Univer-sidad Técnica Particular de Loja (UTPL)This research was funded by a scholarship from the Vice-rectorate of Research of the Univer-sidad Técnica Particular de Loja (UTPL). ==== Body pmc1. Introduction Given their capacity to bioaccumulate in the food chain, heavy metals are of great concern due to their long-term effects on human health, especially in developing countries [1,2]. Mining and metal milling operations are recognized as among the principal sources of heavy metal contamination of soil, water and air [3,4]. Artisanal mining, which refers to informal mining activities carried out with low technology and minimal machinery, is particularly destructive because it is so widespread. Occurring in approximately 80 countries, mainly in the Global South, artisanal mining is practiced by an estimated 100 million people and supplies roughly 20% of the world’s minerals and metals [5,6]. Increasing public awareness of the impacts of artisanal mining on ecosystems and human health has stimulated interest in innovative technologies to remediate the contaminated wastelands that result from this form of mining. Artisanal mining is widespread in South America, occurring primarily in Bolivia, Venezuela, Colombia, Brazil, Peru and Ecuador. In the Andean region, it is often carried out in previously undisturbed ecosystems such as paramos, cloud forests and rain forests, where it represents a threat to the flora, fauna and local communities living downstream [7]. The most common technique used for small-scale gold mining (SSGM) is amalgamation, which has dramatically increased the release of mercury into the environment over the last fifty years [8]. As a result, mercury has become a major pollutant of surface and groundwater in many localities [9]. In the southern Ecuadorian Amazon, heavy metals like Pb and Zn that are naturally present in polysulfides and metamorphic rocks are often released into the environment during both SSGM and large-scale multi-metallic exploitation [10]. As a co-product of Zn extractive metallurgy, Cd has become another major contaminant. The magnitude and complexity of the pollution caused by these xenobiotic elements are due to their indefinite circulation and eventual bioaccumulation [11]. The severe impacts that heavy metals render on human health and the environment underscore the need for methods and tools to collect and remove these contaminants from polluted soils. Physical methods for soil remediation include soil substitution, thermal desorption, membrane filtration and ion exchange, while chemical methods include chemical precipitation, chemical leaching, chemical fixation and immobilization [12]. However, both physical and chemical methods are generally costly, cause irreversible changes in the soil, and result in secondary contamination [13,14]. In contrast, biological methods for soil remediation, known collectively as bioremediation, generally consist of cost-effective and environmentally sustainable processes that detoxify heavy metals in contaminated soils and water bodies [15,16] through the use of bacteria, fungi, plants or a combination of these organisms [12]. Phytoremediation is a type of bioremediation that utilizes plants to reduce the toxic effects of heavy metals in the environment [17]. As an emerging alternative technology to conventional remediation approaches, phytoremediation offers the advantage of being economically and ecologically sustainable [18,19,20]. Among the phytoremediation technologies applicable to soils contaminated with heavy metals, two of the most commonly used are phytoextraction and phytostabilization [21]. In phytoextraction, rapidly growing plants that tolerate high concentrations of metals in their aerial tissues are used. In phytostabilization, plants that possess a strong ability to reduce metal mobility in the rhizosphere or roots are used [22,23]. Phytoextraction is considered a permanent solution for the removal of heavy metals (assuming it includes the final disposal of aerial biomass), unlike phytostabilization, which retains metals underground [21]. High aboveground biomass production, high tolerance, and the ability to extract, transfer and accumulate metals are crucial for the success of phytoextraction. In this regard, accumulator plants with high biomass production, known as hyperaccumulator plants, are the most suitable for phytoremediation. Hyperaccumulator plants can exceed 100 or times more the normal concentrations of accumulated metals or metalloids in their aboveground biomass without showing signs of phytotoxicity, although in some cases they can have low productivity [24]. For this reason, it is generally accepted that species used in phytoremediation with high biomass production capacity can compensate for their relatively low metal accumulation capacity [25]. Phytoextraction potential can be estimated by calculation of the bioconcentration factor (also known as the bioaccumulation factor or biological absorption coefficient) and translocation factor. The bioconcentration factor (BCF) is defined as the ratio of the total concentration of an element in harvested plant tissue to its concentration in the soil where the plant was growing, and the translocation factor (TF) is defined as the ratio of the total concentration of an element in the aerial parts of the plant to its concentration in the roots [22]. In Ecuador, the exploitation of minerals through both large-scale and artisanal mining has increased substantially in recent decades, with serious repercussions for the environment and human health. Conventional remediation techniques have been shown to be effective, but their high construction and operation costs make these methods untenable in regions with limited technological and financial resources. Therefore, it is necessary to develop new, cost-efficient alternatives to clean contaminated mining sites and improve the health of the affected population and environment. Research has shown that certain plants that grow naturally in mine tailings can be used for in situ soil decontamination [26,27,28]. However, there are few studies that have evaluated the phytoremediation potential of native species that grow spontaneously in mining areas of Ecuador. The main objective of this study was to evaluate the potential for heavy metal accumulation of two plant species that grow abundantly around the gold mines of the Chinapintza Mining District in southern Ecuador: Erato polymnioides, which was determined to be a hyperaccumulator for mercury (Hg) in this zone [6], and Miconia sp., which grows spontaneously in areas heavily disturbed by mining. Specifically, we: (1) determined the concentration of heavy metals in vegetative organs and rhizospheric soils; (2) calculated the bioaccumulation and translocation factors in order to propose phytoremediation strategies for the region; and (3) estimated the productivity of each species. 2. Results 2.1. Metal Concentration in Soils Mean (± SD) concentrations of the four heavy metals in soil are shown in Table 1. The relative error, which represents the standard deviation relative to the mean [29], is related to the accumulation capacity of these heavy metals for the plants analyzed. As expected for mining areas, metal concentrations in soils were highly heterogeneous, especially in the polluted zone (PZ), where the relative errors of the means ranged from 12–60%, reaching a maximum for Hg in the rhizospheric soil of Miconia sp. Heterogeneity was lower in the natural zone (NZ), where relative errors of the means ranged primarily between 20–40%, with the exception of Cd and Zn for E. polymnioides. Plants of Miconia sp. were exposed to mean soil concentrations of Cd, Zn and Hg that were 2.7 to 4.6 times higher in PZ than those in NZ. Soils around E. polymnioides exhibited concentrations of the same elements that were 3.5 to 12.3 times higher in PZ relative to NZ. The exception in both cases was Pb, which varied little between zones. Soils in NZ exhibited lower concentrations of Cd, Pb and Hg for E. polymnioides than Miconia sp., while Zn concentration for E. polymnioides was double that observed for Miconia sp. The rhizospheric soils of E. polymnioides in PZ showed substantially higher concentrations of all metals than those of Miconia sp. except Pb. 2.2. Metal Concentration in Plants In Miconia sp. plants, all four metal concentrations were significantly higher in PZ than NZ, but none were significantly different between leaves, stems and roots (Figure 1; Supplementary Table S1). In E. polymnioides plants, the concentration of Cd and Hg was significantly higher in PZ than NZ. Hg was also significantly higher in leaves and roots than stems of this species, while Zn was significantly higher in leaves and stems than roots. Like Miconia sp., no differences in Cd were observed between plant parts in E. polymnioides, but the maximum concentrations reached in PZ were 55 and 40 mg kg−1 in roots and leaves, respectively. Hg reached a maximum value of 13 mg kg−1 in E. polymnioides roots, while Zn reached nearly 1000 mg kg−1 in this species’ leaves in PZ. The total weight and biomass distribution among plant parts of the two species be-tween the zones are shown in Table 2. For Miconia sp., total weight was 11 and 2 g in NZ and PZ, respectively. For E. polymnioides, total weight was 52 and 18 g in NZ and PZ, respectively. Biomass distribution between leaves, stems and roots of Miconia sp. ranged from 39–41, 30–45, and 10–30% in NZ, respectively. However, these percentages were different in PZ, where the biomass found in leaves constituted 60% of the total mass of the plant, whereas that of stems and roots dropped to 20–25% and 14–19%, respectively. In E. polymnioides, the relative contribution of each part was similar between the zones. The contribution of roots was between 10–30% in PZ and 5–10% in NZ. Stems showed values of 75–80% in NZ and 40–80% in PZ, while leaves represented between 15–35% in both zones. 2.3. Bioaccumulation and Translocation Factors For both species, the bioaccumulation factor (BCF) of Cd, Zn and Hg were greater than 1, with the exception of the BCF for stems (Table 3). In the case of Pb, the only BCF close to one (0.80) was that of E. polymnioides roots in PZ. The translocation factor (TF) of leaves/roots were greater than or very close to 1 for Cd, Zn and Hg in both species. For Miconia sp., BCF of Cd, Pb and Zn in PZ was higher in roots than stems and leaves, while BCF of Hg was higher in leaves (Table 3). The highest BCF value for this species was observed for Cd in roots in PZ (6.50). When comparing BCF between NZ and PZ, we observed that BCF of Cd, Pb, Zn and Hg in Miconia sp. leaves and stems changed very little, while that of roots doubled for all metals except Hg. TF of Miconia sp. leaves/roots of was higher than that of stems/roots, and a decrease in PZ relative to NZ was observed for the four metals evaluated. The highest TF value observed for this species was for Hg in leaves/roots (2.53). For Cd, Pb, and Zn, the highest TFs were 1.14, 0.75, and 1.00, respectively (Table 3). For E. polymnioides, higher values of BCF were also observed for Cd in PZ, with BCF in leaves (6.19) being very close to the BCF in roots (5.64). Only for Cd and Pb was an increase in BCF observed in PZ compared to NZ (Table 3). The values of BCF for Pb and Zn in E. polymnioides were generally higher than in Miconia sp. (Table 3). As in Miconia sp., TF leaves/roots were higher than TF stems/roots in E. polymnioides and, in general, TF values in NZ with respect to PZ increased or remained the same. The highest TF values observed for E. polymnioides were stems/roots (2.79) and leaves/roots (2.40) for Zn. For the metals Cd, Pb, and Hg, the highest TFs were 1.55, 0.53, and 1.27 leaves/roots TF, respectively (Table 3). 2.4. Tolerance Index (TI) of Plant Yield The tolerance index was evaluated based on the biomass yield of each species, projecting a planting of 10,000 plants per hectare. For Miconia sp. we estimated a total of 108,800 g plants ha−1 in NZ, and 19,600 g plants ha−1 in PZ. For E. polymnioides, we estimated 523,200 g plants in NZ and 186,300 g plants in PZ. The analysis yielded a TI of 0.18 for Miconia sp. and 0.36 for E. polymnioides. 3. Discussion Soils of both the natural and polluted zones contained high concentrations of the four metals evaluated according to the Ecuadorian Environmental Technical Standard for soils. According to this standard, allowable limits for Cd, Pb, Zn and Hg are 2, 100, 200 and 0.8 mg kg−1, respectively [30]. These limits were exceeded by all four metals in soils of the polluted zone and by Pb and Hg in soils of the natural zone. High concentrations in the substrates that support the native forest of the area could be a natural occurrence, given the existence of numerous natural polymetallic deposits throughout the region [31]. On the other hand, mining exploitation has occurred since pre-colonial times [32], so it is plausible that our reference ecosystem corresponds to a secondary forest established in a formerly mined area. Regardless of the reasons for the high Pb and Hg levels, the concentrations of Cd, Zn and Hg were substantially lower in soils of the natural area compared to the currently mined area. Soil concentrations of Cd and Zn detected in the PZ of our study were similar to those reported in soils contaminated by mining in the same area with values of 5 and 650 mg kg−1, respectively [33]. In contrast, Pb concentrations were roughly half (430–523 mg kg−1 in our study vs. 1000 mg kg−1 in [33]) and Hg concentrations were twice those reported previously from the area (6.1–10.2 mg kg−1 in our study vs. 4.8 mg kg−1 in [6]). It should be emphasized that the amalgamation process is used extensively to mine gold in the region (i.e., Zamora-Chinchipe), which generates an Au-Hg alloy that is then heated in open vessels to separate these metals, yielding volatilized Hg. Given that the region experiences high rainfall throughout much of the year, Hg eventually accumulates in the soil [8] and can move into river systems [34]. The variability found in Hg and the other metals was likely a result of the haphazard nature of mining operations in the polluted zone, as there is currently no specific waste collection or treatment plan for this area. Cd levels detected in Miconia sp. and E. polymnioides plant tissues were high in comparison with other studies of heavy metal contamination in plants growing in mining areas. For example, average Cd concentration in plants growing in the polluted zone varied between 20 and 40 mg kg−1, which is more than a magnitude higher than that found in Artemisia vulgaris (0.62 mg kg−1) from mining areas in Vietnam [35] and Datura inoxia (0.44 mg kg−1) from artisanal mines in Nigeria [36]. Regarding Hg, the mean concentrations detected in both species (2.0 and 7.0 mg kg−1) were similar to those observed for most dicots growing on acid mine tailings containing heavy metals in Niger [37]. They were also similar to values reported in forage plants from artisanal mining areas in Indonesia (9.90 mg kg−1) [38]. Contrary to Cd and Hg, Pb and Zn concentrations in the two study species were lower than those reported for plants from other mining areas. Stipa capensis growing on abandoned mine sites in Morocco accumulated 282.3 mg kg−1 Pb in its roots [38], while Miconia sp. and E. polymnioides from the same area of our study showed lower Pb values. In Miconia sp., Pb concentrations of 400, 100 and 150 mg kg−1 were observed in roots, stems and leaves, respectively, while in E. polymnioides, Pb was 200 mg kg−1 in leaves [33]. Similarly, we found Zn levels for Miconia sp. and E. polymnioides to vary between 50 to 1500 mg kg−1. These values are low compared to species growing spontaneously on extremely contaminated acid mine tailings in Spain, where Zn levels in stems varied from 94 mg kg−1 in Erica arborea to 3391 mg kg−1 in Coincya monensis [38]. A high Zn concentration of 2938 mg kg−1 was also reported in Reseda lutea plants growing near a Pb-Zn mine in Iran [35]. Thus, while Cd was high in the tissues of our study species, Hg was similar, and Pb and Zn were lower compared to other plants growing on mine sites. Bioaccumulation (BCF) and translocation factors (TF) showed that Miconia sp. possessed a high capacity for Cd and Zn phytostabilization, whereas E. polymnioides had high capacity for Cd and Zn phytoextraction. In Miconia sp., BCF indicated a high tolerance for Cd in the polluted zone with the potential to accumulate 6.5 times more of this metal in its roots than the immediate soil. Similarly, Miconia sp. accumulated 2.3 times more Zn in its roots than the soil in the polluted zone. The fact that TF was less than one in this zone indicating metal accumulation in the roots, in addition to the large increase in BCF from the natural to polluted zone in the roots relative to leaves and stems, supported classification of Miconia sp. as a potential phytostabilizer of Cd and Zn [33]. In contrast, E. polymnioides accumulated Cd to the same degree in roots and leaves in the polluted zone. This result, along with TF > 1 in both zones indicating metal accumulation in the aerial parts of the plant, supported classification of this species as a potential Cd phytoextractor. BCFs of Cd in E. polymnioides reported in previous studies from the same area were also greater than 1, with values of 1.10 and 1.68 for roots and leaves, respectively [28]. Notably, BCFs of Cd found in our study were high compared to those reported for other species such as Stipa tenacissima growing in Pb-Zn mining areas in Morocco (2.72) [35]. E. polymnioides also exhibited bioaccumulation of Zn in sufficient proportions and TF > 1 in both zones to be considered a phytoextractor of this metal. BCFs that we found for Zn were high relative to those for Artemisia herba alba in Morocco, which had a maximum of 1.69 [35], as well as E. polymnioides roots (1.64) and leaves (1.40) from the same polluted zone [34]. Evidence thus suggests a high potential of Miconia sp. for Cd and Zn phytostabilization and a high potential of E. polymnioides for Cd and Zn phytoextraction. Hg accumulation in plant roots is reported to be a defense mechanism that hinders phytoextraction of this highly toxic metal [39]. Low amounts of Hg in roots relative the surrounding soil [39] suggest a reticence on the part of some plants to absorb this metal. In this context, the search for native plants with the ability to bioaccumulate and transfer mercury to the stem has become a priority in phytoremediation efforts, especially in economically emerging countries suffering from Hg contamination. Our results confirm the ability of E. polymnioides to accumulate mercury in its roots as reported by [33]. Unlike that study, however, we also found BCF > 1 for leaves in the polluted zone, which, together with evidence for Hg accumulation in the leaves relative to the roots (TF > 1), suggests that E. polymnioides has potential for phytoextraction of this metal. The evidence for Miconia sp. as an effective candidate for Hg phytoextraction was even stronger, given a higher TF in leaves and thus greater efficiency in translocating Hg to its aerial organs than E. polymnioides. Further research conducted under controlled conditions (e.g., greenhouse) is necessary to assess how these attributes change between varying levels of contamination and at different growth stages of each species. Neither study species showed a capacity for phytostabilization or phytoextraction of lead. All BCFs of Pb were less than one and lower than those reported by previous studies, for example, BCF was 2.67 in A. herba alba [35] and 1.20 in Zea mays [40]. Moreover, the TF of both species was less than one in the natural and contaminated zones. According to the Ecuadorian Environmental Technical Standard for soils, Pb concentration in the study area was 3 to 5 times higher than the recommended limit. However, we only measured total concentrations and not bioavailable fractions. It is possible that much of the lead present in the soil cannot be taken up by plants. Lead’s low bioavailability limits its uptake from the soil, reducing the effectiveness of phytoextraction [21]. On the other hand, the higher Pb concentration in the roots of both species suggests the possibility of phytostabilization of the bioavailable fraction of the metal, especially in E. polymnioides, whose BCF of Pb in roots was 0.8. Studies are needed to determine how limited bioavailability affects the capacity of these species to phytostabilize and phytoextract Pb and other heavy metals. Accumulators are plant species that tolerate heavy metal concentrations above the limits established by national and international regulations and meet the criteria of both BCF and TF > 1. According to [41], the metal hyperaccumulation limits in plant shoots for Zn, Pb, Cd and Hg are 10,000, 1000, 100 ppm and 10 ppm, respectively. According to these criteria, E. polymnioides can provisionally be considered a hyperaccumulator of Hg, although thresholds for Hg need to be more clearly established. Currently, plants that concentrate more Hg in their stems have been defined as potential accumulators, but in most, BCF < 1 [42]. On the other hand, unlike hyperaccumulator plants, which concentrate large amounts of metals in their biomass, accumulator plants concentrate less but compensate with higher biomass production [42]. Thus, in addition to tolerating heavy metals, one of the most important conditions for phytoextraction is that the accumulator plant produces enough biomass to accumulate metals to the same degree as that of a hyperaccumulator plant [43]. To fully assess the phytoremediation capacity of a potential accumulator species, it is necessary to estimate its productivity through controlled experiments and field data to verify the overall heavy metal uptake. Despite the limitations of field sampling, this study contributes evidence that Miconia sp. and E. polymnioides have high potential for phytoremediation and could be incorporated into regional strategies for bioremediation of mining liabilities in Amazonian forests. In mining areas throughout the world, high values of Cd, Zn and Hg translocation have been reported for various species, indicating their potential use as phytoextractors. For example, TF for Cd of 1.69 and TF for Zn of 1.9 were observed in Euphorbia hyssopifolia and Pueraria montana in gold mining areas in Ghana, respectively [22,44]. In an artisanal mining area in Indonesia, TF for Hg of 0.84 was observed for a native Guava sp. [45], and in a mining area of southeast China, TF for Hg of 2.62 was observed for the native species Cyrtomium macrophyllum [46]. Although total plant weight and the relative weights of plant organs are generally not used as criteria to evaluate species’ capacity for phytoremediation, we consider these parameters important because they provide a clearer picture of how contaminants are distributed within the plant. The percent weight of leaves in Miconia sp. and of leaves and roots in E. polymnioides increased from NZ to PZ. This could be important to help determine the best phytoremediation strategies for metals with BCF > 1 in these organs, as was the case for Cd, Zn and Hg, because large proportional biomass coupled with large BCF of the organ implies greater accumulation of the metal in question and thus, in effect, more efficacious phytostabilization or phytoextraction. Total plant weight also allowed us to calculate the tolerance index (TI), which provided a rough indicator of the level of stress experienced by plants growing in the polluted zone relative to the natural zone. TI was substantially less than one for both study species, which suggests high levels of stress in the mining area. By comparison, TI values reported for grasses and legumes that became highly resistant to ash from bituminous combustion were higher than those in our study [47], which suggests that resistance to contaminants could develop over time. The higher TI of E. polymnioides implies that this member of the Asteraceae family was more productive than Miconia sp. in the polluted zone, which may be due to higher tolerance of heavy metals and/or resistance to the continual disturbance caused by mining activities. Several Asteraceae species have been recommended for phytoremediation of pollutants, such as Fe, Cd, Hg, Cr, As, Ni, Cu, Cd, Co, Mn, Pb, Cr, Zn, polycyclic aromatic hydrocarbons, and radionuclides [48]. The higher TI of E. polymnioides implies a more effective capacity than Miconia sp. for phytoremediation in the contaminated zone, although strategies combining the two species should not be ruled out. 4. Materials and Methods 4.1. Study Area This study was carried out in Zamora-Chinchipe Province in southern Ecuador, where approximately 23% (282,998 ha) of the total area is dedicated to artisanal mining [49]. The study site was located near the settlement of Chinapintza, which is adjacent to the Peruvian border in the Condor Mountain range (402016S, 7834014W; Figure 2). Characterized by rugged terrain and a high incidence of geological faults, the study site was centered on an active mining settlement located at an elevation of 1854 m.a.s.l. and sur-rounded by tropical montane cloud forest. We identified two zones at the site: (1) a natural zone (NZ) consisting of secondary forest located upslope from the mining area and free of the influence of mining activity; and (2) a highly degraded polluted zone (PZ) subjected to continuous artisanal mining of gold and other precious metals from stream sediments and banks. This latter zone was under constant change from mining activities such as soil excavation, stream channel modification, and construction of paths and infrastructure, which caused persistent disturbance and severely limited regeneration of the native vegetation. 4.2. Plant and Soil Sampling We selected two native plant species that were relatively abundant in both NZ and PZ for assessment as potential heavy metal hyperaccumulators: Erato polymnioides DC. (Asteraceae) and Miconia sp. (Melastomataceae). Miconia sp. represents a single morphospecies that did not exhibit fertile characters at the time of sampling, so we were unable to identify it to species level. Despite this limitation, we selected it for study due to the high number of juvenile individuals found in both zones relative to other species. Seven individuals of E. polymnioides and Miconia sp. were collected in each zone. In addition, we collected the soil around the base of each individual, which included the roots and rhizosphere. Sample sizes were limited due to the low number of individuals of both species in PZ, presumably due to continued disturbance of the area from mining activities. According to the recommendations of [50], sampling took place far from active roads and the surface of fresh plant material was checked to be free of dust. 4.3. Plant and Soil Analysis In the laboratory, plant samples were washed with ultra-pure water (Merck Millipore Milli-Q, Darmstadt, Germany), placed in paper bags, and dried in an oven at 50 °C for one week. Soil samples (0.5–1.0 kg each) were dried at 50 °C until the weight was constant. Dried samples of both plant tissues and soil were weighed and mechanically grounded using a stainless steel grinder (particle diameter 100 µm) for digestion. Individual plants were divided into leaves, stems and roots. Therefore, there were 7 replicates of 2 species from 2 zones divided into 4 parts (3 plant organs + 1 soil sample). In total, 112 samples were processed to determine heavy metal concentrations. Subsamples (~0.2 g for plants and ~1 g for soil) were weighed for digestion and subsequently added to a mixture of HCl and HNO3 in a 3:1 ratio (v/v) (aqua regia). Considered effective for measuring the “total” amount of trace elements in soils, the aqua regia digestion method (USEPA 3050 or ISO standard 11466) provides an estimate of the maximum availability of elements to plants [51]. Samples were left for 1 week to soak in the acid, after which they were digested in an open heat block (Environmental Express 54 HotBlock SC154) for 2 h. After cooling, samples were diluted to 100 mL with 0.1 M HCl and stored until the metal concentration analyses. Upon filtering the sample solutions through filter paper, concentrations of heavy metals in the digested solutions were analyzed immediately using an atomic absorption spectrophotometer (Perkin-Elmer, AANALYST 400, Akron, OH, USA). The respective wavelength (nm), precision measured as relative standard deviation (%), and detection limit (mg kg−1) of the elements studied were as follows: 283.31, 1.0 and 0.05 for Pb; 213.86, 2.31 and 0.005 for Zn; and 228.80, 1.7 and 0.002 for Cd. Reproducibility of the method used for digesting the leaf samples was verified using triplicate analyses [52]. We applied the hydride generation technique coupled to an AA using an electrodeless discharge lamp to determine total Hg concentration in the samples [53]. A Hg standard calibration curve (100, 200, and 300 µg L−1) was prepared in 10 mL of acid mixture containing 1.5% HNO3 by triplicates. We also ran two blank samples simultaneously to estimate the background metal contamination from the digestion procedure. For each sample, 10 mL of an acid mixture containing 1.5% HNO3 were added to 5 mL of the digestion mixture (prepared by triplicates). Hg was determined using an aqueous solution of 3% (w/v) NaBH4 in a 1% (w/v) NaOH solution that was freshly prepared and filtered as a reducing agent. Analytical grade chemical reagents and highly purified deionized water were used throughout the process. Accuracy of the analytical methods was verified based on certified reference materials and standard solutions: CRM029-50G Trace Metals—Sewage Sludge 2 (RT Corporation, 2931 Soldier Springs Rd-USA, Tokyo Japan) and CRM027-50G Trace Metals—Sandy Loam 10 (RT Corporation-2931 Soldier Springs Rd-USA, Tokyo Japan). 4.4. Calculation of Bioaccumulation and Translocation Factors To evaluate the potential accumulation of heavy metals in the study plants [54], the bioaccumulation factor (BCF) was calculated as the ratio of the trace metal concentration in plant tissue to that in soil (BCF = Cplant/Csoil), where Cplant and Csoil are the concentrations given in units of mg kg−1 of dry weight. To evaluate the capacity of the plants to transfer heavy metals from the soil to aerial parts of the plants, the translocation factor (TF) was calculated as the ratio of metal concentration in the aerial parts to that in the roots: TF = Caerial/Croots, where Caerial and Croots are the concentrations given as mg kg−1 of dry weight in aerial parts of the plant and root, respectively [55]. TF values < 1 indicate metal accumulation in the roots while values > 1 indicate accumulation in the aerial parts of a plant. Thus, concerning the aforementioned, the following criteria apply: if TF and BCF > 1, phytoextraction occurs; if TF < 1 and BCF > 1, phytostabilization is attained [35]. 4.5. Calculation of the Tolerance index (TI) Finally, we calculated the tolerance index, which is commonly employed to evaluate the effect of various types of stress on plant growth and yield, as TI = YTr/YCt [56]. YTr and YCt, where Tr stands for treatment and Ct for control, are measured in units of g ha−1 DM and represent the total yield of plants growing in PZ and NZ, respectively. It was estimated that 10,000 plants could be planted per hectare as a phytoremediation strategy. 4.6. Statistical Analysis The effect of zone (natural vs. polluted) and plant part (leaves, stems, roots) on the concentration of the four metals was analyzed separately for Miconia sp. and E. polymnioides with linear models. Given that the interaction between zone and plant part was not significant for any of the metals in either species, we analyzed only the main effects of these factors. Structures allowing for different variances by zone or the interaction between zone and plant part were included in the models to account for within-group heteroscedasticity [57]. Residuals were examined graphically in the final models to ensure that assumptions of normality and homogeneity of variance were met. Tukey’s HSD tests were used to make multiple comparisons between zones or plant parts when these variables were significant in the final model (p < 0.05). Linear models and Tukey tests were implemented using the nlme [58] and emmeans [59] in R version 4.1.3, respectively [60]. 5. Conclusions As in areas throughout southern Ecuador, mining activities in Chinapintza have produced massive deposits of inadequately managed hazardous waste. This study found that the concentration of Cd, Pb, Zn and Hg in soils of the area exceeded the regulatory thresholds of Ecuador, thereby constituting a serious threat to human health and the environment. Our findings provide new insights into the tolerance of two woody plants native to tropical montane cloud forest, Erato polymnioides and Miconia sp., to xenobiotic heavy metals. High bioconcentrations of Cd, Zn and Hg were reported in situ, mainly in roots and leaves, and two of the phytoextraction criteria (BCF and TF > 1) were met for Cd, Zn and Hg. Specifically, evidence showed that Miconia sp. has potential for Cd and Zn phytostabilization, E. polymnioides has potential for Cd and Zn phytoextraction, and both species have potential for Hg phytoextraction. Neither species met the standards to be considered hyperaccumulators of Cd, Pb or Zn; however, some individuals of E. polymnioides exhibited Hg concentrations > 10 ppm in their roots, which suggests that this species could be a hyperaccumulator of Hg. Although the projected productivity of these species was not high, their adaptation to the edaphoclimatic conditions of the region and the possibility of using amendments to increase their biomass, could compensate for the effectiveness of these species for the reclamation of soils contaminated by mining. Acknowledgments We are grateful to the Universidad Técnica Particular de Loja (UTPL) for supporting this investigation and open access publication. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11091186/s1, Table S1: Estimated marginal mean (EMM, also known as least-squares mean) and 95% confidence interval (CI) of Cd, Pb, Zn and Hg concentrations in two species of woody plants native to the Ecuadorian Amazon (Miconia sp. and Erato polymnioides) as a function of zone (natural versus polluted) and plant part (leaves, stems, roots). EMMs and CIs of each factor (e.g., zone or plant part) were averaged over the levels of the other factor. Pairwise comparisons using Tukey tests are shown for significant effects of zone and/or plant part based on linear models that were executed separately for each metal and species. Differences between zones or plant parts were considered significant if the 95% CI did not include zero (shown in bold). EMMs for non-significant effects (NS) are not shown. Click here for additional data file. Author Contributions Conceptualization, M.J.G.; Data curation, I.C.-E. and M.J.G.; Investigation, I.C.-E., D.M.G., C.K. and J.R.; Methodology, M.J.G.; Supervision, M.J.G.; Writing—original draft, I.C.-E.; Writing—review and editing, I.C.-E., D.M.G., C.K. and M.J.G. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 1 Concentration of heavy metals (mg kg−1) in leaves, stems and roots of Miconia sp. and E. polymnioides in a natural zone and polluted zone of southern Ecuador. Figure 2 Location of the study area and pictures of the two studied zones and two native species selected for study. plants-11-01186-t001_Table 1 Table 1 Mean concentration of cadmium (Cd), lead (Pb), zinc (Zn) and mercury (Hg) (mg kg−1) in soils of the two study species in a natural zone (NZ) and polluted zone (PZ) of an active mining area in southern Ecuador. Standard deviations and relative errors (%) are shown. NZ was a tropical montane forest located upslope from the mining area and free from contamination, whereas PZ was subjected to continuous artisanal mining of gold and other precious metals from stream sediments and the adjacent soils. Cd Pb Zn Hg Miconia sp. NZ 1.62 ± 0.59 37% 510 ± 150 29% 85 ± 16 19% 1.62 ± 0.54 33% PZ 4.44 ± 1.68 38% 523 ± 63 12% 390 ± 140 36% 6.1 ± 3.6 60% E. polymnioides NZ 0.42 ± 0.24 57% 326 ± 75 23% 169 ± 93 55% 1.20 ± 0.22 18% PZ 5.15 ± 2.23 43% 430 ± 250 58% 589 ± 305 52% 10.2 ± 6.0 59% plants-11-01186-t002_Table 2 Table 2 Total weight of the study plants with their respective percentage by weight of each part (leaves, stems and roots). Total Plant Weight (g) Plant Part Weight as Percentage of Total (%) Leaves Stems Roots Miconia sp. NZ 10.7 ± 3.6 39.96 ± 0.92 39.7 ± 4.7 20.3 ± 5.6 PZ 1.96 ± 0.27 60.0 ± 1.3 23.5 ± 1.9 16.5 ± 2.1 E. polymnioides NZ 52 ± 11 17.1 ± 2.3 75.7 ± 1.8 7.23 ± 0.74 PZ 18.6 ± 2.9 25.5 ± 5.6 56 ± 12 18.4 ± 7.1 plants-11-01186-t003_Table 3 Table 3 Bioaccumulation and translocation factors for the two study species in a natural zone (NZ) and polluted zone (PZ) subjected to artisanal mining in southern Ecuador. Cd Pb Zn Hg NZ PZ NZ PZ NZ PZ NZ PZ Bioaccumulation factor (BCF) Miconia sp. Leaves 3.47 3.82 0.14 0.23 1.11 0.99 2.05 1.66 Stems 3.08 2.91 0.12 0.20 0.87 0.78 1.03 0.57 Roots 3.09 6.50 0.20 0.49 1.48 2.34 1.10 1.05 E. polymnioides Leaves 2.88 6.19 0.25 0.53 4.48 3.28 0.73 1.19 Stems 2.85 4.62 0.12 0.38 4.53 2.08 0.17 0.31 Roots 2.42 5.64 0.55 0.80 1.71 1.52 0.76 1.12 Translocation factor (TF) Miconia sp. Leaves/roots 1.14 0.84 0.75 0.65 1.00 0.73 2.53 1.72 Stems/roots 1.00 0.59 0.65 0.50 0.70 0.44 0.87 0.36 E. polymnioides Leaves/roots 1.27 1.55 0.46 0.53 2.40 2.21 1.04 1.27 Stems/roots 1.08 1.07 0.23 0.43 2.79 1.45 0.25 0.41 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Mahugija J. Kasenya Z. Kilulya K. Variations of Concentrations of Lead, Zinc, Iron, Copper and Cadmium in Urine of Primary School Pupils in Relation to Age, Sex and Academic Performance Tanzan. J. Sci. 2020 46 190 204 10.4314/tjs.v46i2 2. Mng’ong’o M. Munishi L. Ndakidemi P. Blake W. 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PMC009xxxxxx/PMC9099853.txt

==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092621 molecules-27-02621 Article Fiber Preparation from Micronized Oat By-Products: Antioxidant Properties and Interactions between Bioactive Compounds https://orcid.org/0000-0003-4879-4404 Dziki Dariusz 12 https://orcid.org/0000-0002-7308-4335 Gawlik-Dziki Urszula 23* https://orcid.org/0000-0001-9680-1328 Tarasiuk Wojciech 24 https://orcid.org/0000-0002-3249-8929 Różyło Renata 5 Martín Diana Ana Belén Academic Editor 1 Poland Department of Thermal Technology and Food Process Engineering, University of Life Sciences in Lublin, Głęboka 31, 20-612 Lublin, Poland; dariusz.dziki@up.lublin.pl 2 Fibrecare Sp. z o.o., Słowackiego 16, 40-094 Katowice, Poland; w.tarasiuk@pb.edu.pl 3 Department of Biochemistry and Food Chemistry, University of Life Sciences in Lublin, Skromna 8, 20-704 Lublin, Poland 4 Faculty of Mechanical Engineering Bialystok, Bialystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland 5 Department of Food Engineering and Machines, University of Life Sciences in Lublin, Głęboka St. 28, 20-950 Lublin, Poland; renata.rozylo@up.lublin.pl * Correspondence: urszula.gawlik@up.lublin.pl 19 4 2022 5 2022 27 9 262123 3 2022 18 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This study aimed to investigate the possibility of utilizing oat by-products for fiber preparation. Oat husk (OH) and oat bran (OB) were micronized and used to prepare a novel product rich in fiber and with enhanced antioxidant properties. The basic chemical composition and phenolic acid profile were determined in OH and OB. The antioxidant properties of OH and OB were also analyzed. The type and strength of interactions between the biologically active compounds from their mixtures were characterized by an isobolographic analysis. The analyses showed that the sum of phenolic acids was higher in OH than in OB. Ferulic acid was dominant in both OH and OB; however, its content in OH was over sixfold higher than that in OB. The results also suggested that both OH and OB can be used for preparing fiber with enhanced antioxidant properties. The optimal composition of the preparation, with 60–70% of OH and 30–40% of OB, allows for obtaining a product with 60–70% fiber and enhanced antioxidant activity due to bioactive substances and their synergistic effect. The resulting product can be a valuable additive to various food and dietary supplements. oat by-products micronization phenolic acids antioxidant properties fibre isobolographic analysis ==== Body pmc1. Introduction Cereals have been an important component of a daily diet for centuries. In particular, the high consumption of fiber-rich cereal products has been shown to reduce the risk of several diseases [1,2]. The protective effect of such products is mainly attributed to dietary fiber [3] and polyphenols [4]. Oat (Avena sativa L.) is a valuable cereal crop in the developing world. Until recently, oat was primarily used as animal feed and, to some extent, as human food. Of late, oat has been gaining interest as a health food for humans, and its use as animal feed is steadily declining. Due to its nutritional benefits, as revealed by researchers around the world, oats are currently used in the food industry as an ingredient in various food products, including infant foods, bread, beverages, breakfast cereals, biscuits, and muesli, and also sold in the form of oat milk and oat flour [5]. Another reason for the growing popularity of oats is that their cultivation requires lesser nutrients than other cereals [6]. Oat is predominantly grown in American and European countries, mainly Russia and Canada [7]. Oat consumption by humans has been increasing because of the awareness of the health benefits of dietary fiber such as β-glucan and bioactive phytochemicals. These compounds are known to reduce the risk of type 2 diabetes and cardiovascular diseases and decrease the level of cholesterol and glucose in the blood. β-Glucan can also attenuate glycemic response, increase satiety after a meal, and benefit gut microflora [8]. It is mainly found in the oat bran (OB) fraction [9]. In addition to β-glucan, the OB fraction contains various phenolic compounds, including ester-linked glycerol conjugates, ester-linked alkyl conjugates, ether- and ester-linked glycerides, anthranilic acid, and avenanthramide, with a high antioxidant capacity [10]. OB, which is a by-product obtained from the milling of oat flour, is relatively inexpensive and is believed to provide health benefits when added to food [11,12]. Kim and Dale [13] reported that the processing of crops (oats, wheat, rice, corn, or sorghum) results in approximately 1.5 billion metric tons of waste biomass worldwide. Although the waste occurs primarily in the form of straw, the operation of postharvest lines, which removes the remnants of native plants and husks from processed crops, generates a large amount of waste biomass that is unsuitable for transportation and combustion [14]. Oat husk (OH) is a by-product produced during oat processing for food purposes. It makes up about 25–33% of the weight of oat. Around 2.75–3.3 million tons of OH are generated each year by oat processing [15]. As a low-value lignocellulosic residue, OH can have environmental consequences. However, their polymers can be converted into several value-added products, but this requires efficient pretreatment methods for their fine separation for further valorization [16]. As a raw material rich in fiber with low energy and low protein, OH is primarily used as animal feedstock and biofuel [17]. Nevertheless, due to a very low bulk density of about 144 kg/m3, the handling of OH is also challenging [18,19]. On the other hand, OH is an excellent source of insoluble fiber, with a documented health effect on humans [20]. Its fiber is resistant to fermentation in the human colon, has no impact on serum lipids, and provides no energy to the body. The inclusion of insoluble fiber in the human diet can help maintain healthy colon function and reduce constipation [21]. The food industry is in constant search of novel sources of insoluble fiber. Traditionally, OH has been discarded during oat processing. Still, the need for concentrated, insoluble fiber sources for human consumption has paved the way for the production of oat hull fiber. Although the effects of oat hull fiber have been analyzed in animals such as rats, pigs, chickens, and cattle [22], studies focusing on the possibility of using OH as a food additive are very limited. Piwińska et al. [23] studied the effect of adding a mixture containing OH and soluble oat fraction to wheat pasta. Oliveira et al. [24] proved that OH could be a valuable cellulose fiber source for hydrogel production. Due to the high fiber content, the traditional size reduction method is insufficient for grinding OH. Ultrafine grinding or micronization is a new technique used for making a super fine powder with a particle size of 1–100 μm and good surface properties [12,25]. This very fine powder is characterized by higher solubility, dispersibility, and water absorption, which improves the quality of the target food products. Moreover, micronization considerably enhances the efficiency of extraction of phytochemicals [20], and is widely employed to extract natural polysaccharides from different bioresources [26]. This study aimed to investigate the possibility of utilizing micronized OB and OH to prepare a new fiber-rich product with enhanced antioxidant properties. In addition, the study analyzed the interactions between the biologically active compounds from OH and OB. 2. Results and Discussion 2.1. Basic Composition of Raw Materials Table 1 presents the results of the basic chemical composition of OH and OB. Compared to OB, OH had a higher content of ash (3.41 and 2.74 g/100 g in OH and OB, respectively). It was also fat-free, whereas OB had 5.52% of fat. OB was characterized by a several-fold higher protein content (16.30%) than OH (1.31%). This is in line with a previous study [27] that showed that OH protein content does not exceed 4%. Furthermore, OB contained 6.05 g/100 g dry weight (DW) of β-glucan, whereas in OH, the amount of this compound was only 0.25 g/100 g DW. Higher total fiber content was found in OH (91.11 g/100 g DW) than in OB (23.60 g/100 g DW). A similar composition of OH and OB was reported by Dziki et al. [20] and Xue et al. [12], respectively. OH is especially rich in insoluble fiber such as cellulose, hemicelluloses, and lignin [24], whereas both soluble and insoluble fiber occurs in OB in a ratio of 1:5 [28]. It is worth emphasizing that OB has a higher soluble dietary fiber content (especially β-glucan) than wheat or rice bran [28]. Soluble dietary fiber has many health effects, including preventing cardiovascular diseases, diabetes, and obesity [29]. On the other hand, insoluble dietary fiber supports normal intestinal peristalsis [30]. Moreover, consumption of insoluble fiber-rich products can help to reduce appetite and food intake [31]. 2.2. Identification and Quantification of Phenolic Compounds Phenolic acids are mainly located in the outer part of the cereal grain. The content of these compounds is almost 15–18-fold higher in the bran compared to the endosperm [32,33]. Due to the presence of hydroxyl groups and phenolic rings, phenolic acids can exhibit antioxidant activity (AA), which is one of their most important properties [33]. As shown in Table 2, both OH and OB contained a significant amount of phenolic acids. The sum of phenolic acids was higher in OH than in OB (456.58 and 151.98 µg/mg DW, respectively). In both OH and OB, the dominant phenolic compound was ferulic acid. However, its content in OH was more than sixfold higher than in OB. In OH, ferulic acid accounted for more than 95% of all phenolic acids, whereas in OB, it constituted about 47%. An earlier study [34] also confirmed that ferulic acid was the major phenolic acid in OH. Sevgi et al. [35] showed that ferulic acid exhibited the highest AA compared to other phenolic acids such as p-hydroxybenzoic, caffeic, gallic, protocatechuic, vanillic, and rosmarinic acid. OB also contained a high amount of p-coumaric acid (61.53 µg/mg DW), whereas, in OH, the content of this acid was above the limit of detection and below the limit of quantification. Caffeic acid was present in similar amounts in both OH and OB (6.08 and 5.72 µg/mg DW, respectively). OH was also richer in protocatechuic, p-hydroxybenzoic, and vanillic acids than OB, while OB contained a higher amount of synaptic acid (6.55 µg/mg DW). Salicylic acid was found in a small proportion only in OB (0.09 µg/mg DW). It was shown that 1 g of OH contained 269.9 µg of p-coumaric acid, 309 µg of ferulic acid, 11.2 µg of vanillic acid, 1.4 µg of sinapic acid, 6.8 µg of syringic acid, and 10.9 µg of p-hydroxybenzoic acid [34]. These values differ from those estimated in our study, which may be due to genetic factors and the method of extraction. A study [36] showed that oat grain is rich in the following phenolic acids: p-hydroxybenzoic, dihydroxybenzoic, caffeic, p-coumaric, ferulic, vanillic, sinapic, gallic, and syringic acid. OH contains about fourfold higher ferulic acid content than oat grain [34]. Dziki et al. [20] determined a similar amount of phenolic acids in micronized OH. Hitayezu et al. [4] found that OB contained five main phenolic acids: vanillic, caffeic, p-coumaric, ferulic, and cinnamic acid. The authors observed ferulic acid constituted about 64% of all phenolic acids in the fine bran fraction. They also noted that lower granulation of bran contributed to the improved extraction of phenolic acids. 2.3. Total Phenolic Content (TPC) and AA of OH and OB Several methods can evaluate the phenolic content and AA of plant foods. The analytical technique involves using nonspecific methods to determine the overall content of phenolic compounds, which is usually expressed as an index such as gallic acid, chlorogenic acid, or catechin equivalent [37]. As presented in Figure 1B, both OH and OB contained comparable amounts of buffer-extractable phenolics (0.97 and 1.01 mg gallic acid equivalent (GAE)/g DW for OH and OB, respectively), whereas a significantly higher TPC was found in the hydroalcoholic extract of OH (2.31 mg GAE/g DW) compared to that of OB (1.47 mg GAE/g DW). A similar level of phenolics (2.6 mg/g DW) was found in OH by other authors after its extraction with 75% aqueous methanol [34]. By contrast, Călinoiu and Vodnar [33] showed a lower TPC in OB (0.25 mg GAE/g DW) extracted with 80% methanol using an ultrasonic bath. The content of extracted phenolic compounds depends on the extraction method used and the particle size of raw materials. A higher degree of fineness of OB and OH is associated with higher TPC [4,20]. Most studies investigating the anti-free-radical scavenging activity of oats have used the DPPH (2,2-diphenyl-1-picrylhydrazyl) assay [38,39,40]. However, a study [41] indicated that the ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) assay may also be used to determine the activity of both hydrophilic and hydrophobic antioxidants. This study also indicated that ABTS is not influenced by the ionic strength and reacts with most of the antiradical compounds. The results obtained for different food samples suggested that the ABTS assay better estimates the antioxidant content than the DPPH assay [37]. Higher AA was found in hydroalcoholic extracts of both OB and OH (Figure 1A). The extracts of both raw materials exhibited significant ABTS•+-quenching ability, and the hydroalcoholic extracts of both extracts showed more than twofold higher radical quenching activity. Regardless of the type of extract, the samples obtained from OB were characterized by higher ABTS•+ quenching ability. The highest AA was found in hydroalcoholic extracts from OB (EC50 = 24.07 mg DW/mL). Phenolic compounds mainly determine the AA of OH and OB extracts; however, the amount of phenolic acids, rather than the composition of extracts (the type of phenolic compounds and their proportion), seems to play a key role in the AA of the extracts. Kruma et al. [37] showed that hulled oats exhibited significantly higher ABTS scavenging activity than hull-less oats. Other authors [42] demonstrated that the insoluble phenolic fraction of oat showed significantly higher AA (ABTS) compared to the free phenolic fraction. AA was determined by both the method of sample preparation and extraction procedure. Liu et al. [26] proved that the antiradical activity of the polysaccharide extracts of OB obtained via superfine grinding was significantly higher than that of extracts obtained from coarse particles of OB. Notably, the ABTS scavenging activity of the extracts increased from 38.87% to 62.29%. Considering the chelating power (CHEL), a significantly higher AA (lower EC50) was observed for the OH extracts than for the OB extracts. The OH extract containing the hydroalcoholic extractable compounds was characterized by lower EC50 values compared to buffer extracts (EC50 = 32.4 and 35.37 mg DW/mL, respectively). An opposite trend was observed in the case of OB extracts, in which a significantly higher activity was observed compared to buffer-extractable compounds (EC50 = 80.73 and 115.03 mg DW/mL for buffer and hydroalcoholic extract, respectively). Metals such as Fe2+ (in the free form) can participate in the Fenton reaction, generating hydroxyl radicals. HO∙ radicals are characterized by the strongest reactivity and oxidation power than other reactive oxygen species (ROS). Thus, substances that can chelate free Fe2+ ions are critical in reducing HO∙radicals and associated damage [43]. Another process that has a deleterious effect on foods and is most damaging to living organisms is lipid peroxidation [1]. Interesting results were observed by analyzing the ability of products to inhibit lipid oxidation. In this study, the OH extracts, regardless of the type, showed a higher ability to inhibit lipid peroxidation compared to OB extracts. This suggests that both raw materials contain potentially bioaccessible compounds that can effectively inhibit lipid oxidation (Figure 1B). In fact, molecules with higher peroxyl radical (ROO) scavenging activity do not often exhibit higher metal chelating properties. This is because the chelating activity is determined by the binding characteristics of the active molecule. In contrast, the ROO∙ activity depends on the ability of a molecule to transfer electrons or protons [4]. Phenolic acids, including ferulic, caffeic, p-coumaric, and cinnamic acids, have been reported to differently inhibit the oxidation of linoleic acid, with ferulic acid being the most active. Phenolic acids identified in OB extracts certainly contributed to their activity [1]. 2.4. Interaction Assay The interaction between bioactive components influences the final activity of their mixture [44]. Thus, this study analyzed the strength of interactions occurring between the biologically active compounds from OH and OB. In the first step of the analysis, the type and strength of interactions were determined using normalized isobolograms. As shown in Figure 2, synergism was observed between compounds that indicated their antiradical activity and CHEL regardless of the type of extract, while buffer-extractable compounds additionally exhibited the ability to inhibit lipid peroxidation. Synergism was found in 50% of methanol-extractable compounds that could prevent lipid oxidation. Considering the beneficial interactions between antioxidant compounds, it seemed justified to prepare a mixture of OH and OB and evaluate its antioxidant properties. To determine the optimal composition of the OH-OB mixture, the combination index (CI) of each of the tested mixtures was determined (Table 3). The best antiradical activity was observed in the samples with the highest proportion of OH (60–90%), among which the higher activity was noted for hydroalcoholic extracts. These observations and the average CI values clearly indicated the synergism between antiradical compounds present in both OH and OB. A higher metal-chelating ability was observed in the samples containing at least 50% OB. The average CI values determined in both extracts indicated synergism between active compounds. The solvent used for extraction did not seem to affect the activity of the tested samples. The analysis of the ability to inhibit lipid peroxidation yielded interesting results. For buffer-extractable phytochemicals, the highest activity was found in the samples containing a higher proportion of bran, while this relationship was not observed for hydroalcoholic extracts. Moreover, the average CI value indicated the synergism of buffer-extractable phytochemicals and compounds extracted by 50% MeOH. Based on the CI value, compounds with the highest activity were selected. As presented in Table 4, the CI index for the selected composition differed from the average value. Taking into account the antiradical activity, the highest effect was observed in the mixtures containing 90% OH (extracted using phosphate-buffered saline (PBS) buffer) and 70% and 90% OH (extracted using 50% methanol). In terms of the ability to chelate transition metal ions, in the case of PBS buffer extract, the highest activity was noted for the mixture containing equal proportions of both components, while the mixture with 70% OH exhibited the highest activity among the 50% methanol extracts. Analysis of the influence of the mixture composition on the ability to inhibit lipid peroxidation revealed that the mixture containing 60% husk had the optimal composition. 3. Materials and Methods 3.1. Chemicals All the chemicals used were of analytical grade. DPPH, ABTS, Folin–Ciocalteu reagent (2 N), methanol, linoleic acid, ammonium thiocyanate, gallic acid, and ferrozine were purchased from Sigma-Aldrich (Poznan, Poland). Acetonitrile (high-performance liquid chromatography-grade) was purchased from Merck (Darmstadt, Germany). Kaempferol was purchased from Fluka AG (Buchs, Switzerland). Formic acid (liquid chromatography-mass spectrometry-grade) was obtained from Merck (Darmstadt, Germany). A purification system (Milli-Q-Simplicity-185, Millipore Corp., Burlington, MA, USA) was used for obtaining ultrapure water. 3.2. Plant Materials The plant raw materials used in the study were OB and OH. OB were purchased from ZPZM Kruszwica Sp. z o.o. (Kruszwica, Poland), and OH was purchased from AG Feeding Sp. z o.o. (Gdynia, Poland). Before their use, both raw materials were sterilized and micronized, as described previously [20,45]. 3.3. Determination of Basic Chemical Composition The basic composition of OB and OH was determined using the standard methods as follows [AOAC, 2010] [46]: moisture content—Method 925.10, protein content—Method 992.33 (Nx6.25), ash content—Method 942.05, fat content—Method 30–10, and β-glucan content—Method 995.16. 3.4. Phenolic Acid Analysis For phenolic acid analysis, the UPLC-MS/MS (ultra-performance liquid chromatography-mass spectrometry) method was used. Pulverized samples of OB and OH were analysed and calculated according to the method described by Dziki et al. [20]. 3.5. TPC and AA 3.5.1. Extract Preparation To study the antioxidant properties of OH, OB, and their mixtures (OH with OB: 9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8 and 1:9), their buffer extract (phosphate-buffered saline) and 50% methanol extract were prepared [47,48,49]. 3.5.2. TPC Estimation The TPC of the extracts obtained from OH and OB and their mixtures was determined as described by Singleton et al. [50] with slight modifications [51]. The values were expressed as GAE/g DW. 3.5.3. Antiradical Activity (ABTS) The ABTS•+-quenching ability of OH and OB and their mixtures was determined as described previously [52] using the following equation:SC = [(AC − AA)/AC)] × 100%(1) where SC is scavenging ability, AC is the absorbance of the control, and AA is the absorbance of the sample. 3.5.4. Metal-Chelating Activity (CHEL) The metal-chelating activity (CHEL) of OH and OB and their mixtures was determined as described previously [53] using the following formula:IN = [1 − (AS/AC)] × 100%(2) where IN is inhibiting ability, As is the absorbance of the sample, and Ac is the absorbance of the control. 3.5.5. Inhibition of Linoleic Acid Peroxidation The inhibition of linoleic acid peroxidation was determined as described previously [54], but using an aqueous solution of 10 mmol/L FeCl2 instead of hemoglobin. 3.5.6. AA Determination For all the assays used to determine the AA of OH and OB and their mixtures, the half-maximal inhibitory concentration or EC50 values were calculated by interpolating the dose-response curves. The EC50 values were calculated in fitted models as the concentration at which the tested compound exhibited 50% of the maximum inhibition based on a dose-dependent mode of action. 3.6. Interaction Analysis The type and strength of interactions between biologically active compounds from OH and OB mixtures were determined by isobolographic analysis based on CI values proposed by Chou [55]. The CI value at which the drug combination exhibited x% inhibition was calculated as follows [55]:(3) CI=(D)1(Dx)1+(D)2(Dx)2 where CI is the sum of the dose of the components that exert x% inhibition when combined and Dx is the dose (D) as a single substance that inhibits a system at x%. A CI value of <1, >1, and 1 indicates that the type of interaction is synergistic, antagonistic, and additive, respectively. OH and OB were mixed in ratios for the interaction analysis as described in the “Results and Discussion” section. 3.7. Statistical Analyses All tests were performed in triplicate unless stated otherwise. The results were presented as mean values and standard deviations. The data were also subjected to a one-way analysis of variance, and Tukey’s test determined the differences between means. The significance level (α) was established at 0.05. 4. Conclusions The obtained results justify the use of OH as a hitherto underappreciated ingredient in the production of fiber preparations with enhanced antioxidant properties. The optimal composition of the micronized oat preparation containing 60–70% OH and 30–40% OB can allow the obtainment of a product rich in fiber (about 60%) with exceptional health properties and high AA due to the presence of bioactive substances from both husk and bran, as well as their synergistic effect. Such a product can be a valuable additive for various food products such as bread, pastry, and pasta. Appropriate fragmentation with micronization enables the use of the preparation in the dairy industry and the production of beverages. Such highly fragmented preparation can also be applied in the pharmaceutical industry as an additive to dietary supplements. Author Contributions Conceptualization, D.D., W.T. and U.G.-D.; methodology, D.D. and U.G.-D.; validation, D.D., formal analysis, D.D., investigation, D.D., W.T., U.G.-D. and R.R.; data curation, D.D.; writing—original draft preparation, U.G.-D. and D.D.; writing—review and editing, D.D.; supervision, U.G.-D. and D.D. All authors have read and agreed to the published version of the manuscript. Funding The research was partially funded by the project “Obtaining a fiber preparation from the husk and fruit-seed coat of oat grain with the use of innovative hulling and crushing technologies” (Grant Number POIR.01.01.01.-00-0289/17) co-financed by the European Union from the European Regional Development Fund under the Intelligent Development Operational Programme for 2014–2020. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available upon request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 TPC (A) and AA (B) of micronized OB and OH. PBS—buffer extract; 50% MeOH—hydroalcoholic extract; ABTS—antiradical activity; CHEL—chelating power; LPO—ability to protect lipids against oxidation. Means followed by different lowercase letters (a–d) are significantly different at p < 0.05. Figure 2 Dose-normalized isobolograms for the antioxidant activity of OH and OB components: antiradical activity of buffer extract (A) and 50% hydroalcoholic extract (B); CHEL of buffer extract (C) and 50% hydroalcoholic extract (D); and lipid peroxidation-inhibiting ability of buffer extract (E) and 50% hydroalcoholic extract (F). molecules-27-02621-t001_Table 1 Table 1 Comparison of The Basic Composition of Oat Husk and Bran (g/100 g DW). Parameter Husk Bran Moisture content 3.5 ± 0.12 4.2 ± 0.08 Ash content 3.41 ± 0.10 2.74 ± 0.16 Protein content 1.31 ± 0.08 16.30 ± 0.29 Fat content nd * 5.52 ± 0.28 β-glucans content 0.25 ± 0.04 6.05 ± 0.25 Total fiber content 91.11 ± 1.35 23.60 ± 1.7 Total carbohydrates 91.90 ± 1.63 69.4 ± 1.10 * Not detected. molecules-27-02621-t002_Table 2 Table 2 Comparison of The Basic Composition of Oat Husk and Bran (g/100 g DW). Phenolic Acid Husk Bran Caffeic 6.08 ± 0.20 5.72 ± 0.04 Ferulic 435.71 ± 20 70.74 ± 0.86 p-coumaric >LOQ 61.53 ± 0.89 p-hydroxybenzoic 4.98 ± 0.12 3.14 ± 0.06 Protocatechuic 0.71 ± 0.01 0.22 ± 0.00 Salicylic <LOD 0.09 ± 0.01 Sinapic 1.82 ± 0.12 6.55 ± 0.15 Vanillic 4.27 ± 0.15 1.59 ± 0.06 Syringic 3.01 ± 0.35 2.40 ± 0.17 Sum 456.58 ± 19.90 151.98 ± 1.58 >LOQ—above the limit of detection and below the limit of quantification, <LOD—below the limit of detection. molecules-27-02621-t003_Table 3 Table 3 Antioxidant Activity of OB and OH Mixtures and Combination Index. Mixture OB:OH Antiradical Activity Metal-Chelating Activity Inhibition of Lipid Peroxidation PBS 50% MeOH PBS 50% MeOH PBS 50% MeOH EC50 * CI EC50 CI EC50 CI EC50 CI EC50 CI EC50 CI 9:1 67.8 ± 0.3 d** 0.95 25.2 ± 1.8 c 0.90 32.4 ± 0.8 b 0.84 36.4 ± 1.0 a 0.94 10.7 ± 0.2 a 0.94 17.5 ± 0.5 a 0.69 8:2 65.8 ± 2.7 d 0.92 25.0 ± 0.8 c 0.92 27.8 ± 0.6 a 0.72 34.7 ± 1.1 a 0.88 11.0 ± 0.1 a 0.99 21.7 ± 1.6 c 0.85 7:3 59.5 ± 0.8 c 0.80 23.8 ± 0.9 c 0.80 33.4 ± 1.2 b 0.75 37.4 ± 0.9 a 0.82 12.8 ± 0.2 b 1.02 18.1 ± 0.7 ab 0.66 6:4 53.8± 1.3 a 0.71 21.4 ± 0.7 ab 0.76 33.8 ± 0.4 b 0.73 41.3 ± 0.8 b 0.82 13.5 ± 0.8 b 1.02 21.6 ± 0.8 c 0.76 5:5 53.2 ± 0.6 a 0.70 20.0 ± 0.6 ab 0.69 34.5 ± 0.9 b 0.68 45.7 ± 2.1 c 0.81 14.6 ± 0.1 cb 1.03 17.5 ± 0.4 a 0.59 4:6 56.1 ± 1.3 b 0.72 20.1 ± 0.7 ab 0.62 41.6 ± 1.0 c 0.76 52.0 ± 1.6 d 0.83 14.5 ± 0.3 cb 0.88 19.1 ± 0.8 b 0.57 3:7 55.2 ± 0.7 ab 0.69 19.9 ± 0.3 a 0.59 45.5 ± 0.2 d 0.76 48.7 ± 6.2 dc 0.69 14.9 ± 0.1 c 0.90 18.1 ± 0.7 ab 0.66 2:8 55.9 ± 0.6 ab 0.69 20.8 ± 0.7 ab 0.60 46.8 ± 0.4 d 0.71 63.5 ± 1.0 e 0.79 16.8 ± 0.8 d 0.93 19.5 ± 0.7 b 0.59 1:9 55.5 ± 0.3 b 0.68 21.4 ± 0.8 ab 0.59 52.6 ± 1.2 e 0.74 72.2 ± 1.8 f 0.76 18.0 ± 0.2 d 0.91 21.5 ± 0.3 c 0.61 * EC50—half maximal inhibitory concentration, CI—combination index, ** Means in rows followed by different lowercase letters (a–e) are significantly different at p < 0.05. molecules-27-02621-t004_Table 4 Table 4 The Best Connections OB:OH for The Tested Antioxidant Activities, Combination Index (CI), and The Type of Interaction. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095030 ijerph-19-05030 Article Student and Nature Interactions and Their Impact on Mental Health during the COVID-19 Pandemic Trevino Jonah E. 1 Monsur Muntazar 2 Lindquist Carol S. 3 https://orcid.org/0000-0002-8164-4275 Simpson Catherine R. 1* Dong Zhengchao Academic Editor Gorriz Juan Manuel Academic Editor Zhang Yudong Academic Editor Tchounwou Paul B. Academic Editor 1 Department of Plant and Soil Sciences, Texas Tech University, Lubbock, TX 79409, USA; jonah.trevino@ttu.edu 2 Department of Landscape Architecture, Texas Tech University, Lubbock, TX 79409, USA; mmonsur@ttu.edu 3 Department of Sociology, Anthropology, and Social Work, Texas Tech University, Lubbock, TX 79409, USA; carol.lindquist@ttu.edu * Correspondence: catherine.simpson@ttu.edu; Tel.: +1-806-834-5691 21 4 2022 5 2022 19 9 503010 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Passive and active interactions with nature reduce stress, anxiety, and depression. Populations that experience increased stress often have fewer interactions with nature due to many factors. More recently, the COVID-19 pandemic has created a new stressor for all populations due to sickness, isolation, financial burdens, or other factors. University students were particularly impacted due to the change to online modalities, which isolated them from other students. To assess if any negative or other consequences were experienced and if nature factors could mitigate them, we examined how plant interactions affected university students (N = 353) in an online learning environment. Two modified Depression Anxiety Stress Surveys (DASS; Depression Anxiety Stress and Academic Stress, DASA) were administered over two semesters in 2020 to survey students on these interactions with nature. During the two semesters, most students experienced extremely severe self-reported mental health adversities. Further correlations between DASA scores and responses about nature interactions, home environments, plant exposure, and plant access showed that outdoor interactions were positively related to better self-reported mental health scores. However, the concerning and lingering effects of the pandemic were evidenced in our research as DASA scores increased across the two semesters. Nevertheless, going outdoors and interacting with nature brings some benefits that lessen the severity of depression, anxiety, and stress. university students mental health COVID-19 pandemic nature interactions passive impacts plants ==== Body pmc1. Introduction The COVID-19 pandemic has profoundly altered students’ lives at all grade and developmental levels. During normal years, university students are subject to many stressful conditions that are highly influenced by their environment, peers, and academic disciplines. These stressful factors were then exacerbated by the pandemic. During the height of the pandemic, students faced the rigors of university education in an online learning environment, which enhanced stress on many levels. This circumstance ultimately changed how students experienced the college lifestyle and introduced new stressors to the academic setting, some of which are still present to date. Along with changes in social activities such as ceasing students’ recreational activities on campus, negative emotions due to the online shift have also been found [1,2]. In addition to pressure associated with class performance, students were burdened with problems such as financial insecurity, uncertainties in romantic relationships, health, family, death, and their own isolation from peers and loved ones [3,4,5]. Literature published within the last two years has shown that the COVID-19 pandemic has resulted in increased psychological stress in students [6]. Furthermore, university student-athletes, and females, have been increasingly affected by depression, anxiety, and stress [2]. This has been a common theme worldwide, and a study of online learners in China found that depression, anxiety, and stress were high, specifically among males and those studying subjects other than medicine [1]. This snapshot of literature attests to the wide-spread impact of COVID-19, but the question remains on what factors or resources may counteract or ameliorate the stressors and negative consequences associated with a pandemic. Furthermore, how do nature or natural elements (e.g., greenspaces, window views, and interior plants) affect online learners at an American university? Natural environments provide space and distance from other people while still allowing interaction with fewer safety risks [7,8]. However, few studies have had the opportunity to explore the impacts of nature on students in online learning environments, and furthermore, how the experience of a nationwide pandemic affected students with regard to depression, anxiety, stress, and academic stress. Outside of the pandemic, previous studies have shown that natural environments can affect student performance, mental health, and satisfaction with their academic courses [9,10]. These studies found that that window views, campus greenspaces, and passive and active interactions with plants can have overall positive effects on students [11,12,13]. Interactions with these nature-based elements are therefore potential mechanisms to reduce mental strains and improve emotional well-being. Most university campuses have greenspaces, and many college students frequently use these greenspaces for personal enjoyment [14]. In one study, students deemed outdoor spaces with more greenery to be more likely to have mentally restorative effects [15]. Overall, outdoor greenspaces and walks through natural settings on college campuses can improve the quality of life among those who utilize these areas [13]. Window views of nature also play a role in people’s mental state. When university students have the option of studying with plants or natural window views versus rooms without plants or windows with hardscape views, they are often drawn to areas with natural elements [11]. Benfield et al. [16] researched students’ exposure to window views with nature versus concrete walls and showed that students who were exposed to natural views had a higher level of satisfaction with their courses and higher end-of-semester grades. Moreover, a researcher interviewed students who were exposed to plants in a classroom environment and found that these items boosted social comfort and enhanced collaboration [17]. Interior plants in a classroom have also been shown to increase student participation in lectures [9]. These results provide evidence that greenspaces can greatly influence a student’s daily life in a university setting. However, mental health and well-being are subjective factors that can be difficult to measure [18]. Therefore, tools have been developed to measure factors that detract from positive mental health, such as depression, anxiety, and stress [19]. Among these tools is the Depression, Anxiety, and Stress Scale (DASS) [20] which has been used frequently to assess mental health on many populations and has been a proven tool to measure student populations [2,6]. Depression, anxiety, and stress are common in populations throughout the world and are triggered by different individual experiences [21]. They are interrelated and can have common symptoms and degrees to which they are expressed [22]. Depression is characterized by negative feelings, sadness, low self-esteem, and loss of interest which can affect appetite, sleep, energy levels, and cognitive function [23]. Anxiety differs in that feelings of anxiety and fear are more predominant than those related to sadness [23]. Stress is more difficult to define in that it can be associated with an environment or environmental factors (e.g., cold or heat), be a response to external pressures, or a psychological response [24]. Academic-induced stress is stress experienced in association with or because of pressures related to academic activities [25]. Academic-induced stress is common in students who are enrolled in a college or university, and the causes can range from the cost of tuition to interpersonal relationships or the pressures of demanding programs [26]. Once students are in a university setting, academic performance-related stress becomes one of their more common stress factors, with 55% of students in the U.S. reporting this claim [27]. Students may also experience difficulties in their interpersonal relations from the strains associated with going through a life-stage change, adjusting to a new living environment, and working to fulfil academic requirements [28,29]. A recognized stressor is the financial burden associated with attendance [30]. Due to these stressors, students may seek mechanisms to alleviate the stress, such as experiencing nature by going outside, or negative escapes such as drugs or alcohol. Nature has long been a source of restoration and refuge for those who are experiencing hardships. This is also true for populations suffering from the COVID pandemic, who used this opportunity to go outside and experience nature in various ways. Buckley and Westaway [7] hypothesized that outdoor tourism would be essential for recovery from COVID, especially for urban women with families. This claim coincided with another study that theorized that using nature to heal the mental anguish that was induced by COVID could be extended to all demographics [31]. Furthermore, it was noted that people were going out into nature more often during the pandemic. Morse et al. [32] saw that Vermont residents were spending more time in nature and that this heightened relationship was extremely important during the first few months of the pandemic. Local state parks and natural recreational areas also had an increase in the number of visitations during 2020, when compared to 2019 [8,33]. Additionally, sales of outdoor gear increased, and forest therapy became increasingly popular [8]. To date, there is little to no literature about how students in in-home learning environments interacted with nature or plants. Therefore, we hypothesized that interacting with nature or plants would positively affect students that were subject to at-home orders during the COVID-19 pandemic. The objectives of this study were to assess students’ stress in home-learning environments and determine if plant interactions played any role in reducing their stress. 2. Materials and Methods 2.1. Survey Topics Both surveys had the same questions for all participants to answer; however, some questions had additional answer choices for the Fall 2020 survey for students who participated in in-person classes. The additional answer choices were adjusted for students who studied on-campus. For example, if a question asked, “Where do you spend time working?”, the Summer 2020 survey listed areas that were in their own home environment, while the Fall 2020 survey listed “on-campus” as an option. The survey contained multiple sections to evaluate demographics, mental health, attitudes towards COVID-19, work/study environments, and interactions with natural elements. The DASS survey section was followed by academic stress questions, workplace environment questions, COVID-19 stress questions, and nature exposure questions. 2.1.1. DASS Survey The survey used in this study was based on the Depression Anxiety Stress Scale (DASS), which was slightly modified for this project [20]. To the original range of responses, we added a fifth option: “Applies all the time.” This extra answer was added to adjust for the high-stress situation many students were facing during the COVID-19 pandemic. The first 23 questions of this current survey were taken directly from the DASS; however, two questions were omitted from analysis to match questions listed in the DASS 21 for analysis (Table A1). 2.1.2. Academic Stress Questions The remaining questions, written specifically for the DASS survey section, were designed to focus on stressors induced by academia and the academic environment. This section is referred to as “academic stress.” To distinguish it from the original survey, this study’s modified DASS survey is referred to as the DASA (Depression, Anxiety, Stress, Academic Stress) survey throughout the remainder of this article (Table A1). 2.1.3. Workplace Information Survey Questions Questions regarding the “at-home” environment assessed students’ COVID-19 learning environment by asking about workplace set-up (referred to as ‘workplace functionality’) (Table A2) during at-home learning and any limitations (referred to as ‘workplace constraints’) (Table A3) that an individual may have experienced. Researchers also asked if there were any difficulties getting online (Table A4). Workplace functionality had participants select which environmental factors were present at their workplace, and workplace constraints listed different factors that may have made working at home difficult. 2.1.4. COVID-19 Stressor Survey Questions The COVID-19 Stressor survey questions were located within the section dedicated to asking students not only about their experiences working at home, but also how COVID-19 affected them on a personal level. Questions related to the effects COVID-19 had on finances (Table A5), student experiences (Table A7), if any positive effects were experienced (Table A8), etc., were asked to determine if any direct consequences of the pandemic were experienced that were not covered in other sections. These were taken into consideration due to the mental struggle that may, or may not have, affected students during the COVID-19 pandemic 2.1.5. Exposure to Nature Survey Questions Another section assessed the “Individual’s Exposure to Nature”. This section asked 12 questions which evaluated students’ active and passive interactions with nature on a daily basis. This section also contained questions on hobbies, jobs, or regular interactions with plants. Researchers sought to record participants’ plant access (Table A6) during their hours working from home, indoor plant exposure they experienced at home (Table A9), and their amount of time experiencing nature in the outdoors (Table A10). This section assisted in printing a snapshot of a student’s daily plant exposure over the course of the initial stay-at-home order during the pandemic. 2.2. Participants This survey assessed depression, anxiety, stress, and academic stress of university students who engaged in online learning during the COVID-19 outbreak, and the impact of nature-based associations on these variables. The first part of the survey collected information on respondents’ basic demographics: age, student status, gender, and major. Any student who was currently enrolled in Texas Tech and over the age of 18 was eligible to participate. This guaranteed that only students would be allowed to participate in the survey, ensuring that the target population was achieved. An Institutional Review Board (IRB) review was conducted prior to administering the surveys and permission was granted to perform this research. 2.3. Survey Distribution Students at Texas Tech University were sent “TechAnnounce” messages about this survey at regular intervals throughout the summer and fall semesters of 2020. Out of the 40,322 students enrolled during the 2020 school year, we had a total of 353 respondents, making the response rate 0.9%. Texas Tech’s “TechAnnounce” is a daily email that contains university-wide notifications and information. In accordance with their recruitment periods, the responses to these surveys were grouped into “Spring/Summer 2020” and “Fall 2020” categories. Survey Monkey (Momentive, San Mateo, CA, USA) was used to distribute surveys to students. 2.4. Data Analysis and Reliability Data were prepared by coding similar answers with a uniform designator so that a mixed-methods analysis could be performed. Timeline, data collected, number of responses, timeframe of data collection, user groups, population, and distribution were compared using analysis of variance where appropriate. Where DASA scores were collected, questions were separated by category and correlated to DASA scores using fit models in JMP 15.0.0 (SAS, Cary, NC, USA). A Cronbach’s alpha test was performed on data obtained from DASA questions, showing a reliability of >0.9 for depression, anxiety, and stress sections and >0.8 for the academic stress section, indicating suitable reliability for the overall instrument [34]. The DASA questions were the same for both Spring/Summer and Fall semesters. Since the surveys differed slightly in the Spring/Summer and Fall 2020 semesters, they were not compared directly during analysis. Survey questions that were used for correlations had a list of answers for participants to select. A cumulative score was derived from summing the number of answers selected for each categorical question that can be found in the Appendix A. Those scores were then correlated to DASA scores using multivariate (Spearman’s ρ) correlation analysis. 3. Results 3.1. Demographics The demographics for the student populations are shown in Table 1. The sample size for Spring/Summer and Fall 2020 were 159 and 194, respectively. Most students fell within the 18–24-year-old age range and graduate students represented the largest proportion of respondents. Gender distribution was skewed to a female-student majority and most students did not have an academic major or minor that involved plants or plant sciences. Finally, prior to the issuance of COVID-19-related off-campus learning orders, almost all of the participants were on-campus students. 3.2. DASA Scores For the depression, anxiety, and stress scores, during both semester categories, most of the participants scored “extremely severe,” the highest score possible. There was a bimodal, almost multi-modal nature to responses for the DASS scores. The second most-frequent score ranking was “normal,” the lowest score possible. For the academic stress scores, Spring/Summer semester respondents had mostly normal and mild scores, while Fall semester respondents had mostly mild and moderate scores (Table 2). 3.3. Anxiety and Stress Scales by Gender Participants’ gender had significant effects on anxiety and stress scores. Female participants on average reported moderate to severe anxiety scores during the Spring/Summer semester, and moderate to severe stress scores during both Spring/Summer and Fall (Table 3). Comparatively, male respondents reported lower scores for anxiety and stress. 3.4. Response to Nature When looking at responses to the question, “If you do go outside, how do you feel when you return home?”, time outside correlated with significant effects on all depression, anxiety, stress, and academic stress scores in both semesters. Participants who reported feeling an enhancement of focus when they returned home after being outdoors had significantly lower DASA scores compared to those who felt worse when returning home, and/or did not go outside at all (Table 4). 3.5. Correlation between DASA Scores and Responses to Nature For the Spring/Summer semester, there were several significant correlations between measured survey responses. Indoor plant exposure was positively correlated with outdoor exposure, school–workplace constraints, and plant access (Table 5). Notably, outdoor exposure was negatively correlated with COVID-19 stress and DASA stress scores, and positively correlated with plant access and indoor plants. Furthermore, COVID-19-related stress was negatively related to outdoor exposure and beneficial effects perceived by respondents, but it was positively related to workplace constraints, difficulties getting online, financial issues, and DASA scores. This further illustrates not only the impacts of COVID-19 stress on students but also that some of this stress could possibly have been overcome by outdoor exposure (Table 5). Some notable trends worth mentioning were that outdoor plant exposure seems to be negatively correlated with DASA scores and COVID-19-related stressors. Furthermore, students with a highly functional workplace and plant access seemed to also have lower DASA scores (Table 5). During the Fall semester, many findings were similar to those in the Spring/Summer survey. Respondents who reported higher indoor plant exposure also reported higher outdoor exposure, workplace functionality, and plant access (Table 6). Higher outdoor exposure ratings were significantly related to lower depression, stress, and academic stress scores in participants. If participants deemed COVID-19 to be beneficial for their academic experiences, then their depression, anxiety, stress, and academic scores were lower. Furthermore, students that saw the beneficial aspects of COVID-19 also had higher outdoor exposure, workplace functionality, plant access, and lower reported COVID-related stress scores. Another noticeable trend that correlations expressed was that indoor plant exposure, outdoor plant exposure, and workplace functionality were negatively correlated with DASA scores (Table 6). 4. Discussion During the Fall semester, a significant upward trend was found for depression, anxiety, stress, and academic stress compared to the Summer semester. Early research published in March 2020 anticipated that many mental health issues would arise during the months following the initial quarantine and shelter-in-place orders, and that they would worsen as these restrictions continued [35,36]. This evidence illustrates the severity of the COVID-19 outbreak on student stress and overall mental health. Many students were aware of the impact that COVID-19 had had on their lives, whether it was through being at home, impacts on relationships, or an increased amount of academic stress. Recently published literature regarding the COVID-19 pandemic also found signs of deteriorating mental health in the general public [37,38]. We show that, in the college-student demographic, the pandemic had profound effects on mental and emotional health, both of which declined as the pandemic restrictions progressed. During the Fall semester, students’ depression scores significantly decreased when they started spending more time outdoors, and they recognized an improvement in mood. When asked, “Do you find yourself spending more time outdoors due to the current outbreak? If you are, do you think this is affecting your mood/ stress?”, students who responded positively to both questions had significantly lower depression scores in comparison to those who were not going outside. Students who felt worse when they went home after being outside or were not going outside at all had notably higher DASA scores in both semesters when compared to those who felt “an enhancement of focus” when returning home. This extended benefit of outdoor exposure is promising, particularly in regard to maintenance of mental health over a long period of time. Students who reported feeling “very good” after returning from nature had significantly lower DASA scores compared to those who felt worse or did not go outside, but that improvement was not as profound as it was among students who experienced restorative effects when returning home. These results showed that a person’s attitude and their awareness of noticeable differences in mood, in addition to how environments may influence both factors, are extremely important in realizing benefits from nature encounters. These findings significantly reflect Stephen Kaplan’s [39] research focusing on nature’s role in Attention Restoration Theory, which shows how the restorative effects of nature can play a role in decreasing stress and improving mental health. The research conducted in this study, together with the recently published literature regarding people’s involvement with nature during COVID-19, demonstrates that people recognize the benefits of being outside and are actually going outdoors more frequently, which underscores the importance of exposure to nature in their mental wellbeing. A multivariate correlation analysis of both semesters’ question categories along with DASA scores further illustrated the impact and relationships between DASA scores and student stress factors that were related to COVID-19 issues. During the Spring/Summer semester, outdoor exposure significantly reduced COVID-19-related stress, but the relationship was not significant during the Fall semester. This difference may have been due to the likelihood that outdoor activities increased during the Spring/Summer, with the accompanying effects of exposure to nature that alleviated stress [40,41]. Outdoor exposure did significantly reduce stress scores during the Spring/Summer while specifically reducing depression, stress, and academic stress during the Fall. These results show that nature still had a beneficial effect on student mental health; however, it was not enough to entirely mitigate all factors related to pandemic-induced stressors. Interestingly, when students had a positive attitude toward COVID-19 compared to being consciously, negatively affected by the pandemic, they also reported lower depression, anxiety, stress, and academic stress. Yet, those who reported more beneficial effects of the pandemic also reported higher outdoor exposure and workplace functionality. These results indicate that attitudes towards COVID-19 may not be straightforward but may be influenced by environmental or socioeconomic advantages. Conversely, those who reported more COVID-19-related stress also reported more workplace constraints, difficulties getting online, financial issues, and worse mental health. This also implies that accessibility to outdoors, a good workplace, and adequate internet may disproportionally affect those without financial means. While students who reported higher COVID-19-related stress in the Fall semester did not significantly benefit from outdoor exposure, this was most likely due to the chronic stress from the pandemic [42]. This finding coincides with other research, as those who experience high-stress situations cannot gain significant relief in coping with their circumstances from just nature alone, although the natural interactions do assist in improving quality of life [43]. While these results indicate that the best method to reduce COVID-19-related stress was by keeping a positive outlook on the situation, that might not have been possible for everyone, particularly those with more financial and environmental constraints. However, it is clear that plant interactions did play a significant role in alleviating some of the mental taxation, stress, and other negative factors that were associated with the worldwide pandemic. Limitations This research sought to investigate how pandemic-induced learning environments affected students during COVID-19 and how the influence of interactions with nature impacted DASA-related outcomes. Nonetheless, the study had several limitations. A key limitation in the overall study design was the usage of two different survey tools. This led to an inability to directly compare results from spring/summer to fall. Second, there was a lack of ability to follow individuals over time. Third, female participants represented a large percentage of the participant pool. The university population is approximately 50% female. However, our respondents were disproportionally female (77%). We believe this is likely due to more interest in plants or the incentives given to participants. Female survey participants have been shown to have higher reporting rates for negative emotions over male counterparts [44]. This tendency could have affected the present results. Fourth, a question about race/ ethnicity would have been useful to see if race had any significant effect on the data. The same is true of a question about location of participants. During the Spring/Summer, all students were attending classes online and could have been living anywhere. Different locations could have produced different results, especially when comparing outdoor experiences in urban and rural areas. Furthermore, this could also affect plant access selections due to the number of natural elements that could or could not be present in either rural or urban environments. Another limitation was that we did not inquire why individuals choose to go outside. This might have provided valuable data on the choices being made (e.g., smoke break, humanitarian effort, outdoor exercise) which might have influenced the individuals and also our findings. Researchers adding another response choice in the DASS-based questionnaire holds another limitation since this extra option was not psychometrically validated. Finally, this was written, survey-based research, without an opportunity for follow-up questions and probes to clarify or expand the content of responses. The information given was subject to interpretation by investigators, made through the filter of their personal understanding. Despite these limitations, the data from this study present unique findings and suggest some ways in which greenspace and experiences of nature can be designed into students’ surroundings, both in classrooms and throughout campus, to maximize their mental health, academic achievement, and satisfaction with campus life. Future research could enhance these findings by further evaluating the longer-term impacts of the extended pandemic period and considering more variables and how they apply to mental health and nature interactions. 5. Conclusions COVID-19-related stress was found to be a powerful factor that influenced self-reported mental health scores in this study, indicating that the COVID-19 situation itself negatively affected students’ lives. However, plants and nature were found to positively influence students’ mental health and diminish stress to varying degrees. In this study, outdoor experiences positively influenced DASA scores, which showed restorative effects. Furthermore, in the short-term, interactions with plants and nature, both indoors and outdoors, provided some benefits, which was evidenced in reduced depression, anxiety, and stress scores. Nevertheless, this alone does not appear to alleviate the effects of chronic stress and it should be noted that, over time, the benefits of interactions with nature and plants declined. Acknowledgments The authors would like to acknowledge Alicia Thomas and James Surles for their assistance with research and statistical analysis. The authors would also like to thank the lab group with their support and insight over the project. Author Contributions Conceptualization, C.R.S. and J.E.T.; methodology, C.S, J.E.T., C.S.L. and M.M.; validation, C.R.S. and J.E.T.; formal analysis, J.E.T., C.R.S. and C.S.L.; investigation, J.E.T. and C.R.S.; resources, C.R.S.; data curation, J.E.T.; writing—original draft preparation, J.T and C.R.S.; writing—review and editing, M.M., C.S.L., J.E.T. and C.R.S.; visualization, J.E.T. and C.R.S.; supervision, C.R.S.; project administration, C.R.S.; funding acquisition, C.R.S. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Texas Tech University (IRB2020-333, 26 May 2020). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement Data are not available upon request because of confidentiality and assurances made to participants. Conflicts of Interest The authors declare no conflict of interest. Appendix A ijerph-19-05030-t0A1_Table A1 Table A1 DASA scoring rubric for student surveys. Meaning Depression * Anxiety * Stress * Academic Stress Normal 0–9 0–7 0–14 0–9 Mild 10–13 8–9 15–18 10–19 Moderate 14–20 10–14 19–25 20–29 Severe 21–27 15–19 26–33 30–40 Extremely Severe 28+ 20+ 34+ - Based off DASS 21 scale *. ijerph-19-05030-t0A2_Table A2 Table A2 Workplace functionality rubric for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Workplace Functionality Scoring Rubric Desk 2 Separate room 1 No defined space −1 Window 1 Next to indoor plants 1 Outside 1 On campus 1 ijerph-19-05030-t0A3_Table A3 Table A3 Workplace constraints rubric for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Workplace Constraints Scoring Rubric Loud 1 Cluttered 1 Poor lighting 1 Sleeping roommate 1 Small work area 1 Children 1 Time 1 Bandwidth 1 ijerph-19-05030-t0A4_Table A4 Table A4 Difficulties getting online assessment rubric for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Difficulties Getting Online Scoring Rubric Equipment 1 Getting online 1 Accessing video/meetings 1 Accessing blackboard 1 Accessing posted materials or lectures 1 Other remote access issues 1 Everything running smoothly 0 ijerph-19-05030-t0A5_Table A5 Table A5 Financial issues assessment for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Financial Issues due to COVID-19 Scoring Rubric I lost my job 1 A family member lost their job 1 My partner lost job 1 No one lost their job 0 Other 0 ijerph-19-05030-t0A6_Table A6 Table A6 Plant access assessment for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Plant Access Inside Home Working Environment Scoring Rubric Window views 1 Real plants 1 Fake pants 1 Work outside 1 Other 0 ijerph-19-05030-t0A7_Table A7 Table A7 COVID-19-related stress assessment for surveys. Participants were told to select all that applied, and scores were cumulative based on their selections. COVID-19related Stress Scoring Rubric No, it has made life easier 0 Yes, this outbreak has caused financial stress on me. 1 Yes, this outbreak has affected my relationships with people. 1 Yes, this outbreak has caused me stress due to being at home. 1 Yes, this outbreak has caused me an increase amount of academic stress. 1 Yes, this outbreak has caused me stress for a reason not listed. 1 ijerph-19-05030-t0A8_Table A8 Table A8 Beneficial views of COVID-19 assessment based on survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Beneficial Viewpoints of COVID-19 as a Student Scoring Rubric No, this event is stressful on me as a student (See above). 0 Yes, the recent shift to online school has made school easier. 1 Yes, working at home alleviated commutes to campus making life easier. 1 Yes, the shift made my classes less intense. 1 Yes, the shift allowed me to gain more focus at home. 1 Yes, but for reasons not listed. 1 ijerph-19-05030-t0A9_Table A9 Table A9 Indoor plant exposure assessment for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Indoor Plant Exposure Scoring Rubric No plants 0 Few plants (1–3) 1 Moderate (4–10) 2 Many plants (11+) 3 ijerph-19-05030-t0A10_Table A10 Table A10 Outdoor exposure assessment for survey answers. Participants were told to select all that applied, and scores were cumulative based on their selections. Outdoor Exposure Scoring Rubric Does not take care of plants outside/No job outside/<2 h outside a week 0 Takes care of plants outside 1 Job outside 1 3–6 h outside a week 1 7–12 h outside a week 2 13–20+ h outside a week 3 ijerph-19-05030-t001_Table 1 Table 1 Demographics of student respondents to the COVID-19 questionnaire in Spring/Summer and Fall 2020. Question Answer Spring/Summer 2020 * Fall 2020 * N = 159 N = 194 Age group 18–24 121 76.1% 153 78.9% 25–34 31 19.5% 29 15% 35–44 4 2.5% 8 4.1% 45–54 3 1.9% 3 1.5% 55–64 0 - 1 0.5% 65+ 0 - 0 - Total: 159 100% 194 100% Student status Freshman 15 9.4% 43 22.2% Sophomore 25 15.7% 29 15% Junior 29 18.2% 33 17% Senior 41 25.8% 38 19.6% Graduate Student 48 30.2% 50 25.8% Not sure 0 - 1 0.5% Total: 158 99.4% 194 100% Gender Male 30 18.9% 45 23.2% Female 123 77.4% 142 73.2% Non-binary 4 2.5% 4 2.1% Prefer not to say 1 0.6% 3 1.5% Total: 158 99.4% 194 100% Major/Minor involve plants? Yes 14 8.8% 30 15.5% No 144 90.6% 164 84.5% Total: 158 99.4% 194 100% Pre-COVID-19 learning method On-campus student 145 91.2% 167 86.1% Online student 13 8.2% 26 13.4% Total: 158 99.4% 193 99.5% * Totals are for the respondent participation, not for additive questions which resulted in amounts less than 100%. Numbers were rounded to ±1 significant figure for calculation, which led to minor variations in the totals. ijerph-19-05030-t002_Table 2 Table 2 Distribution of students in depression, anxiety, stress, and academic stress rating scales. Spring/Summer 2020 * Fall 2020 * Scale N = 159 N = 194 Depression rating Normal 38 23.90% 38 19.60% Mild 17 10.70% 15 7.70% Moderate 24 15.10% 33 17% Severe 18 11.30% 22 11.30% Extremely severe 54 34% 69 35.60% Total: 151 95% 177 91.20% Anxiety rating Normal 46 28.90% 52 26.80% Mild 10 6.30% 11 5.70% Moderate 22 13.80% 22 11.30% Severe 13 8.20% 11 5.70% Extremely severe 60 37.70% 81 41.80% Total: 151 95% 177 91.20% Stress rating Normal 38 23.90% 46 23.70% Mild 14 8.80% 11 5.70% Moderate 28 17.60% 21 10.80% Severe 16 10.10% 30 15.50% Extremely severe 55 34.60% 69 35.60% Total: 151 95% 177 91.20% Academic stress rating Normal 40 25.20% 33 17% Mild 40 25.20% 54 27.80% Moderate 29 18.20% 41 21.10% Severe 22 13.80% 34 17.50% Total: 131 82.40% 162 83.50% * Totals are for the respondent participation, not for additive questions which resulted in amounts less than 100%. Numbers were rounded to ±1 significant figure for calculation, which led to minor variations in the totals. ijerph-19-05030-t003_Table 3 Table 3 Effects of DASA scores on gender. Gender Spring/Summer Fall Depression scale Female 3.34 3.47 Male 2.67 3.03 p depression 0.203 0.227 Anxiety scale Z Female 3.46 a 3.49 Male 2.33 b 2.74 p anxiety 0.0065 0.1285 Stress scale Z Female 3.45 a 3.58 a Male 2.5 b 2.71 b p stress 0.0164 0.0153 Academic stress scale Female 2.3 2.54 Male 1.91 2.25 p academic stress 0.279 0.054 Z Different lowercase letters within a column represent significant differences between designated values as specified by Student’s t-tests (p ≤ 0.05) as appropriate. Italicized p-values represent significance at p ≤ 0.05. Spring/Summer is missing n = 5 and Fall is missing n = 7 due to low number of people identifying as non-binary or preferring not to say their gender. ijerph-19-05030-t004_Table 4 Table 4 Influence of going outside on depression, anxiety, stress, and academic stress by semester. If You Do Go Outside, How Do You Feel When You Return Home? Spring/ Summer Fall Depression Z Anxiety Z Stress Z Academic Depression Z Anxiety Z Stress Z Academic Stress Z Stress Z I don’t go outside 23.38 b 20.46 ab 33.08 ab 16.67 b 31.23 a 22.67 ab 30.38 ab 23.6 a I feel worse when I go home 39.86 a 28.29 a 37.29 a 28.73 a 32.23 a 26.71 a 36.71 a 23.47 a I feel the same when I go home 22.8 b 16.32 b 20.72 d 16 b 21.26 bc 14.32 c 23.11 c 17.94 ab I feel a little better when I go home 23.05 b 17.16 b 27.61 bc 16.2 b 23.49 b 20.25 ab 29.27 b 20.19 ab I feel very good when I go home 17.05 bc 11.52 b 21.33 cd 16.5 b 22.58 abc 16.82 bc 25.65 bc 14.63 bc I feel an enhancement of focus 12.19 c 11.71 b 18.29 d 8.5 c 14.64 c 11.71 c 17.79 c 12.62 c p value 0.0001 0.0041 0.0001 0.0001 0.0011 0.0026 0.0001 0.0029 Z Different lowercase letters within a column represent significant differences between designated values as specified by Student’s t-tests (p ≤ 0.05. Italicized p-values represent significance at p ≤ 0.05. ijerph-19-05030-t005_Table 5 Table 5 Multivariate Correlation Analysis of DASA and survey categories for Spring/Summer. Spearman’s ρ correlation was performed to assess relationships between variables. Indoor Plant Exposure Outdoor Exposure Workplace Functionality Workplace Constraints Difficulties Getting Online Financial Issues Plant Access COVID-19-Related Stress Beneficial Effects of COVID-19 Depression Rating Anxiety Rating Stress Rating Academic Stress Score Indoor plant exposure 1 Outdoor exposure 0.174 * 1 Workplace functionality 0.104 0.127 1 Workplace constraints 0.0.147 0.021 −0.164 * 1 Difficulties getting online 0.093 0.130 0.011 0.363 *** 1 Financial issues −0.022 −0.049 −0.077 0.025 0.028 1 Plant access 0.343 *** 0.236 ** 0.195 * −0.103 0.019 −0.061 1 COVID-19-related stress 0.014 −0.190 * −0.084 0.292 *** 0.207 * 0.201 * −0.051 1 Beneficial effects of COVID-19 0.060 0.0059 0.019 −0.139 −0.038 −0.007 0.075 −0.212 ** 1 Depression rating 0.103 −0.117 −0.163 * 0.298 ** 0.104 0.085 −0.124 0.432 *** −0.150 1 Anxiety rating 0.164 * −0.135 −0.134 0.240 * 0.161 0.085 −0.038 0.380 *** −0.091 0.748 *** 1 Stress rating 0.121 −0.181 * −0.064 0.353 *** 0.179 * 0.082 −0.067 0.455 *** −0.189 0.790 *** 0.834 *** 1 Academic stress score −0.024 −0.088 −0.101 0.244 ** 0.135 0.129 −0.093 0.251 ** −0.195 * 0.532 *** 0.534 *** 0.597 *** 1 Values that have *** indicate significance < 0.001. Values that have ** indicate significance < 0.01. Values that have * indicate significance < 0.05. ijerph-19-05030-t006_Table 6 Table 6 Multivariate Correlation Analysis of DASA and survey categories for Fall. Spearman’s ρ correlation was performed to assess relationships between variables. N = 194 Indoor Plant Exposure Outdoor Exposure Workspace Functionality Workspace Constraints Difficulties Getting Online Financial Issues Plant Access COVID-19-Related Stress Beneficial Effects of COVID-19 Depression Rating Anxiety Rating Stress Rating Academic Stress Score Indoor plant exposure 1 Outdoor exposure 0.307 *** 1 Workspace functionality 0.267 ** 0.011 1 Workspace constraints 0.074 0.025 0.006 1 Difficulties getting online 0.044 0.029 −0.061 0.378 *** 1 Financial issues −0.046 0.076 −0.067 0.162 ** 0.159 * 1 Plant access 0.332 *** 0.079 0.316 *** −0.057 −0.002 −0.082 1 COVID-19-related stress 0.005 −0.021 −0.017 0.415 *** 0.341 *** 0.196 ** −0.009 1 Beneficial effects of COVID-19 0.137 0.164 * 0.169 * −0.135 0.028 −0.074 0.175 * −0.289 1 Depression rating −0.106 −0.236 ** −0.022 0.179 * 0.040 0.189 −0.004 0.441 *** −0.173 * 1 Anxiety rating −0.076 −0.133 −0.009 0.340 *** 0.124 0.189 * 0.11 0.502 *** −0.228 ** 0.645 *** 1 Stress rating −0.023 −0.170 * −0.011 0.30 *** 0.135 0.049 0.057 0.496 *** −0.253 ** 0.641 *** 0.785 *** 1 Academic stress score −0.044 −0.248 *** 0.009 0.283 *** 0.191 * 0.188 * 0.036 0.407 *** −0.241 * 0.583 *** 0.522 *** 0.590 *** 1 Values that have *** indicate significance < 0.001. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095115 ijerph-19-05115 Article The Health Impacts and Life Challenges Caused by the COVID-19 Pandemic on Hong Kong Chinese Women https://orcid.org/0000-0001-9989-9498 Hung Maria Shuk Yu 1* https://orcid.org/0000-0001-7426-7603 Chan Liliane Chui King 2 Liu Sisi Pui Shan 2 Binns Colin W. Academic Editor Lee Mi Kyung Academic Editor 1 School of Nursing, Tung Wah College, Hong Kong, China 2 Hong Kong Federation of Women’s Centres, Hong Kong, China; liliane.cck@gmail.com (L.C.K.C.); sisi.liu@womencentre.org.hk (S.P.S.L.) * Correspondence: mariahung@twc.edu.hk; Tel.: +852-3468-6804 22 4 2022 5 2022 19 9 511514 3 2022 20 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The COVID-19 pandemic has caused a massive global crisis. The adverse impacts on Asian women, including Hong Kong Chinese women, have been considerable. The pressure on Hong Kong women is immense due to cultural, social, familial, and personal responsibilities. This study aims to illustrate the health impacts and life challenges for Hong Kong Chinese Women during the pandemic. An interpretive phenomenological approach with purposive sampling was adopted. Semi-structured, face-to-face, in-depth interviews were conducted from August 2020 to January 2021. Twenty-five women participated in the interviews, lasting an average of 48 min. The transcribed interviews were analyzed using interpretative phenomenological analysis. The core theme identified was “Perceived family caregiving as paramount self-obligation in times of the pandemic”, in the context of the role of daughter, wife, or mother (or a combination). Three interconnected themes have been identified in individual, relational, and external contexts: deterioration of personal health, unfavorable to family relationships, and adaptation to social challenges. Eight subthemes have emerged related to health impacts and life challenges. The pandemic has increased women’s perception of their caregiver roles in the family, but it has diminished their quality of life. The promotion of strategies and activities that could enhance women’s physical, psychological, emotional and social quality of life is recommended. COVID-19 pandemic health impacts life challenges Hong Kong women qualitative ==== Body pmc1. Introduction The COVID-19 pandemic has been causing global human health, social, economic, and environmental crises since late 2019. Up to mid-April 2022, about 500 million and more than 6 million people had been infected and died, respectively [1]. The United Nations [2] has highlighted the impact of COVID-19 on women, including the complex negative economic consequences, adversely affected health, demands for increased unpaid care work, etc. These adverse impacts on women, and especially on Asian women, have been substantial, as these women have traditional and cultural responsibilities as families’ primary informal caregivers. Previous evidence has shown that women in China with parental caregiving obligations had poorer self-reported health than those who were not caregivers [3]. During COVID-19, the closing of schools to limit disease transmission has placed extra care demands and workloads on women, who are forced to care for their children at home. The increases in care demand have probably raised stress levels. This could affect women’s mental health and employment opportunities [4]. The significant reductions in financial status due to the pandemic have further induced worries and life quality changes [5,6]. A recent review identified the considerable risk of specific adverse health outcomes negatively affecting women’s health during the COVID-19 pandemic [7]. Although Hong Kong citizens have experienced various previous public health emergencies [8,9], e.g., the SARS and H1N1 pandemics, evidence has shown that Hong Kong citizens’ emotional, social, and economic status has been unavoidably affected by COVID-19, especially women [10,11]. A local survey assessed 126 middle-aged women who had experienced a psychological burden during SARS in 2003; their emotional distress was primarily related to financial losses and risk perception [8]. Most respondents (77%) were afraid of contracting the disease, and about two-thirds (70%) limited their social activities more than usual. A quarter of respondents reported restless sleep. Forty percent worried about their financial situation and their reduced household incomes [8]. Another local survey found that uninfected citizens with moderate anxiety levels and higher risk perceptions were more willing to endure thorough precautionary measures to prevent SARS infection [9]. Furthermore, those who had severe anxiety and feelings of helplessness may take inappropriate and potentially harmful remedies. In early 2020, the COVID-19 pandemic triggered panic in communities, leading to the purchasing of personal protective and disinfectant items [12]. The authors of this paper also conducted an initial online survey of 417 women aged 18 years or above in Hong Kong, seeking to assess the impacts of COVID-19 on this group [10]. The results show a high percentage of negative emotions such as stress (32.2%), anxiety (42.4%), and depression (44.9%), with significant negative correlations between emotional state and different aspects of quality of life [10]. Concerning the evidence that public health emergencies affect women most severely, this in-depth qualitative study aimed to understand the health impacts and life challenges related to COVID-19 on Chinese women in Hong Kong. The objectives were to explore their experiences of psychological and physical health impacts, and to identify the life challenges associated with caring for family during lockdown. The findings provide useful information that could assist local government officials, non-government organizations, and healthcare professionals in designing appropriate support systems and activities that will assist women during or after a pandemic. 2. Materials and Methods 2.1. Design and Participants This study adopted a qualitative phenomenological approach. The ultimate goal of this method is to illustrate, comprehend and explain the informants’ experience [11]. The purposive sampling of subjects offering high amounts of information was employed in this study. Each participant could provide their experiences and share their viewpoints [13]. The subjects invited were the 417 women who had participated in the previous online survey [10], with the following inclusion criteria: Chinese females aged over 18, living in Hong Kong, who could understand Cantonese. Those who voluntarily agreed to participate in this extended study were called on the phone to invite them to participate in the individual interview. 2.2. Ethical Considerations Ethical approval was obtained from the Research Ethics Sub-Committee of the researcher’s institution (Ref. No. REC2020071). The study was implemented based on the general principles of the Declaration of Helsinki. The detailed information of the structure and purpose of the study was given to the potential participants. Before the interviews, written informed consent was received from each subject. They had the right to refuse to join, and could refuse to answer any questions during the interviews. All the research information was kept confidential, and the anonymity of the participants was guaranteed. 2.3. Data Collection After a preliminary quantitative analysis of the above-mentioned online survey [10], the participants’ emotional state, life experiences, and self-perceived coping ability and strategies were queried via guiding questions. Semi-structured, face-to-face, in-depth interviews were commenced in late August 2020. Semi-structured interviews are the most appropriate data collection method for interpretative phenomenological analyses [13]. Through semi-structured interviews, researchers were able to modify the initial questions based on participants’ responses, and explore any meaningful and significant content that emerged [13]. The interviews were conducted at the centers of different districts in Hong Kong that were familiar to the participants. The rooms used were comfortable, quiet, and free from distractions. Following the Hong Kong government’s policy of social distancing, only one-to-one interviews with appropriate distancing and adequate disinfection measures were performed. Before the interview, casual conversation was undertaken with the participants, and general questions were asked to learn more about them and establish a level of trust. For those women who brought their children, the center staff offered assistance in taking care of them in another room. This allowed both the participants and the interviewer to engage in a confidential and natural conversation without interruption. The interview’s purpose, how the information would be used, and the estimated duration of the interview were also explained. Consent for audio recording was also sought. An interview guide was developed to aid in our understanding of the participants’ life experiences during the COVID-19 pandemic. For instance, “How did the COVID-19 pandemic affect your health and daily life? Please describe the unforgettable feelings or experiences in the past few months that COVID-19 caused.” Participants were asked to relate their feelings and experiences related to the health impacts and life challenges of COVID-19. They were encouraged to freely express their views and concerns. The interviewers were careful when asking questions and expressing responses, maintaining a friendly atmosphere and good rapport in order to encourage the participants to share their personal experiences. The researcher remained aware of her own preconceptions and personal experiences regarding COVID-19, and did not communicate these to participants, as this may have influenced their views. Subsequent probing and clarification questions were asked if required. Any coping strategies the participants used during the pandemic were also asked about. The participants’ emotional conditions were closely observed throughout the interviews. Five of them experienced emotional distress, and three began to cry. Appropriate supportive actions, e.g., a short period of silence or rest, were undertaken. If required and the participant consented, they were referred for further support, such as psychological and financial counseling or legal advice on marital issues. The interviews lasted from 30 to 90 min, with an average of 48 min. The interviews yielded a valuable in-depth understanding of participants’ experiences during the pandemic. All the audio-recorded interviews were then transcribed for analysis. Field notes were taken to document the participants’ demographics, emotions, gestures and physical expressions, and the researcher’s impressions during and after the interviews. These notes were later incorporated into the verbatim transcripts. The researchers reviewed the first semi-structured interview transcript for interview skills, text accuracy, and overall understanding, and made minor adjustments before the following interviews. 2.4. Data Analysis The data analysis was based on interpretative phenomenological analysis (IPA) [13]. The purpose of IPA is to recognize the essential subjective meaning of a participant’s experience of an event, instead of quantifying the data. The researchers sought to understand and interpret the participants’ personal, psychological and social world through IPA [13]. A step-by-step process was utilized to explore the participants’ perceptions [13]. The researchers explored themes derived from the transcription of the first participant’s interview, linking the themes and then progressing with the analysis of other participants [13]. Two team members individually read through the transcribed interviews and notes, and they wrote comments on the transcript to help identify recurrent ideas and expressions. Meaningful sections were further organized and categorized according to their characteristics. The researchers then sought themes and subthemes that would reveal the similarities and natural variations among individual participants, forming relationships within the data. The analysis was then continued with other participants. The researchers’ pre-existent understanding and knowledge regarding the pandemic assisted them in forming interpretations during the data analysis. After 24 participants had been interviewed, data saturation occurred. The researchers continued to interpret and refine the main themes and subthemes. Then, one more participant was recruited for an interview in January 2021 to verify these themes and subthemes. 3. Results Of the 25 women participants, most (72%) were aged between 30 and 39 (40%) or 60 and 69 (32%). Fifteen were married, and six were separated/divorced. In total, 21 (84%) participants had caregiving responsibilities with children and/or elderly parents, and 8 (32%) had to care for two family members. Eight (32%) were housewives, and five (20%) were retired. The demographic information of these participants is summarized in Table 1. The impact of COVID-19 on the Hong Kong women’s health and lifestyles is illustrated through the core theme, the three other themes, and the eight subthemes shown in Table 2. The core theme was “Perceived family caregiving as paramount self-obligation in times of the COVID-19 pandemic” as a daughter, wife and/or mother (or in combined roles). Three other interconnected themes arose in individual, relational, and external contexts, respectively: deterioration of personal health, unfavorable to family relationships, and adaptation to social challenges. 3.1. Deterioration of Personal Health The first theme, “deterioration of personal health”, relates to the individual impacts on personal health. The two subthemes included emotional and psychological distress due to fear of COVID-19’s consequences, and physical exhaustion in performing the family caregiver role during COVID-19. 3.1.1. Emotional and Psychological Distress Due to Fear of COVID-19’s Consequences In relation to this subtheme, almost all participants (22 out of 25) expressed emotional and psychological distress due to the fear of themselves or family members becoming infected, e.g., unhappiness, worries, anxiety, stress, and depression. Apart from the fear of the direct consequences of being infected, the participants also expressed worries and concerns about the indirect impacts on their health and their family, e.g., financial losses. A mother with two children who was separated from her husband verbalized her fear of being infected, and its consequences. I’ve been under tremendous stress and anxiety. I’m a private tutor of school children and should have frequent contact with them and their parents. I was afraid that I would get infected or my kids would be infected as they followed me to my workplace. If I got infected, I could not work, and my income would be affected. (P17) A retired participant expressed that her fear of contracting coronavirus led her to experience physiological symptoms that affected her physical wellbeing. She commented as below: I had gone to the places with confirmed cases. I’ve inadequate masks. I’m afraid I got infected and then transmitted it to others. I was so scared that I would die. I started to have a stomachache weeks ago and could not sleep well. (P1) 3.1.2. Physical Exhaustion in Performing the Family Caregiver Role during COVID-19 Among the 25 participants, 21 were caregivers of family members, and 8 had responsibilities of care for two family members. The participants mentioned additional caregiving responsibilities that had arisen under the lockdown policy put in place during the pandemic, further aggravating their burdens. Seventeen participants experienced physiological symptoms of palpitations, inadequate rest, poor sleep quality, etc., primarily due to their additional family caregiving roles. A housewife with two small children shared her daily experience as follows: I have a boy, aged five, and a girl, aged one. The kindergartens have been closed in the past few months due to the social distancing and lockdown policy. My son used to learn at the kindergarten during the daytime on weekdays. Apart from my busy family routines such as meal cooking, clothes washing, and extra household cleaning …, I should take care of them also. They are active, energetic, and playful at home. You know, just like having a battle every day! I’m so tired and exhausted every day. I cannot sleep well and have sufficient rest. (P16) A few participants experienced severe physical impacts, including weight loss, chest pain and exhaustion, when performing their intense or continuous family caregiving roles. A retired lady who lived with and cared for her husband and daughter verbalized her recent challenges as follows: My husband is old and has a mobility problem, and my daughter is developmentally disabled. Due to the pandemic, the elderly daycare center and the shelter workshop were closed. They could not go there on weekdays and must stay at home. I take care of them by myself all around the clock without a break. I’m so exhausted. It’s too difficult for me. I have had insomnia with a 3 kg weight loss recently. (P8) Although this lady voiced her difficulties, she stated she was still willing to care for her loved ones until such time as she was unable. 3.2. Unfavorable to Family Relationships The second theme, “unfavorable to family relationships”, regards the impacts on the relationship with the family as a daughter, wife, or mother. The three subthemes identified were regrets of unfulfilled filial responsibilities, unhealthy marital relationships, and tension in parent–child relationships due to the prolonged pandemic. All these subthemes strongly reflect the traditional family values of Chinese women. 3.2.1. Regrets of Unfulfilled Filial Responsibilities Due to social distancing measures and the lockdown policy, elderly care centers were also closed. Six of the twenty-five participants stated they could not visit or take care of their sick family members, especially their parents who did not live with them in Hong Kong or mainland China. The participants’ comments reveal that filial piety and parental care are significant traditional concepts deeply rooted in their minds. For instance, they exemplify the Chinese values of caring for parents and being present as a family during parents’ final moments. One participant’s experiences speak of a feeling of regret about unfulfilled filial responsibilities, as follows: My father-in-law fell from a height with a broken leg a few months ago in China. He was admitted to the hospital for an operation. We (participant and husband) should have, but we could not go there to care for him due to the lockdown. Our children and we missed him so much! (P16) Another participant expressed the painful experience, and the guilty feelings associated with it, of not being present in the hospital during her father’s last moments due to the lockdown policy. I was so guilty about my father’s death. In June, my father’s health deteriorated suddenly. He was admitted to a hospital in mainland China. Due to the lockdown policy, we could not take care of him or bring him back to Hong Kong for treatment. We should be with him during his last moment. We were so regretful and felt helpless. He passed away finally. (P25) 3.2.2. Unhealthy Marital Relationship Besides unfulfilled filial responsibilities, participants also mentioned that their marital relationships were affected during the pandemic. Eleven of the twenty-five participants described strained or deteriorating relationships primarily related to prolonged separation, financial losses, and inadequate communication during the pandemic. Besides this, their husbands’ underemployment or unemployment led to decreased monthly household incomes, which further induced family conflicts and indirectly damaged the harmony of the marriage. For example, one participant described a broken relationship with her husband. My husband had previously worked at a hotel but was terminated by his employer in March 2020. Since then, he could not find a full-time job. After that, we started to have more quarrels and conflicts than before due to financial problems. We are separated, and he moved out for a few months. (P13) In addition to the financial problems, the prolonged separation due to the lockdown in Hong Kong may have limited couples’ intimacy, giving rise to an unhealthy marital relationship. A young housewife expressed her worries. My husband works in China on weekdays and comes back every Saturday. Apart from my daughter, I have no other relatives in Hong Kong. After having the lockdown policy, he stayed in China and could not come back for months. I’m so worried about our relationship as some of my friends divorced due to a long-time separation. (P6) 3.2.3. Tension in the Parent–Child Relationship Thirteen of the twenty-five participants fulfilled caregiving roles for their children during the pandemic, and eleven of them experienced conflict and tension in their parent–child relationships due primarily to children’s distance/online learning at home and the limited opportunities for outdoor activities. One mother experienced stress and difficulties when supervising her child, causing an increase in the harshness of her parenting and greater tension in the parent–child relationship. After school closure several months ago, my 8-year-old daughter should have online learning at home. She was not interested and concentrated on her online learning. She is naughty and did not finish her home assignments as scheduled. I was so angry and punished her sometimes. I could not control myself, and I regretted beating her. Also, I’m not familiar with her school assignments and using a computer. (P5) Another participant echoed this sentiment, and further described difficulties in resolving the tension: I’ve two naughty and energetic little boys. They are studying K2 and K3. They don’t understand why they should stay at home. They always request to go to the playgrounds or child centers. (P12) 3.3. Adaptation to Social Challenges The third theme, “Adaptation to social challenges”, regards the external impacts on the female participants as citizens. The three subthemes identified were: avoidance of social activities due to the social distancing policy; changes in environmental and household hygiene practices; and underemployment/unemployment/resignation causing financial losses. The participants adapted to these social challenges to ensure their safety and safeguard their families. 3.3.1. Avoidance of Social Activities Due to Social Distancing Policy Due to the local government’s social distancing policy and the closure of public facilities, eighteen participants showed understanding and mainly stayed at home, avoiding social activities or gatherings. Although nearly all outdoor activities were suspended, the participants had their own means of maintaining necessary social interactions and family connections. One participant described her approach as follows: I like swimming, table tennis, shopping and traveling with my family or friends. But I have seldom gone out with them in recent months because of the closure of the public facilities and the social distancing policy. Although we had reduced social gatherings, we maintained contacts via phone or social media networks. (P7) Another participant supported the social distancing policy, understanding its importance in preventing the transmission of the disease. However, she emphasized concerns about her mother and elderly people with medical or mental health issues. I agree and support that we should minimize social gatherings or group activities to limit the spread of the disease. But I should regularly visit my mother as she has early dementia. Even though I could not visit her every day, I gave her a phone call daily. As a daughter, I think we should closely observe and care for our parents and those elderly in need, do not just leave them alone unattended. (P4) 3.3.2. Environmental and Household Hygiene Practices Changes Sixteen participants cleaned their households more often, or changed their hygiene practices, during the pandemic to prevent the transmission of the disease. Twelve participants showed an awareness of the need to maintain environmental and household hygiene. A mother with two children referred to using sanitizing products and wearing masks to prevent infection. You know, it’s not easy to buy sanitizing products for household cleansing and suitable masks for my kids to put on as there was inadequate supply in the early stage. (P17) One housewife lived with her husband and was the caregiver of her parents, who were not living together. Although the additional cleaning was time-consuming and labor-intensive, these actions increased her sense of safety. I have comprehensive household cleansing increased to twice a day instead of twice a week before. After every shopping or visiting my parents, I take a shower, thoroughly cleanse my body and sanitize my clothes. When I go out, I try to avoid touching public facilities and decrease visits to the wet market. (P9) 3.3.3. Underemployment/Unemployment/Resignation Causing Financial Losses In this subtheme, we can see that the employment opportunities of the participants or their husbands largely affected their financial situation. Of the 25 participants, 12 had full-time (3) or part-time jobs (9) before the pandemic. Ten of them had their voluntary or involuntary work reduced or terminated. Seven were underemployed or unemployed. Three resigned from their job, as they perceived their responsibility for taking care of their children was greater. Two of these three participants divorced, and one was separated from her husband. Some participants also reported that the additional expenses of protective masks, sanitizing products, or online-learning resources for their children further increase their financial burden. A retired participant who was divorced with one daughter described the challenges as below: Being the mother, I should care for my daughter at home during the pandemic. Thus, I resigned from my part-time job. Only the monthly allowance from the government supports our daily expenses! (P3) Another participant (separated from her husband) complained that her monthly income was reduced, but her expenses had increased. My husband has been underemployed for a few months with decreased salary. However, my son has online learning at home. In these few months, we should have extra expenses to purchase protective masks, sanitizers, cleansing products, and my son’s online learning equipment. (P19) 4. Discussion Our findings show that the impacts of the COVID-19 pandemic on Hong Kong women were substantial. Even during the pandemic, they perceived family caregiving as their highest obligation, given their role as daughter, wife, or mother (or combinations thereof). All the themes and subthemes identified are interconnected. No matter the participants’ roles in life, they encountered personal (psychological and physical), familial, societal, and cultural challenges during the COVID-19 pandemic. 4.1. Deterioration of Personal Health Most participants (22 out of 25) experienced a physical or psychological health deterioration, or both. They articulated a fear of being infected, or of their family being infected, along with the other indirect consequences of COVID-19. As in the previous online survey, the respondents had high levels of fear and perceived strong influences related to COVID-19 [10]. Additionally, the participants in this study came to understand the direct or indirect negative consequences of the pandemic through social media and news information. A local survey of 500 citizens in 2020 found that most respondents were afraid of becoming infected, and suspected they were infected [14]. There was a negative association between fear of infection and health-related quality of life [14]. Another local study investigating the psychological burden felt by middle-aged women during the SARS outbreak reported that high emotional stress was significantly associated with feeling scared, disrupted sleep, and financial losses [8]. Most Hong Kong citizens adopted good hygiene habits after SARS, and began wearing face masks in public areas when sick [8]. However, the unexpected global need for face masks and personal protective items in early 2020 caused supply shortages, leading to an increase in the fear of Hong Kong citizens [12]. Citizens who could not access adequate stock of face masks or sanitizer may have felt a greater fear of infection. A recent systematic review and meta-analysis of 44 articles found that respondents’ fear of COVID-19 was higher in Asia [15]. The provision of clear information related to COVID-19, along with psychological support services, could ease women’s fear and stress. The participants mentioned increases in caregiving responsibilities under lockdown conditions, which further aggravated their burdens. The study participants who were family caregivers felt stress and physical exhaustion due to a lack of sufficient rest when performing their caregiving roles non-stop during the months of the pandemic. Evidence has shown that informal family caregiving roles are often demanding, and negatively affect caregivers’ physical and emotional health [16,17,18]. Motivations and enthusiasm for informal caregiving are significantly based upon cultural and societal matters [18]. Caregiving is commonly perceived as women’s responsibility, especially in Chinese culture. Liu and Dupre found that Chinese women who provide one or more hours of care for their parents show poor health levels compared to non-caregivers [3]. Another recent study identified an adverse impact on the wellbeing of Chinese women who provided long-term care for elderly parents at home [19]. Nowadays, there is a greater prevalence of nuclear families, with a smaller domestic household size in Hong Kong than before. The local government’s statistics show that the average domestic household size was 2.7 at the time of the study [20]. Thus, the family caregivers, especially the women, may be responsible for intergenerational care. Given that schools and childcare and elderly care centers were closed in mid-2020 in Hong Kong, our study participants’ caregiving responsibilities further intensified, increasing the caregiver burden, which echoes the findings of Connor et al.’s study [7]. Even though some of our study participants were aware of their own physical exhaustion, they continued their caregiving roles and showed unconditional love for their families. Thus, caregiving’s impact on women’s health and quality of life should be addressed during public health crises. If family caregivers become sick, infected, or have to enter quarantine, those under their care may suffer. The careful consideration of local governments or healthcare professionals in providing adequate support for caregivers’ physical and mental wellbeing are recommended, such as the provision of respite services. 4.2. Unfavourable to Family Relationships In some Asian countries, filial piety and parental care are important traditional concepts [19]. The virtues of filial piety are established and cultivated in people at an early age via school education, family teaching, etc. [18]. Our study found that Hong Kong women perceived and accepted their roles and responsibilities related to caring for their parents, regardless of whether they lived with them. Daughters play a crucial role in family caregiving, while sons and daughters-in-law are vital family caregivers of parents in Chinese culture [21]. Holroyd conducted ethnographic research, and interviewed 20 caregiving daughters in Hong Kong [22]. Her results show that Chinese daughters have a strong sense of their caregiving obligations towards frail elderly parents, which is consistent with our findings. Although a few participants in this study had committed their parents to elderly care settings, they loved their parents, and visited them regularly. In the past, placing parents in an elderly care attention home was regarded as non-filial. Nowadays, though, it is widely recognized as an adequate substitution for filial piety in urban areas [21], as in Hong Kong. Similar to other countries, the Hong Kong Special Administrative Region’s government enforced social distancing measures and a lockdown policy during the pandemic [23]. Thus, this study’s participants expressed regret that they could not visit their parents who resided in elderly care institutions, or who lived in nearby cities in mainland China. Ho and his team reported that the COVID-19 pandemic caused loneliness in elderly people in care institutions [24]. Two study participants regretted not being present during their parents’ dying moments due to the lockdown. The presence of children during their parents’ final moments is an integral part of filial piety in Chinese tradition [25,26]. Providing emotional support and physical connection are deemed essential to the parents’ “good death” [25]. A recent review reported that filial piety is worthwhile to promote and support. It can reduce the stresses and burdens of adult Chinese offspring, thus improving their sense of spirit and caregiving satisfaction [27]. Similarly, Western women are generally their parents’ caregivers [28]. Although cultural expectations have tended to differ between Eastern and Western countries, the elements of filial piety have become more alike throughout global changes and evolution [27,29]. Besides unfulfilled filial responsibilities, unhealthy marital relationships represent another significant issue felt by families during the pandemic. Our study participants related their marital experiences and challenges, such as separation and financial losses due to the pandemic and lockdown. The experience of broken or deteriorated relationships is consistent with the findings of Maiti et al. [30]. They identified several issues that may interfere with married couples’ harmonic relationships during a pandemic, such as inappropriate communication, overworking, unemployment or salary reduction, relationship breakdown or even later divorce, etc. [30]. Indeed, the findings are not only specific to Chinese women, but could apply across countries and cultures. Prime et al. stated that significant life events could intensify pre-existent marital dissatisfaction, resulting in relationship breakdowns or even divorce [31]. A recent study review conducted in various countries, including Singapore, Canada, Spain, and Australia, found that reduced marital intimacy could further intensify the probability of domestic violence [32]. In contrast, some of our participants experienced improved relationships, showing how good communication with the sharing of emotional needs and bonding is required during times of hardship [32,33]. Due to the closure of schools and study centers in 2020 in Hong Kong, parents, especially mothers, had to take up some responsibilities related to their children’s learning from school teachers. The study participants experienced stress and difficulties in facilitating their children’s distance or online learning at home. They found that their children had problems finishing their assignments on time, mainly due to insufficient study interest and concentration. Sonnenscheina and Stites reported that parents’ stress levels and technology competency largely affect children’s involvement in online/distance learning [34]. A local online study assessed 6702 parents, 93% of whom were mothers, regarding their views on young school children’s distance learning [35]. Inadequate support and home–school communication were the parents’ foremost complaints. Besides this, the impact of the pandemic and stay-home policies increased parental stress, damaging the parent–child relationship and increasing the possibility of harsh parenting [36], which echoes our findings. Thus, it was recommended the schools offer adequate technological support, strengthen communication with parents, and offer flexible learning activities. Several study participants (mothers of young children) stated that they occasionally lost their temper. They regretted inappropriate actions taken towards their children, which agrees with the study findings of Sonnenscheina and Stites [34], revealing that parents might experience unstable moods and conflicting feelings under lockdown. The authors also suggested that parents could stabilize their emotions by performing other activities with their children, and thus avoiding conflict. Campbell [37] also highlighted that child abuse or neglect might be underreported during the pandemic. 4.3. Adaptation to Social Challenges Apart from individual and relational impacts, the participants also encountered external impacts, demonstrating a certain degree of adaptation to social challenges. The social distancing policies of the Hong Kong Special Administrative Region Government aimed to reduce the possibility of COVID-19 spreading in the community [38]. The majority of study participants agreed with and supported minimizing social activities and performing personal and environmental hygiene practices. Indeed, most Hong Kong citizens were already aware of their infection control responsibilities, and were willing to take preventive measures and undertake social distancing and environmental hygiene practices [39,40]. Evidence has shown a positive correlation between fear of COVID-19 and changes in public health behavior, as greater fear enhances citizens’ compliance with public health behaviors, such as maintaining social distancing, mask-wearing, and hygiene practices [10,41,42]. Due to the closures of schools, childcare centers, and playgrounds, most women caregivers showed concern over whether their children would have adequate support and care at home [2,43]. Women caregivers may reduce their working hours and withdraw from the labor market [44]. Among the twelve participants who had full-time or part-time jobs before the pandemic, seven had voluntarily or involuntarily reduced their working hours, and three had resigned from their jobs, as they perceived their caregiving roles at home to be of greater significance. These ten participants’ family income and financial status were significantly affected. According to the Census and Statistics Department of Hong Kong [45], the number of unemployed females significantly increased to 92,400 (5.7%) in 2020, which is more than double the figure before the COVID-19 pandemic in 2019 [45]. The number of those underemployed increased to 54,700 in 2020, which is about five times greater than the 11,300 in 2019 [45]. Besides this, the additional expenses required for protective masks, sanitizing products, or online-learning resources, and reductions in families’ monthly household incomes, could cause significant financial burdens. Although the local government has implemented a series of anti-virus policies, including establishing an Anti-Pandemic Fund, to meet the challenges facing society [23], continuous and comprehensive support should not be neglected. Women’s self-perceptions and actions are essentially formed out of social and cultural circumstances [46]. Although Hong Kong is now a modern urbanized city, our study has demonstrated that Hong Kong women still generally preserve traditional cultural values and duties, echoing the findings of Han [46]. The traditional Chinese view of the significance of family is evident through the female Hong Kong participants’ behaviors. These behaviors show that the Chinese women adhered to familial duties and responsibilities in order to maintain family concord and stability [46]. Based on the tradition of filial piety, there was a feeling of mutual responsibility among parents and children. Children cared for their parents. In turn, parents are devoted to loving their children [46], which explains our participants’ displays of selfless sacrifice and unconditional love for their families during the pandemic. The COVID-19 pandemic gave rise to various physical and psychological health impacts and general life challenges for Hong Kong Chinese women. To support the women who have been substantially affected by the pandemic, local governments, non-governmental organizations, and healthcare professionals should consider strategies and activities that will enhance women’s physical and mental well-being, and help them tackle the life challenges experienced during hardship. For instance, online counseling services or telephone hotlines for psychological support, and instant preventive and protective measures regarding COVID-19, may help reduce their fear. It is also recommended to increase the anti-pandemic financial assistance available for those who have lost their jobs or are underemployed, as well as to provide domestic help, childcare, or elderly respite services for those in need. 4.4. Limitations This study has several limitations. Firstly, the selection of participants. Participants were recruited voluntarily from a pool of 417 Chinese women who had previously responded to an online survey, and who were primarily members of Hong Kong Federation Women Centres. Another limitation is the small sample size, comprising 25 participants from different centers, which may not be sufficiently representative for drawing generalizable conclusions. However, the objective of the interpretive phenomenological approach is to describe, understand and interpret participants’ experiences [11]. Carefully identifying the subjects with wide variations and information-rich can facilitate a more in-depth understanding [47]. 5. Conclusions The COVID-19 pandemic has had unpredictable and prolonged impacts on human health, society, the economy, and the environment worldwide. Understanding the health impacts and life challenges related to the pandemic for Hong Kong women will help improve their resilience and mitigate adverse consequences. This study’s findings illustrate that Hong Kong women perceived family caregiving as their paramount obligation during the pandemic. The pandemic increased their awareness of their caregiving roles, and has diminished their quality of life. Apart from re-thinking the sharing of care duties among family members in order to relieve women’s burdens, the promotion of strategies or activities that could enhance women’s physical, psychological, emotional and social health, and general quality of life, is recommended. Comprehensive and continuous support from the local government, non-governmental organizations, and healthcare organizations is required. Acknowledgments The authors would like to express their gratitude to Wong Yiu Tung Matthew for his assistance in data analysis. The authors would like to acknowledge the women participants for their contributions to the study. Author Contributions Conceptualization, M.S.Y.H., L.C.K.C. and S.P.S.L.; data curation, M.S.Y.H.; formal analysis, M.S.Y.H.; funding acquisition, M.S.Y.H., L.C.K.C. and S.P.S.L.; investigation, M.S.Y.H.; methodology, M.S.Y.H., L.C.K.C. and S.P.S.L.; project administration, M.S.Y.H.; resources, L.C.K.C. and S.P.S.L.; validation, M.S.Y.H.; writing—original draft, M.S.Y.H.; writing—review and editing, L.C.K.C. and S.P.S.L. All authors have read and agreed to the published version of the manuscript. Funding This research received funding from the School Research Grant of Tung Wah College (2019-04-52-SRG190402). Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of Tung Wah College (Ref. No.: REC2020071, date of approval: 2 July 2020). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data presented in this study are available on reasonable request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. ijerph-19-05115-t001_Table 1 Table 1 Demographic information of participants (n = 25). No. Age Group Marital Status Working Status before (during) COVID-19 Caregiving Role (Caregiver of) 1 60–69 Single Retired Yes (father) 2 60–69 Widowed PT* No 3 30–39 Divorced PT (resigned) Yes (daughter) 4 50–59 Married PT Yes (mother) 5 40–49 Separated PT (resigned) Yes (daughter) 6 30–39 Married Housewife Yes (daughter) 7 40–49 Divorced PT (resigned) Yes (mother and daughter) 8 60–69 Married Retired Yes (husband and daughter) 9 40–49 Married Housewife Yes (father and mother) 10 30–39 Married FT# (underemployment) Yes (daughter) 11 30–39 Married PT (unemployment) Yes (mother-in-law and son) 12 30–39 Married PT (underemployment) Yes (two sons) 13 30–39 Separated PT (underemployment) Yes (son) 14 60–69 Single Retired Yes (niece) 15 60–69 Married Retired Yes (husband) 16 30–39 Married Housewife Yes (son and daughter) 17 30–39 Separated FT (underemployment) Yes (two daughters) 18 20–29 Married Housewife Yes (son) 19 30–39 Separated Housewife Yes (son) 20 60–69 Married PT (underemployment) No 21 50–59 Married Housewife No 22 30–39 Single FT (unemployment) No 23 50–59 Married Housewife Yes (husband) 24 60–69 Married Retired Yes (husband) 25 60–69 Married Housewife Yes (father and mother) FT#—full time, PT*—part-time. ijerph-19-05115-t002_Table 2 Table 2 The main theme, the three interconnected themes, and the eight subthemes. 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PMC009xxxxxx/PMC9099856.txt

==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092977 molecules-27-02977 Article Allosteric Binding of MDMA to the Human Serotonin Transporter (hSERT) via Ensemble Binding Space Analysis with ΔG Calculations, Induced Fit Docking and Monte Carlo Simulations https://orcid.org/0000-0002-6322-1295 Islas Ángel A. 12* https://orcid.org/0000-0003-2196-2682 Scior Thomas 2 Roccatano Danilo Academic Editor 1 Vicerrectoría de Investigación y Estudios de Posgrado, Benemérita Universidad Autónoma de Puebla, Puebla 72592, Mexico 2 Laboratory of Computational Molecular Simulations, Departamento de Farmacia, Benemérita Universidad Autónoma de Puebla, Puebla 72592, Mexico; tscior@gmail.com * Correspondence: angel.islasn@correo.buap.mx 06 5 2022 5 2022 27 9 297708 3 2022 14 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Despite the recent promising results of MDMA (3,4-methylenedioxy-methamphetamine) as a psychotherapeutic agent and its history of misuse, little is known about its molecular mode of action. MDMA enhances monoaminergic neurotransmission in the brain and its valuable psychoactive effects are associated to a dual action on the 5-HT transporter (SERT). This drug inhibits the reuptake of 5-HT (serotonin) and reverses its flow, acting as a substrate for the SERT, which possesses a central binding site (S1) for antidepressants as well as an allosteric (S2) one. Previously, we characterized the spatial binding requirements for MDMA at S1. Here, we propose a structure-based mechanistic model of MDMA occupation and translocation across both binding sites, applying ensemble binding space analyses, electrostatic complementarity, and Monte Carlo energy perturbation theory. Computed results were correlated with experimental data (r = 0.93 and 0.86 for S1 and S2, respectively). Simulations on all hSERT available structures with Gibbs free energy estimations (ΔG) revealed a favourable and pervasive dual binding mode for MDMA at S2, i.e., adopting either a 5-HT or an escitalopram-like orientation. Intermediate ligand conformations were identified within the allosteric site and between the two sites, outlining an internalization pathway for MDMA. Among the strongest and more frequent interactions were salt bridges with Glu494 and Asp328, a H-bond with Thr497, a π-π with Phe556, and a cation-π with Arg104. Similitudes and differences with the allosteric binding of 5-HT and antidepressants suggest that MDMA may have a distinctive chemotype. Thus, our models may provide a framework for future virtual screening studies and pharmaceutical design and to develop hSERT allosteric compounds with a unique psychoactive MDMA-like profile. ecstasy entactogen psychoactive Molly serotonin benzofuran NPS cathinone bath salts cocaine methamphetamine designer drugs escitalopram antidepressant 3D-RISM electrostatic complementarity XED free energy Gibbs free energy citalopram Cresset ==== Body pmc1. Introduction Currently, there is great interest and much debate about the clinical use of MDMA (3,4-methylenedioxy-methamphetamine). Double-blind phase 3 trials and meta-analysis of its use in assisted psychotherapy to treat post-traumatic stress disorder (PTSD) have yielded promising results. Hence, its approval by the Food and Drug Administration (FDA) appears to be imminent [1,2,3,4,5]. Despite this topicality, research on the actions of MDMA at a molecular level [6,7,8] remain rare. The physiological and psychological effects of MDMA stem from its ability to cause the release of dopamine, 5-HT and norepinephrine to supraphysiological levels through interactions with their reuptake transporters DAT, NET and SERT [9,10]. The 5-HT transporter (SERT) is particularly involved in the behavioural response to MDMA in humans [11]. In fact, amphetamine derivatives that are more selective to the SERT than to the DAT are more likely to induce entactogenic MDMA-like effects with lower abuse liability [12], thus having pharmaceutical potential. Accordingly, the MDMA-induced release of 5-HT in the nucleus accumbens via the SERT is necessary and sufficient to explain its prosocial effect, but not its non-social drug reward in rodents [13]. Moreover, MDMA promotes fear extinction learning in a SERT-dependent manner [14], providing a mechanistic basis for its beneficial effects in treatment-resistant PTSD [10]. The hSERT constitutes the molecular target of the majority of antidepressants and it is comprised of twelve helical transmembrane segments with two known binding sites: a central (S1) and an allosteric (S2) site. They are located midway along the transporter and at the extracellular vestibule, respectively. The SERT is a neurotransmitter Na+ symporter that terminates serotonergic neurotransmission by coupling the transit of Na+ and K+ along their electrochemical gradient, to the internalization of 5-HT into the presynaptic cell. Antidepressants impede 5-HT reuptake through the SERT by stabilizing an outward open conformation [10,15]. Most drugs and test substances bind S1 with higher affinity with the exception of vilazodone and ligand Lu AF60097. It is noteworthy that the occupation of the ‘low-affinity’ allosteric site has a synergistic effect, deterring the dissociation of antidepressants and potentiating the binding for the central site [16,17,18,19]. On the one hand, MDMA occupies the central site of the hSERT acting as a substrate, as it is transported across the presynaptic serotonergic neuron. This process is energetically coupled to the drug-induced reverse flow of 5-HT, i.e., the SERT-assisted transport from the cytosol of the pre-synapse into the synaptic cleft. On the other hand, MDMA also inhibits the reuptake of 5-HT in a similar manner to a typical antidepressant. It is believed that it is by this dual profile that MDMA elicits its psychoactive effects that are clinically valuable, e.g., the therapeutic bonding and trauma reprocessing without emotional distress [3,10]. Previously, we proposed a mode of action for MDMA at S1, characterising the ‘ensemble binding space’ throughout the hSERT conformational cycle on trajectories from interpolated elucidated structures. In addition, we correlated the docking interaction energies of MDMA and a set of metabolites and analogues to their 5-HT uptake experimental activities (EC50s), providing a pharmacophore model [8]. Here, to shed light on the mechanism of action of this entactogen, we tested all current experimentally elucidated structures of the hSERT to demonstrate how favourably it occupies the allosteric site. The concomitant MDMA occupation of both hSERT binding sites was investigated by a combination of ensemble binding space analysis with enthalpic-entropic ΔG calculations, energy perturbations with the Monte Carlo method, extra-precision semi-automatic induced fit dockings, 3D-RISM solvation thermodynamics and electrostatic complementarity calculations. Our modelling approach was validated with experimental data by correlating the 5-HT reuptake inhibitory activities of MDMA and a group of related psychoactive agents, to their computed ΔG values upon their binding to the two binding sites. Of note, we made use of the ‘Molecular Field point Technology’ developed by Cresset™ under the XED force field, that provides a multipole electron distribution at a near quantum level [20]. This structure-based characterization of the energetically favourable features of the allosteric binding of MDMA provides: (i) a dynamic mechanistic hypothesis of the early molecular events in the transport of this drug along the hSERT; (ii) a guidance for the design of further experimental and computational studies of the MDMA/hSERT interplay; (iii) a theoretical framework for virtual screening and rational design of MDMA-like novel hSERT allosteric modulators. 2. Results and Discussion 2.1. MDMA Allosteric Occupation of the hSERT Overlaps the Binding Site of Escitalopram High-performance induced fit docking of MDMA was carried out on S1 and S2 of the hSERT in an outward open conformation (PDB:5I73). At the allosteric site, this phenylethylamine adopts a conformation, perpendicular to the z-axis (aligned to the lumen of the transporter), docking between transmembrane segments TM1, TM6 and TM10 (Figure 1A). The simulation of the system with the reference interaction site model (3D-RISM) provided a quantitative thermodynamic description of the aqueous environment in which MDMA binds the extracellular vestibule of the hSERT. The methylammonium moiety of MDMA simultaneously formed a salt bridge and a H-bond with the backbone and side chain of Glu494, in addition to a H-bond with a water molecule that favourably accommodates inside the binding pocket. While the benzodioxol group of MDMA forms a cation-π and a parallel π-π interaction with Arg104 and Phe335, respectively (insert in Figure 1A). Of note, these three residues constitute key elements for the allosteric binding of escitalopram (Figure 1B), as the substitution R104K decreases its affinity for this transporter [17]. Likewise, mutant E494Q significantly reduced the potency of this antidepressant but did not affect that of imipramine, which also binds S2 [18]. Glu494 is also fundamental for the binding of the high-affinity allosteric inhibitor Lu AF60097 and importantly, Phe335 is part of a motif that propagates the allosteric communication between S1 and S2 [18]. Thus, the occupation of S2 by MDMA may be coupled to its binding on the orthosteric site and in turn may play a role in its mechanism of action on the hSERT. Next, to validate our modelling results we tested the hypotheses of whether the 5-HT block activities of MDMA and a set of related psychoactive hSERT inhibitors could correlate with the estimated Gibbs free energies (ΔG) on either their central or their allosteric binding. We selected five benzofurans, one cathinone, methamphetamine, MDMA, and cocaine (Figure 2) and carried out the simulations on an outward open hSERT structure. Of note, our estimations of binding free energies go beyond classical docking scores by including entropic, enthalpic, polar ligand desolvation, and non-polar solvent contributions (see methods Section 3.2). 2.2. Experimental-Computational Quantitative Correlation of 5-HT Reuptake Inhibitors to S1 and S2 of the hSERT The ΔGs of the MDMA congeneric hSERT blockers, methamphetamine, and cocaine were calculated upon their molecular association to the central and the allosteric binding sites of the hSERT. In agreement with the affinity data for allosteric antidepressants [17,18], all compounds showed higher binding strength to S1 than to S2. The in vitro inhibitory activities of tritiated 5-HT via the hSERT, heterologously expressed in cell assays [7] were linearly correlated to their theoretical free energy values. Of note, the correlations were possible despite the use of racemic mixtures in the experiments, while only the (R)-enantiomers were used for calculations. In line with previous experimental findings on other MDMA analogues [6], the occupation of S1, rather than S2 best explained the inhibitory potencies of this set of ligands (Figure 3A,B). Of note, the computed binding modes of the selected compounds in S1 only have Glu98 in common with the binding of classical antidepressants and novel hSERT inhibitors [21], with the exception of escitalopram that may interfere with Tyr176 and Ser336 [22]. In particular, the orientation and modes coincide with the previously described binding mode for MDMA (on the paroxetine-induced conformation) [8] with the addition of Ala96 and S336 side chain contributions that likely reflect the escitalopram-induced conformational rearrangement. Our proposed MDMA binding pose and binding residues were highly conserved among all compounds, even for cocaine and methamphetamine at S1 (Figure 3C). In contrast, the binding modes for S2 were more heterogenous (Figure 3D). Notably, the docking solution of cocaine correctly explains why the mutant Y176C significantly decreases its affinity [23]. In stark contrast, methamphetamine with the lowest 5-HT inhibitory potency docked between S2 and S1 (in light green in Figure 3D). Our interpretation is that this reflects the propensities of the drugs to be internalized through the transporter eliciting the efflux of 5-HT, e.g., MDMA and methamphetamine or to merely block the neurotransmitter reuptake, e.g., cocaine. Thereafter, we focused on characterizing the allosteric binding of (R)-MDMA on the conformational landscape of the hSERT given by experimentally determined structures, by carrying out an ensemble binding space analysis. This approach mimics the dynamic ligand-protein cooperation, accounting for protein flexibility and ligand mobility within the active site by considering the best alternative binding modes on an ensemble of protein conformations [24]. 2.3. Ensemble Binding Space Analysis of MDMA on the Allosteric Site of the hSERT: 5-HT and Escitalopram Analogous Binding Modes The simulations were carried out first on the six recently elucidated cryo-EM structures of the hSERT, which characterize the allosteric site of 5-HT. Importantly, these structures are believed to recapitulate all fundamental states of the neurotransmitter transport cycle from holo open outward, holo occluded, over holo inward open toward apo inward open states. Holo structures are in complex with two molecules of 5-HT at S1 and at S2, [25]. Several low-energy ‘escitalopram-like’ binding modes similar to the previously proposed in Figure 1A were identified on the outward open and occluded holo states (PDBs:7LIA and 7MGW), i.e., the conformations in NaCl in which the substrate internalization cycle begins. (Figure 4A). In this binding solution, it is only Glu494 that binds both 5-HT and MDMA by their cationic amines and again, the thermodynamic calculations indicate that it is probable that this moiety interacts with water (insert in discontinuous lines, Figure 4A). Intriguingly, at this conformation, MDMA lies deeper inside the extracellular vestibule of the transporter, protruding its ring system towards the sodium (~7.6 Å apart) located in between transmembrane helixes TM1a and TM1b, which also binds Asp98, a key residue for MDMA binding to the central site [6,8]. The ensemble binding space of MDMA at the allosteric site was analysed and parameterized with the 80 most energetically favourable binding solutions. Figure 4B shows the number of solutions obtained at each state and the parameters based on the calculated Gibbs free energies. Statistical metrics (mean, standard deviation, mode, and range values) indicated little ΔG disparity and reflected a heterogeneous ensemble of solutions, implying relevancy to the MDMA allosteric mechanism of action. The sensitivity score encodes the capacity of the ligand to vary its free energy values by adjusting its own conformation [24]. Compared with the ensemble binding space reported for MDMA at the central site, the sensitivity score for S2 denotes a higher intramolecular flexibility, as the range score was double of the one previously obtained for S1 [8]. Meanwhile, the average rmsd at S2 reflected a considerable exploration of the binding space or ‘binding mode displacement’. It is noteworthy that the parameters obtained here originate from more precise ΔG terms (see methods Section 3.2) rather than classical enthalpic docking scores previously obtained for the central site [8]. Additionally, in the previous work, an equal number of poses per structure was obtained, here only the highest scoring poses were considered. As a consequence, it was clear to see that MDMA preferentially binds the occluded and the outward open states, suggesting that the affinity of the drug increases as the hSERT transits from the latter to the former, being sequestered similarly to the endogenous substrate. Furthermore, although no energetically favourable poses were found for the transporter in the occluded apo state (PDB:7LI7), some were found for the inward open apo states with Na+ and K+ cations, conditions under which MDMA could initiate the reverse translocation of 5-HT. Remarkably, the top binding solution from this analysis, identified in the occluded state, coincides with the orientation and binding mode of 5-HT. All solutions were clustered and the pose from the most populated cluster (highest number of solutions) interacted with three of the six 5-HT binding residues in this state (Figure 4C). The reduced distribution of thermodynamically stable water molecules at S2 in the occluded state compared with the outward open state accounts for the diminished solvent accessibility. However, in the occluded state, the ammonium groups of 5-HT and MDMA can directly interact with Asp328 or via H-bond networks of waters as it is predicted by the 3D-RISM to be heavily hydrated. In contrast with Glu494, which preserves the salt bridge with these monoamines, observed in the outward open state. In addition, both ligands bind Tyr495, albeit in a different manner i.e., 5-HT forms a H-bond with its backbone and MDMA an edge-to-face π-π with its side chain (Figure 4C). Moreover, the global results show that MDMA can interact with all six 5-HT binding residues in the occluded state and specify the individual bonding incidences along the whole conformational ensemble (Figure 4D). Clearly, the aforementioned salt bridges with the two anionic residues at TM10 and TM6 are critical for MDMA allosteric binding throughout the hSERT transport cycle. The high prevalence of the amine-Glu494 bonding in the allosteric binding of MDMA to hSERT is explained by the often simultaneous interactions with the side chain and backbone atoms of this glutamate. Likewise, Thr497 may form two concomitant H-bonds, and finally alternating aromatic interactions, e.g., with Tyr495, Phe556 and Tyr579, may also play a role in the occupation of the allosteric site by MDMA. Of note, Phe556 is also involved in the recognition of escitalopram at the allosteric site [26]. Taken together these results indicate that both the ‘escitalopram-like’ and the ‘5-HT-like’ MDMA binding modes at S2 may contribute to the allosteric mode of action of this psychoactive compound. In agreement with the literature, multiple binding modes, such as these two, can be exploited for the drug design of new allosteric compounds of the hSERT [27]. In the pursuit of a compelling theoretical pathway for MDMA along the hSERT, we then wondered whether these two symmetrically opposed configurations of MDMA could alternate within the S2 binding pocket. 2.4. Intermediate Poses between the ‘5-HT-like’and the ‘Escitalopram-like’ Binding Modes of MDMA Indeed, low-energy binding solutions in between the ‘5-HT-like’ and the ‘escitalopram-like’ orientations of MDMA were found in the ensemble binding space analysis. The polar cavity in which these intermediate binding modes lie (Figure 5A,B) suggest the ligand is able to flip from one orientation to the other in situ. Most probably during the transition process from the outward open to the tightly bound occluded conformation. Since ‘the most intermediate conformations’ detected (in magenta, Figure 5B), i.e., almost parallel to the substrate pathway (z-axis) are exclusively present in the occluded state. The binding residues involved in such proposed reorientation from the outward open to the occluded state (at transmembrane helixes TM1b, TM6 and TM10) are shown in Figure 5C. Of note, the allosteric residue Phe335 drastically rearranges its side chain along the transition from one state to the other (black arrow in Figure 5C). The following three steps provided a more thorough and dynamic characterization of the early allosteric MDMA binding events on the hSERT: (i) selection of the most energetically favourable MDMA/hSERT complexes, in the ‘5-HT-like’ and the ‘escitalopram-like’ configuration from the ensemble space; (ii) high-accuracy induced fit docking of this ligand to the central site yielding double-bound models occupying both S1 and S2; (iii) subjecting these models to stochastic energy perturbations with the MC method. These steps were followed with the aim of evaluating the stability of the MDMA-hSERT interactions quantitatively and to capture the protein rearrangements induced by occupation of the two binding sites. Four energetically favoured poses from the ensemble binding space were chosen for the simulations, two in which MDMA is in the ‘5-HT-like’ binding mode and two in which it is in the ‘escitalopram-like’ orientation. A ‘control simulation’ was run with MDMA only at the central site to distinguish the protein intramolecular changes induced by the occupation of S2. 2.5. Monte Carlo (MC) Simulations on Double-Bound MDMA/hSERT Models to Identify Allosteric Determinants The preservation and stability of the MDMA-hSERT intermolecular and intra-residue interactions along the MC trajectories in the lowest ΔG outward open models were assessed. To this end, the interatomic donor-acceptor, π-π or cation-π distances were monitored as a quantitative marker of bonding contacts (Figure 6A–C and Figure 7B,C). Figure 6D shows the two resulting complexes after the stochastic energy jumps in the ‘5-HT-like’ configuration and Figure 7D shows the models in the ‘escitalopram-like’ orientation. The most frequently contacted amino acid in the full MDMA allosteric ensemble space (Figure 4D) was Glu494 and the prevalence of its interactions with the cationic amine of MDMA was corroborated. Accordingly, in three of the four MC simulations, an intermolecular salt bridge was preserved or acquired (in red and orange, Figure 6A), while, simultaneously in two of them, a backbone H-bond with this residue was conserved (in light and dark blue, Figure 6A). Of note, these interactions distinctly enhanced the intracellular Glu494-Arg104 salt bridge, slightly pulling TM10 and TM1b together and briefly inducing an extracellular Glu494-Lys490 interaction. In contrast, in one case, the loss of the MDMA-Glu494 salt bridge precluded the formation of the Glu494-Arg104 bond and coincided with the optimization of the Glu494-Lys490 ionic interaction (Figure 7B,C). Importantly, the conformational coupling between Glu494-Arg104 and Glu494-Lys490 is crucial to the allosteric inhibition of escitalopram [26], as well as for the binding of the allosteric SSRI vilazodone [19]. Likewise, the strengthening and stabilization of the cation-π attraction between Arg104 and the benzodioxol of the drug (Figure 6B in blue) in the simulations with the escitalopram-like orientation, also optimizes the Glu494-Arg104 interaction compared with the control MC simulation, to the expense of losing the Glu494-Lys490 bond in one system, while it is preserved and optimized in the other with the aid of Glu493, that also binds Arg104 (Figure 7B,C). Aromatic bonds also prevailed along MC trajectories in both ligand orientations, the most solvent exposed ‘5-HT-like’ pose of MDMA formed a stable π-π stacking with Phe556 or Tyr495 and a short-lived π-π interaction with Tyr579, three residues involved in the allosteric binding of 5-HT (Figure 6B,D) but not escitalopram. Subsequent energy minimization of one of the ‘5-HT-like’ systems allowed to retain these interactions while forming a cation-π tie between the methylammonium of MDMA and the phenyl ring of S2-S1 allosteric propagating residue Phe335 [18]. Of note, this residue is also contacted by MDMA in one of the escitalopram systems albeit via a π-π contact displaced stacking, while retaining the electrostatic interactions with Ar104 that engages one of the oxygens of MDMA (Figure 6B and Figure 7A). The comparison of the MC-induced conformational readjustments with those occurring in the occluded state (Figure 5C) suggest that these interactions may not only be key to the allostery of this compound but may participate in the substrate-induced transition from the outward open to the occluded state [18]. Lastly, the stability of the aforementioned Thr497 and particularly Gln332 H-bonds with the amine of MDMA was verified in the ‘5-HT-like’ and ‘escitalopram-like’ configurations, respectively. It is noteworthy that in the latter case, the salt bridge with Asp328, previously observed in the occluded conformation, may exist simultaneously in the outward open state (Figure 6C and Figure 7A). Aforementioned residues are also involved in the allosteric modulation of the hSERT, as the high affinity ligand Lu AF60097 but not escitalopram also H-bonds with the side chain of Gln332, while Thr497 caps the binding pocket of the allosteric SSRI vilazodone [19]. In short, the MC trajectories show how the presence of MDMA at S2 in the two symmetrically opposed orientations affect the conformation of the hSERT at sites critically involved in the allosteric regulation of this transporter. In turn, these results suggest that the binding of MDMA to the allosteric site may synergistically affect that of the central site. In addition, we revealed that MDMA shares molecular allosteric features with 5-HT, antidepressants, and allosteric ligand Lu AF60097, reflecting the electrophysiological evidence of its role as a substrate and as an uptake inhibitor [10]. Together, the ensemble binding space dockings and MC simulations imply that MDMA may constitute a unique chemotype for structure-based drug design. We next wondered how likely it was for MDMA to navigate from the allosteric to the central binding site, in view of the small tunnel there is between them in the open outward state [25] that, nevertheless, is proposed to be a gateway for the bulkier escitalopram to reach the orthosteric site [26] and since some putative allosteric ligands elongate between the two sites [28]. 2.6. Ensemble Binding Space Analysis and Electrostatic Complementarity of the Pathway of MDMA from S2 to S1 Pursuing the idea of a path for MDMA between the allosteric and the central site, we first carried out a binding space analysis sampling both active sites simultaneously on a double-bound complex subjected to MC simulations. Low-energy intermediate binding poses were identified (Figure 8A in grey thin sticks). Figure 8B shows how often these poses involved residues from the central site (in asterisks) and from S2. In addition to previously identified ‘allosteric residues’ Arg104, Gln332 and Phe335, the contribution of Tyr95 along the proposed S2–S1 pathway stands out. The high contact frequency of this residue is due to alternating or simultaneous cation-π contacts and backbone H-bonding with the ionic head group of MDMA. Moreover, Tyr95 may play a dual role, in the allostery of MDMA and in its transport as a substrate, since it is a binding residue for the occupation of vilazodone at S2 [19] and its displacement is necessary for the release of 5-HT into the cytoplasm [25]. This binding space analysis revealed that the amine of MDMA persistently interacts with Asp98 of the central site, which seems to be the driving force for the entry of MDMA into the central site. To account for protein conformational freedom, we next carried out an ensemble binding space docking, probing a more extensive region on all 26 available hSERT structures. The preferential ‘high-affinity’ binding of this drug to S1 was corroborated and intermediate poses were detected (Figure 8C). On the one hand, the MC results in one of the systems with the escitalopram-like orientation suggest MDMA could access the orthosteric site adopting this orientation without visiting the ‘5-HT-like’ binding pathway (arrow in Figure 7A). On the other hand, the occurrence of an extracellular energetically favoured binding pose found in the occluded state (insert of Figure 8C) and some found in complexes bound to sertraline (PDBs: 6AWQ and 6AWO) reinforces the notion of the reorientation of the ligand at the vestibule of S2 (Figure 5B and Figure 8C). It is noteworthy that in the outward conformation of the hSERT, S2 and S1 are fused into one cavity. However, both sites are separated in the occluded state by the Tyr176-Phe335 gate [29]. Fundamentally, it is clear from the electrostatic complementarity calculations of the models subjected to MC energy perturbations (Figure 8D) that: (i) MDMA in an escitalopram-like orientation can traverse the narrow cavity between the allosteric and the central site, without the energetic cost of changing its orientation, perhaps driven by the protein conformational rearrangements that ensure the closing of the extracellular vestibule in the occluded state before the sequestering of the substrate [25]. (ii) Two MDMA molecules may concurrently occupy the allosteric site, one with the ‘5-HT’ and the other with the ‘escitalopram-like’ orientations (Figure 8D, insert). This way, the passage of one molecule from S2 to S1 is possible, while another remains bound, stabilizing allosteric changes, e.g., Glu494-Arg104 and Glu494-Lys490 coupled salt bridges [26], and possibly facilitating substrate internalization. Finally, to challenge our allosteric models, we screened a set of hSERT active compounds [30] (Figure 9A). All 5-HT releasers fitted the MDMA ‘5-HT-like’ conformation (Figure 9B) and the ‘escitalopram-like’ binding mode of S2, albeit with statistically different ΔG mean values (Table 1). These results emphasise the dual allosteric binding mode of 5-HT releasers via the hSERT, suggesting the contact with 5-HT binding residues is particularly important for the reverse flow of 5-HT. 3. Methods 3.1. Induced Fit and Ensemble Docking The ligands and the proteins were prepared in Flare4.0.2 (Cresset®, Litlington, Cambridgeshire, UK) [20]. Missing hydrogens were added and optimal ionization states of ligands and protein residues were assigned (pH = 7), using the thermodynamic sampling of amino acid residues (TSAR) method, a graph-theoretical approach from BioMolTech (Toronto, ON, Canada) [31]. The spatial positions of polar hydrogen atoms were optimized to maximize H-bond interactions and minimize steric strain. The side-chain orientation of His, Asn, and Gln residues for which X-ray analysis can return flipped orientations due to apparent symmetry were optimized. Gaps in the protein sequences of 1 or 2 residues if any, were filled. Residues with unresolved sidechains were detected and reconstructed and atoms from residues with incomplete backbone if any, were removed. Steric clashes were relieved by allowing small sidechain movements. Protein chains were capped if truncated, native ligands were extracted and template ligands for induced fit docking were selected manually. No native water molecules were around the active site (see Section 3.2). However, ions, when present, were included in the simulations. Active site was defined as the set of residues within 6 Å of the template ligand. The three-dimensional structures of ligands were generated in Flare 4.0.2 and energy-minimized before docking. High-precision flexible-receptor/flexible-ligand docking was performed with LeadFinder™ (BioMolTech) in Flare 4.0.2 under the XED force field by Cresset™. This patented second-generation force field redefines the charge toward a multipole electron distribution akin to a quantum orbital description [20]. The ‘Very Accurate but Slow’ mode was used. This increases the accuracy and reliability of predictions by performing induced fit dockings by triplicate, making use of the most rigorous sampling and scoring genetic algorithm search combined with multilevel local optimization procedures and smart exploitation of the knowledge collected during a search run. To simulate the process of induced fit: automatic ‘on-the-fly’ and manual post docking local rearrangements of the active site were carried out, in conjunction with energy minimizations, thereby: (i) identifying the best pre-existing complementarity and (ii) simulating multiple tentative collisions with mutually induced conformational adjustments of the interacting species to achieve the most appropriate match. To account for active site flexibility and to simulate the conformational selection binding mechanism, ensemble docking was carried out in Flare4.0.2. This enables the inclusion of multiple protein structures in the same docking run. Proteins were superimposed and the binding site on each was defined by one or more template ligands as indicated in each results section. The maximum number of poses was set to 80, retaining the highest scoring poses across all proteins. The ‘Normal’ docking mode used is optimized provide and accurate and exhaustive search. The default parameters of the LeadFinder™ genetic algorithm (pool size, population size and maximum constrain penalty) were used. 3.2. Solvation Thermodynamics and Gibbs Free Energies Calculations The location and thermodynamic stability (ΔG values) of water molecules in the protein was investigated using the Reference Interaction Site Model (3D-RISM). Conceptually, this analytical method is equivalent to running an infinite time Molecular Dynamics simulation on the solvent with a fixed solute and then extracting the density of solvent particles, based on the Molecular Ornstein Zernike equation: h(r12) = c(r12) + ∫dr3 c(r13) ρ(r3)h(r23), where: h(r12) is the total correlation function (‘What is the distribution of the solvent around the solute’), c(r12) is the direct correlation function (‘How does a solvent molecule interact with the solute?’), and dr3 c(r13)ρ(r3)h(r23) is the indirect influence through all possible chains of mediating third particles (‘What is the effect of a solvent molecule interacting with another solvent molecule which is interacting with the solute?’). Gibbs free energies (ΔG) were calculated in Flare 4.0.2. This function performs accurate estimation of the free energy of protein-ligand binding for a given complex. Besides the typical enthalpic energy terms, binding free energies include entropic contributions: A polar component of ligand desolvation upon binding using an adapted version of the Born model, energy penalties accounting for the accessibility of each H-bond donor/acceptor for water molecules and the strength of lost H-bonds upon ligand transfer from water to protein environment, also accounting for the loss of protein H-bonds induced by ligand binding, nonpolar solvation favoured by hydrophobic contacts in the complex, internal energy losses of the ligand upon transition from solvent to protein bound state, and entropic losses due to freezing ligand’s degrees of freedom upon binding. This method has demonstrated a rmsd of 1.5 kcal/mol with respect to observed binding constants [31]. 3.3. Monte Carlo (MC) Energy Perturbations and Electrostatic Complementarity (EC) Conformational search simulations by random perturbation of the torsional angles were carried out in AMMP 2.4.1(c) in VEGA ZZ 3.0.5 [32] using the MC Boltzmann jump method at a temperature of 1000 K, with a torsion rmsd of 60° to generate significantly different conformations at each step, followed by 20 energy minimization steps. This method allows upward jumps in energy to explore the conformational landscape and employs the Metropolis criterion to accept or reject perturbed conformations. The EC was calculated in Flare 4.0.2 from the comparison of the protein and ligand electrostatic potential (ESP) based on the polarizable XED force field, describing atomic charge anisotropy. ESP values are generated and mapped at all vertices of a ligand or protein solvent accessible surface (SAS). Local visualization of the EC is allowed by the calculation of the EC score as follows: EC = ∫∫S [1 − ESPL + ESPP/max (ESPL, ESPP, k)] dS, where the integral is over the ligand SAS, ESPL, and ESPP are the ligand and protein ESP values, and max (ESPL, ESPP, k) is the protein or ligand ESP value with the largest deviation from zero, or a constant k if that is larger. A k value of 5 was chosen heuristically. Both ESPL and ESPP were capped to a maximum deviation from zero of 12 for the EC score. The capping value was derived from the XED ESP. 3.4. Ensemble Binding Space Analysis Derived from property space analysis, the ensemble binding space analysis, developed by Vistoli et al. (2017), constitutes an approach to account for the dynamic processes of protein flexibility and ligand mobility. Briefly, this incorporates the statistically confirmed idea that alternative binding modes and the degree and ease of mobility of a ligand within a binding site significantly contribute to the observed affinity [24]. Accordingly, multiple low-energy docking poses are generated and analysed to calculate the relative frequencies of ligand interaction. The docking grid included all residues within 6 Å of the ligands unless otherwise specified. All elucidated structures of the hSERT from the PDB database at RCSB were included for ensemble binding space analysis. 4. Conclusions MDMA occupation of the allosteric site (S2) of the hSERT is energetically favourable and it triggers protein readjustments of known allosteric regulation under Monte Carlo (MC) simulations. Upon association to S2, MDMA and a group of hSERT releasers can adopt two orientations. One that coincides with the binding site of 5-HT and the other with that of escitalopram. The concomitant occupation of these sites by MDMA may be possible and intermediate conformations were identified. The experimental activities of a set of psychoactive hSERT inhibitors were quantitatively correlated to their computed ΔG values for the orthosteric (central) and the allosteric binding solutions, thereby validating the modelling in principle. Based on ensemble binding analyses on all reported hSERT structures in distinct conformations and ΔG calculations, we proposed a pathway for MDMA from the extracellular vestibule to the orthosteric site. The main residues identified in the allosteric binding of MDMA lie between transmembrane segments TM10, TM11, TM6, and TM1b, i.e., Glu494, Asp328, Phe556 Phe335, Arg104, Thr497, and Gln332. These molecular models may be used for virtual screening and may pave the way to the identification and development of allosteric modulators of the hSERT with a MDMA-like chemotype. In conjunction with previous computational findings by our group [Islas et al. (2021)], these results constitute a structure-based mechanistic hypothesis for some of the molecular determinants that underlie the action of MDMA on the hSERT. Acknowledgments We would like to kindly thank Ygnacio Martinez-Laguna, Vice-chancellor of the Vicerrectoría de Investigación y Estudios de Posgrado of the Benemérita Universidad Autónoma de Puebla for acquiring funding for the publication of this work. Author Contributions Á.A.I. designed the study, carried out the research, conceptualization, methodology, formal analysis, writing (original draft), visualization and acquired funding for proprietary software. T.S. supervised, edited the manuscript, conceptualised, acquired funding and was the project administrator. All authors have read and agreed to the published version of the manuscript. Funding The APCs were funded by the Vicerrectoría de Investigación y Estudios de Posgrado, Benemérita Universidad Autónoma de Puebla. Grant number: [n.a.]. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Key model structures (.pdb files) are available (free of charge for non-profit use) upon request. Conflicts of Interest The authors declare no conflict of interest. Sample Availability Samples of the compounds are not available from authors. Figure 1 (A) The human serotonin transporter (hSERT) bound to two molecules of MDMA at the central (S1) and the allosteric binding site (S2), midway between the intracellular and extracellular side and on the extracellular vestibule, respectively. Cylinders represent the transmembrane segments. The insert shows the binding residues associated to their bond distance with 3D-RISM solvation in a green (energetically favourable) to red (energetically disfavourable) colour scale. (B) Binding mode of escitalopram from the intact complex PDB:5I73. Figure 2 Selected hSERT blockers to simulate the ligand-protein interaction of S1 and S2 of the hSERT associated to their [3H]5-HT uptake inhibitory potencies (IC50s) from functional reuptake assays of HEK-293 cells stably expressing the hSERT. Experimental values taken from [7]. Figure 3 Correlation between experimental and computed data. The calculated Gibbs free energies of drugs in Figure 3, bound to S1 (A) or S2 (B) are plotted as a function of their [3H]5-HT reuptake block activities. The linear regressions (y = x × a + b) are associated to Pearson correlation coefficients. Binding modes of these compounds to S1 (C) and to S2 (D) of the hSERT. Ionic and aromatic bonds (in purple) and H-bonds (in cyan = weak, green = strong) are associated to their distances. MDMA is in ball and stick representation in magenta. Figure 4 Ensemble binding space analysis of MDMA at the allosteric site of the hSERT. (A) Induced fit model of MDMA binding to the holo outward open state obtained in complex with 5-HT (in black). The sodium ion located between TM segments 1a and 1b is shown. Dotted lines represent intermolecular bonds associated to their distances. Ionic and aromatic bonds (in purple) and H-bonds (in cyan = weak, green = strong) are associated to their distances. The 3D-RISM solvation of the methylammonium group of MDMA is shown in the insert. (B) Number of energetically favourable MDMA poses per hSERT structure on S2. PDB codes are given in parenthesis. (C) The most representative binding pose from the cluster analysis of MDMA (in purple) on the occluded state of the hSERT with 3D-RISM solvation, waters are associated to their ΔG values. The intact position of 5-HT at S2 is in black. (D) Relative occurrences of the repertoire of MDMA interacting residues at the allosteric site. Asterisks denote the residues that interact with 5-HT at S2 on the occluded conformation. Figure 5 (A) Molecular surface of the hSERT complex in the occluded state showing the locations of MDMA at the central site and at S2. Including an intermediate configuration between the ‘5-HT-like’ and the ‘escitalopram-like’ orientation (in magenta). (B) Molecular surface of the binding pocket of S1 in the occluded state. The ‘5-HT-like’ binding mode in green, the ‘escitalopram-like’ binding mode in yellow and intermediate modes in magenta. Colour code: beige = hydrophobic, blue = polar. (C) Binding poses on S2 in the open outward and occluded states, ‘5-HT-like’ binding mode is in green, ‘escitalopram-like’ mode is in yellow, intermediate modes in magenta. The main participating binding residues are shown in the same colour code. Figure 6 Ligand-protein electrostatic interactions under Monte Carlo (MC) simulations of four holo double-bound MDMA/hSERT complexes. Data correspond to either the ‘5-HT-like’ (replicas 1 and 2 in superscript) or the ‘escitalopram-like’ orientation (replicas 3 and 4 in superscript). (A) Interatomic distances from the cationic amine of MDMA and either the oxygen from the side chain (carboxylate) or the backbone (carbonyl) of Glu494 along the MC trajectories, as a marker of stability and occurrence of electrostatic bonds (according to the rectangles in the right-hand side of the plot). (B) Occurrence and stability of aromatic interactions evaluated by centroid-to-centroid distances with a 5 Å cut-off, as a function of the MC energy jumps. Data points represent allowed π interactions. (C) Interatomic ligand-protein H-bond/salt bridge distances as a function of the MC energy jumps (n = 4 in a total of 1000 Boltzmann steps). (D) MDMA/hSERT complexes in the ‘5-HT-like’ orientation of the drug at the end of the MC simulations. Replica 1 in blue and replica 2 in green. MDMA corresponding molecules are in ball and stick representation. The residues with the most favourable interactions in this orientation are shown. The salt bridges involved in the allosteric modulation of the hSERT are black rectangles. Figure 7 (A) MDMA/hSERT double-bound complexes subjected to Monte Carlo (MC) simulations in the ‘escitalopram-like’ orientation of the drug. Replica 3 in pink, replica 4 in blue. MDMA corresponding molecules are in ball and stick representation, MDMA at S1 is in translucent blue. The residues proposed to participate in the allosteric binding of MDMA are shown. The residues involved in the allosteric modulation of the hSERT are black rectangles. Donor-to-acceptor distance as a function of the MC energy perturbation steps for the allosterically coupled salt bridges between (B) Glu494 and Arg104, and (C) Glu494 and Lys490. (D) Rotated view of (A), in the absence of TM segments (ribbons) showing the proposed entry pathway for MDMA from S2 to S1. Figure 8 Ensemble binding space of MDMA on the allosteric and central site of hSERT. (A) Electrostatic surface of the MC hSERT complex model from the external vestibule S2 (in pink) to the central site (in cyan) intermediate conformations in thin sticks in grey. Surface colour scale: light blue = negative, red = positive charge. (B) Bonding incidences of the intermediate conformations of MDMA found on the double-bound complex after MC simulations. Asterisks mark the residues involved in the binding of MDMA to the central site. (C) Top 20 most energetically favoured poses from ensemble docking to all available hSERT structures. The insert shows a ‘flipped’ intermediate conformation on the extracellular vestibule of S2. (D) Electrostatic complementarity surface of a MC double-bound hSERT/MDMA model showing an alternative location of MDMA at S2 with a ‘5-HT-like orientation’. Intact escitalopram locations are shown in thinner dark red sticks. Figure 9 (A) 5-HT releasing compounds selected to test the MDMA/hSERT allosteric model. (B) Binding of these molecules to the allosteric site of the hSERT model in the ‘5-HT-like orientation’ of MDMA (highlighted in blue). Dotted lines represent intermolecular bonds associated to their distances. H-bonds are in cyan and a stronger H-bond in green. Salt bridges and π-π interactions are in purple. molecules-27-02977-t001_Table 1 Table 1 Gibbs free energies of compounds in Figure 9A on the allosteric site of the MDMA/hSERT models. 5-HT-Like Orientation Escitalopram Orientation p-Value −7.1 ± 0.9 kcal/mol * −6.3 ± 0.6 kcal/mol 0.03 Means ± SD. Significance obtained with a t-test. * p < 0.05. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Bauer M.R. Mackey M.D. Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein–Ligand Complexes J. Med. Chem. 2019 62 3036 3050 10.1021/acs.jmedchem.8b01925 30807144 2. Berger U.V. Gu X.F. Azmitia E.C. The substituted amphetamines 3,4-methylenedioxymethamphetamine, methamphetamine, p-chloroamphetamine and fenfluramine induce 5-hydroxytryptamine release via a common mechanism blocked by fluoxe-tine and cocaine Eur. J. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095078 ijms-23-05078 Article Enamel Matrix Derivative Decreases Pyroptosis-Related Genes in Macrophages Sordi Mariane Beatriz 12 Cabral da Cruz Ariadne Cristiane 2 https://orcid.org/0000-0003-3557-3493 Panahipour Layla 1 Gruber Reinhard 13* Kloc Malgorzata Academic Editor 1 Department of Oral Biology, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria; mariane.sordi@kcl.ac.uk (M.B.S.); layla.panahipour@meduniwien.ac.at (L.P.) 2 Department of Dentistry, Federal University of Santa Catarina, Florianopolis 88040-900, Brazil; ariadne.cruz@ufsc.br 3 Department of Periodontology, School of Dental Medicine, University of Bern, 3010 Bern, Switzerland * Correspondence: reinhard.gruber@meduniwien.ac.at; Tel./Fax: +43-1-40070-2660 03 5 2022 5 2022 23 9 507813 4 2022 29 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Background: Pyroptosis is a caspase-dependent catabolic process relevant to periodontal disorders for which inflammation is central to the pathophysiology of the disease. Although enamel matrix derivative (EMD) has been applied to support periodontal regeneration, its capacity to modulate the expression of pyroptosis-related genes remains unknown. Considering EMD has anti-inflammatory properties and pyroptosis is linked to the activation of the inflammasome in chronic periodontitis, the question arises whether EMD could reduce pyroptosis signalling. Methods: To answer this question, primary macrophages obtained from murine bone marrow and RAW 264.7 macrophages were primed with EMD before being challenged by lipopolysaccharide (LPS). Cells were then analysed for pyroptosis-signalling components by gene expression analyses, interleukin-1β (IL-1β) immunoassay, and the detection of caspase-1 (CAS1). The release of mitochondrial reactive oxygen species (ROS) was also detected. Results: We report here that EMD, like the inflammasome (NLRP3) and CAS1 specific inhibitors—MCC950 and Ac-YVAD-cmk, respectively—lowered the LPS-induced expression of NLRP3 in primary macrophages (EMD: p = 0.0232; MCC950: p = 0.0426; Ac-YVAD-cmk: p = 0.0317). EMD further reduced the LPS-induced expression of NLRP3 in RAW 264.7 cells (p = 0.0043). There was also a reduction in CAS1 and IL-1β in RAW 264.7 macrophages on the transcriptional level (p = 0.0598; p = 0.0283; respectively), in IL-1β protein release (p = 0.0313), and CAS1 activity. Consistently, EMD, like MCC950 and Ac-YVAD-cmk, diminished the ROS release in activated RAW 264.7 cells. In ST2 murine mesenchymal cells, EMD could not be tested because LPS, saliva, and IL-1β + TNF-α failed to provoke pyroptosis signalling. Conclusion: These findings suggest that EMD is capable of dampening the expression of pyroptosis-related genes in macrophages. enamel matrix derivative pyroptosis inflammasomes periodontal diseases macrophages mesenchymal cells University Clinic of Dentistry in ViennaThis research was funded by the University Clinic of Dentistry in Vienna. ==== Body pmc1. Introduction Periodontal disease is a global health problem [1]. Currently, peri-implant disease has reached the same level of concern as periodontal disorders. In this scenario, mucointegration—the attachment of soft tissues to the transmucosal portion of an implant—is just as relevant for implant success as osseointegration [2]. A disruption in mucointegration can manifest as peri-implant mucositis and, if not resolved, can progress to inflammatory peri-implantitis [3,4]. Periodontitis and peri-implantitis are universally agreed to begin with a breakdown in the soft tissue attachment and bone loss progression [5,6]. Consequently, methods to strengthen, maintain, or regenerate the soft tissue attachment around the tooth or the dental implant are critical for improving the protection sealing against microbial infections or endogenous danger signals [7]. The underlying pathogenesis of periodontitis/peri-implantitis is a chronic inflammation that drives downstream catabolic cellular events ultimately leading to tooth loss due to a lack of supporting tissues [6,8,9]. There is thus a critical requirement to understand the fundamental pathological mechanisms on a cellular and molecular basis to implement therapies aiming to regulate inflammation and thereby pave the way for regenerative strategies [8,9]. Thus, understanding the pathways connecting inflammation and tissue destruction will help to develop strategies to prevent and treat periodontitis and peri-implantitis. Pyroptosis is an inflammatory caspase-dependent catabolic process that is relevant for innate immunity. This process is mainly mediated by the activation of caspase-1 (CAS1) by the nucleotide-binding domain (NBD) and leucine-rich repeat (LRR)-containing protein 3 (NLRP3) inflammasome [10]. Then, CAS1 cleaves the gasdermin D (GSDMD), which is responsible for cell membrane perforation and the release of interleukins-1β (IL-1β) and -18 (IL-18) [10], which, in turn, trigger a robust inflammatory response on the surrounding tissues [11]. NLRP3 and CAS1 are important for bacterial clearance; however, if overexpressed, they may lead to cellular self-destruction, inflammation, and tissue damage [12]. Immunostaining images showed a stronger signalling intensity for NLRP3, cleaved CAS1, and IL-1β in the connective tissue of periodontitis compared to a healthy gingiva [13]. Additionally, using a periodontitis mouse model, higher amounts of NLRP3 and IL-1β were visible in the inflamed gingiva [13]. There is thus evidence for pyroptosis to occur in periodontal diseased tissues. In vitro periodontal models in pyroptosis research focus on the NLRP3/CAS1/GSDMD-mediated pyroptosis pathway in monocytes, macrophages, and periodontal ligament cells [11,14,15,16]. NLRP3 inflammasome can react to a wide range of bacterial ligands and play a pivotal role in the pathogenesis of inflammatory diseases. Lipopolysaccharide (LPS) is a virulence factor and a strong agonist of toll-like receptor (TLR) that is able to initiate pyroptosis signalling [16,17]. LPS is produced by Gram-negative bacteria [18]. Considering that oral diseases are mainly mediated by Gram-negative bacteria, it makes sense that LPS is related to periodontal disorders [12,18,19]. Taking advantage of this in vitro model, glycogen synthase kinase-3β (GSK-3β) deficiency was identified to lower the LPS-induced pyroptosis through the inactivation of NLRP3 inflammasome [16]. Accordingly, NLRP3/CAS1/GSDMD-mediated pyroptosis bioassays are suitable for identifying the components that lower pyroptosis signalling. Furthermore, considering the impairment caused by pyroptosis on periodontal disorders, finding ways to inhibit or reduce pyroptosis downstream brings prospects for periodontal therapies. Enamel matrix derivative (EMD) is a xenograft applied to support periodontal regeneration [20] that was also considered a treatment for venous leg ulcers [21]. EMD is an extract of enamel matrix from the tooth germ of piglets and propylene glycol alginate serves as a matrix. Proteome analyses confirmed the presence of enamel matrix proteins amelogenin and ameloblastin [22], and growth factors such as TGF-β have also been identified [23,24]. More importantly for this paper, EMD has been shown to exert anti-inflammatory activity in vitro. LPS-stimulated rat monocytes exposed to EMD exhibited a decrease in TNF-α production [25]. In human blood-derived cells exposed to LPS and peptidoglycan, EMD lowered TNF-α release [26]. In LPS-stimulated human osteogenic cells and immortalized human epithelial gingival keratinocytes, EMD lowered the expression of inflammatory cytokines including TNF-α [27]. Nevertheless, the expression of pyroptosis factors in cells stimulated with pyroptosis-triggering dangers—and primed with EMD—has not yet been explored. It might be hypothesized that the beneficial effects of EMD [25,26,27] are caused by lowering the pyroptosis-mediated cellular self-destruction and inflammation in periodontitis. Since there is strong in vitro evidence that EMD has anti-inflammatory properties [25,26,27] and pyroptosis is linked to the activation of the inflammasome in chronic periodontitis and peri-implantitis [11,12,16], the question arises whether EMD could reduce pyroptosis in vitro. Therefore, we tested the hypothesis that the anti-inflammatory activity of EMD is at least partially involved in a lowering of the LPS-mediated pyroptosis factors. 2. Materials and Methods 2.1. Primary Macrophages, RAW 264.7 Macrophage-like Cells, and ST2 Mesenchymal Cells BALB/c mice of 6 to 8 weeks old were purchased from Animal Research Laboratories, Himberg, Austria. Bone marrow cells were collected from the femora and tibiae as previously described [28]. Briefly, mice were sacrificed, and the femora and tibiae were removed. Bone marrow cells were seeded at 1 × 106 cells/cm2 into 24-well plates and grown for 7 days in Dulbecco’s Modified Essential Medium (DMEM; Sigma Aldrich, St. Louis, MO, USA) supplemented with 10% fetal calf serum (FCS; Capricorn Scientific GmbH, Ebsdorfergrund, Germany), 1% antibiotics (PS; Sigma Aldrich, St. Louis, MO, USA), and 20 ng/mL macrophage colony-stimulating factor (M-CSF; ProSpec, Ness-Ziona, Israel). RAW 264.7 macrophage-like cells (LGC Standards, Wesel, Germany) were expanded in growth medium and seeded at 3 × 105 cells/cm2 into 24-well plates. ST2 murine mesenchymal cells (Riken Cell Bank, Tsukuba, Japan) isolated from mouse bone marrow were seeded at 3 × 105 cells/cm2 into 24-well plates. Cells were primed with 30 µg/mL of enamel derivative matrix (EMD; Straumann AG, Switzerland) for 1 h and then exposed to 100 ng/mL of LPS from Escherichia coli 055:B5 (Sigma Aldrich, St. Louis, MO, USA) for 6 h to induce an inflammatory response. Alternatively, 5% saliva [29] or 20 ng/mL IL-1β (ProSpec, Ness-Ziona, Israel) and TNF-α (ProSpec, Ness-Ziona, Israel) were used for cell stimulation. Pyroptosis-specific inhibitors were applied to establish the in vitro LPS-induced pyroptosis model: MCC950 (CP-456773 Sodium, Selleck Chemicals GmbH, Houston, TX, USA) was applied at 8 µM for 30 min before cells were exposed to LPS and Ac-YVAD-cmk (≥95%, HPLC; Sigma Aldrich, St. Louis, MO, USA) was applied at 5 µM for 20 h prior to the LPS challenge. All cell lineages were exposed to the respective treatments under standard conditions of 37 ◦C, 5% CO2, and 95% humidity. 2.2. Reverse Transcription Quantitative Real-Time PCR (RT-qPCR) and Immunoassay For RT-qPCR, after stimulation, total RNA was isolated with the ExtractMe total RNA kit (Blirt S.A., Gdańsk, Poland) followed by reverse transcription and polymerase chain reaction (LabQ, Labconsulting, Vienna, Austria) on a CFX Connect Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA). The mRNA levels were calculated by normalizing to the housekeeping gene GAPDH using the ΔΔCt method. The primer sequences were mNLRP3-F: 5′-TCACAACTCGCCCAAGGAGGAA-3′; mNLRP3-R: 5′-AAGAGACCACGGCAGAAGCTAG-3′; mCAS1-F: 5′-GGCACATTTCCAGGACTGACTG-3′; mCAS1-R: 5′-GCAAGACGTGTACGAGTGGTTG-3′; mCAS11-F: 5′-CCTGAAGAGTTCACAAGGCTT-3′; mCAS11-R: 5′-CCTTTCGTGTAGGGCCATTG-3′; mGSDMD-F: 5′-GGTGCTTGACTCTGGAGAACTG-3′; mGSDMD-R: 5′-GCTGCTTTGACAGCACCGTTGT-3′; mIL-1β-F: 5′-CAACCAACAAGTGATATTCTCCATG-3′; mIL-1β-R: 5′-GATCCACACTCTCCAGCTGCA-3′; mIL-18-F: 5′-CAAACCTTCCAAATCACTTCCT-3′; mIL-18-R: 5′-TCCTTGAAGTTGACGCAAGA-3′; mGAPDH-F: 5′-AACTTTGGCATTGTGGAAGG-3′; mGAPDH-R: 5′-GGATGCAGGGATGATGTTCT-3′. RT-PCR data are represented compared to the untreated control. Supernatants and the respective cell lysates prepared with 0.3% Triton X-100 (Sigma Aldrich, St. Louis, MO, USA) were analyzed for IL-1β secretion by immunoassay (R&D Systems, Minneapolis, MN, USA) according to the manufacturer’s instruction. 2.3. Western Blot RAW 264.7 cells were seeded at 3 × 105 cells/cm2 into 12-well plates. On the following day, serum-starved cells were primed with EMD for 1 h and then exposed to LPS for another 6 h. Extracts containing SDS buffer with protease and phosphatase inhibitors were separated by SDS-PAGE (cOmplete ULTRA Tablets and PhosSTOP; Roche, Mannheim, Germany) and transferred onto PVDF membranes (Roche Diagnostics, Mannheim, Germany). Membranes were blocked and the binding of the Caspase-1 (D7F10), gasdermin D (E8G3F), and cleaved gasdermin D (E7H9G) first antibodies (rabbit IgG, 1:1000; Cell Signaling Technology, Danvers, MA, USA) were detected with the second antibody labelled with HRP (goat anti-rabbit IgG, 1:10,000; Cell Signaling Technology, Danvers, MA, USA). After exposure to the Clarity Western ECL Substrate (Bio-Rad Laboratories Inc., Hercules, CA, USA) chemiluminescence signals were visualized with the ChemiDoc imaging system (Bio-Rad Laboratories Inc., Hercules, CA, USA). Quantification of band intensity was performed using ImageJ software. 2.4. Mitochondrial Reactive Oxygen Species (ROS) Release RAW 264.7 cells were seeded at 3 × 105 cells/cm2 into 96-well plates and followed the standard stimulation with EMD, MCC950, or Ac-YVAD-cmk, then challenged with LPS for 6 h. Cells were analysed for the release of mitochondrial reactive oxygen species (MitoROS 580, AAT Bioquest, Inc., Sunnyvale, CA, USA) according to the manufacturer’s instructions. 2.5. Statistical Analysis All experiments were performed at least three times. Statistical analyses of gene expression and immunoassays were performed with paired t-tests, while ROS release statistical analyses were performed with one-way ANOVA followed by Dunnett’s multiple comparison test. Analyses were performed using Prism v.9 (GraphPad Software, La Jolla, CA, USA). Significance was set at p  < 0.05. 3. Results 3.1. Pyroptosis Inhibitors Validate Macrophages to Serve as a Pyroptosis Model To establish a pyroptosis model, primary macrophages generated from murine bone marrow were exposed to E. coli LPS. MCC950 and Ac-YVAD-cmk were introduced as inhibitors raised against NLRP3 and CAS1, respectively. MCC950 significantly reduced the forced expression of NLRP3, CAS11, and IL-1β, but also consistently decreased CAS1 and IL-18 gene expressions in primary macrophages. Likewise, Ac-YVAD-cmk significantly reduced the expression of NLRP3 and IL-18, and showed a trend to the reduction in the expression of CAS1, CAS11, and IL-1β, in primary macrophages (Figure 1). These findings support the LPS-induced primary macrophages to serve as a bioassay to test EMD and its potential for reducing pyroptosis signalling. 3.2. EMD Reduces the Expression of Pyroptosis Markers in LPS-Induced Primary Macrophages To test EMD and its potential for reducing pyroptosis in the established bioassay, primary macrophages were primed with EMD before being challenged by LPS and then were analysed for gene expression of pyroptosis signalling components. Our chosen dose of 30 µg/mL EMD did not lead to any cytotoxicity either alone or in combination with LPS (data not shown); therefore, we proceeded with the gene expression analyses. LPS caused a robust increase in the expression of the pyroptosis genes NLRP3, CAS1, CAS11, IL-1β, and IL-18 in primary macrophages, with particularly strong expressions of NLRP3 and IL-1β. EMD significantly lowered the LPS-induced expression of NLRP3, CAS1, and IL-18, suggesting that primary macrophages are susceptible to EMD and its pyroptosis-lowering activity (Figure 2). 3.3. EMD Reduces the Expression of Pyroptosis Markers in LPS-Induced RAW 264.7 Macrophages To implement a cell line-based pyroptosis model, RAW 264.7 macrophages were exposed to LPS followed by the screening for the respective pyroptosis marker genes. Consistent with the findings regarding the primary macrophages, EMD significantly reduced the LPS-induced expression of NLRP3 and IL-1β. There was also a trend toward reducing CAS1 and CAS11 expression (Figure 3). Differently from primary macrophages though, it was mainly the IL-1β but not the IL-18 expression that was reduced by EMD in RAW 264.7 cells. As expected [30], immunoassays of RAW 264.7 cells revealed negligible amounts of IL-1β in the supernatant (Supplementary Figure S1A). Nevertheless, under the permeabilization of the cell membrane, IL-1β could be confirmed in LPS-stimulated RAW 264.7 cells as well as the significant IL-1β reduction with the treatment with EMD (Figure 4A). Moreover, EMD reduced cleaved CAS1 at the protein level (Figure 4B), suggesting a decrease in the CAS1 activity and that EMD could lower the expression and the activation of CAS1 by NLRP3 reduction. The bands were quantified regarding intensity (Supplementary Figure S2), confirming what can be pictured in the Western blot images. Thus, the RAW 264.7 macrophages are suitable to identify EMD for lowering a pyroptosis response. 3.4. EMD Reduces Reactive Oxygen Species (ROS) in LPS-Induced RAW 264.7 Macrophages RAW 264.7 macrophages were again exposed to LPS and analysed for mitochondrial ROS release. EMD reduced the mitochondrial ROS release in RAW 264.7 cells to levels comparable to the untreated control, suggesting a reduction in cellular stress levels by the EMD treatment. Consistently, the pyroptosis specific inhibitors, MCC950 and Ac-YVAD-cmk, diminished ROS release in activated RAW 264.7 cells (Figure 5). 3.5. ST2 Mesenchymal Cells Are Not Suitable to Test the Potential Role of EMD on Pyroptosis Finally, we introduced LPS and saliva stimulation over ST2 murine mesenchymal cells to serve as a model for pyroptosis testing. However, both, LPS and saliva, failed to considerably increase the expression of the most sensitive pyroptosis marker—the NLRP3—and all other pyroptosis markers, including IL-1β and IL-18, suggesting that neither LPS nor saliva stimulation in ST2 cells were suitable models to evaluate EMD to change pyroptosis (Supplementary Figure S3). When ST2 cells were exposed to IL-1β and TNF-α, there was a strong increase of interleukin-6 (IL-6) and chemokines CCL2 and CXCL2, which were further reduced by EMD application (Figure 6). Nevertheless, no changes in NLRP3 or any other pyroptosis markers were found (Supplementary Figure S3). Thus, LPS, saliva, or IL-1β + TNF-α stimulations on ST2 cells are not applicable to evaluate the potential role of EMD to reduce pyroptosis signalling. 4. Discussion Pyroptosis is a major driver of inflammatory disorders and is chiefly activated by NLRP3 inflammasome and caspases. Thus, NLRP3 and CAS1, the hallmarks of pyroptosis signalling, are increasingly expressed in periodontal disease compared to healthy tissue [10,12,31]. Considering EMD is widely used in periodontal regeneration and has demonstrated anti-inflammatory properties in vitro [20,25,26,27], we hypothesized that part of the beneficial activity of EMD might involve the modulation of pyroptosis signalling. Indeed, our major finding was that EMD lowered the forced expression of NLRP3 and CAS1 activity in murine macrophage models. Taken together, our findings suggest that EMD diminishes pyroptosis signalling in macrophages. If we relate our findings to those of other studies, our data completes the overall picture of the anti-inflammatory activity that EMD has in vitro on various models from human, rat, and mouse cells [25,26,27,32]. However, these models mainly used TNFα or interferon-gamma (IFNγ) to simulate inflammation, but TNFα and IFNγ are not drivers of pyroptosis signalling. It was merely the study on LPS-stimulated human osteoblastic cells and human gingival keratinocytes that assessed EMD lowering the expression of IL-1β, however, this study was not focused on pyroptosis [27]. Hence, our findings that EMD reduces the expression of IL-1β in RAW 264.7 macrophages support the existing knowledge on the anti-inflammatory properties of EMD; nevertheless, this observation is not sufficient to support the involvement of EMD in the reduction of the pyroptosis signalling. Macrophages can be polarized into either classically activated pro-inflammatory (M1) or alternatively activated anti-inflammatory (M2) macrophages depending on the stimulation [33]. M1 macrophages are induced by pathogen-associated molecular patterns (PAMPs), such as the bacterial LPS used herein, or Th1 cytokines such as IFNγ, producing a wide range of cytokines, such as TNF-α, IL-1β, IL-6, and inducible nitric oxide synthase (iNOS), to aggravate inflammation. In contrast, M2 macrophages are induced by Th2 cytokines such as IL-4 and IL-13, and they possess the ability to express arginase-1 (Arg1), chitinase-like 3 (or Ym1), and IL-10 to promote reparative processes and relieve inflammation [34]. Therefore, since we have applied LPS, we know that we are working with M1 pro-inflammatory macrophages. Furthermore, since this is a pyroptosis-related article, we did not focus on IFNγ, TNF-α, IL-6, or iNOS, but on IL-1β and IL-18, which are directly related to pyroptosis. Moreover, the production of ROS is a hallmark of M1 macrophages, which also contributes to the M2 polarization switch [35]. Nevertheless, since we have scientific support that we are working with M1 macrophages, we can affirm that the release of ROS is related to the pro-inflammatory aspect. Our data showing that EMD significantly reduced the expression of IL-18 in primary macrophages and that NLRP3 and CAS1 specific inhibitors (MCC950 and Ac-YVAD-cmk, respectively) exert a similar activity, can be considered indirect support for EMD to attenuate pyroptosis activity. These findings are in line with other observations showing that MCC950 inhibited IL-18 release in THP1 and monocytes [36,37], reversed the forced IL-1β and IL-18 expression on periodontal ligament fibroblasts [38], HCT116 colorectal cells [39], and canine kidney epithelial cells [40]. Furthermore, Ac-YVAD-cmk reduced the forced expression of IL-18 in whole blood cells [41], in THP-1 cells [42], and also in sepsis-induced acute kidney injury [43]. Even though EMD performs similarly to MCC950 and Ac-YVAD-cmk inhibitors and reduces the expression of NLRP3, CAS1, and IL-18 in primary macrophages, this is not sufficient evidence that EMD reduces pyroptosis activity and should be supported by additional investigation. Support for EMD to regulate pyroptosis arises from findings that EMD reduces the LPS-induced expression of NLRP3 and IL-1β in RAW 264.7 macrophages. Considering that NLRP3 together with IL-1β and IL-18 are NF-kB-target genes, it can be hypothesized that EMD lowers the LPS-driven NF-kB signalling pathway and thereby the transcription of NLRP3 and IL-1β/IL-18. Consequently, the assembly of the inflammasome is limited by the accessibility of the reduced NLRP3, and our observation that EMD lowers the LPS-induced CAS1 activity supports this concept. Thus, our findings add to the existing knowledge of the anti-inflammatory properties of EMD and guide it towards the regulation of the pyroptosis pathway in macrophages. Furthermore, our data on EMD reducing inflammation in ST2-challenged cells also give additional support to the anti-inflammatory activity of EMD in vitro. Consistent with other reports [30], immunoassays failed to detect IL-1β in the extracellular media in LPS-stimulated RAW 264.7 cells, while the cell membrane permeabilization allowed the detection of IL-1β in LPS-challenged cells. This seems to be related to the weak GSDMD activity that is herein reported. GSDMD is required for IL-1β release in pyroptotic cells or hyperactivated cells [30]. GSDMD knockout cells are unable to form pores and release IL-1β or lactate dehydrogenase (LDH), a molecule that shows signs of membrane pore formation [30]. This agrees with our finding that LDH release was not substantially increased in LPS-stimulated RAW 264.7 macrophages (Supplementary Figure S1B). Likewise, GSDMD is necessary for the release of cleaved IL-1β during infection but is not required for IL-1β processing within cells [30]. Hence, it seems that our model failed to cause membrane pore formation due to reduced GSDMD activity. Thus, our model is valid to test for pyroptosis signalling but not for full pyroptosis induction, including membrane disintegration. Regarding ROS release, EMD in LPS-stimulated RAW 264.7 macrophages reduced mitochondrial ROS, such as the NLRP3 and CAS1 specific inhibitors (MCC950 and Ac-YVAD-cmk, respectively). In agreement with our findings, MCC950 inhibited the excessive production of ROS in chondrocytes [44], and Ac-YVAD-cmk blocked the forced ROS production in HT22 cells [45] and cerebellar granule neurons [46]. Increased ROS levels drive the transcription nuclear factor and induce the pyroptosis of nucleus pulposus cells through the NLRP3 pathway which is related to the mechanism of degenerative disorders [47]. More importantly, ROS acts downstream of gene transcription, mRNA translation, and IL-1β converting enzyme activation [46]. Furthermore, ROS production occurs after K+ deprivation [30,46], which can induce pyroptosis [12]. Therefore, evaluating ROS release is relevant to pyroptosis signalling as part of the downstream events occurring in the pyroptotic cells. The clinical relevance of our findings remains at the level of speculation. Clinically, EMD stabilizes blood clots and improves clinical healing in deep pockets after non-surgical periodontal treatment [48]. Minimally invasive periodontal surgery with EMD in periodontitis-affected subjects results in lower values of C-reactive protein as no inflammatory perturbation was noticed [49]. EMD treatment also reduced bleeding on probing and periodontal pocket depth, and post-surgical gingival recession was lowered [49]. Moreover, EMD shows an antibacterial effect on the viability of ex vivo supragingival dental plaque flora collected from patients with periodontitis [50]. Considering that EMD lowers the inflammation also in vivo [51] and that periodontitis is linked to pyroptosis [11,12,16], we can speculate that EMD exerts its beneficial effect by reducing pyroptosis signalling at sites of chronic periodontitis, likely involving the NLRP3 expression. The complexity of the in vivo situation, however, is not fully represented by primary macrophages or cell lines. Primary macrophages are closer to the in vivo situation than cell lines and, therefore, we have used the primary macrophages to establish the pyroptosis system and to perform the proof-of-principle experiments. However, to reduce and replace animal organ donation—in this case, bone marrow—once we had established our model with the use of pyroptosis-specific inhibitors and evidence that EMD reduces pyroptosis-related genes, we switched to a macrophage cell line. As expected, the cell line performed similarly, although not identically, to the primary cell line. Furthermore, in vitro models are useful for identifying potential cellular responses and signalling pathways that can later be evaluated in a complex in vivo environment. By showing that EMD lowers the LPS-induced expression of pyroptosis-related genes, we provide a fundament for future research in this direction. This study has the limitations of the in vitro research. For instance, which and how EMD component molecules responsible for the anti-pyroptosis activity reach the target cells in vivo were not explored. Since we have not discovered the molecular structure and the characteristics of the anti-inflammatory components of EMD, the in vitro findings cannot easily be translated into a clinical perspective. Hence, further studies of EMD in the inhibition of pyroptosis in periodontal tissues should be conducted in vivo. Since EMD is available for clinical purposes, studies on its impact on the periodontium are feasible. Another limitation of our model is that LPS alone was not sufficient to increase the expression of, or activate, GSDMD, an executor of pyroptosis required for the IL-1β secretion in macrophages [52]. Future studies could therefore include pyroptosis agonists such as α-hemolysin [53,54], nigericin [30], or ATP [55], together with LPS to impulse cytotoxicity and IL-1β secretion other than the gene expression of pyroptosis-related factors, i.e., the full picture of pyroptosis [52]. It might also be worth considering the impact of EMD on the CAS3 dependent apoptotic pathway, downstream of CAS1 and independent of GSDMD [56]. Further proof for EMD to reduce pyroptosis-mediated periodontal destruction might be based on mouse models with a genetic deletion of NLRP3, CAS1, or GSDMD; hypothetically, EMD cannot exert its beneficial activity when pyroptosis is blocked at the genetic level. In conclusion, our findings suggest that EMD is capable of dampening pyroptosis-related genes in macrophages. This is relevant as the clinical use of EMD in periodontal therapies could comprise the reduction of pyroptosis downstream under conditions of periodontal tissue inflammation. Acknowledgments M.B.S. was supported by Osteology Foundation (Luzern, Switzerland) Scholarship. EMD was a kind gift from Straumann Österreich (Vienna, Austria). Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23095078/s1. Click here for additional data file. Author Contributions M.B.S., A.C.C.d.C., L.P. and R.G. have contributed to the conception, analysis, and development of this article. M.B.S. has conducted experiments. M.B.S., A.C.C.d.C., L.P. and R.G. have been involved in drafting and revising the manuscript critically and have approved the final version for publication. M.B.S., A.C.C.d.C., L.P. and R.G. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The bone marrow cells were collected from the femora and tibiae of BALB/c mice, which were purchased from Animal Research Laboratories, Himberg, Austria. According to Austrian law, organ donation from mice required an informal approval of the local veterinarian authorities but not a formal approval by the Ethics Committee. Informed Consent Statement Not applicable. Conflicts of Interest The authors state no conflict of interest related to this project. Figure 1 LPS stimulation caused an increase in the expression of the pyroptosis genes NLRP3, CAS1, CAS11, IL-1β, and IL-18 in primary macrophages. The application of MCC950 prior to LPS stimulation in primary macrophages led to a significant reduction in the expression of NLRP3, CAS11, and IL-1β, and a trend in the reduction of CAS1 and IL-18. The application of Ac-YVAD-cmk prior to LPS stimulation in primary macrophages led to a significant reduction in the forced expression of NLRP3 and IL-18, and a trend in the reduction of CAS1, CAS11, and IL-1β. Different symbol shapes mean independent experiments. Paired t-test statistical analysis was applied to compare the groups. Figure 2 LPS stimulation caused an increase in the expression of the pyroptosis genes in primary macrophages. The application of EMD prior to LPS stimulation in primary macrophages led to a reduction in the forced expression of NLRP3, CAS1, and IL-18, and a trend in the reduction of CAS11 and IL-1β in primary macrophages. Different symbol shapes mean independent experiments. Paired t-test statistical analysis was applied to compare the groups. Figure 3 LPS stimulation caused an increase in the expression of the pyroptosis genes in RAW 264.7 macrophages. The application of EMD prior to LPS stimulation in RAW 264.7 cells led to a significant reduction in the forced expression of NLRP3 and IL-1β, and a trend in the reduction of CAS1 and CAS11 gene expression. Different symbol shapes mean independent experiments. Paired t-test statistical analysis was applied to compare the groups. Figure 4 EMD reduces the pyroptosis factors in LPS-induced RAW 264.7 macrophages. (A) EMD protection on LPS-stimulated RAW 264.7 macrophages led to IL-1β reduction detected from the immunoassay. Different symbol shapes mean independent experiments. Paired t-test to compare LPS and EMD + LPS groups was applied. (B) Confirming the gene expression, Western blot analyses showed less cleaved CAS1 (20 KDa) protein expression in RAW 264.7 cells primed with EMD. Cleaved GSDMD was present in cells stimulated with LPS and GSDMD was present in all groups. Figure 5 LPS stimulation caused an increase in the reactive oxygen species (ROS) release in RAW 264.7 macrophages. The application of EMD, MCC950, or Ac-YVAD-cmk in LPS-induced RAW 264.7 cells showed a significant reduction in ROS release. Different symbol shapes mean independent experiments. Repeated measures of one-way ANOVA followed by Dunnett’s multiple comparison tests, comparing every group to the LPS group, were applied. Figure 6 IL-1β and TNF-α stimulation caused a strong expression of IL-6, CCL2, and CXCL2 in ST2 cells. The application of EMD prior to IL-1β + TNF-α stimulation in ST2 cells led to a trend in the reduction of the forced expression of inflammatory markers. Different symbol shapes mean independent experiments. Paired t-test statistical analysis was applied to compare the groups. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Petersen P.E. Ogawa H. The global burden of periodontal disease: Towards integration with chronic disease prevention and control: Global periodontal health Periodontol. 2000. 2012 60 15 39 10.1111/j.1600-0757.2011.00425.x 22909104 2. Klinge B. Meyle J. Soft-tissue integration of implants Clin. Oral Implant. Res. 2006 17 93 96 10.1111/j.1600-0501.2006.001366.x 16968385 3. Berglundh T. Armitage G. Araujo M.G. Avila-Ortiz G. Blanco J. Camargo P.M. Chen S. Cochran D. Derks J. Figuero E. 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==== Front Animals (Basel) Animals (Basel) animals Animals : an Open Access Journal from MDPI 2076-2615 MDPI 10.3390/ani12091089 animals-12-01089 Article Wind Farms and Power Lines Have Negative Effects on Territory Occupancy in Eurasian Eagle Owls (Bubo bubo) https://orcid.org/0000-0002-7015-5011 Husby Magne 1* Pearson Martin 2 Solonen Tapio Academic Editor 1 Section of Science, Nord University, 7600 Levanger, Norway 2 Odontovet, Nedre Vikan 5, 7240 Hitra, Norway; martin@odontovet.no * Correspondence: magne.husby@nord.no 22 4 2022 5 2022 12 9 108926 3 2022 20 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary Wind power can contribute to a necessary reduction in CO2 and other greenhouse gas emissions. However, wind farm construction and infrastructure might cause other problems, for example, reducing biodiversity. In parts of their distribution area, eagle owls are scarce and declining, and not much is known about their tolerance for different kind of disturbances. Here, we investigated the presence–absence of Eurasian eagle owls (Bubo bubo) in 48 territories in the central part of Norway before the construction of eight wind farms and power lines started, and shortly after the construction period. Eagle owls living within 4–5 km away from the disturbance left their territories to a higher extent than eagle owls living even further away. Abstract Wind power is useful for reducing greenhouse gas emissions, but the construction and operation might have negative effects on biodiversity. The purpose of this study was to investigate any effects of wind farm and power line construction on territory occupancy in the vulnerable Eurasian eagle owl. We investigated 48 eagle owl territories before and after the whole construction period and a short operation period with the use of sound meters. We found that territorial eagle owls within 4–5 km from the wind farm and power line construction disturbance left their territories to a significantly higher extent (41% reduction in the number of territories with eagle owls) compared with the eagle owls in territories further away (23% reduction). The distance from the nest site to the disturbance was significantly shorter for those territories that were abandoned compared with territories where the birds stayed. Possible reasons for this decline might be a higher mortality caused by collisions, desertion and avoidance of wind power areas caused by the noise and disturbance from their construction. In addition, there are possible indirect effects, for example reductions in prey species may force eagle owls to abandon their territories. The construction period lasted much longer than the period with active wind turbines and power lines in this investigation, but we cannot separate the effects of the two because the investigations were only possible in the eagle owl breeding season, and the wind turbines were activated shortly after the construction period. Our results imply that careful investigations are needed to detect the possible occurrence of eagle owls near any type of construction work. Studies of these territories should strongly influence how and when the construction work can be carried out, but more investigations are needed to find details about the influence of distance. anthropogenic disturbance birds construction influence area territory tolerance turbines wind energy wind farm construction ==== Body pmc1. Introduction It is unequivocal that human influence has warmed the Earth’s atmosphere and oceans more in the last 50 years compared to the last 2000 years, causing many weather and climate extremes worldwide. Strong reductions in CO2 and other greenhouse gas emissions in the coming decades are needed [1]. The adverse effects of climate change on biodiversity are expected. With a global temperature increase of 1.5–2 °C, the majority of terrestrial species ranges are projected to dramatically shrink [2]. Many bird species have already experienced declines caused by global warming [3,4]. Wind power is one of several possible mitigation actions to reduce greenhouse gas emissions [5]. However, much of nature has already been lost, and what remains continues to decline. Now, only 23% of global land area is classified as wilderness [2]. Land-based wind farms require huge areas, and this effort to reduce global warming might increase biodiversity losses. Investigations have shown the negative effects of landscape disturbance and land use on many bird populations [6,7,8], including boreal owl (Aegolius funereus) and northern saw-whet owl (Aegolius acadicus) in Canada [9]. More specifically, the construction and operation of wind farms negatively impacts birds both by habitat alteration and disturbance [10], as well as direct mortality [11,12,13,14]. The fatality rate due to wind turbines is relatively high for some owl species compared with some other bird species [15]. Eagle owl mortality has been associated with both wind turbines [16] and power lines [17,18,19,20,21,22]. During wind farm construction, strong anthropogenic noise is likely an important disturbing factor for birds in the surrounding areas [23,24,25,26,27]. Farmland birds decline more significantly near urban areas compared with rural areas with less anthropogenic impact, including noise [28]. Owls are, to a large extent, acoustically specialized predators, and therefore potentially vulnerable to noise. The morphology of eagle owl wings makes it possible to fly almost silently [29], as an adaptation to finding prey by listening while flying. Anthropogenic noise was found to reduce the hunting success of northern saw-whet owl (Aegolius acadius) by 8% for each decibel increase in the noise [30]. Helicopter overflights caused Mexican spotted owls (Strix occidentalis lucida) to flush when the distance was less than about 105 m, but there were no effects on reproductive success or the number of fledglings. Chainsaws were found to be more disturbing to this owl species than helicopter flights at comparable distances, but there was still no visible negative effect beyond 105 m [31]. Noise from low-intensity chainsaws operated at an 100 m distance from roost sites did not elicit a detectable increase in physiological stress levels in California spotted owls (Strix occidentalis occidentalis), but chronic and intense noise from for example road construction was not included in the experiment [32]. Human activity increases in remote areas during the period of wind farm construction. It has been found that Mexican spotted owls leave their roosting site when approached by hikers, but mostly when the hikers were within 55 m [33]. Breeding females of the Mexican spotted owl decreased the amount of time spent handling prey and daytime maintenance during experimental hiking. Therefore, the authors concluded that the cumulative effects of high levels of short-duration recreational hiking near nests may be detrimental [34]. Very little is known about the effect of human presence on Eurasian eagle owls. Roosting eagle owls are not especially shy [35,36,37], and flush distances of 50 m or less are observed [35]. However, in parts of the area used in the present study, an increase in the number of hikers within 2 km from the nest site reduced eagle owl breeding performance [38]. Eagle owls might be more sensitive to approaching humans in areas where they have been heavily persecuted than in other areas. High rates of persecution during more than hundred years is one important factor resulting in declining eagle owl populations in Norway [18,36,37]. The eagle owl population in Norway is estimated at 451–681 pairs [39], classified as endangered (EN) on the Norwegian red list [40]. Other investigations of eagle owl found that the number of breeders declined when the number of hikers and climbers increased in a national park in Croatia, but the number of pairs investigated was low [41]. In 327 clutches studied for 20 years in Bulgaria, human activities near the nests were the main reason for nest failures [42]. In the Italian Alps, territories were located at a lower elevation and closer to intensively cultivated, urbanized valley floors where there was more prey available, but the eagle owls on the valley floors suffered a higher anthropogenic-associated mortality [43]. The present study investigates the effects of the noise and other disturbance on eagle owls in Norway during the construction of both wind farms and power lines, and a short period with active wind turbines. We investigated areas with eagle owls before the disturbance started and in the first breeding season after the wind turbines were activated. According to the literature introduced above, we expected to (1) find fewer occupied eagle owl territories after the construction period than before the construction started within the influence areas compared with reference areas further away from the disturbances. In addition, (2) we expected a lower breeding performance within the influence area compared with the reference areas. 2. Materials and Methods 2.1. Study Area The study area comprises eight wind farms and associated power lines in the central part of Norway (Figure 1). In 2014–2015, we investigated 70 areas known for having eagle owls commonly recorded or where eagle owls might have been recorded during the last decade. This is the pre-disturbance investigation. The home ranges of the eagle owls vary considerably between areas, likely as a response to variable food supply, sex and season [18,35]. Some individuals might have home ranges of several tens of km2 [18,35,44,45,46]. They also hunt outside of the strictly defended area [35]. Therefore, we started this investigation by defining all areas within 5 km from the wind farms or power lines as belonging to the influence area (Figure 1), and other areas to be outside the influence area and used as reference areas. However, in this study, we investigated at which distance we still observed the negative effects of the constructions. All areas included in the investigation were close to the coast, and the influence areas and reference areas were situated in the same region (the wind farm areas and the areas around and between them in Figure 1) and are therefore comparable. Parts of the study area are mountains without forests and suitable for wind farms, with steep cliffs that are the preferred breeding ground of the eagle owl. In addition, there are forests, farmland areas, bogs, lakes and rivers, and some human settlements. Islands with breeding eagle owls in the study area lack the mammalian predators common on the mainland, such as the red fox (Vulpes vulpes), Eurasian lynx (Lynx lynx), European badger (Meles meles) and pine marten (Martes martes). The eagle owl diet analyzed from pellets, remnants found on and near the nesting cliffs of our study area show a wide variety of prey, especially various species of birds, mammals and reptilians [47,48,49,50]. This is also normal also in other areas of its breeding range [18]. 2.2. Construction Disturbance The disturbance to eagle owls investigated in the present study are mainly in the construction period of the wind farms and power lines, both power lines connected to the wind farms and other new power lines constructed in the same time period. In addition, the areas have more human activity than before. The main disturbances in connection with wind farm construction are supposed to be road construction and the construction of platforms for wind turbines, transport and installation of the turbines with the use of cranes and large trucks. Disturbance along the power line network involves clearing a belt free of trees in the power line ride by using logging machines or chain saws; helicopters are used to transport materials and to install the electric lines. Normally several rig areas and storage spaces are constructed along the power lines. Mechanical diggers and dynamite are used in the infrastructure construction. There were no restrictions in where and when construction work was permitted, except when eagle owl were detected within about 1 km from the working area. As a result of our findings in 2014–2015, before the construction period started, a few wind turbines and roads were moved relative to the original plans to reduce the disturbance of neighboring eagle owl nests or possible nests. During the wind farm construction period, there was more human activity than usual in the remote areas, but this was not quantified. That, together with the construction disturbance, most likely reduced the preferred habitats for the eagle owls and for some of the prey species. After the construction period, the wind turbines started to produce electricity and generated a different but significant type of noise. The disturbance in the influence areas lasted, therefore, 2–3 years during the construction period and a few months with active wind turbines and power lines. It was impossible for us to start the investigations immediately after the construction period because we had to wait until the first breeding season afterwards. After construction, the power lines might also have caused mortality by collisions [22]. 2.3. Observing Eagle Owls We used wildlife acoustic sound meters (SM 2+, SM4 and SM Mini), in 2014–2015, programmed to continuously record for about seven days in March (February–April), termed the spring investigation. In 2020–2022, the sound meters were programmed to record from one hour before sunset to one hour after sunrise for about 14 days, thus increasing the probability of detecting eagle owls if present. In areas where the eagle owl was not registered in the spring investigation, we used recorders in the autumn (September), similarly programmed according to sunset and sunrise as in the spring. Three localities were investigated in February–March 2022. The autumn investigation is of course not completed yet, but our experience is that there will be almost no eagle owls registered in the autumn if they were not present in the breeding season. Different areas were investigated in different years, and each area was investigated only one year before the disturbance and one year after the disturbance period. The recordings were analyzed by the programs Audacity(R) editing software (v. 2.4.2. Boston, MA, USA) and Kaleidoscope (Pro Analysis Software v. 5.1.9g, Wildlife Acoustics. Maynard, MA, USA) to find eagle owl sounds, and Raven (Pro v. 1.6, Cornell Lab of Ornithology. Ithaca, NY, USA) for studying the details. The song of the eagle owl is not learned; therefore, it has little variation over time [51]. It is therefore possible to recognize different males via variations in spectrogram measures if the recordings are high-quality [52,53,54]. We used the program Raven to find details in the song of males to quantify the differences, and thereby concluded if it was the same or a different male in neighboring areas, treating the areas as one territory if it was the same male. In addition, we measured the distance from the nest, or the most probable nesting place, to both the closest wind turbine and the shortest distance to a new power line constructed after the preliminary investigation. We are not sure whether or not every area with observations of eagle owls was a breeding territory, but because most areas had regular observations of eagle owls during the years before this investigation started, we considered all the areas as territories. 2.4. Statistics To test hypothesis 1, if the eagle owls changed their presence status in the territory from 2014–2015 to 2020–2022, we used generalized linear mixed effects model (GLMM) analysis (IBM Statistics v. 27. Chicago, IL, USA). Alternatives were 1 = abandoned (n = 14); 2 = no change in observation, meaning observed in both periods (n = 23); and 3 = observed only in the last period, termed reestablished (n = 2). Instead of reestablishment, it is possible that the eagle owls used the territories in 2014–2015 without being detected, but statistically, we treated these territories as reestablished. Explanatory variables were: (1) inside or outside the influence area, varying from 1 to 5 km (values 1 and 2, respectively); (2) distance between the nest area and the closest wind turbine; and the (3) shortest distance between the nest area and a new power line. Both distances were measured to the nearest 0.1 km. Each wind turbine and the powerlines are visible on norgeskart.no, which has a tool for measuring distances. In addition, (4) we included the island and mainland as explanatory variables with values 1 and 2, respectively. The observations in the different territories were not in the same year, and year was therefore included as random factor. The GLMM analysis was run with a multinomial probability distribution and cumulative logit link function. In the data exploration for the GLMM analysis with the target variable, if eagle owl changed their presence in the territory or not from 2014–2015 to 2020–2022, we first used Spearman rank correlations between the explanatory variables. We used the variables for: (1) within or outside the influence area, (2) the distance from the nest area to the nearest wind turbine, and (3) the closest distance to the power line. The correlations were quite high (rs = 0.6–0.8) and around the suggested maximum limit of 0.7 [55]. The variation inflation factor (VIF) values were >5.5, which were above most recommendations [56,57]. We therefore run the GLMM analyses with the island, mainland and only one of the other explanatory variables that were highly correlated separately (distance from nearest wind turbine, distance to power line, within or outside the influence area) and by varying the influence area from 1 to 5 km from the nearest turbine or power line. We compared the different models with Akaike information criteria corrected (AICC), and with ΔAICC > 2 from the best model, the other models were normally rejected [56]. Because of the small number of reestablished territories, we ran a nonparametric Fisher–Freeman–Halton exact Test (FFHET) to test if the decline in the number of active territories was statistically significant with various influences of distance. We tested hypothesis 2 with a GLMM analysis using only nests with known breeding performances for all the seven years from 2015 to 2021, produced fledglings in at least one of the seven years. The year 2015 was before the actual disturbance started and 2021 was the year after. The target variable was a breeding performance ranked from 1–6: 1 = eagle owls were not observed in the territory, 2 = observed in the territory, 3 = eggs were laid, 4 = one chick was produced, 5–6 = one additional point for each additional chick. Chicks produced is the number of young alive at ringing age during the first 15 days of June, about three weeks old. Explanatory variables were: (1) islands Hitra and Frøya, values 1 and 2, respectively; (2) disturbance with value 1 in the years without disturbance, and value 2 in the years with disturbance; (3) distance to the nearest disturbance factor (wind turbine or power line) in km; and (4) year (during the seven year period, 2015–2021). The GLMM analysis was conducted with multinomial probability distribution and cumulative logit link function. The sample size was relatively low in this analysis, but with the selected limitations, we were sure that the adult birds were living in a territory where reproduction was possible. No explanatory variables were excluded because the maximum values of the correlations between the explanatory variables were within the recommended limits (|rs| < 0.46 and VIF < 2.2). Territory number was a random factor in these analyses. GLMMs were used because they removed variability in responses that were associated with random factors rather than the conditions of experimental interest, thus reducing Type I error rate [58]. GLMM may be the best tool for analyzing non-normal data that involve random effects [59]. Because of the strong probabilities of negative effects of the disturbances, statistical tests are one-tailed with an α-level of 0.05. 3. Results Of the 70 areas investigated in 2014–2015, 22 were excluded from further investigations due to the lack of eagle owl activity in 2014–2015 and earlier registrations were scarce and/or several years old. In the remaining 48 territories, we registered eagle owls in 37 territories, and continued to investigate the other 11 territories despite the fact that no eagle owls were observed in 2014–2015. This is because the potential for reestablishment/detection was expected to be higher here than in the 22 territories that we excluded according to the earlier history of the locations. In 2 of these 11 areas, eagle owls were registered in 2020–2022. Therefore, we had 39 eagle owl territories with registrations in at least one of the two time periods that were included in the analyses. Before the construction period started, we observed at least one eagle owl in 15 territories within the influence area of 5 km from the closest wind turbine or power line and 22 territories outside the influence area. Of the territories within the influence area, nine were abandoned. In addition, two territories within the influence area with no eagle owls detected before the construction period were detected afterwards. We therefore registered seven fewer territories of the 17 with eagle owls in at least one of the two periods (41% reduction). Outside the influence area, five of the 22 territories were abandoned, and there were no reestablished territories (23% reduction). The decline in the number of active territories was statistically significant both with an influence distance of 5 km (FFHET value = 7.39, p = 0.008) and with an influence distance of 4 km (FFHET value = 4.97, p = 0.028). The other tested influence areas from 1 to 3 km did not yield significant results in the same test (p > 0.15 in all tests). Abandoned eagle owl nests (n = 14) were significantly closer to the nearest disturbance source than those that remained (n = 23) (MW U-test: Z = −1.817, n = 37, p = 0.035) (Figure 2). We found a similar result for the distance from power lines (MW U-test: Z = −1.645, n = 37, p = 0.050), and the results were not so significant for the distance to the nearest wind turbine (Z = −1.472, n = 37, p = 0.071). The two areas where eagle owl reestablished were 2.5 and 4.0 km away from the nearest wind turbine, respectively. None of the GLMM analyses testing hypothesis 1 were statistically significant. An influence distance of 4 km gave the best model, as judged from AICC values, and all the other variables achieved a value of ΔAICC > 2 compared with the best model. The data to test hypothesis 2 were from only 11 nests on Hitra and Frøya, investigated yearly in the period 2015–2021, and with production of young for at least one of the years. A GLMM analysis with the breeding performance (values 1–6) as the target variable was statistically significantly higher inside than outside an influence distance of 3 km (coefficient = 2.244 ± 1.15, t = 1.953, p = 0.028). The other explanatory variables—year, island and disturbance or no disturbance for each year—were far from being statistically significant. However, this strange result that contrasted with our hypothesis was caused by a higher breeding performance within the influence area of 3 km already before the disturbance started, and the breeding performance did not change, neither in the influence areas nor in the reference areas when the disturbance started (Figure 3). The use of distance to the disturbance as an explanatory variable instead of influence area had a far-from-significant effect, as well as with other influence areas than 3 km. 4. Discussion The present investigation focuses on the immediate response of territorial eagle owls to disturbances from the full construction period of wind farms and power lines, and a short period with active wind turbines and power lines. We found that territorial birds within 5 km from the disturbance left their territories to a significantly higher extent (41%) compared with the eagle owls in territories further away (23%). In addition, the distance from the nest site or the central part of the territory to the disturbance was significantly shorter for those territories that were abandoned compared with territories where the birds stayed. Our findings of the detrimental effects are in accordance with our prediction 1, and with other investigations on how eagle owls react on human disturbance [38,42,60]. The decline in eagle owl populations near the construction areas of wind farms and power lines might have three main explanations. Firstly, the increased mortality of eagle owls may have been caused by power lines and wind turbines. It is known by telemetry investigations that eagle owls fly more than 20 m above the ground 25% of the time [45], and that they can fly upwind like raptors high up in the air [18,61]. It is therefore reasonable that they are judged to be vulnerable to becoming killed by wind turbines [62], and eagle owls have been killed by wind turbines in several European countries [16]. It is also well-known that many eagle owls are killed by power lines [17,18,19,20,21,22]. Secondly, the desertion and avoidance of wind power areas by eagle owls caused by the noise and disturbance from the constructions. Several publications show the negative effects of noise and disturbances on bird populations (see Introduction). Thirdly, the possible indirect effects are that prey species of the eagle owl might die or leave the area, and that the eagle owl also leaves because less prey is available. Unfortunately, we do not know whether the abundance of prey was affected by the construction. Others have shown a positive correlation between the amount of prey and eagle owl population density [18,63]. The availability of prey is an important factor determining the density of breeding eagle owls, and it is unlikely that the shortage of nest sites limits its breeding density because of their flexibility in choice of nest sites [18]. Nest sites seem to be frequently available in the rocky environments that the eagle owls use as nesting sites in our investigation areas. Eagle owls might skip breeding in years with low food availability [64,65], and there is a positive relationship between territory occupancy and habitat quality [66]. Food availability is among the most important factors influencing fluctuations in eagle owl populations [18], and the occupation rate of eagle owl territories is found to be positively correlated with food availability and negatively correlated with mortality risk [67]. If one pair of eagle owl leaves the territory, there will be more space and less competition for the others. Inter-individual effects contribute to shaping space use and movement patterns in eagle owls [68]. Eagle owls seem to have considerable individual consistency in their movements with the repeated use of similar routes within their fixed home range, but might significantly change this route pattern between years, even if the same territory is occupied [69]. If a neighboring pair leaves their territory, remaining pairs can exploit a larger area without the restrictions caused by neighbors, and thereby breed equally or even more successfully. It is therefore possible that the negative effects of wind farm constructions might be easier to detect in the number of occupied territories than in breeding performance. This might be the reason why we did not find any negative effects on breeding performance after the constructions started compared with before, the opposite to our prediction. We found that the breeding performance seemed to be better within the influence area of 3 km compared with the reference areas further away from the disturbances. However, this difference was present before the constructions started, and it did not change after the constructions started (Figure 3). The difference might be because eagle owls mostly hunt in open areas [37,70] in the same type of landscape used to construct wind farms. There were also few nests within the influence area of 3 km (n = 3) that could cause bias in the data. There were no statistically significant differences in breeding performance when we used distances of 4 or 5 km as the influence area, and we found no support for hypothesis 2. There might be a time lag in population declines after disturbances if the eagle owls leave their territories after the most important prey species become less numerous. Eagle owls have a strong nest site fidelity [37], and the same breeding cliff in the middle part of Norway has been used for nearly 4000 years [47]. It is important to note that the disturbances in the present paper were in established eagle owl territories with little other human activity before the construction started. If there are appropriate nesting sites and a good food supply, the eagle owl can adapt to living closer to humans [43], and eagle owls can even breed in large cities [18]. This is also known in other eagle owl species, such as the Mackinder’s eagle owl (Bubo capensis mackinderi) in Africa [71], and the rock eagle owl (Bubo bengalensis) in India, which breeds in higher densities in highly human-altered landscapes that are richer in larger prey, such as rodents and birds [72]. In the present study, the nestling production we used was the number of young in the first half of June, when they were old enough to be ringed. Eagle owls in Norway suffered a high mortality rate that gradually declined during their first three years [73]. There might be several reasons for this high early mortality [18], and similar mortality patterns were found in other owl species [74] as well as in other bird species [75,76,77]. It is therefore uncertain how the results would be if we could follow the young for a longer period. A lowered density in eagle owls might reduce the need for vocalizing among the remaining individuals, thus reducing the probability of being detected by passive auditory surveys such as sound meters [78]. Our experience in more than ten remote territories in our investigated area is that the territorial birds were detected by the use of sound recorders in all years. The eagle owls are not singing only to defend their territories, but also have intra-pair contact sounds uttered by both males and females [18]. There are many other possible threats to eagle owls [18], including pesticides, pollution [79,80], and mobbing corvids [37,38]. We believe that there should be no differences in the probability that eagle owls will leave their territories because of these factors; therefore, we assume that many changes in territory occupancy inside and outside the influence area can be described as wind farm and power line constructions. To our knowledge, no other study exists that shows the importance of the distance to various kinds of disturbances from eagle owl nests. However, there is some advice given by researchers to protect the eagle owl from disturbances, e.g., all building of houses and other disturbances should be at least 1 km from the nest site [81,82]. We found the highest impact of the disturbances on eagle owls when we used 4–5 km as the influence area. Fewer territories were within a 1–3 km distance from the closest wind turbine or power line, which gave a low statistical precision. However, more research is still needed to quantify the magnitude of human-related eagle owl mortality and its effects on the populations [18]. For the more effective conservation of eagle owls during different types of constructions, it might be interesting to know the disturbance contributions from each factor. In a wind farm and power line construction, that can be achieved by increasing human presence before the construction period starts to the same level that is expected during construction, then continue with the construction period, and thereafter activate the wind turbines. Before and after each step, the effects on eagle owls and prey abundance should be investigated during their breeding seasons. To be successful, there should be no delay in the effects of the different disturbance factors. The construction disturbance in our investigated area is finished, but the disturbance from rotating wind turbines and their sounds might also deter the eagle owls or their prey, which can be investigated in the ongoing study of these territories. This is the first published survey in Norway that has investigated the short-term effects of establishing wind farms in or nearby the territories of eagle owls. The construction activities in the present study were performed throughout the year, including during the eagle owl breeding period, and were similar for the short period with active wind turbines. The eagle owls are stationary in the breeding area and were therefore continuously exposed to the disturbances for quite a long period. Despite not being very shy, at all times, the early breeding stages represent the most sensitive and fragile period for the eagle owl. If the female is disturbed at the nest during incubation or shortly after hatching, she might abandon the nest [18]. A conservation recommendation learned from the present investigation is that it should be investigated if an eagle owl is present in a construction area before the constructions start, and any eagle owl territories should affect how and when the construction can be carried out. 5. Conclusions The effects of disturbance from the whole wind farm and power line construction period and a short period with active wind turbines were measured in 39 territories with eagle owls present before and/or after disturbances. More territories were abandoned within an influence area of 4–5 km compared with reference areas that were further away. Testing to discover if the influence area was shorter yielded no significant results, probably because there were few territories within the influence areas with shorter distances from the disturbances. The mean distance from the disturbances was shorter in the abandoned territories compared with the territories where eagle owls were observed both before and after the disturbance period. These results show that the eagle owl is vulnerable to anthropogenic disturbance in areas with little prior disturbance. This investigation is a contribution to a field with a lack of knowledge, and more investigations are needed to ensure a better conservation of this threatened bird species, and especially to find the real influence distance for different kind of disturbances. Acknowledgments We acknowledge John Øystein Berg, Jan Ove Bratset, Anita Husby, Livar Ramvik, Tore Reinsborg, Morten Venås and Tom Roger Østerås for assistance in the field; Georg Bangjord for data from some of the reference areas; and Hilde Dørum and Tom Roger Østerås for assistance in analyses of the sound recordings. We also appreciate Tore Slagsvold and James D. M. Speed for valuable comments on the manuscript, Ørjan Werner Jenssen for construction of Figure 1, and the concessionaires for financing the investigation. Author Contributions Conceptualization, methodology, data curation, formal analysis, project administration, M.H.; fieldwork, writing, M.H. and M.P. All authors have read and agreed to the published version of the manuscript. Funding The research is funded by the different concessionaires. Institutional Review Board Statement Not applicable. Data Availability Statement The data set is available from MH upon request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 The study area with the eight wind farms (blue) with area names (yellow), and the 420 kV (red) and 132 kV (violet) power lines. The influence area for eagle owls is defined to be up to five kilometers from these constructions, and reference areas are further away. Nearly no new powerlines were constructed in Frøya. Figure 2 Mean distance (km) ± 2 SE between the central places of eagle owl territories or nest sites that were abandoned (n = 14) or no change in occupation (n = 23) and distance to closest disturbance (wind turbine or power line). The change is a comparison of territory occupancy before the construction of the wind farms and power lines started compared with the similar investigation shortly after the construction was finished. Figure 3 Breeding performance (score 1–6, see text) ± 2 SE for the 11 nests on Hitra and Frøya with at least one year with the production of young in the time period 2015–2021. The nests were within or outside an influenced area of 3 km from the closest wind turbine or power line before and after the disturbance started (n = 3 nests), or in reference areas further away (n = 8 nests). Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. IPCC Climate Change 2021. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27092683 molecules-27-02683 Article Dating Sediments by EPR Using Al-h Centre: A Comparison between the Properties of Fine (4–11 µm) and Coarse (>63 µm) Quartz Grains https://orcid.org/0000-0002-8897-6627 Kabacińska Zuzanna 1* https://orcid.org/0000-0003-4799-3866 Timar-Gabor Alida 12 Karakirova Yordanka Academic Editor Yordanov Nicola D. Academic Editor 1 Interdisciplinary Research Institute on Bio-Nano-Sciences, Babeș-Bolyai University, Treboniu Laurian 42, 400271 Cluj-Napoca, Romania; alida.timar@ubbcluj.ro 2 Faculty of Environmental Science and Engineering, Babeș-Bolyai University, Fântânele 30, 400000 Cluj-Napoca, Romania * Correspondence: zuzanna.kabacinska@ubbcluj.ro 21 4 2022 5 2022 27 9 268330 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The possibility of EPR dating for sediments using Al-h signals of fine (4–11 μm) grains of quartz has not been previously discussed. Here, the Al-h and peroxy EPR spectra of fine (4–11 μm) and coarse (63–90, 125–180 μm) sedimentary quartz from thoroughly investigated loess sites in Eastern Europe were examined. By comparing experimental spectra with a simulated signal, we evaluated the overestimation observed when using the standard approach established by Toyoda and Falguères to measure Al-h intensity for different doses of radiation, up to 40,000 Gy. This overestimation, caused by the presence of peroxy signals, was much more pronounced for fine grains. Fine grains exhibited some additional dose-dependent signals, which, for some samples, caused a complete distortion of the Al-h spectra at high doses, making it impossible to measure the standard amplitude. We propose a new approach to measuring Al-h signal intensity, focusing on the peak-to-baseline amplitude of the part of the signal at g ≈ 2.0603, which is not affected by the peroxy signals and therefore has the potential of providing more accurate results. The shapes of dose response curves constructed for coarse and fine grains using the new approach show considerable similarity, suggesting that Al-h centre formation in fine and coarse grains upon artificial radiation at room temperature follows the same pattern. electron paramagnetic resonance (EPR) electron spin resonance (ESR) quartz Al-h centre fine grains dose response curve ==== Body pmc1. Introduction Quartz (SiO2) is a material of great importance in many areas of Earth sciences, as well as in industry. As all crystals, it contains a vast number of point defects, which may be either intrinsic (involving only atoms of the host lattice—vacancies, interstitial atoms and excess atoms) or extrinsic (belonging to foreign atoms in lattice and inter-lattice positions) [1,2]. Those of most interest in the field of geochronology include Si- and O-vacancies and impurity related defects. Among the latter, Al3+ always presents in quartz, substituting for Si4+ with charge compensation generally achieved by Li+, Na+ or H+, which gives rise to [AlO4/M+]0 (where M+ denotes an alkali metal or hydrogen ion) [3]. Ti4+ may substitute for Si4+ in quartz with no charge compensation, creating [TiO4]0 [4]. Ge centre, namely [GeO4/M+]0 (most notably [GeO4/Li+]0) is sometimes observed in irradiated natural quartz [4,5]. A neutral oxygen vacancy can trap an electronic hole, forming a paramagnetic oxygen vacancy (E1′ centre) [4,5]. Performing systematic investigations on quartz using electron paramagnetic resonance (EPR) (or electron spin resonance—ESR) spectroscopy, a method of high sensitivity, allows for gaining a deeper understanding of the mechanisms involved when the defects in quartz are subjected to irradiation. EPR has been applied in dating geological and archaeological materials for over 40 years. Together with optically stimulated luminescence (OSL) and thermoluminescence (TL), the so-called trapped-charge dating methods have been extensively used for dating sediments using quartz (e.g., [6,7,8,9]). Quartz records the amount of ionising radiation it has been exposed to as a latent signal within its crystal lattice, and therefore can be used as a natural dosimeter for quantifying the radiation history of materials. Irradiation at room temperature leads to the dissociation of [AlO4/M+]0, resulting in the formation of [AlO4/H+]0 and [AlO4]0 [10]. [AlO4]0, also referred to as Al-hole or Al-h, as a paramagnetic centre, is therefore detectable by EPR, and has been extensively used for dating sediments [6,7,9,11,12,13,14,15,16,17,18,19]. Ti centres have also been widely used for dating [6,11,18,19,20]. Upon room temperature irradiation, Ti4+ may trap an electron together with an alkali ion M+ for charge compensation, forming [TiO4/M+]0, where M+ can be either Li+, H+ or Na+ [4]. Trapped-charge dating methods are based on the assumption that the natural growth of the signal of interest can be reproduced by laboratory irradiation, which leads to the construction of a dose response curve—a plot of EPR intensity versus the doses of irradiation, obtained separately for every investigated sample. The equivalent dose—a total dose of radiation absorbed by the crystal, giving rise to the signal measured in the natural sample—is determined by extrapolation (in the case of additive dose protocols) or interpolation (in the case of regenerative protocols) of the dose response curve. In many luminescence and EPR dating studies, the choice of grain size fraction used for analysis has been most often dictated predominantly by the nature and availability of the material. Based on a series of previous research, Timar-Gabor et al. [21] showed that there is a discrepancy in the ages obtained by the single aliquot regeneration protocol (SAR) OSL between different grain sizes and an age underestimation for finer grains, and suggested a potentially worldwide phenomenon. However, defects giving rise to luminescence in quartz have not yet been unambiguously identified, and their correlation with the defects detected by EPR remains unestablished. Consequently, observations concerning grain size effects based on luminesce results are not directly transferable to EPR defects, which leaves this topic largely unexplored. To fill this gap, a systematic approach needs to be employed, starting with a thorough investigation of the dependence of EPR intensity of defects on grain size, and followed by experiments showing their behaviour when subjected to laboratory irradiation, which would expose any possible differences between fine- and coarse-grained quartz. It goes without saying that such experimental studies should also be complemented by the development of appropriate models. The first objective was addressed by Timar-Gabor [22] by showing the dependence of EPR intensity of the main paramagnetic defects in quartz with grain size, for fraction 4–11, 63–90, 90–125, 125–180, and 180–250 μm. The intensity of the E1′ and Al-h signal in natural samples was found to decrease with increasing grain size, while [TiO4/Li+]0 signals, detected only in coarse fractions, increased with increasing grain size. The second objective, the investigation the behaviour of the defects under laboratory irradiation, is the subject of this work. To achieve any of these goals, or, in fact, to obtain any reliable dating, an accurate measurement of EPR signal intensity is crucial. In this study, we focus on the Al-h signal in fine (4–11 μm) and coarse (>60 μm) grains. The Al-h signal can only be measured by EPR at cryogenic temperatures due to the very short spin–lattice relaxation time of the defect. It produces a complex EPR spectrum arising from the interaction of the unpaired electron with nearby magnetic nuclei. The Al-h signal consists of a central set of peaks around g ≈ 2.008 displaying a distinct hyperfine structure, and a much less intense set of peaks at about g ≈ 2.06. In early attempts at dating using the Al-h signal [23,24,25], different peaks from the central set were considered for evaluating its intensity. Their reliability was compared in a study by Lin et al. [26]. Eventually, a common approach proposed by Toyoda and Falguères [27] was adopted, and it has been widely used ever since by the EPR dating community (e.g., [6,9,15,16,17]). This approach is based on the measurement of a peak-to-peak amplitude between the top of the first peak (g = 2.018) and the bottom of the last peak (g = 1.993) of the central part of the 27Al hyperfine structure. This method has been extremely useful due to its simplicity and the fact that it focuses on the most distinct peaks, which are clearly distinguishable even for very weak signals. However, its applicability is sometimes limited by the presence of additional signals superimposed on the central part of the Al-h signal. These additional signals are referred to as “peroxy” species or, for simplicity, sometimes as a peroxy centre (singular), although their spectrum is clearly composed of many overlapping signals. They are visible most clearly at room temperature, when the Al-h is not detectable. Friebele et al. [28] first established a peroxy radical in neutron or gamma-ray irradiated 17O-enriched fused silica and suggested it derives from pre-existing bridging peroxy linkages (≡Si–O–O–Si≡, where ≡ represents three Si-O bonds), which shed an electron to form peroxy radicals by irradiation and/or thermal treatment. Peroxy radicals in crystalline SiO2 (α-quartz) have been suggested by several EPR studies and established by Botis et al. [29,30], Nilges et al. [31,32] and Pan et al. [33,34] in their very detailed studies. Based on their research, it was concluded that most of the discrepancies in the literature concerning the g-factor values, linewidths and hyperfine structure reported for the peroxy centres can be attributed to incompletely resolved site splittings in previous X- and Q-band studies. For an in-depth investigation of these species, higher microwave frequencies should be applied, but the accessibility of such equipment is very limited. Despite the wealth of information provided by these studies, there are still many unanswered questions regarding the nature of these signals, answers to which are essential considering their relevance in EPR dating and provenance investigations. It should be noted that, apart from peroxy centres, another type of oxygen excess centre has been identified, namely, the non-bridging oxygen hole centre (NBOHC), described as oxygen dangling bonds ≡Si–O∙ (where ∙ represents an unpaired electron) [35]. For simplicity, however, in this study, we use the term “peroxy” to describe all signals observed between g ≈ 2.01 and g ≈ 1.99 at room temperature, with the exception of E’ and Ge centres. The complexity of the peroxy spectrum, combined with the limitations of the X-band spectroscopy routinely used for dating, makes attempts at isolating these signals to obtain an undisturbed signal from the Al-h centre extremely challenging. Perhaps for this reason, the issue of peroxy signals interfering with Al-h measurements has been largely ignored in the literature. However, some amendments have been occasionally employed to circumvent the problem, such as subtracting the overlapping peroxy signal intensity using its EPR signal intensity after annealing [36]. The assumption here is that the peroxy signal changes neither with heating nor with the dose of irradiation, and the same signal can be used for subtraction at low and high doses. While the latter assumption is generally accepted in the case of coarse grains, this has not been confirmed for fine grains. Indeed, Timar-Gabor [22] reported on a dose-dependent signal at g ≈ 2.011, detected in a fine-grained fraction of quartz, which suggests that this approach might not be applicable in every case. Moreover, it introduces additional uncertainty related to the determination of peroxy signal intensity. Another approach, used by Tsukamoto et al. [7] for Al-h measurements conducted at 123 K, when the 27Al hyperfine structures were not visible, was based on the measurement of the peak-to-peak intensity of the first central peak. It was reported to be consistent with the peak-to-peak intensity of the whole peak minus the intensity of the peroxy centre, which was measured at 183 K. No significant changes in the peroxy centre intensity were observed when raising the temperature from 123 to 183 K, and the Al-h signal at 183 K became almost undetectable. As in the previous example, this method bears some additional uncertainties. Additionally, the authors noted that their measurements might not be directly comparable with other studies, which use the traditional approach. It is clear that developing an alternative approach to measuring Al-h signal intensity, unaffected by the presence of the peroxy signals, would improve the accuracy of age determination and, therefore, greatly benefit the EPR dating community. The aforementioned study by Timar-Gabor [22], conducted on several samples, including two (ROX 1.14 and STY 1.10) studied in this work, shows that peroxy signals have significantly higher intensities in fine grains (4–11 μm) and decrease when grain size increases. Extended etching experiments resulted in obtaining partial evidence that these defects are concentrated in damaged areas of the grains. The weaker signals of peroxy centres would suggest that coarser fractions should be preferred for conventional EPR dating using the approach of Toyoda and Falguères [27] to measure Al-h intensity. However, assuming that the issue of accurately determining Al-h signal intensity could be solved, finer grains would not have to be automatically dismissed solely on that basis, especially as they are the main constituents of many sedimentary archives, such as loess, lake or marine sediments. That would allow for a thorough comparison of the properties of the Al-h signal observed in fine and coarse grains and open the possibility of EPR dating based on fine grains, which has not been discussed before. In this work, we propose an alternative method of evaluating Al-h signal intensity, which circumvents the issue of interfering peroxy signals. The results obtained for the new approach and the standard approach by Toyoda and Falguères [27] are compared using the measurements of fine (4–11 μm) and coarse (63–90, 125–180 μm) quartz separates from thoroughly investigated loess palaeosol sites in Eastern Europe (Roxolany, Stayky and Mircea Vodă), which were used in the previous investigations carried out by our group. By comparing the experimental spectra with a simulated signal of the Al-h centre, we evaluated the overestimation that results from using the standard approach for different doses of radiation, up to 40,000 Gy. We then used the dose response curves constructed from the intensities obtained with the new approach to compare, for the first time, the response of the Al-h signal to laboratory irradiation displayed by fine- and coarse-grained fractions. 2. Materials and Methods 2.1. Samples Experiments presented in this study were conducted on archived quartz separates of different grain sizes from previous investigations carried out by our group. Sample Rox 1.14 originates from Roxolany, loess palaeosol section, Southern Ukraine, and was collected below the Brunhes/Matuyama polarity transition. The results of EPR dating using a multicentre approach, along with optically stimulated investigations using both the standard single aliquot regenerative (SAR) multigrain OSL procedure, as well as single grain investigations, are presented in detail in [37]. Quartz sample Sty 1.10 comes from Stayky, loess palaeosol section, Northern Ukraine. The OSL chronology of this section, as well as extended SAR-OSL dose response curves on the Styky samples, are presented in detail in [38]. Sample 2 MV 80 was collected near the village of Mircea Vodă, which is situated in the Dobrogea plateau of SE Romania, about 15 km from the Danube River. Optical dating results for this site were published in [39] (including this sample) and in [8,40] (previous sampling). Preparation protocol, following the standard OSL preparation guidelines, is described in detail in the aforementioned references, as well as in [22]. The selection of samples for the current study was based on the availability of sufficient material for EPR investigation and the high purity of the quartz extracts, as confirmed by routine tests in OSL dating as well as by scanning electron microscopy imaging, coupled with energy dispersive X-ray spectroscopy (EDX) [22]. 2.2. EPR Measurements EPR measurements were performed on an X-band Bruker EMX Plus spectrometer at Babeș-Bolyai University, Cluj-Napoca, Romania. All samples were placed in quartz glass tubes filled with a mass of 200 mg ± 10% for coarse grains (>63 μm) and 100 mg ± 10% for fine grains, maintaining the same volume, and with measurements later normalized to 100 mg for intercomparison. Care was taken that all samples were centred inside the cavity. Samples were rotated in the cavity using a programmable goniometer and measured at 3 different angles (every 120°, 1 scan per angle). Measurements were usually repeated 2–5 times at a few weeks’ intervals. Details of reproducibility tests are described in [22]. A mean EPR intensity was used for constructing a dose response curve, and standard error was indicated in all the plots. Exposure of samples to sunlight during measurements was restricted to a minimum. Measurements were carried out at 90 K for Al-h centres and at room temperature (295 K) for peroxy centres, using a variable temperature unit. Spectra were acquired using the following settings: 3350 ± 150 G scanned magnetic field, modulation amplitude 1 G, modulation frequency 100 kHz, microwave power 2 mW, conversion time 40 ms, time constant 40 ms. Baseline correction was performed when necessary using Bruker’s Xenon software. Samples were gamma-irradiated with doses up to 10,000 Gy on top of the natural dose for sample ROX 1.14, and up to 40,000 Gy for samples STY 1.10 and 2 MV 80. Due to the limited availability of the material, fewer aliquots were obtained for the coarse fraction than for the fine fraction. Gamma irradiations were performed at room temperature at the Department of Health Technology at DTU (Dosimetry Research Unit) in Denmark using a calibrated 60Co gamma cell with a dose rate of 2 Gy/s (dose rate to water) at the time of irradiation. Dose rate to quartz was estimated to be 96% of dose rate to water based on Monte Carlo simulation considering the irradiation geometry used, as in [22]. 2.3. Al-h Signal Simulations Al-h signal was simulated with EasySpin [41] using parameters listed in Table 1. Initial parameters were based on [5,42] and adjusted to fit the experimental spectra. The values of quadrupole splitting were used as in [42] with no adjustments. When comparing with an experimental spectrum, an average of baseline-corrected experimental spectra obtained for a given dose was used. A spectrum recorded at a microwave frequency of 9.42 GHz was chosen as a reference, and magnetic field values for all spectra were adjusted to match the position of the signal. A simulated spectrum is shown in Figure 1, together with the principal components of the g-tensor values mentioned in the text. 3. Results and Discussion 3.1. Contribution of Peroxy Signals to Al-h Signal Measurements The spectra of the Al-h and peroxy signals obtained for the coarse and fine grains of the quartz irradiated with different doses were compared based on three examples: sample ROX 1.14, STY 1.10 and 2 MV 80. Due to the different sources of quartz, one would expect these three samples to have different types and concentrations of defects, which makes them great subjects for studying the diversity of signals recorded by EPR. 3.1.1. Sample ROX 1.14 Figure 2 shows a comparison of coarse (125–180 μm) and fine (4–11 μm) quartz EPR spectra for sample ROX 1.14 acquired at 90 K and at room temperature. Both fractions exhibit clear differences in the shape of the spectra. Experimental spectra of natural and additionally irradiated (with 1000 and 10,000 Gy) samples recorded at 90 K were overlaid with a simulated spectrum of Al-h (Figure 2a,b). The shape of the experimental spectra differs from the simulated one, which is caused by overlapping with signals assigned to the so-called peroxy species. The difference between Al-h simulation and experimental spectra recorded at 90 K is much more significant in the case of fine grains, as the peroxy signals in 4–11 μm quartz are much stronger than in the bigger fractions, which was previously reported by Timar-Gabor [22]. In the case of coarse grains, this difference is visible only in the centre of the spectra and remains more pronounced for the smaller doses of irradiation. The peroxy signals can be clearly registered at room temperature, when Al-h signal is not detectable (Figure 2c,d). The structure of the spectra is complex and consists of several overlapping signals. Their detailed characterisation and interpretation have been a subject of several studies (e.g., [29,30,31,32,33,34]) and is beyond the scope of this work. What is relevant for this study is whether the intensity of some of these signals depends on the dose of irradiation, an issue which, to our knowledge, has not been addressed in the literature. The only exception is a mention of a dose-dependent signal at g ≈ 2.011 detected by Timar-Gabor [22] in fine grains. The spectra of coarse-grained quartz shown in Figure 2c indicates that only two signals, at g ≈ 2.000 and g ≈ 1.996, increase with the applied dose, while the rest do not show any changes. The signal at g ≈ 2.000 can be ascribed to the E’ centre and the peak at g ≈ 1.996 to the Ge centre, namely, [GeO4/Li+]0 [5] (their EPR spectra can be found therein). The peroxy signals detected in fine grains (Figure 2d) are much stronger, and the presence of some additional peaks is visible. In addition to the E’ signal observed at g ≈ 1.999 and the Ge signal at g ≈ 1.994, at least two other signals, at g ≈ 2.009 and g ≈ 2.001, also show an increase with an increasing dose. The presence of these dose-dependent signals strongly influences the overall shape of the spectrum at high doses. The precise relationship between the intensity of these signals and the dose of laboratory irradiation cannot be determined at this point, as it requires separating them from the overlapping peaks, which is not possible without the aid of simulations and/or measurements at higher microwave frequencies. As the peroxy signals in the 125–180 μm fraction of ROX 1.14 (and most of them in the case of the fine grains) are not dose-dependent, their contribution to the overall intensity of the signals registered in the considered range at low temperature decreases with the dose of radiation due to the increase in the Al-h signal. For coarse grains (Figure 2a), the experimental spectrum at high doses is very close in shape to the simulated one, while for fine grains (Figure 2b), the difference is still clearly visible. When overlaying the experimental spectrum with a simulated one, we were faced with the issue of properly adjusting the amplitude of the latter. Since the central part of experimental spectra has proven to be distorted, to a varying degree, it should not be used as a reference point to adjust the simulated spectra. Therefore, a logical course of action was to choose peaks in the low-field part of the spectral range, specifically, the centre of the peak around g ≈ 2.0603 (see Figure 1), and match the amplitude of the simulated spectrum to the experimental spectrum each time, using this point as a reference. The interference of peroxy signals may naturally cause problems for accurate measurements of Al-h signal intensity. A well-established method of measuring the intensity of the Al-h signal is based on the measurement of peak-to-peak amplitude between the top of the first peak of the central signal (g = 2.018) and the bottom of the last peak (g = 1.993) (see Figure 1) [27]. In Figure 2a,b we mark the amplitudes measured using this approach (denoted further as “A”), obtained from the experimental (Aexp) and simulated (Asim) spectra of ROX 1.14 sample. As demonstrated for the additional dose of 10,000 Gy, both Aexp and Asim give basically the same value (less than 2% difference) for coarse grains at high doses. However, due to the greater contribution of peroxy signals, Aexp amplitude at low doses is slightly overestimated compared to Asim for the natural sample, giving about 13% and, for 1000 Gy, about 5% higher value compared to Asim. For fine grains, the overestimation of Aexp compared to Asim is much more significant. For the natural sample of 4–11 μm ROX 1.14, it amounts to approximately 54%; for natural + 1000 Gy, about 38%; and for natural + 10,000 Gy, about 27%, as a result of the increasing contribution of dose-dependent Al-h signals and the decreasing contribution of mostly non-dose-dependent peroxy signals. It is therefore clear that, although the approach based on measuring amplitude A works very well for samples of coarse-grained quartz which have accumulated a high dose of irradiation (e.g., very old samples and laboratory-irradiated samples), it can result in a significant overestimation in the case of fine-grained and young coarse-grained quartz, which can affect the slope of the dose response curve. A more reliable method for quantitatively describing the changes in Al-h concentration with the dose would be using the simulated signals and calculating the area under the curve with double integration. This value is directly proportional to spin concentration and will not be affected by any contributions from other paramagnetic species. Despite these advantages, this method is very time consuming, demands more signal processing and is not always accessible. However, as mentioned previously, adjusting the simulated spectrum to the experimental one requires a reference point (or, to be precise, a second reference point, the first being the baseline), which in this case, was chosen as the centre of the peak around g ≈ 2.0603 (see Figure 1). This provides the possibility of obtaining a reliable measurement of the amplitude simply by measuring the peak-to-baseline height of this peak of the experimental spectra, further referred to as “B”. The values of Bexp and Bsim (Figure 2) will therefore always be, by definition, equal to each other for every example of coarse and fine spectra. This approach allows for a much more accurate representation of Al-h signal intensity for fine grains, and may also improve the measurement of coarse grains, particularly for younger samples. To investigate the effect of this overestimation on the shape and slope of the dose response curve (DRC), two sets of DRCs were constructed for sample ROX 1.14 (coarse and fine grains), using amplitudes Aexp (DRC A) and Bexp (DRC B) (Figure 3a,b). A sum of two exponential functions was used to fit the datapoints. As expected from the comparison between simulated and experimental spectra, the lower dose part of DRC A for coarse grains (Figure 3a) bends upwards compared to DRC B due to the contribution from peroxy signals, while at higher doses, curves A and B overlap. As a result, the equivalent dose obtained from DRC A is overestimated. The divergence between DRC A and B is more pronounced in the case of quartz fraction 4–11 μm (Figure 3b). Because of the peroxy contribution, DRC A has a much smaller slope, which leads to a considerable overestimation of the equivalent dose obtained from this curve compared to curve B. It should be kept in mind that, while both these curves seem to almost overlap at high doses, the values of amplitude A are still over 25% overestimated compared to amplitude B at 10,000 Gy. Since the exact nature of the dose-dependency of the signals observed in the case of fine grains is not known, the relationship between A and B values for doses above 10,000 Gy cannot be predicted at this point, and may further affect the shape and slope of DRC A. 3.1.2. Sample STY 1.10 The second example of comparison between Al-h measurements for coarse and fine grain spectra is sample STY 1.10 (Figure 4). The spectra of the 125–180 μm fraction demonstrate a similar situation to ROX 1.14—the shape of experimental spectra differs slightly from the simulation for smaller doses, and this difference becomes less significant as the radiation dose increases (Figure 4a). The value of Aexp amplitude overestimates Asim by about 13% for the natural signal, and about 5% for 1000 Gy and 10,000 Gy. For fine grains represented by a 4–11 μm fraction (Figure 4b), the spectra for the natural sample and the sample irradiated with 1000 Gy show differences between simulation and experiment analogous to the ones observed for sample ROX 1.14. Due to the contribution of peroxy signals, Aexp overestimates Asim by about 24% for the natural sample and about 21% for 1000 Gy irradiated sample. However, for higher doses, the situation becomes even more complex, as the overestimation of Aexp compared to Asim increases again, to about 30% for 10,000 Gy. The explanation for this fact comes from analysing the peroxy signals observed at room temperature (Figure 4c,d). While the spectra of coarse grains (Figure 4c), as in the case of sample ROX 1.14 (Figure 2c), do not show significant changes as the dose increases, with only the Ge signal at g ≈ 1.996 and E’ signal at g ≈ 2.000 being more prominent at high doses, the spectra of fine grains (Figure 4d) exhibit some additional signals, which increase their intensity with the laboratory dose. Most of them—the signal at g ≈ 2.002, 2.010, the Ge signal (g ≈ 1.995) and the E’ signal (g ≈ 2.000)—are also detected in sample ROX 1.14 (Figure 2d), but two other signals at g ≈ 1.991 and 2.016 are not. In particular, the signal at g ≈ 2.016 exhibits a considerable growth, and due to its position, which almost coincides with the top of the first peak of the central Al-h signal (g = 2.018) used for Aexp estimation, it strongly affects the outcome of this measurement for higher doses. As a result, Aexp amplitude obtained for fine grains provides unreliable measurements not only for lower doses, but also for higher doses, making it unsuitable for Al-h intensity determination. It is worth mentioning that, at first glance, the low-temperature spectrum of STY 1.10 irradiated with 10,000 Gy does not show clear signs of distortion around g = 2.018, as it still resembles the shape of the Al-h signal quite well, which can be very misleading, as it encourages attempts to measure Aexp. It is only through analysing the room temperature measurements that the dose-dependent nature of the signal at g ≈ 2.016 can be revealed. In cases such as this, measuring Aexp, although technically possible, results in unreliable data, leading to a distorted shape in the dose-response curve. Amplitude B, however, remains unaffected by the contribution of other signals, and therefore provides a reliable representation of Al-h signal intensity changes. 3.1.3. Sample 2 MV 80 The third example is based on measurements of sample 2 MV 80 (Figure 5). The spectra recorded at 90 K for coarse grains (Figure 5a), in this case represented by a 63–90 μm fraction, show a more significant distortion than in the case of samples ROX 1.14 and STY 1.14. This is most likely due to the smaller size of the coarse grains—63–90 μm instead of 125–180 μm—compared to the other two samples. As shown by Timar-Gabor [22], the intensity of peroxy signals decreases with increasing grain size. For the natural sample, Aexp overestimates Asim value by as much as 82%, by 62% for 500 Gy and by 44% for 5000 Gy. A small part of this overestimation might be attributed to performing a baseline correction, as the original baselines displayed a steeper slope and more complex shape, but even then, the differences between Aexp and Asim are still very considerable. Due to a limited availability of coarse material, fewer additional doses could be investigated; therefore, the overestimation present at 10,000 Gy could not be determined. The spectra recorded at room temperature resemble those obtained for samples ROX 1.14 and STY 1.10, with the same dose-dependent signals at g ≈ 2.000 and g ≈ 1.996, ascribed to the E’ and Ge centre, respectively, being visible. As with samples ROX 1.14 and STY 1.10, for fine grains (Figure 5b), the contribution of the peroxy signals in the central part of the spectrum is clearly visible, leading to an overestimation of Aexp by 50% compared to Asim for the natural sample and 64% for the 1000 Gy irradiated sample. At the higher doses, as shown for 10,000 Gy, the spectrum becomes very distorted, to the point that the measurement of Aexp is basically impossible, as it is clearly too affected by the overlapping signals. Measurements performed at room temperature (Figure 5d) show the same dose-dependent signals, as in the case of the samples ROX.1.14 and STY.10—at g ≈ 2.010, 2.002, and at g ≈ 1.999 (E’ centre) and 1.995 (Ge centre), as well as very strong dose-dependent signals at g ≈ 2.015 and g ≈ 1.991, also observed in sample STY 1.10, in addition to the non-dose-dependent signals, also visible in the coarse grains. In the case of samples like 2 MV 80, with very strong dose-dependent signals overlapping with the Al-h signal, the measurement of amplitude B is not only more reliable, but also appears to be the only viable option for obtaining Al-h amplitude without the use of simulations. It should be noted that the presence of dose-dependent signals will also cause problems when attempting to remove the peroxy signals by subtracting the spectra recorded after heating (as performed by Richer and Tsukamoto [36]), since the peroxy spectrum will look different for every dose, and the heating will likely affect the ratio of dose-dependent and non-dose-dependent peroxy signals. These arguments further support using amplitude B for Al-h intensity determination for both fine and coarse grains. The comparison between the results obtained for the three presented examples by measuring the amplitude A and B shows the advantage of using amplitude B for Al-h intensity estimation. Contrary to amplitude A, it is not affected by the peroxy signals present in the centre of the analysed range, and it can therefore provide more accurate results, or, in fact, any results in cases where a spectrum is too distorted to allow for an estimation of amplitude A. Measurements of amplitude B were used in the second part of this study to compare the response of coarse and fine grains of quartz to laboratory irradiation. 3.2. Comparison of DRCs of Coarse and Fine Grains Dose response curves obtained using amplitude Bexp were used for comparing the response of coarse and fine grains of quartz to laboratory irradiation (Figure 6). The behaviour of the Al-h signal was investigated up to 10,000 Gy on top of the natural dose for sample ROX 1.14, and up to 40,000 Gy for samples STY 1.10 and 2 MV 80. Due to the limited availability of the material, fewer aliquots were obtained for the coarse fraction than the fine fraction. No correction for the residual dose was applied. A note of caution regarding the fitting is necessary before proceeding to describe these results. A sum of two saturating exponential functions was used to fit the data. This choice was dictated by the results obtained in a recent study by Benzid and Timar-Gabor [43], where a phenomenological model of Al-h formation upon room temperature irradiation was proposed. In this model, the Al-h centre is considered to be formed upon laboratory irradiation by two processes: (i) directly by transforming [AlO4/M+]0 into Al-h, and (ii) indirectly by transforming [AlO4/M+]0 into [AlO4/H+]0, and then [AlO4/H+]0 into Al-h. By assuming that the dissociation rates of these centres are proportional to their concentrations, the model shows that the increase in the Al-h EPR signal with increasing dose can be well described by a sum of two exponential functions. Benzid and Timar-Gabor, however, acknowledge the dangers of interpreting the parameters derived through fitting with multiple exponentials, stating that, for quantitative assumptions using the derived parameters to be made, the DRC needs to be raised until it reaches full saturation; otherwise, the parameters depend on the maximum given dose, as was shown previously by Timar-Gabor et al. [21] for DRCs obtained for OSL signals fitted with a sum of two saturating exponentials. As is clear from Figure 6, this is not the case for DRCs constructed in the current study; therefore, we refrain from deriving any conclusions based on parameters obtained from the fittings. As such, the fitted curves presented in Figure 6 should be regarded primarily as a visual aid in comparing the response to laboratory irradiation. Additionally, due to a smaller number of datapoints for coarse-grained samples STY 1.10 and 2 MV 80 and their noticeable scatter, a fitting was not performed. Proceeding to the comparison of fine and coarse quartz DRCs, it is immediately apparent from Figure 6a that both the 4–11 μm and 125–180 μm fractions of the ROX 1.14 sample show almost identical DRC shape. While the number of datapoints is limited, it can be assumed that the effect of increasing the laboratory dose on the Al-h signal, even if not identical, is remarkably similar in fine and coarse grains. As mentioned before, the data obtained for the coarse fraction of samples STY 1.10 (Figure 6b) 2 MV 80 (Figure 6c) did not allow for a satisfactory fitting, as the shape of the fitted curves would be largely affected by an arbitrary choice of parameters. Instead, the datapoints were overlaid on the DRCs obtained for fine grains. Some differences can be observed for the sample STY 1.10 (Figure 6b), namely, the datapoints obtained for coarse (125–180 μm) grains seem to indicate a faster saturation of the DRC. However, the shape of the curves is likely affected by a noticeable scatter of the datapoints and the absence of data for coarse grains above 20,000 Gy, so the divergence observed in Figure 6b may very well be exaggerated. In the case of sample 2 MV 570 (Figure 6c), as far as the doses up to 5000 Gy are concerned, the data for coarse (63–90 um) and fine grains is in very good agreement, suggesting that, in this range, there are no significant differences in the behaviour of the Al-h signal in coarse and fine grains for this sample. Despite the aforementioned issues with the fitting, simply by visually following the datapoints, it can be observed that, in all three cases, the intercept of the DRCs with the x axis seem to be the same for both fractions. It can therefore be stated that no significant divergence in the behaviour of the Al-h signal with the increasing radiation dose in the investigated range can be observed between the fine (4–11 μm) and coarse (63–90 μm and 125–180 μm) grains of quartz studied in this work. A logical conclusion is to assume that Al-h centre formation in fine and coarse grains due to artificial radiation is governed by the same processes. To our knowledge, the influence of grain size on the formation of the Al-h centre has not been discussed in the literature. The phenomenological model of Al-h formation upon laboratory irradiation at room temperature proposed by Benzid and Timar-Gabor [43] does not suggest that this process would be significantly different for coarse and fine grains. As Al-h centres are extrinsic, impurity-related defects, it is to be expected that they would have a relatively homogeneous distribution in the volume of a sedimentary quartz grain. Indeed, Timar-Gabor [22] report that no significant effect could be observed when measuring Al-h signals as a function of etching time. More experimental and theoretical studies are certainly needed to further examine the mechanism of Al-h centre formation; however, our results show that, in the first approximation, the response of both fine- and coarse-grained quartz to artificial irradiation is remarkably similar. Should coarse and fine grains of quartz therefore be provided with the same equivalent dose when dated using the Al-h centre? The answer to this question requires a separate consideration and cannot be answered at this point. It is generally accepted that sunlight exposure does not completely bleach the EPR intensity of Al-h, and the signal is reset only to a non-bleachable residual level (e.g., [6,7,44,45,46,47,48]). Our study focuses on unbleached samples, which have a residual signal composed of bleachable and unbleachable components. These components can be of different magnitudes for coarse and fine grains and should be determined separately for every fraction, which is beyond the scope of this work. To our knowledge, the only study showing the effects of grain size on the results of the EPR dating of quartz was conducted by Liu et al. [49] for the Ti-Li centres of fluvial and lacustrine sediments. They assumed complete bleaching and reported that, for grain sizes above 100 μm, the equivalent dose decreased with the increase in grain size. However, for the smallest fraction (50–100 μm, which, in our study, would still be considered coarse), the equivalent dose was smaller than for the larger fraction. They also showed that the beta irradiation dose rate of grains with different sizes accounts for only about 6% of the total deviation of dating results, making if far less significant than the effect of grain size on EPR sensitivity. No similar studies have been conducted on the Al-h centre of sedimentary quartz. It should be mentioned that the effect that the size of grains has on the obtained equivalent dose was investigated for E’ and the Al-h centre in quartz from fault gouge (e.g., [50,51]), but due to the different mechanism involved in resetting the signal (mechanical deformation and high temperature), these results cannot be of use for other types of environments. The effects of natural irradiation and light exposure on fine- and coarse-grained quartz should certainly be investigated in order to reach conclusions regarding the equivalent dose estimation. 4. Conclusions We examined the Al-h and peroxy EPR spectra of fine (4–11 μm) and coarse (63–90, 125–180 μm) sedimentary quartz separates extracted from three well-characterised samples collected from thoroughly investigated sites (Roxolany, Stayky and Mircea Vodă). Based on the data presented in this work, as well as in the study conducted by Timar-Gabor [22], it is clear that Al-h measurements of fine grains are affected by the presence of peroxy signals to a much greater extent than coarse grains. However, the degree to which this affects the standard amplitude measurement following the approach of Toyoda and Falguères [27] seems to be sample-dependent. It ranges from causing an overestimation, which is much stronger for smaller doses (sample ROX 1.14), to a complete distortion of the spectra at high doses (sample 2 MV 80) due to the presence of dose-dependent peroxy signals in fine grains. For a proper understanding of the observed differences, a much larger set of samples would certainly have to be analysed, which is not an easy task, since it requires a large amount of material from different sites divided into fractions. The new approach to measuring Al-h signal amplitude proposed in this study, focusing on the peak-to-baseline amplitude of the part of the signal at g ≈ 2.0603, has the potential to provide more accurate results. This region of the spectrum is not affected by strong peroxy signals overlapping the central part of the Al-h signal and causing the overestimation. While using the strongest absorption line, as in the standard approach, increases the signal-to-noise ratio, which leads to greater precision in the dating result, our study shows that this precision comes at the cost of accuracy. In other words, while the errors associated with the standard approach may be smaller, the dates themselves may not reflect the true age of the material. We believe that more accurate results, even if less precise, are of much greater value to the dating community and the researchers using the reported values in their studies. It should be mentioned that, while very useful for samples with strong Al-h signals, as the ones investigated here, the new approach might not be applicable to very weak signals. This part of the Al-h spectrum is considerably less intense than the central signal typically used for measuring amplitude, and in the case of some samples, it may simply be undetectable. Additionally, more studies are needed on the individual signals composing the peroxy spectra in order to rule out the possibility of some weaker lower-field peaks being present around g ≈ 2.0603, which could affect the amplitude measurement following this new approach. We compared the response of the Al-h signal to laboratory irradiation displayed by the fine- and coarse-grained fractions, which has not been previously shown in the literature. The shapes of dose response curves constructed for coarse and fine grains using the new approach show a considerable similarity, which suggests that Al-h centre formation in fine and coarse grains upon artificial radiation follows the same pattern. These observations have significant implications for the dating community and will hopefully inspire more research, experimental and theoretical, allowing for a thorough comparison of dating results obtained for different fractions of sedimentary quartz, which in turn will deepen our understanding of the underlying processes and increase the accuracy of EPR dating. It should be stressed that the behaviour of the Al-h signal in coarse and fine grains upon laboratory irradiation might differ from behaviour observed in nature. Depending on the grain size, the amount of alpha and beta radiation penetrating the grain will be different, influencing the formation of defects. Understanding the processes induced in fine and coarse grains by gamma radiation in a controlled laboratory environment is the first step towards the development of a comprehensive model. The effect of grain size on the formation and bleachability of Al-h centres under natural conditions needs to be thoroughly studied before any conclusions are drawn regarding the overall result of EPR dating using different fractions. We hope that our work will stimulate such studies. Author Contributions Conceptualization, Z.K. and A.T.-G. Investigation, Z.K. and A.T.-G. Writing—original draft preparation, Z.K. Writing—review and editing, A.T.-G. Visualization, Z.K. Supervision, A.T.-G. Project administration, A.T.-G. Funding acquisition, A.T.-G. All authors have read and agreed to the published version of the manuscript. Funding This study received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, ERC-2015-STG (grant agreement No [678106]). A.T.-G. acknowledges the financial support of the research project EEA-RO-NO-2018-0126. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data analysed in this study are available from the corresponding authors upon reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 A simulated EPR signal of the Al-h centre with the g-factor values mentioned in the text. The parameters used for simulation are listed in Table 1. Figure 2 EPR spectra of the coarse (125–180 μm) and fine (4–11 μm) fraction of sample ROX 1.14, natural and additionally irradiated with 1000 and 10,000 Gy, recorded at 90 K ((a,b), respectively) and at room temperature ((c,d), respectively), and simulated spectra of Al-h signal (in red). Amplitudes Aexp, Asim, Bexp and Bsim are marked with arrows. Major dose-dependent signals observed at room temperature are marked with blue dashed lines. The 90 K spectra for coarse grains are multiplied by a factor of 2.6. Figure 3 Dose response curves obtained for samples ROX 1.14 125–180 μm (a) and 4–11 μm (b) by measuring amplitudes Aexp and Bexp. Data normalised by the maximum value. Negative dose values indicate the dose accumulated in the material prior to laboratory irradiation. Figure 4 EPR spectra of the coarse (125–180 μm) and fine (4–11 μm) fraction of sample STY 1.10, natural and additionally irradiated with 1000 and 10,000 Gy, recorded at 90 K ((a,b), respectively) and at room temperature ((c,d), respectively), and simulated spectra of Al-h signal (in red). Amplitudes Aexp, Asim, Bexp and Bsim are marked with arrows. Major dose-dependent signals observed at room temperature are marked with blue dashed lines. The 90 K spectra for coarse grains are multiplied by a factor of 5.9. Figure 5 EPR spectra of the coarse (63–90 μm) and fine (4–11 μm) fraction of sample 2 MV 80, natural and additionally irradiated with 500 and 5000 Gy for coarse grains, and 1000 and 10,000 Gy for fine grains, recorded at 90 K ((a,b), respectively) and at room temperature ((c,d), respectively), and simulated spectra of Al-h signal (in red). Amplitudes Aexp, Asim, Bexp and Bsim are marked with arrows. Major dose-dependent signals observed at room temperature are marked with blue dashed lines. The 90 K spectra for coarse grains are multiplied by a factor of 5.6. Figure 6 Dose response curves for the amplitude Bexp obtained for fine (4–11 μm) and coarse (125–180 μm) grains of samples ROX 1.14 (a), STY 1.10 (b), and fine (4–11 μm) and coarse (63–90 μm) grains of sample 2 MV 80 (c). Data were normalised to the maximum datapoint (a) or maximum datapoint for fine grains and overlaid on the curve for coarse grains (b,c). Negative dose values indicate the dose accumulated in the material prior to laboratory irradiation. molecules-27-02683-t001_Table 1 Table 1 Spin Hamiltonian parameters used for simulating [AlO4]0 spectrum. A—hyperfine splitting, Q—quadrupole splitting. S = 1/2, 27Al (I = 5/2), Lorentzian peak-to-peak linewidth 0.185 mT. Parameter x y z g-Tensor 2.0603 2.0083 2.0021 A (MHz) 14 17 18.2 (mT) 0.499 0.606 0.649 Q (MHz) −0.62 −0.43 1.05 (mT) −0.022 −0.015 0.037 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Preusser F. Chithambo M.L. Götte T. Martini M. Ramseyer K. Sendezera E.J. Susino G.J. Wintle A.G. Quartz as a Natural Luminescence Dosimeter Earth-Sci. Rev. 2009 97 184 214 10.1016/j.earscirev.2009.09.006 2. Götze J. Chemistry, Textures and Physical Properties of Quartz—Geological Interpretation and Technical Application Mineral. Mag. 2009 73 645 671 10.1180/minmag.2009.073.4.645 3. Malik D.M. Kohnke E.E. Sibley W.A. Low-temperature Thermally Stimulated Luminescence of High Quality Quartz J. Appl. Phys. 1981 52 3600 3605 10.1063/1.329092 4. Toyoda S. Paramagnetic Lattice Defects in Quartz for Applications to ESR Dating Quat. Geochronol. 2015 30 498 505 10.1016/j.quageo.2015.05.010 5. Ikeya M. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095129 ijms-23-05129 Article Pharmacologic Tumor PDL1 Depletion with Cefepime or Ceftazidime Promotes DNA Damage and Sensitivity to DNA-Damaging Agents Murray Clare 1 https://orcid.org/0000-0002-6104-2605 Galvan Eva 23 Ontiveros Carlos 1 Deng Yilun 4 Bai Haiyan 4 Padron Alvaro Souto 4 Hinchee-Rodriguez Kathryn 4 Garcia Myrna G. 1 Kornepati Anand 1 https://orcid.org/0000-0001-6431-4074 Conejo-Garcia Jose 5 https://orcid.org/0000-0001-6962-9411 Curiel Tyler J. 124*† Fiering Steven Academic Editor 1 Graduate School of Biomedical Science, University of Texas Health, San Antonio, TX 78229, USA; murrayc1@livemail.uthscsa.edu (C.M.); ontiverosc@livemail.uthscsa.edu (C.O.); garciam64@livemail.uthscsa.edu (M.G.G.); kornepati@livemail.uthscsa.edu (A.K.) 2 UT Health Mays Cancer Center, University of Texas Health, San Antonio, TX 78229, USA; galvane@uthscsa.edu 3 Department of Radiation Oncology, University of Texas Health, San Antonio, TX 78229, USA 4 Department of Medicine, University of Texas Health, San Antonio, TX 78229, USA; dengy@uthscsa.edu (Y.D.); baih@uthscsa.edu (H.B.); soutopadron@uthscsa.edu (A.S.P.); khinchee@gmail.com (K.H.-R.) 5 Department of Immunology, Moffitt Cancer Institute, Tampa, FL 33612, USA; jose31.conejo-garcia@moffitt.org * Correspondence: tyler.j.curiel@dartmouth.edu † Current addresses: Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA; Dartmouth College, Hanover, NH 03755, USA. 04 5 2022 5 2022 23 9 512901 4 2022 29 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The interaction between tumor surface-expressed PDL1 and immune cell PD1 for the evasion of antitumor immunity is well established and is targeted by FDA-approved anti-PDL1 and anti-PD1 antibodies. Nonetheless, recent studies highlight the immunopathogenicity of tumor-intrinsic PDL1 signals that can contribute to the resistance to targeted small molecules, cytotoxic chemotherapy, and αPD1 immunotherapy. As genetic PDL1 depletion is not currently clinically tractable, we screened FDA-approved drugs to identify those that significantly deplete tumor PDL1. Among the candidates, we identified the β-lactam cephalosporin antibiotic cefepime as a tumor PDL1-depleting drug (PDD) that increases tumor DNA damage and sensitivity to DNA-damaging agents in vitro in distinct aggressive mouse and human cancer lines, including glioblastoma multiforme, ovarian cancer, bladder cancer, and melanoma. Cefepime reduced tumor PDL1 post-translationally through ubiquitination, improved DNA-damaging-agent treatment efficacy in vivo in immune-deficient and -proficient mice, activated immunogenic tumor STING signals, and phenocopied specific genetic PDL1 depletion effects. The β-lactam ring and its antibiotic properties did not appear contributory to PDL1 depletion or to these treatment effects, and the related cephalosporin ceftazidime produced similar effects. Our findings highlight the rapidly translated potential for PDDs to inhibit tumor-intrinsic PDL1 signals and improve DNA-damaging agents and immunotherapy efficacy. PDL1 immunotherapy DNA damage drug repurposing β-lactam antibiotics NIH T32GM113896 (STX MSTP) AwardT32GM113896 National Center for Advancing Translational Sciences, National Institutes of HealthTL1 TR002647 Clayton FoundationNCICA204965 CA054515 This research was funded by the NIH T32GM113896 (STX MSTP) Award (C.M.) and by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant TL1 TR002647 (C.M.). The Clayton Foundation (no grant number) and the NCI (CA204965, CA054515) supported Curiel. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. ==== Body pmc1. Introduction Programmed death ligand 1 (PDL1, CD274) is a B7 homology family immune cosignaling molecule that interacts with T cell PD1 to blunt antitumor immunity [1,2]. Antibodies that block the PDL1, PD1, or CTLA4 immune checkpoints are collectively termed “immune checkpoint blockade” (ICB), which is highly effective as cancer immunotherapy, but only in select patients [2,3,4,5]. ICB against cell-surface-expressed PDL1 is well known and investigated, but recent evidence for immunopathogenic tumor-intrinsic PDL1 signals demonstrates additional PDL1 effects that are important in treatment resistance and that are aside from its canonical surface role [6]. Genetic tumor PDL1 depletion suppresses pathologic tumor-intrinsic PDL1 signals and can improve the efficacy of cytotoxic agents and small molecules in distinct cancers [7,8,9,10], and genetic PDL1 cytoplasmic tail deletion in tumor cells heightens αPD1 ICB efficacy [11,12]. We reported that tumor PDL1 can also promote stemness and mTORC1 signals and suppress autophagy [7,8,13], all of which are clinically actionable targets. However, it is not currently possible to genetically deplete tumor PDL1 in cancer patients as cancer treatment. We recently reported that genetic tumor PDL1 depletion reduced homologous recombination DNA damage repair, increased DNA damage, and improved tumor PARP inhibitor (PARPi) and gemcitabine sensitivity [14], which expands the current knowledge of tumor-intrinsic PDL1 functions in treatment resistance. αPDL1 was unable to sensitize tumors to PARPi [14], which highlights the need to investigate other methods to deplete tumor PDL1 for clinical applications. As genetic PDL1 depletion is not currently clinically actionable, we hypothesized that pharmacologic tumor PDL1 depletion could inhibit tumor-intrinsic PDL1 signals to replicate genetic PDL1 depletion effects on DNA damage and sensitize tumors to DNA-damaging agents. To identify pharmacologic tumor PDL1-depleting drugs (PDDs), we screened the Prestwick and LOPAC molecule libraries, which are enriched for FDA-approved drugs, in order to identify those that depleted tumor PDL1 in distinct mouse and human cancer cell lines. We validated the fourth-generation β-lactam cephalosporin antibiotic cefepime, which is FDA-approved to treat serious Gram-negative bacterial infections [15], as a bona fide PDD. We chose to study cefepime in further detail as it is inexpensive, relatively safe, and many FDA-approved β-lactams are available for the initial structure–activity-relationship studies. We investigated whether cefepime could improve the standard-of-care DNA-damaging agents, or those currently in clinical trial, in aggressive and treatment-resistant tumor types, including glioblastoma multiforme (GBM), melanoma, ovarian cancer, and bladder cancer. Gemcitabine is a standard-of-care cytotoxic chemotherapy for bladder cancer treatment, and temozolomide (TMZ) is a standard-of-care cytotoxic chemotherapy agent for GBM [16,17]. Chk1 inhibitors (Chk1i) enhance the cytotoxic-drug and/or radiation-therapy efficacy by inhibiting cell cycle checkpoints and DNA-damage repair, and they are currently in cancer clinical trials [18]. We hypothesized that the combination of cefepime, which replicates the genetic PDL1-depletion effects on DNA damage, with other DNA-damaging agents could improve cancer treatment response. Here, we show that cefepime phenocopies genetic PDL1 depletion in robustly increasing DNA damage and increasing sensitivity to distinct cytotoxic drugs and small molecule DNA-damage-inducing Chk1 inhibitors in various aggressive cancer cell lines, including glioblastoma multiforme, melanoma, bladder cancer, and ovarian cancer. Cefepime induced tumor STING signals, which could improve the ICB efficacy, and it treated distinct cancers in vivo in both an immune-independent manner and in wild-type mice, where it elicited distinct immune effects. The structurally related FDA-approved β-lactam antibiotic ceftazidime was also validated as a PDD that improved DNA-damaging agent efficacy. As cefepime is FDA-approved and generally well tolerated at doses that are needed to augment the DNA-damaging agent efficacy, it could be rapidly advanced clinically. Here, we provide the proof-of-concept that pharmacologic tumor PDL1 depletion can overcome treatment resistance to selected agents, which merits much additional study. Additional PDDs also merit further study to define the best agents for initial human clinical translation. 2. Results 2.1. Cefepime Is a Pharmacologic Tumor PDL1-Depleting Drug To identify FDA-approved drugs that deplete tumor PDL1, we undertook high-throughput drug screening of molecules in the Prestwick and LOPAC libraries, which are enriched for FDA-approved agents (Figure 1A). Briefly, we treated mouse B16 melanoma cells expressing RFP-tagged PDL1 with candidate agents, and we assessed the loss of the RFP signal but not cell viability. A total of 15 candidate drugs from the screen were validated as PDL1-depleting drugs (PDDs) by flow cytometry for PDL1 expression reduction. Among the top hits was cefepime, which potently reduced the tumor PDL1 from RFP-B16 cells by flow cytometry. To validate the flow cytometry data, we used immunoblots of the total PDL1 content in various tumor lines (Figure 1B), which confirmed cefepime as a bone fide tumor PDD in all of the cell lines tested. Easily used murine transplantable or human tumor lines that represent difficult-to-treat human cancers and express PDL1 were selected for detailed studies. We selected cefepime among the identified PDDs on the basis of its relative efficacy in tumor PDL1 depletion, its FDA approval, its relative safety, its low cost, and the existence of many commercially available β-lactam molecules for the initial structure–activity-relationship studies. 2.2. Cefepime Induces DNA Damage and Sensitizes to DNA-Damaging Agents In Vitro We and others have reported that tumor cells overexpress PDL1 in response to DNA damage, and we have shown that genetic tumor PDL1 depletion increases DNA damage and sensitizes tumors to DNA-damaging agents [14,19]. Thus, we investigated whether cefepime could overcome tumor PDL1 induction after DNA damage and whether it could sensitize resistant tumor cells to DNA damage-inducing drugs. We examined standard-of-care treatments, such as temozolomide for glioblastoma multiforme (GBM) and gemcitabine for bladder cancer, as well as more recently developed Chk1 inhibitors (Chk1i). Chk1i are currently in clinical trial as monotherapy and in combination with DNA-damaging chemotherapy or radiotherapy with preclinical success [18]. We reported that DNA-damaging agents increase PDL1 various tumor cell lines [14] and here in the mouse GL261 GBM line using rabusertib (Figure 1B), demonstrating that these cell lines do not have an atypical PDL1 response to DNA damage. Cefepime significantly depleted the PDL1 in ID8agg murine ovarian cancer cells, and it mitigated the PDL1 overexpression in response to DNA damage caused by the Chk1i rabusertib (Figure 2A), which suggests that PDDs could overcome PDL1 overexpression as a result of DNA damage. Furthermore, cefepime as a single agent induced γH2AX, which is a marker for DNA damage, similar to the DNA-damaging agent rabusertib (Figure 2A). On the basis of these data, we anticipated that, as the cefepime depleted tumor PDL1, it would induce DNA damage and further sensitize tumors to DNA-damaging agents. Consistent with our predictions, cefepime improved temozolomide efficacy in GL261 (Figure 2B) and gemcitabine in T24 human bladder cancer cells (Figure 2C), but modestly. By contrast, cefepime strikingly improved the efficacy of the Chk1i prexasertib in T24 cells and the Chk1i rabusertib in ID8agg cells (Figure 2D,E). However, cefepime did not sensitize MB49 mouse bladder cancer cells to PARPi (Supplementary Figure S1). Thus, cefepime induces DNA damage and sensitizes resistant mouse and human tumor cells to distinct DNA-damaging agents in tumor lines from differing cancers. 2.3. Cefepime-Induced DNA Damage and Synthetic Lethality Is Tumor-Cell-PDL1-Dependent and Can Include ROS Contributions To define the PDL1 dependence of the cefepime-mediated improvement in the DNA-damaging-agent efficacy, we tested cefepime combined with rabusertib in genetic PDL1 knock-out (PDL1KO) ID8agg cells. Cefepime failed to improve the rabusertib efficacy in PDL1KO ID8agg cells (Figure 3A), which is consistent with the tumor PDL1 dependence of the cefepime efficacy. Some β-lactam antibiotics can induce reactive oxygen species (ROS), which damage DNA [20]. To test the ROS contributions to PDL1-dependent cefepime-mediated DNA damage, we added the ROS scavenger N-acetyl-L-cysteine (NAC) to cultures with cefepime, and we assessed γH2AX as a measure of the DNA damage. In human T24 cells, the γH2AX induction by cefepime was unaltered by NAC (Figure 3B), which implies that the cefepime-mediated DNA damage is not from ROS contributions in this cell line. However, in murine B16 and ID8agg cells, we found that NAC moderately attenuated the γH2AX induction (Figure 3B), which is consistent with a minor role for ROS generation in PDL1 depletion-mediated DNA damage induction by cefepime in distinct tumor models (B16 quantification in Supplementary Figure S2). 2.4. Cefepime Improves Rabusertib Sensitivity In Vivo and Skews towards TH1-Polarized Immunity We next tested the in vivo efficacy of cefepime at improving DNA-damaging agent efficacy. As cefepime most potently improved Chk1i efficacy in vitro, we tested the Chk1i rabusertib in in vivo studies. In NSG mice challenged with T24 cells, rabusertib alone was ineffective at improving mouse survival, but cefepime significantly improved mouse survival (Figure 4A), which is similar to genetic PDL1KO T24 [9]. Strikingly, the combination of cefepime with rabusertib potently improved mouse survival over either single agent (Figure 4A), which demonstrates the in vivo utility of our in vitro findings. These data from severely immunodeficient NSG mice essentially eliminate the microbiota contributions to cefepime antitumor efficacy, as microbes contribute through immune mechanisms [21], which is further supported by the in vitro efficacy where no immune mediators are present. Thus, cefepime can improve Chk1i efficacy in vivo in an immune-independent manner, which is likely related to augmented DNA damage. We also tested cefepime efficacy in wild-type (WT) mice, as detrimental immune consequences from antibiotic treatments are a possibility as has been noted for various cancer treatments [22]. In WT mice challenged with B16 cells, neither cefepime nor rabusertib alone controlled tumor growth. However, the combination was significantly effective (Figure 4B). Flow cytometry analysis of tumor-infiltrating cells demonstrated that cefepime increased dendritic cells, CXCR3+ CD8+ T cells (consistent with TH1-polarized immunity [23]), and IFNγ+ NK cells, whereas rabusertib alone did not (Figure 4C). Surprisingly, other immune cell populations and effector cell functions that are typically altered through tumor PDL1 depletion (e.g., IFNγ+ CD8+ T cells, Granzyme expression) were unaffected by cefepime treatment (Supplementary Figure S3), which suggests differential immune consequences of PDDs. As the immune effects of the combination treatment were similar to cefepime alone, we were unable to ascribe a specific immune mechanism to the in vivo treatment efficacy from these data. 2.5. PDDs Promote Tumor STING Activation As faulty DNA damage repair can activate the immunogenic STING pathway [24,25], we studied cefepime effects on the STING pathway. Cefepime increased total tumor STING after 24 h of treatment, which could augment STING signals. We observed a significant increase in pTBK1 by immunoblots (Figure 5A), which is a clear marker for functional STING activation and consistent with elevated STING [24]. As β-lactams can induce DNA-damaging ROS [20], which could potentially induce STING signals, we assessed the ROS contributions to the cefepime-mediated STING signals by immunoblot. By using samples as in Figure 3B, which had significant tumor PDL1 depletion with the cefepime treatment, we found that 48 h of cefepime treatment moderately induced pTBK1 with total STING protein unchanged (Figure 5B,C). Although the total STING protein content is unchanged at 48 h versus 24 h of cefepime treatment, elevated pTBK1 signals are indicative of active STING signals. We hypothesize that STING protein content recovers by 48 h of treatment versus elevation at 24 h, although the signals remain active as is evident by elevated pTBK1. Notably, NAC treatment slightly attenuated STING signals in B16 and ID8agg (Figure 5B,C; B16 quantifications in Supplementary Figure S2). These findings support a minor role of ROS in cefepime-induced STING induction. In further support of STING activation, we used RT-qPCR to show increases in the transcription of the downstream STING-activation products Cxcl9, Cxcl10, Ccl5, and IFNβ (Figure 5D,E). CXCR3 is the receptor for CXCL9 and CXCL10, and the increase in CXCL9 transcription supports the in vivo immune consequences in Figure 4; however, further investigations are needed for a mechanistic confirmation of cefepime-activated STING in in vivo treatment efficacy, as we hypothesize, and for STING effects in PDL1-deficient tumors. 2.6. Cefepime Regulates Tumor PDL1 Post-Translationally To understand how cefepime regulates tumor PDL1 protein content, we first assessed Cd274 mRNA-encoding PDL1 in ID8agg and B16 cells; however, it was not reduced in either cell line (Figure 6A). We next interrogated protein stability mechanisms through the proteasome inhibitor mg132 and the lysosomal inhibitor bafilomycin A1 in cells collected from the same experiment, which proved the consistency of the tumor PDL1 depletion. Strikingly, mg132 significantly preserved the PDL1 protein content in the presence of cefepime (Figure 6B; quantified in Supplementary Figure S2), which is consistent with ubiquitin-mediated degradation as a cefepime PDL1-depletion mechanism, and which is supported by the increased PDL1 with bafilomycin A1. Additional work is required in order to elucidate the PDL1-depletion mechanism fully. 2.7. The Cefepime β-Lactam Ring Appears Dispensable for PDD and Cytotoxic Effects Cephalosporins are a class of β-lactam antibiotics, which are reported to have negative effects on treatment outcomes in cancer patients [22]. Thus, we investigated whether the cefepime β-lactam ring is required for its PDD effects. We tested several β-lactam antibiotics for tumor PDL1 depletion, including penicillin G and other β-lactams in the cephalosporin and related antibiotic classes, such as the first-generation cephalosporin cefazolin, the carbapenem meropenem, the third-generation cephalosporin ceftriaxone, and the fourth-generation cephalosporin ceftazidime. Surprisingly, penicillin G, meropenem, cefazolin, and ceftriaxone all induced tumor-PDL1 expression (Figure 7A), which was potentially from DNA damage induction through ROS production. However, ceftazidime potently depleted the tumor PDL1 with a similar efficacy to cefepime (Figure 7A), which we replicated in B16 and ID8agg cells over time (Figure 7B). Ceftazidime suppressed the PDL1 induction in ID8agg cells with rabusertib treatment and induced γH2AX, which phenocopies Figure 2A. cefepime effects (Figure 7C). We next tested STING activation and cytotoxicity, and we found that ceftazidime both induces STING signals through increased STING protein and pTBK1 induction and reduces cell viability similar to cefepime (Figure 7D,E). Most of the other β-lactam antibiotics that were used in our preliminary structure–activity-relationship studies that did not deplete tumor PDL1 also failed to reduce the U251 GBM cell viability, except for modest cefazolin activity (Figure 7E), which merits further investigation. Because ceftazidime is similarly able to induce γH2AX as for cefepime, we tested whether ceftazidime could elicit sensitivity to DNA-damaging agents. Ceftazidime induced rabusertib sensitivity in ID8agg cells similarly to cefepime which was eliminated in PDL1KO cells, implying tumor PDL1 dependence of rabusertib sensitivity (Figure 7F,G). Ceftazidime is structurally very similar to cefepime, whereas the other β-lactam antibiotics that were tested shared few structural similarities, apart from their β-lactam rings (Supplementary Figure S4). These data support the concept that the β-lactam ring is dispensable for PDD effects, and they provide opportunities for structure–activity-relationship studies to define potentially better molecules that mediate tumor PDL1 depletion and that are derived from cefepime/ceftazidime structures. 2.8. PDDs Phenocopy Other Genetic Tumor-PDL1KO Outcomes We reported that tumor PDL1 promotes mTORC1 and stemness and suppresses autophagy [7,8,13], all of which are actionable treatment targets, so we tested whether PDDs replicated genetic tumor PDL1 depletion effects on these pathways. We found that cefepime and ceftazidime reduced the transcription of several stemness genes, including Sox2 and Nanog (Figure 8A), which is similar to what we reported for PDL1lo ID8agg clones [13]. We found that both cefepime and ceftazidime induce autophagy through LC3A/B induction in ID8agg cells, which is consistent with our findings on genetic PDL1 depletion [7]. However, they minimally affect mTORC1 activity, seen through phospho-S6, when tumor PDL1 is depleted (Figure 8B; quantified in Supplementary Figure S2). Therefore, cefepime and ceftazidime can phenocopy other tumor-intrinsic PDL1 depletion effects aside from DNA damage and sensitivity to DNA-damaging agents, which could be clinically exploitable. 3. Discussion Although the paradigm of surface-expressed PDL1 engaging immune cell PD1 to thwart antitumor immunity is valid and has led to important ICB drugs, it is incomplete. Recent work demonstrates the immunopathologic import of tumor-intrinsic PDL1 signals, which notably includes their role in the tumor treatment resistance to distinct classes of therapies, including cytotoxic agents, targeted small molecules, irradiation, and immunotherapies (reviewed in [6]). Although genetic tumor PDL1 depletion can define mechanistic insights, it is not a logistically tractable approach to cancer treatment with current technology. Recent interesting reports from several groups have shown that small molecules can deplete tumor-intrinsic PDL1 to alter cancer treatments. For example, FDA-approved verteporfin, which is a benzoporphyrin molecule that is used to treat retinal diseases, depletes PDL1 from distinct human tumor cells and improves PARP inhibitor (PARPi) treatment in vivo in mouse ovarian tumors [26]. Curcumin is a natural product that can reduce tumor cell PDL1 content by promoting its ubiquitination and can improve in vivo anti-CTLA-4 immunotherapy efficacy in murine 4T1 triple-negative breast cancer [27]. Small molecule inhibitors of PDL1 and PD1 are also described [28,29], but these were generally designed to replace the antibody action of inhibiting PDL1/PD1 interactions at cell surfaces and not to reduce tumor-intrinsic PDL1. We used a drug screen to identify a variety of FDA-approved drugs that can be repurposed to deplete tumor PDL1 and phenocopy important genetic PDL1 depletion outcomes, including suppressing mTORC1 and tumor stemness, and promoting tumor autophagy. These are actionable pathways that merit additional study and that are not fully addressed here. We found that the fourth-generation cephalosporin cefepime potently reduces tumor PDL1 in a wide variety of human and mouse tumor cell lines to phenocopy the recently described role of PDL1 in DNA damage repair [14,30]. Although the drug screen used up to 10 μM of the candidate drugs to assess the PDL1 with minimal cytotoxic effects, we found that drug concentrations higher than used in the drug screen, and yet still clinically achievable, could inhibit tumor cell growth in vitro and in vivo. Cefepime PDL1-dependently increased tumor DNA damage and sensitivity to DNA-damaging agents, including the cytotoxic chemotherapy drug gemcitabine and two small molecule Chk1 inhibitors. We recently reported that tumor PDL1 specifically promotes homologous recombination DNA repair, and its genetic depletion augmented PARPi cytotoxicity in vitro and in vivo [14]. Cefepime phenocopied genetic PDL1 depletion in augmenting DNA damage and sensitivity to targeted small molecule DNA-damaging agents in vitro, but the effects were modest versus genetic PDL1 depletion, and it did not sensitize to PARPi as did genetic PDL1 depletion [14]. This difference in the effect size could be due to tumor-specific factors, incomplete cefepime effects on PDL1-regulated DNA damage repair molecules, incomplete PDL1 depletion, or a requirement for immune contributions, which could be seen in vivo. Further work to determine PDD impairment of homologous recombination, sensitivity to PARPi, and whether PDL1-dependent STING activation contributes to in vivo efficacy is required. By contrast, cefepime robustly increased tumor cell death mediated by the cytotoxic chemotherapy drugs gemcitabine and temozolomide, which have mechanisms of cytotoxicity that are distinct from each other [31,32] and that are distinct from small molecule Chk1 inhibitors. These data suggest that cefepime affects DNA damage repair pathways aside from homologous recombination and that it could affect the mechanisms that are specific to the actions of temozolomide and gemcitabine. Much additional work is required in order to understand how cefepime (and ceftazidime) influences cytotoxicity that is mediated by distinct molecules with differing mechanisms of action; however, we hypothesize that the influence on the distinct DNA damage sensing or repair pathways is among the mechanisms, including the specific effects on the Chk2 DNA damage sensing pathway as we recently reported [33]. It is now recognized that tumor PDL1 affects gene product expression through distinct mechanisms, including through gene expression [12,13] and post-translational mechanisms. Tumor PDL1 also promotes sister chromatid cohesion [34] and genomic stability [35], which could contribute to cytotoxic effects in PDL1 depletion. We further found that ROS could contribute to cytotoxicity or DNA damage in selected cell lines. All these mechanistic details require additional work. In vivo data show that cefepime affects immune cells in a distinct manner, including by potentially augmenting Th1 polarization, and shows signs of STING activation, such as an increase in CXCR3+ immune cells [23,25]. It will be important to understand how the cefepime-mediated immune effects are generated, as well as their contributions to the cefepime in vivo efficacy. We predict that increased tumor STING activation with cefepime will contribute to and could improve ICB efficacy, despite the reduced tumor PDL1 expression. In support, we just reported that the PDD chlorambucil renders αPDL1-resistant tumors αPDL1-responsive [36]. These lines of investigation are also important, as many antibiotics appear to worsen cancer treatments including ICB [22]. Many cytotoxic agents also increase tumor immunogenicity, including through immunogenic cell death [37], warranting further investigation. The effects of cefepime and rabusertib on several aggressive cell lines, particularly GBM, are notable for their potency. PDDs can thus help to fill the GBM treatment gaps, as most highly express PDL1 [38]. Notably, both cefepime (and ceftazidime) and rabusertib cross the blood–brain barrier, which is required for effective systemic GBM treatment. GMB has demonstrated a poor response to single-agent ICB, and combination ICB is currently in clinical trial; thus, combining PDDs with single agent ICB or DNA damage-inducing molecules could improve GBM treatments and outcomes, as we have shown with the PDD chlorambucil in ovarian cancer [36]. Our work has limitations in addition to those mentioned above. We have yet to show that in vivo cefepime efficacy is tumor PDL1-dependent, which we expect, as well as whether it is tumor-selective in PDL1 depletion, both of which we demonstrated for the PDD chlorambucil [36]. Some PDL1 effects are related to the subcellular location, such as the cytoplasmic PDL1 regulation of DNA damage repair gene expression [30] and the nuclear PDL1 regulation of αPD1 resistance and pyroptosis [12,13]. We have yet to understand if the cefepime effects on sensitization to DNA-damaging agents is related to a specific subcellular PDL1 location and whether specific PDDs deplete PDL1 from specific subcellular locations. Furthermore, the precise mechanisms for the sensitization to these agents has yet to be defined, as do the immune contributions. Finally, we expect the PDD effects to differ by tumor type, which we demonstrated here; however, much work is needed in order to understand these differences, which likely relate to the underlying tumor mutational landscape among other considerations. In summary, we show that pharmacologic tumor PDL1 depletion increases DNA damage, sensitizes a wide variety of human and mouse tumor cells to cytotoxicity from distinct DNA-damaging agents, augments the treatment efficacy to a Chk1 inhibitor in vivo, and activates STING signals, all of which are clinically translatable. Cefepime was our proof-of-concept FDA-approved agent as a PDD, but it has limitations. Although it is inexpensive, relatively safe, and well tolerated, its antimicrobial effects (which likely do not contribute to its treatment efficacy here) and its short half-life could be problematic. Our initial structure–activity-relationship studies suggest that the antimicrobial β-lactam ring is not required for the outcomes that we observed, nor are its antimicrobial effects likely mechanistic in the treatment efficacy here. Better agents could be designed from the structures that have been identified here or from among other PDDs that we are now evaluating. Combination therapies of PDDs with other immunotherapies are also worth exploring. Pharmacologic tumor PDL1 depletion merits additional studies for its clinical ability to target other PDL1 effects, such as mTORC1 signals and rapamycin sensitivity, and for its potential to improve selected ICB as we have demonstrated with chlorambucil. 4. Materials and Methods Cell lines and constructs. B16 murine melanoma cells were obtained from ATCC. ID8agg cells were generated through serial in vivo passage, as we previously described [8]. T24 human bladder cancer cells were a gift from Dr. Robert Svatek, MD. Cell lines were not revalidated for this study, except for T24. B16 and ID8agg cells were cultured in RPMI-1640 medium, while T24 was cultured in McCoy’s medium. GL261 murine glioblastoma cells were a kind gift from Sandeep Burma, PhD. GL261 were cultured in DMEM. U251 human glioblastoma cells were a gift from the Agenus Corporation (Lexington, MA, USA) and were cultured in EMEM. PDL1KO cell lines were generated by using commercially available CRISPR/Cas9 plasmids, as we previously described, and were validated through flow cytometry, Western blot, and sequencing [9]. Chemicals, reagents, and X-rays. Gemcitabine was purchased from Sigma-Aldrich (St. Louis, MO, USA) and was diluted in sterile water for in vitro studies. Cefepime was purchased from Sigma-Aldrich and was diluted in DMSO for in vitro studies, while in vivo USP cefepime was obtained from Oakdell Pharmacy (San Antonio, TX, USA) and was diluted in sterile 0.9% NaCl (Intermountain, West Joran, UT, USA). Penicillin G, meropenem, cefazolin, and ceftriaxone were also obtained from Oakdell Pharmacy and were used without further manipulations. Temozolomide was obtained from the Mays Cancer Center (San Antonio, TX, USA) and was diluted in DMSO. N-acetyl-L-cysteine (NAC) was purchased from Sigma-Aldrich. Gemcitabine was purchased from Sigma-Aldrich. Rabusertib was purchased from Selleck Chemicals (Houston, TX, USA) and was diluted in DMSO for in vitro studies. For in vivo studies, the rabusertib vehicle was 5% DMSO, 30% PEG300 (Selleck Chemicals), 5% Tween 80 (Sigma-Aldrich), 5% Propylene Glycol (Sigma-Aldrich), and sterile 1× PBS. In vitro proliferation and viability. For MTT cell viability assays, 1000 cells/well were plated in 96-well plates in appropriate growth medium. Cells were treated with indicated agents the following day and were incubated for up to 96 h. The drug concentrations shown in these data were carefully optimized in preliminary experiments. A total of 20 microliters of 5 mg/mL 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MPBio, Solon, OH, USA) was added/well, and then the plates were incubated for 2 h at 37 °C. Medium was then decanted, and wells were resuspended in 200 microliters of DMSO. Absorbance was read at 490 nm. RT-qPCR. Cells were lysed with TRIzol (Invitrogen, Waltham, MA, USA) and total RNA was isolated, according to the manufacturer’s recommendations. A NanoDrop 1000 was used to determine the quantity and quality of the RNA. cDNA was reverse transcribed from RNA with SuperScript III Reverse Transcriptase (Invitrogen). Gene expression was determined by quantitative real-time RT-PCR (qRT-PCR) by using TaqMan Gene Expression Assays (Applied Biosystems, Waltham, MA, USA) and a QuantStudio 3 (Applied Biosystems). Values were normalized to GAPDH, which was used as a housekeeping gene with the Applied Biosystems Data Analysis software by using the ΔΔCq method. All TaqMan primers were purchased from ThermoFisher Scientific (Waltham, MA, USA) and were validated by the manufacturer. Immunoblots and antibodies. Cells were lysed with 1× TN1 lysis buffer (125 mM NaCl, 50 mM Tris, 10 mM EDTA, 1% Triton X-100, 10 mM Na4PO7, 10 mM NaF) with a 1:100 Halt protease/phosphatase inhibitor cocktail (ThermoFisher Scientific). Protein concentrations were measured by BCA Assay (ThermoFisher Scientific). A total of 40 micrograms of protein per sample were run on 4 to 15% DS-PAGE Precast TGX gels (Bio-Rad, Hercules, CA, USA) and were transferred to 0.2-micron nitrocellulose membranes (Bio-Rad) by using a Trans-Blot Turbo transfer system (Bio-rad). Membranes were incubated with the appropriate primary antibodies overnight at 4 °C, washed with 1× TBST, and incubated in species-matched horseradish peroxidase-conjugated secondary antibodies for 2 h at ambient temperature. After wash with 1× TBST, membranes were incubated with Western Lightening Plus reagent (PerkinElmer, Austin, TX, USA) or SuperSignal West Pico PLUS (ThermoFisher Scientific) for chemiluminescent detection. Antibodies purchased from Cell Signaling Technology (Danvers, MA, USA) include phospho-histone H2AX (#9718), PDL1 (#13684), vinculin (#13901), GAPDH (#2118), p-TBK1 Ser172 (#5483), TBK1 (#3013), STING (#13647), LC3 A/B (#12741), S6 ribosomal protein (#64108), and phospho-S6 ribosomal protein (#4858). Mouse-reactive PDL1 antibody was purchased from Abcam (Cambridge, UK) (ab213480). The incubation times used for the PDD experiments differed among distinct cell lines on the basis of the differential optimal time to maximal PDL1 depletion in each cell line. Mice. Wild-type C57BL/6J (BL6) and NOD.Cg-Prkdcˢᶜiᵈ Il2rgᵗᵐ¹ᵂʲˡ/SzJ (NSG) mice were originally purchased from Jackson Labs (Bar Harbor, ME, USA) then bred in-house and maintained under pathogen-free conditions. All animal studies were approved by the UT Health San Antonio Institutional Animal Care and Use Committee. In vivo tumor challenges and treatments. NSG mice were challenged with 2 × 106 T24 cells in 1:1 PBS:Matrigel (Corning, Corning, NY, USA) subcutaneously on each flank (n = 2 tumors per mouse). BL6 mice were challenged with 0.5 × 106 B16 cells subcutaneously on each flank in PBS. Rabusertib (5 mg/kg) and vehicle (5% DMSO, 30% PEG300, 5% Tween 80, 5% Propylene Glycol, 55% PBS) were administered daily, beginning as indicated in the text. Cefepime or sterile 0.9% NaCl vehicle were administered at 200 mg/kg/day, beginning as indicated in the text. Flow cytometry. Mice were sacrificed by cervical dislocation after isoflurane anesthesia. Tumors were excised and then strained in 100-micron cell strainers into serum-free RPMI-1640 medium to create single-cell suspensions. Total cell numbers per sample were counted with a Vi-Cell-XR cell counter (Beckman Coulter). A total of 5 × 106 cells per sample were transferred to U-bottom 96-well plates. Dead cells were excluded by the LIVE/DEAD Fixable Blue Dead Cell Stain Kit for UV excitation (ThermoFisher Scientific). Samples were incubated with anti-CD16/32 (Biolegend) for 30 min at 4 °C and were shielded from light to prevent nonspecific binding. Cells were stained with surface antibodies at 1:100 for 30 min at 4 °C while protected from light. Surface antibodies include αPD1 (566515), αCD11b (748700), αCD4 (612952), αCD8 (563786), αCXCR3 (748700), and αB220 (612972) from BD Biosciences (Franklin Lakes, NJ, USA); αCD3 (80-0032-U100), αNK1.1 (20-5941-U100), and αCD11c (60-0114-U100) from TONBO Biosciences (San Diego, CA, USA); αCD45 (58-0451-82) from Invitrogen; and αCD11b (101226) from BioLegend (San Diego, CA, USA). Cells were washed with FACS buffer (1:50 FBS in 1× PBS) and were then activated with Cell Activator Cocktail (Biolegend), which contained phorbol 12-myristate 13-acetate, ionomycin, and brefeldin A in a CR10 medium (RPMI-1640, 10% FBS, L-glutamine, sodium pyruvate, nonessential amino acids, penicillin/streptomycin, HEPES buffer) for 6 h to perform intracellular cytokine staining. Cells were washed and were then fixed and permeabilized by using the Foxp3/Transcription Factor Staining Buffer Kit (TONBO Biosciences). After fixation and permeabilization, the samples were incubated with intracellular antibodies and diluted to 1:100 at 4 °C for 30 min while protected from light. Intracellular antibodies include αFOXP3 (ThermoFisher Scientific, 15-5773-82), αIFNγ (BD Biosciences, 612769), and αGranzyme B (Biolegend, 515408). Data were acquired by using a Cytek Aurora flow cytometer (Cytek Biosciences, Fremont, CA, USA) and were analyzed with FloJo software V.10.7.1 (BD Biosciences). Generation of Figure 1A and Supplementary Figure S3.Figure 1A was generated by using BioRender software. Images of structures of β-lactam antibiotics were adapted from The National Library of Medicine National Center for Biotechnology Information PubChem database of compound summaries. Images are cited as follows: National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5479537, cefepime; National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5481173, ceftazidime; National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5904, penicillin G; National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 441130, meropenem; National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 33255, cefazolin; National Center for Biotechnology Information (2022). PubChem Compound Summary for CID 5479530, ceftriaxone. Statistics. Statistical analyses of all data were performed using PRISM software (version 9.3.1, GraphPad, San Diego, CA, USA), with the significance defined as p ≤ 0.05. Data for in vivo experiments are represented as mean ± standard error of the mean, while in vitro data are represented as mean ± standard deviation. Survival significance was determined by log-rank test. Tumor growth and viability curves were analyzed by two-way ANOVA. All other data were analyzed with unpaired t-test. Outliers were identified by Grubbs’ test, used only once per data set, and removed from analysis. Acknowledgments Jason Liu at UT Health San Antonio provided equipment support. Flow cytometry data were generated at the UT Health San Antonio Flow Cytometry Shared Resource Facility, supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR002645. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23095129/s1. Click here for additional data file. Author Contributions C.M. was involved in the conception, data collection, data analysis, article drafting, and revision of the article. E.G. assisted in the data collection, data analysis, and critical revision of the article. C.O. contributed to the data collection and data analysis. Y.D. assisted in the data collection, the data analysis and interpretation, and the critical revision of the article. H.B. assisted in the data collection. A.S.P. assisted in the experimental design and the procedures. K.H.-R. assisted in the data collection and the experimental procedures. M.G.G. assisted in the experimental procedures. A.K. contributed to the conception and the design of the work. J.C.-G. contributed to the conception and the design of the work and the critical revision of the article. T.J.C. assisted with the conception and design of the work, the drafting of the article, the critical revision of the article, overall guidance and supervision, and provided the final approval of the version to be published. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest T.J.C. and A.K. have filed a patent on the use of pharmacologic PDL1 depletion. Figure 1 Cefepime depletes tumor PDL1 in multiple distinct tumor cell lines. (A) Schematic of high-throughput drug screen used to identify pharmacologic PDL1-depleting agents (PDDs). (B) Immunoblots of PDL1 and the loading control vinculin (VINC) in B16, ID8agg, and GL261 cells treated with 80 μM cefepime for indicated times. GL261 cells were treated with DMSO (veh), 250 nM rabusertib (rab), or 80 μM cefepime (cef) for 48 h. Cefepime was replenished daily. Figure 2 Cefepime induces DNA damage and sensitizes to DNA-damaging agents. (A) Immunoblot for γH2AX, PDL1, and vinculin loading control (VINC) of ID8agg cells treated with DMSO vehicle (veh), rabusertib (rab) as indicated, 80 μM cefepime (cef), or combination (combo) for 72 h. (B) MTT viability assay of GL261 cells treated with 100 μM cefepime (cef) or vehicle (DMSO) in combination with indicated concentrations of temozolamide (TMZ). p value by two-way ANOVA. (C) MTT viability of T24 cells treated with 80 μM cefepime or vehicle (DMSO) in combination with indicated concentrations of gemcitabine (gem). p value by two-way ANOVA. (D) MTT viability of T24 cells treated with the Chk1i prexasertib (prex) at indicated concentrations combined with DMSO vehicle or 80 μM cefepime. p value by two-way ANOVA. (E) MTT viability of ID8agg cells treated with rabusertib (rab) at indicated concentrations in combination with DMSO or 80 μM cefepime. p value by two-way ANOVA. Drugs were not replenished in these assays. Figure 3 Cefepime-induced DNA damage and sensitivity to DNA-damaging agents is tumor PDL1-dependent with ROS contributions in distinct lines. (A) MTT viability in PDL1KO ID8agg cells treated with 80 μM cefepime (cef) or DMSO (veh) in combination with indicated concentrations of rabusertib (rab). p value by two-way ANOVA. (B) Immunoblots of T24, ID8agg, and B16 cells treated with 80 μM cefepime and/or 0.5 mM N-acetyl-L-cysteine (NAC) for γH2AX, PDL1, and loading control vinculin (VINC) (48 h incubation). Figure 4 Cefepime elicits PDL1-dependent rabusertib sensitivity in vivo and skews towards TH1. (A) Survival of NSG mice (n = 5 per group) challenged with T24 cells. Mice were treated beginning Day 7 with vehicle (veh), 200 mg/kg cefepime (cef), and/or 2.5 mg/kg rabusertib (rab) daily. p value by log-rank test. (B) Tumor growth in WT mice challenged with B16 cells treated with vehicle, 5 mg/kg rabusertib daily, and/or 200 mg/kg cefepime twice daily beginning Day 3 post-challenge. p value by two-way ANOVA. (C) Flow cytometry analyses of immune populations in tumors derived from vehicle-, rabusertib-, cefepime-, and combination (combo)-treated mice. p values by unpaired t test. Figure 5 Cefepime promotes tumor STING activation. (A) Immunoblot for STING, phospho-TBK1 (pTBK1), and loading control vinculin (VINC) of B16 cells treated with DMSO or 80 μM cefepime (cef) for 24 h. (B) Western blot for STING, pTBK1, total TBK1, and loading control vinculin in B16 cells treated with 80 μM cefepime and/or 0.5 mM N-acetyl-L-cysteine (NAC) for 48 h. (C) Immunoblot for targets as in (B) in ID8agg cells treated with 80 μM cefepime and/or 0.5 mM N-acetyl-L-cysteine for 48 h. (D) RT-qPCR assessment of normalized gene expression in B16 cells treated as in (B). p values by unpaired t test. (E) RT-qPCR assessment of normalized gene expression in ID8agg cells treated as in (C). p values by unpaired t test. Figure 6 Cefepime regulates tumor PDL1 post-translationally. (A) RT-qPCR assessment of normalized Cd274 gene expression of B16 and ID8agg cells treated with DMSO (veh) or 80 μM cefepime (cef) for 48 and 24 h, respectively. (B) Western blot of ID8agg cells treated with 0.2 μM mg132 for final 6 h, 100 nM bafilomycin A1 (BafA1) for final 6 h, and/or 80 μM cefepime for 48 h for PDL1 and loading control vinculin (VINC). Figure 7 Tumor PDL1depletion effects of cefepime are likely independent of the β-lactam ring. (A) Immunoblot of PDL1 and loading control vinculin (VINC) of ID8agg cells treated with DMSO (veh) or 80 μM of β-lactam antibiotics cefepime (cef), ceftazidime (cz), penicillin G (pen G), cefazolin (cefaz), ceftriaxone (ceftri), and the carbapenem meropenem (mero) for 48 h. Drugs were replenished daily. (B) Immunoblot for PDL1 and loading control vinculin of ID8agg and B16 cells treated with ceftazidime for indicated time points, replenished daily. (C) Western blot for γH2AX, PDL1, and loading control vinculin of ID8agg treated with DMSO, 2.5 μM rabusertib (rab), 80 μM ceftazidime, or combination for 72 h. (D) Immunoblot of B16 cells treated with DMSO or ceftazidime for 24 h for STING, phospho-TBK1, and loading control vinculin. (E) MTT viability of U251 cells treated with 80 μM of β-lactam antibiotics cefepime, ceftazidime, penicillin G, cefazolin, ceftriaxone, and the carbapenem meropenem for 96 h. p values by unpaired t test. (F) MTT viability assay of ID8agg cells treated with rabusertib (rab) with DMSO or 80 μM ceftazidime for 96 h. p value by two-way ANOVA. (G) MTT viability of PDL1KO ID8agg cells treated with rabusertib, DMSO, or 80 μM ceftazidime for 96 h. p value by two-way ANOVA. Figure 8 PDDs phenocopy additional genetic tumor-PDL1-depletion effects. (A) RT-qPCR for normalized stemness-associated gene expression (Pouf51, Sox2, Nanog, c-Myc) of ID8agg cells treated with DMSO (veh) or 80 μM cefepime (cef) for 48 h, replenished daily. p values by way of unpaired t test. (B) Immunoblot for phospho-S6, total S6, LC3A/B, PDL1, and loading control vinculin (VINC) in ID8agg cells cultured and replenished daily with 80 μM cefepime (cef) or ceftazidime (cz) daily for two days. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Hayashi H. Nakagawa K. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093009 materials-15-03009 Article Microstructure, Properties, and Numerical Simulation of Semi-Solid Aluminum Alloy under Planetary Stirring Process Zhou Bing * Qiu Zhiyan Chen Keping Xu Chun * Wang Zhanyong Łodygowski Tomasz Academic Editor School of Materials Science and Engineering, Shanghai Institute of Technology, Shanghai 201418, China; qiujanice@163.com (Z.Q.); m16605162069@163.com (K.C.); zhanyong@sina.com (Z.W.) * Correspondence: zb521a@sina.com (B.Z.); xuchun1963@163.com (C.X.) 21 4 2022 5 2022 15 9 300903 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In order to solve the problem of insufficient convective heat transfer of uniaxial stirred melt, the temperature field and shear rate of melt under planetary stirring were studied based on CFD simulation. The microstructure and properties of this technology were also experimentally studied. The results show that compared with the uniaxial stirring semi-solid technology, the convective heat transfer ability of aluminum alloy, semi-solid slurry in planetary stirring mode is stronger. In addition, its temperature field can be reduced to the semi-solid range faster and more evenly, which is conducive to a large number of nucleation and improves the nucleation rate. The temperature difference of the whole melt is small, so the preferred direction growth and uniform growth of dendrites are avoided, and the morphology is improved. Properly increasing the revolution and rotation speed of the stirring shaft can refine the grains of semi-solid aluminum alloy parts, improve the grain morphology, and improve the tensile strength. The planetary stirring semi-solid process is very suitable for rheological high-pressure casting. planetary stirring semi-solid numerical simulation microstructure properties ==== Body pmc1. Introduction Semi-solid metal forming is a kind of metal forming based on the good rheological properties of the metal in the semi-solid region during the transition of metal from solid to liquid or from liquid to solid. It has the advantages of stable filling, no turbulence and spatter, low processing temperature, and small solidification shrinkage, so the dimension accuracy of the casting is high. The preparation methods of semi-solid slurry include the mechanical stirring method [1], electromagnetic stirring method [2,3], ultrasonic vibration method [4], gas stirring method [5,6], and casting method [7,8,9,10]. The mechanical stirring method includes single screw stirring, double screw stirring [11,12], cone barrel rheological forming [13], and forced convection rheological pulping [14]. With the rapid development of computational fluid dynamics (CFD) technology, it is more and more widely used in stirring simulation [15,16,17]. Certain numerical calculation methods must be used to simulate the preparation and filling process of semi-solid metal slurry. According to the different research objects and methods, numerical calculation methods can be divided into the Lagrange method and Euler method; another mesh-less method is under exploration. The VOF (Volume-of-Fluid) method is currently the most popular and is used in well-known computational fluid dynamics programs, such as ANSYS Fluent, Star-CD, Geo MATH, CFX, and Flow-3D [18,19,20]. One of the basic contents of numerical simulation of melt filling and solidification process is to solve the continuity equation and the Navier–Stokes equations based on finite difference or finite element methods. Liquid metal is usually regarded as an incompressible fluid, and its flow process obeys the conservation of mass and momentum. Its mathematical form is continuity equation and momentum equation. Through the research on the preparation technology of mechanical stirring semi-solid slurry, it is found that a single stirring shaft can fully stir the melt heat exchange in the center. However the melt far from the center has poor stirring and heat exchange capacity due to the influence of inertial laminar flow. There is a large temperature difference, which takes a long time to make the temperature field of the whole melt uniform, especially when preparing large volume semi-solid slurry. The ability of the stirring shaft to drive melt convection is weaker, the middle temperature of the melt is low, the surrounding temperature is high, the temperature difference is more obvious, and the heat exchange efficiency is lower. Therefore, in order to improve the heat exchange and stirring problem between ordinary stirring shaft and melt, a new planetary composite stirring semi-solid technology with superposition revolution and rotation was used to study the temperature field, microstructure, and properties in the preparation of semi-solid slurry by means of experiment and numerical simulation. 2. Experimental Method and Numerical Simulation A356 aluminum alloy was used as the research material with the solidus and liquidus temperatures of 560 °C and 615 °C, respectively. The alloy was placed in the heating furnace and heated to 710 °C for melting. After slag removal, refining, and other operations, the melt was kept at 660 °C. An appropriate amount of alloy liquid was treated with self-made planetary stirring semi-solid equipment to prepare the aluminum alloy, semi-solid slurry. The planetary mixing semi-solid equipment includes two independent motion controls of the rotation and revolution of the mixing shaft. The rotation of the mixing shaft is realized by the motor and transmission gear located on an intermediate platform. The revolution of the mixing shaft is realized by the rotation of the motor and eccentric shaft through the intermediate platform. The equipment mainly controls the rotation (300, 500 r/min), rotation (0.7, 1.4 rad/s), and mixing time (15~30 s) parameters to prepare the semi-solid slurry. The whole procedure can be automatically controlled by PLC of Siemens S7-200 SMART (Siemens, Beijing, China). The stirring shaft was put into the melt for a certain time, the melt temperature was monitored in real-time through the thermocouple, and the semi-solid slurry was poured into the die-casting mold or water-cooled spindle mold for solidification. Figure 1 shows the eccentric part of the planetary stirring structure and the drawing of the tensile sample mold. After rough grinding, fine grinding, and polishing, the microstructure of the sample was observed and photographed by a metallographic microscope of ZEISS Axio Scop A1 (ZEISS, Jena, Germany). The grain size and roundness were analyzed by image processing software (ImageTool, Version2.0 made by Texas health Science Center in San Antonio, TX, USA). The grain size is represented by the average diameter D and the roundness is represented by the shape factor F. The closer F is to 1, the rounder the solid grain is. The specific formula is: D=4Aπ, F=4πAP2. Where A is the grain area and P is the grain section perimeter. The tensile strength and elongation of the samples were measured by an electronic universal testing of MTS-SANS machine (Ningbo, China). In order to better understand the effects of mixing action plus revolution action on melt flow behavior and temperature field, the numerical simulation of the semi-solid slurry preparation process was realized. The CFD simulation procedures of the melt stirring by the planetary stirring device are as follows: First, the 3D solid model with actual size is created. Second, the standard joint form IGES file is output. Third, the above model is transmitted to Meshing–Geometry Block. Then, the attributes of material, heat exchange coefficient on boundary, rotational speed, initial temperature, and other Pre-Processing parameters are set. After the calculation, simulation results are directly observed through the Post-Processing block. The effect of rotational condition on the flow characteristics and temperature field of the melt planetary stirring device is important to understanding the nucleation and grain growth mechanism. Figure 2 shows the three-dimensional model, meshing, and mixing operation diagram for numerical simulation. The finite difference method was used to generate the hexahedral mesh of the simplified model. To reduce computing time, the total number of cells is about 360,000 and the width of the grid is 2.2 mm, which has no significant difference from 800,000 cells in the final simulation results. The fluid material database of simulation software has provided most material thermal properties of the A356 aluminum alloy, such as specific heat, thermal conductivity, latent heat of fluid, and liquidus temperature. The material data can be directly used for simulation with only minor modifications. The viscosity of melt adopts the original fixed value in the database, which should be better if the non-Newtonian fluid model is adopted. However, the current simulation has not been involved. Several important parameters, such as melt temperature, barrel temperature, and rotation condition of the planetary stirring process, which have a significant influence on microstructure, were set in actual conditions. The actual agitating shaft rotation speed has been treated by the reducer, according to the actual situation. The rotational speed is set to 18.84, 31.4 rad/s, and the revolution speed is 0.7, 1.4 rad/s by the moving module. The specific parameters and computation conditions used in the simulation are shown in Table 1. 3. Results 3.1. Simulation Results 3.1.1. Melt Flow Characteristics of Planetary Mixing and Stirring Process The research of flow characteristics in the planetary mixing semi-solid device will be helpful to understand the way of heat transfer and the change of temperature, and thus provides a theoretical basis for adjusting process parameters and for controlling solidification process. The melt in the planetary mixing semi-solid device has complex stirring-mixed flow characteristics. Figure 3 shows the effect of different revolution and rotation on the shear rate of the melt. It can be seen from Figure 3(b1,b2) that rotation mainly produces a large shear rate on the melt near the stirring shaft, the melt away from the stirring shaft is weakly sheared, and the influence range of rotation on the melt becomes larger with the increase of stirring speed. In Figure 3(c1,c2), the stirring shaft rotates around the middle part of the crucible at a small speed, and the melt on the side of the stirring shaft close to the crucible will be fully sheared and stirred. The melt on the side of the stirring shaft away from the crucible will be sheared weakly and increasing the rotation speed of the stirring shaft cannot change this situation. When the revolution speed of the stirring shaft is increased in Figure 3(c3), the stirring shaft can rotate around the crucible faster, and the melt on the side of the stirring shaft away from the crucible also has a better shear rate. Under the condition of planetary stirring, properly increasing the revolution speed has better shear stirring effect on the whole melt than increasing the rotation speed, but the revolution speed should not be too high to avoid serious turbulent entrainment. 3.1.2. Variation of Melt Temperature Field in Planetary Mixing Stirring Process The melt has sufficient convection in three dimensions, and the temperature field of the melt in the device will be affected enormously. As shown in Figure 4(b1,b2), under the condition of simple rotation, the melt close to the core of the stirring shaft first exchanges heat with the stirring shaft to cool down. With the increase of stirring speed, the melt cooling speed accelerates. With the extension of stirring time, although the total cooling degree of the two is not much different, it is clearly appropriate to increase the stirring speed, and the temperature field changes faster and more uniform. Under the condition of rotation combined with slow revolution in Figure 4(c1), due to the dual cooling of the mixing shaft and the cylinder at the beginning, the cooling speed of the melt between the mixing shaft and thae cylinder exceeds the degree of simple rotation, which can obtain a very large undercooling in a short time. This occurs while the melt far away from the mixing shaft and the cylinder cools slowly, and there is a very large temperature difference between them. With the increase in rotation speed, the shear flow of melt near the stirring shaft is driven, and the temperature difference between different positions is improved, as shown in Figure 4(c3). When the revolution speed is increased, the whole melt can be driven to high-efficiency heat exchange in a short time, the melt can quickly reach a uniform supercooling state, and the temperature difference of the melt is reduced to the greatest extent, which is conducive to the faster and uniform nucleation and uniform growth of the whole melt. This occurs especially to reduce the hanging of material on the mixing shaft due to the formation of the solidified shell near the mixing shaft due to local supercooling. This is very important for the stable and continuous preparation of semi-solid slurry. 3.1.3. Variation of Melt Temperature Difference with Time under Planetary Stirring Process When the stirring shaft rotates only in the middle of the melt, the melt heat transfer in the middle of the stirring shaft and the wall lag behind. Therefore, we analyze the melt temperature in this region and the maximum temperature difference of the whole melt. Figure 5 is the curve of melt temperature in the middle of the stirring shaft and vessel wall with different stirring process parameters with time under simulated conditions. Curve A is the temperature change curve of the stirring shaft inserted into the alloy melt without stirring. The melt temperature at the middle position cools slowly. After 8–10 s, its temperature remains at 628 °C and above the liquidus. When the stirring shaft rotates, the melt cooling speed is faster than that without stirring. After 7 s, the temperature stabilizes at a lower 619 °C. When the rotating stirring speed is increased, the melt cooling speed is further accelerated. The hybrid planetary stirring is an efficient stirring method. Even if the slow revolution is superimposed, the melt at the middle position can be stirred and convected to bring greater cooling capacity. The melt in the middle position reaches a lower temperature of 608 °C at 7 s. Under the condition of increasing the revolution speed, the huge temperature fluctuation at the middle melt during the heat exchange process is observed in Figure 5(c3). It shows that the planetary stirring is efficient and the heat transfer is sufficient, which is more conducive to obtain a uniform temperature field, which is unfavorable to dendrite production. Figure 6 is the time variation curve of the maximum temperature difference of the whole melt under simulated different stirring process conditions, in order to characterize the temperature variation process of alloy solution. The temperature difference increases first and then decreases with time under six different process parameters. The temperature difference of the alloy solution increases fastest in time without stirring, and the maximum temperature difference can reach 50 °C. Compared with uniaxial slow and fast rotation, the latter is slower than the former in the heating stage, and both of them are slower than that without stirring. In the cooling stage, the decline rate of the latter is slightly higher than that of the former. The maximum temperature difference decreases with the increase of rotation and revolution speed, and the change trend of cooling speed is the same. Under the same conditions, it takes the shortest time for the melt to reach a lower temperature difference under the condition of the rapid revolution and compound. In contrast, slightly increasing the revolution speed is more effective in reducing the maximum temperature difference of the whole melt than increasing the rotation speed. 3.2. Microstructure and Mechanical Properties 3.2.1. Microstructure of A356 Aluminum Alloy under Different Stirring Conditions Figure 7 shows the comparison of micromorphology of A356 aluminum alloy semi-solid under different stirring processes. Figure 7a shows the microstructure of A356 aluminum alloy ordinary casting. It can be seen that the structure is a large dendritic structure, which is disorderly and unevenly distributed. Figure 7(b1) shows the semi-solid structure of aluminum alloy prepared by traditional uniaxial rotation. It can be observed that the dendritic structure begins to break and the rose-like grains increase instead. The number of nearly spherical grains in the semi-solid slurry structure obtained in Figure 7(b2) began to increase, but the rosette grains decreased. In Figure 7(c1), the size of the primary solid phase decreases and the number of near spherulites increases. In Figure 7(c2), the grain contour becomes more obvious, and the proportion of spherical grains increases significantly. In Figure 7(c3), the grain shape tends to be round, while the grain size tends to be uniform. The grain size of A356 semi-solid as cast is about 168 μm. At the same time, the shape factor is 0.46. After the semi-solid was prepared by uniaxial slow rotation and rapid rotation, the grain size gradually decreased to 126 μm. At the same time, the shape factor gradually increased to 0.72. After the semi-solid was prepared by using Figure 7(c1–c3) composite rotation and revolution methods. The grain size gradually decreased to 119 μm again. At the same time, the shape factor gradually increased to 0.8 again. 3.2.2. Mechanical Properties of A356 Semi-Solid under Different Stirring Processes Figure 8 shows the mechanical properties of A356 semi-solid under different stirring processes. Under the condition of uniaxial rotation, increasing the stirring speed from 300 r/min to 500 r/min increased the tensile strength by 18.8 MPa and the elongation by 2.5%. The performance is improved by 7 MPa when a small revolution speed is superimposed compared with the single shaft rotation of 300 r/min. When the rotating speed of the mixing shaft is 500 r/min, the composite mixing is 8.2 MPa higher than that of single shaft rotation. Under the condition of compound rotation and revolution, increasing the rotation speed increased the tensile strength by 17.6 MPa and the elongation by 1%. Increasing the revolution speed, the tensile strength of the test bar was increased by 19.9 MPa. It should be noted that the elongation at stirring speed of 300 r/min + 0.7 rad/s is lower than that at 300 r/min alone. The possible reason is that the stirring shaft is too close to the crucible wall, and the slurry between the crucible wall and the stirring shaft cools faster than that during central stirring, while too slow revolution speed does not have much effect on the uniform heat transfer of the melt, as shown in Figure 4c. There may also be a reaction, resulting in a reduction in elongation. However, with the increase of stirring speed, this problem has been improved. Therefore, under planetary stirring conditions, appropriately increasing the stirring shaft speed or increasing the revolution speed can improve the tensile properties of aluminum alloy, increasing the revolution speed more effectively. 3.2.3. Microstructure of Al-Si Alloy of Planetary Stirring Semi-Solid Die Casting Figure 9 shows the microstructure of Al-Si alloy communication parts cast by planetary stirring semi-solid technology. We combine planetary stirring semi-solid technology with die casting to trial produce semi-solid die castings. Figure 9a is a diagram of actual communication cooling shell part. The parameters of the process are the initial melt temperature of 670 °C, rotational speed of 300 r/min, and revolution speed of 1.4 rad/s. The structure at the position of the heat sink of the part is cut for polishing and corrosion, and then analyzed by metallography and scanning electron microscope. As can be seen from Figure 9b,c, the microstructure of the semi-solid die castings is mainly composed of rosiness and nearly spherical primary α-Al particles, and the non-dendritic primary particles are uniformly dispersed in the liquid matrix. The particles size is about 20 μm, which is smaller than normal solidification. Because the quenching effect of the die cavity on the generation of new particles is obvious with significant increases in the number of the particles. The melt at the blades takes a shorter solidification time due to the thinner thickness and the morphology of particles is relatively rounder than the particles in other areas. In addition to the small and rounded primary grains (α1-Al), the average size of the secondary aluminum phase (α2-Al) of semi-solid slurry is decreased to 5~10 μm and the number is increased significantly. 4. Discussion 4.1. Analysis of Melt Temperature Field under Planetary Stirring Process Based on the previous numerical simulation and experimental results, the mechanism between melt temperature change and grain nucleation and growth during composite stirring pulping is analyzed and discussed. Composite stirring superimposes revolution on the basis of traditional uniaxial spin. Compared with traditional uniaxial stirring, composite stirring drives the flow range of melt larger and has higher efficiency. The faster the rotating speed of the stirring shaft or the faster the revolution speed of the stirring shaft, the faster the cooling of the alloy liquid, and the shorter the time required for the alloy liquid to reach the minimum temperature. The higher the revolution speed of the stirring shaft, the higher the convection intensity of the alloy melt driven by the stirring shaft, and the faster the convective heat exchange between the melt with higher temperature and the melt with lower temperature. In the case (c3 in Figure 4), the temperature in the middle of the alloy liquid changes greatly in a short time, and the temperature decreases first and then increases. The temperature of the alloy liquid appears at a peak. Under the same conditions, the temperature difference of the alloy liquid without stirring is the largest, which makes the grain growth direction most obvious and easy to form dendrites. Under the traditional single-shaft slow stirring, the stirring shaft drives a small part of the surrounding liquid to rotate, which can reduce the temperature of the alloy liquid near the stirring shaft. The heat of the alloy liquid diffuses slowly outward from the stirring shaft, resulting in a smaller temperature difference between the alloy liquid at the same time, better cooling effect, and less dendritic crystals than that without stirring. The high-speed rotation of the stirring shaft of the traditional uniaxial rapid stirring drives the convection and exchange of the surrounding alloy liquid. Compared with the first two methods, the temperature difference is the smallest at the same time in the falling stage, which is more conducive to the formation of round-shaped grains. Composite stirring has one revolution more than uniaxial stirring, which expands the volume of alloy liquid involved in the convective exchange and makes the solution reduce to the minimum temperature more quickly. In this way, the overall temperature difference of alloy liquid is also smaller. The directionality of the grain solidification process is smaller. These can reduce the possibility of dendrite formation. The composite fast stirring reduces to the lowest temperature faster than the composite slow stirring, and its convection intensity is stronger than the latter. The decrease of stirring temperature difference with time is the fastest, indicating that the convective exchange between alloy liquids is the most effective. From the temperature field and temperature difference curves of alloy liquid under several stirring modes, the melt has greater overall undercooling under composite stirring. With the increase of the rotation and revolution speed of the stirring shaft, the lower the overall temperature of the alloy liquid, the smaller the temperature difference and the greater the undercooling. Therefore, heterogeneous nucleation requires smaller nucleation work and critical nucleation radius, which is more conducive to nucleation. 4.2. Grain Nucleation and Growth Mechanism under Planetary Stirring The classical theory of semi-solid, near-spherical grain formation is dendrite fragmentation spheroidization. According to the different action modes of an external field, the mechanisms can be divided into dendrite arm mechanical breaking, fusing, and dendrite arm bending induced grain boundary liquid infiltration [21]. Although there are differences, the main viewpoints are that the near-spherical grains are spheroidized after the dendrite formed in the supercooled solution is broken. In recent years, many scholars have found the direct growth of near-spherical crystals in the melt. Li Tao et al. [22] observed the direct formation process of near-spherical grain structure of Sn-15%Pb alloy during solidification. A. Das and Z. Fan et al. [23] studied the double helix stirring semi-solid technology and believed that the penetration of metal liquid into the dendrite arms increased the growth rate of the root and the side of the primary grain dendrite arms, so as to grow into a rose shape. Pilling [24] analyzed and studied the effect of shear rate on grain dissociation through the stress calculation model of the dendrite wall. Zhou Bing [25] and Zhang Jingxin [26] respectively studied the influence and control factors of a uniform temperature field formed by melt convection caused by mechanical stirring and electromagnetic stirring on nucleation and growth. More and more scholars accept the theory of directly controlling melt nucleation and growth into near-spherical grains under stirring treatment. Compared with the traditional stirring, the melt is disturbed at a smaller shear rate in the container due to the existence of inertia. Compared with the traditional stirring, the melt is disturbed at a smaller shear rate in the container due to the existence of inertia. Planetary stirring has two behaviors of rotation and revolution, which has more efficient stirring and mixing effect and can avoid laminar flow caused by simple rotation, which is very conducive to the uniformity of the temperature field and promotes the overall nucleation. According to classical solidification theory, for homogeneous nucleation and heterogeneous nucleation, the critical nucleation radius is inversely proportional to the degree of supercooling. The critical nucleation energy varies inversely as the square of the supercooling degree. Compared with the traditional uniaxial rotation, the melt under the condition of planetary stirring can drop to a lower semi-solid temperature range in a shorter time, reach a lower undercooling faster, reduce the critical nucleation energy and critical nucleation size, and nucleate more easily. At the same time, it will also increase the scouring effect on nucleated grains and increase the number of free grains. The grain dissociation process is shown in Figure 10. Compared with traditional uniaxial stirring, the melt under composite stirring can achieve a more uniform temperature field in a shorter time, reduce the temperature difference and undercooling gradient of the whole melt, and the spin and collision of grains under stirring conditions will also change their local temperature field environment, avoiding the preferred directional growth of grains to the greatest extent. The semi-solid slurry with a large number of fine, uniform, and round grains is obtained. Rheological die casting was carried out by combining planetary stirring semi-solid technology with a die casting machine. Compared with superheated melt, semi-solid slurry has more primary grains and lower melt temperature. Lower melt temperature leads to larger supercooling degree and smaller solidification shrinkage. In the subsequent filling and solidification processes inside the mold, the time used to primary grain growth and secondary grain generation is shorter. Due to the large supercooling degree, the secondary nucleation has a great driving force, resulting in a large number of secondary grains. The grain size and morphology of α1-Al/α2-Al in microstructure was significantly refined as shown in Figure 9. Conversely, the semi-solid slurry has unique performance of high viscosity and good fluidity. The high viscosity of the slurry can reduce the turbulence and splash in the filling process. Less entrapped gas content and smaller solidification shrinkage can effectively reduce the porosity of castings. The castings prepared with semi-solid slurry have better comprehensive performance. 5. Conclusions Compared with the common cast dendrite structure, uniaxial rotation improves the microstructure and properties of A356 aluminum alloy, and its average grain size ranges from 145 μm increased to 126 μm. The shape factor is increased from 0.59 to 0.72, the tensile strength is increased from 248 MPa to 267 MPa. Under the condition of planetary stirring, with the increase of stirring speed, the size of the primary solid phase is about 132 μm reduced to 119 μm. The shape factor increased from 0.65 to 0.8, the tensile strength increased from 256 MPa to 276.2 MPa, and the elongation increased to 6%. The semi-solid slurry prepared by planetary stirring is convenient to be combined with die casting, and the grain size of Al-Si alloy is only 20 μm. The increase of stirring speed is conducive to improve the convective heat transfer of alloy melt, reduce the overall temperature of melt, increase the undercooling, and increase the number of nucleation. Planetary stirring is a complete stirring of the whole melt without a dead angle, which avoids the inertial laminar flow during uniaxial stirring, has higher mixing heat exchange efficiency, can reduce the melt to a greater undercooling faster, and promotes the overall uniform nucleation. At the same time, the convective shear effect of composite stirring is also conducive to grain dissociation and increases the number of nucleation. Conversely, by increasing the nucleation rate, planetary stirring can obtain smaller temperature differences faster, reduce undercooling gradient, avoid preferential growth of melt, and is conducive to uniform growth. Acknowledgments The authors would like to appreciate Beijing General Research Institute for Nonferrous Metals for providing software support of Flow-3D. Author Contributions Investigation—C.X. and Z.W.; test data acquisition—Z.Q.; data curation—K.C.; writing—B.Z. All authors have read and agreed to the published version of the manuscript. Funding The authors gratefully acknowledge the financial support by Shanghai Sailing Program (17YF1407100). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 (a) Eccentric part of the planetary stirring structure and (b) the drawing of tensile sample mold. Figure 2 Three-dimensional model, meshing, and mixing operation diagram for simulation. Figure 3 Melt strain rate of planetary mixing and stirring process. Figure 4 Variation of melt temperature field in planetary mixing stirring process. Figure 5 Temperature variation curve of melt with time under different stirring conditions. Figure 6 Curve of simulated temperature difference of molten alloy with time. Figure 7 Semi-solid microstructure of A356 alloy under different stirring conditions: (a) No stirring; (b1) rotation 300 r/min; (b2): rotation 500 r/min; (c1): rotation 300 r/min revolution 0.7 rad/s; (c2): rotation 500 r/min revolution 0.7 rad/s; (c3): rotation 300 r/min revolution 1.4 rad/s. Figure 8 Mechanical properties of A356 semi-solid alloy under different stirring processes. Figure 9 The microstructure of Al-Si alloy communication parts by planetary stirring semi-solid technology. (a) Communication cooling shell part, (b) Metallographic diagram, (c) SEM diagram. Figure 10 Diagram of crystal dissociation of different stirring process. materials-15-03009-t001_Table 1 Table 1 Process and initial conditions of the simulation. Input Value Unit Initial melt temperature 660 °C Initial barrel temperature 560 °C Initial agitating shaft temperature 25 °C Agitating shaft rotation speed 18.84, 31.4 rad/s Agitating shaft revolution speed 0.7, 1.4 rad/s Thermal conductivity of agitating barrel 23.4 W/m·K Thermal conductivity of agitating shaft 23.4 W/m·K Heat transfer to melt 7 kW/m2·K Heat transfer to air 5 kW/m2·K Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Hu Y. He B.L. Yan H. Rheological behavior of semi-solid Mg2Si/AM60 magnesium matrix composites at steady state Trans. Nonferrous Met. Soc. China 2010 20 s883 s887 10.1016/S1003-6326(10)60600-0 2. Zhao Z.D. Mao W.M. Preparation of semi-solid AlSi7Mg alloy slurry with big capability Rare Met. 2010 29 642 645 3. Young K.P. Method of Producing Shaped Metal Parts U.S. Patent 4687042 18 August 1987 4. Wu S.S. Lin C. Lv S.L. Sha M. Research progress on microstructure evolution of semi-solid aluminum alloys in ultrasonic field and their rheocasting China Foundry 2014 11 258 267 5. Wannasin J. Martinez R.A. Grain refinement of an aluminum alloy by introducing gas bubbles during solidification Scr. Mater. 2006 55 115 118 10.1016/j.scriptamat.2006.04.003 6. Wannasin J. Research and development of gas induced semi-solid process for industrial applications Trans. Nonferrous Met. Soc. China 2010 20 1010 1015 10.1016/S1003-6326(10)60622-X 7. Haga T. Suzuki S. Casting of Aluminum Alloy Ingots for Thixoforming Using a Cooling Slope J. Mater. Process. Technol. 2001 118 169 172 10.1016/S0924-0136(01)00888-3 8. Yiicel B. Cooling Slope Casting and Thixoforming of Hypereutectic A390 Alloy J. Mater. Process. Technol. 2008 207 200 203 9. Guo H.M. Yang X.J. Hu B. Zhu G. Rheo-diecasting process for semi-solid aluminum alloys J. Wuhan Univ. Technol. Sci. Ed. 2007 22 590 595 10.1007/s11595-006-4590-0 10. Guo H.M. Yang X.J. 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RHPDC Process with Forced Convection Mixing Device for Automotive Part of A380 Aluminum Alloy Materials 2014 7 3084 3105 10.3390/ma7043084 28788608 15. Brucato A. Ciofalo M. Grisafi F. Giorgio G. Numerical prediction of flow fields in baffled stirred vessels: A comparison of alternative modelling approaches Chem. Eng. Sci. 1998 53 3653 3684 10.1016/S0009-2509(98)00149-3 16. Luo J. Issa R. Gosman A. Prediction of impeller induced flows in mixing vessels using multiple frames of reference Institution of Chemical Engineers Symposium Series EFCE Cambridge, UK 1994 549 17. Landau L.D. Lifshitz E.M. Fluid Mechanics: Volume 6, Theoretical Physics Course Nauka Moscow, Russia 1986 Elsevier Amsterdam, The Netherlands 1987 18. Sreenivas J. Computational Fluid Dynamics for Engineers and Scientists Springer Dordrecht, The Netherlands 2018 19. Lomax H. Pulliam T.H. Zingg D.W. Fundamentals of Computational Fluid Dynamics Springer Berlin/Heidelberg, Germany 2001 20. Kim N. Yoon J.H. Li D.C. Oh S.I. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094549 ijms-23-04549 Review Comparative Efficacy and Safety of P2Y12 Inhibitor Monotherapy and Dual Antiplatelet Therapy in Patients with and without Diabetes Mellitus Undergoing Percutaneous Coronary Intervention https://orcid.org/0000-0002-1994-6205 Feng Wen-Han 1 Chang Yong-Chieh 2 https://orcid.org/0000-0003-2232-9037 Lin Yi-Hsiung 34 Chen Hsiao-Ling 2 https://orcid.org/0000-0003-3353-7501 Chang Hsiu-Mei 2 https://orcid.org/0000-0003-0275-5442 Chu Chih-Sheng 1* Russo Isabella Academic Editor 1 Department of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80145, Taiwan; hans0426@gmail.com 2 Department of Pharmacy, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung 80145, Taiwan; lovemjayo@gmail.com (Y.-C.C.); hlchen369@gmail.com (H.-L.C.); 880504@kmhk.org.tw (H.-M.C.) 3 Department of Internal Medicine, Division of Cardiology, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan; caminolin@gmail.com 4 Center for Lipid Biosciences, Kaohsiung Medical University Hospital, Kaohsiung 80756, Taiwan * Correspondence: chucs@kmu.edu.tw; Tel.: +886-7-291-1101 (ext. 8074); Fax: +886-7-323-4845 20 4 2022 5 2022 23 9 454931 3 2022 17 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Increasing evidence has shown P2Y12 inhibitor monotherapy is a feasible alternative treatment for patients after percutaneous coronary intervention (PCI) with stent implantation in the modern era. However, patients with diabetes mellitus (DM) have a higher risk of ischemic events and more complex coronary artery disease. The purpose of this study is to evaluate the efficacy and safety of this novel approach among patients with DM and those without DM. We conducted a systematic review and meta-analysis of randomized controlled trials that compared P2Y12 inhibitor monotherapy with 12 months of dual antiplatelet therapy (DAPT) in patients who underwent PCI with stent implantation. PubMed, Embase, Cochrane library database, ClinicalTrials.gov, and three other websites were searched for our data from the earliest report to January 2022. The primary efficacy outcome was major adverse cardiovascular and cerebrovascular events (MACCE): a composite of all-cause mortality, myocardial infarction, stent thrombosis, and stroke. The primary safety outcome was major or minor bleeding events. The secondary endpoint was net adverse clinical events (NACE) which are defined as a composite of major bleeding and adverse cardiac and cerebrovascular events. A total of four randomized controlled trials with 29,136 patients were included in our meta-analysis. The quantitative analysis showed a significant reduction in major or minor bleeding events in patients treated with P2Y12 inhibitor monotherapy compared to standard DAPT (OR: 0.68, 95% CI: 0.46–0.99, p = 0.04) without increasing the risk of MACCE (OR: 0.96, 95% CI: 0.85–1.09, p = 0.50). The number of NACE was significantly lower in the patients treated with P2Y12 inhibitor monotherapy (OR: 0.84, 95% CI: 0.72–0.97, p = 0.019). In DM patients, P2Y12 inhibitor monotherapy was associated with a lower risk of MACCE compared to standard DAPT (OR: 0.85, 95% CI: 0.74–0.98, p = 0.02). Furthermore, P2Y12 inhibitor monotherapy was accompanied by a favorable reduction in major or minor bleeding events (OR: 0.80, 95% CI: 0.64–1.05, p = 0.107). In non-DM patients, P2Y12 inhibitor monotherapy showed a significant reduction in major or minor bleeding events (OR: 0.58, 95% CI: 0.38–0.88, p = 0.01), but without increasing the risk of MACCE (OR: 0.99, 95% CI: 0.82–1.19, p = 0.89). Based on these findings, P2Y12 inhibitor monotherapy could significantly decrease bleeding events without increasing the risk of stent thrombosis or myocardial infarction in the general population. The benefit of reducing bleeding events was much more significant in non-DM patients than in DM patients. Surprisingly, P2Y12 inhibitor monotherapy could lower the risk of MACCE in DM patients. Our study supports that P2Y12 inhibitor monotherapy is a promising alternative choice of medical treatment for patients with DM undergoing PCI with stent implantation in the modern era. P2Y12 inhibitor monotherapy percutaneous coronary intervention (PCI) diabetes mellitus (DM) ==== Body pmc1. Introduction Dual antiplatelet therapy (DAPT) with aspirin plus a P2Y12 inhibitor is the standard treatment for patients undergoing percutaneous coronary intervention (PCI) with stent implantation [1]. Although it is an effective treatment to reduce the risk of ischemic events and stent thrombosis, it increases the risk of bleeding. Newer-generations of drug-eluting stents (DES) have thinner stent struts and better design to lower the risk of stent thrombosis and have more rapid endothelialization. The role of DAPT was challenged by many clinical trials in recent years [2,3,4,5]. Increasing evidence is showing P2Y12 inhibitor monotherapy is a feasible alternative treatment for patients after PCI with stent implantation in the modern era, as it could lower the risk of bleeding complications and still has enough antiplatelet effect to avoid recurrent ischemic events [6]. Diabetes mellitus (DM) and non-DM patients have very much different clinical characteristics. DM patients are associated with a higher risk of ischemic events and usually have more co-morbidities than non-DM patients [5,7,8]. In addition, DM patients usually have more complex coronary artery disease and more stents implanted during PCI than non-DM patients. Globally, the prevalence of DM has increased significantly in the past decade [9]. Therefore, it is important to find the optimal post-PCI therapy for patients with DM. The efficacy and safety of this novel approach among patients with or without diabetes mellitus is uncertain. Although some clinical trials have shown P2Y12 inhibitor monotherapy had favorable outcomes for DM patients [10,11], they were individually underpowered. Therefore, we perform a systematic review and meta-analysis to assess the efficacy and safety of P2Y12 inhibitor monotherapy compared to DAPT in DM and non-DM patients who underwent PCI and stent implantation. 2. Methods 2.1. Data Sources and Study Selection This meta-analysis was conducted following the recommendations of the Preferred Reporting Items for a Systematic review and Meta-analysis of Individual Participant Data (PRISMA-IPD) and the Cochrane Collaboration method. The protocol was registered on PROSPERO (international prospective register of systematic reviews) on 23 February 2022, and is available online (www.crd.york.ac.uk/prospero (accessed on 23 February 2022), CRD42022312669). Starting on the same date, we searched PubMed, Embase, Cochrane library database, ClinicalTrials.gov, and three other websites (www.escardio.org, www.acc.org/cardiosourceplus, www.tctmd.com (accessed on 23 February 2022)) from the earliest record to January 2022. The inclusion criteria of the study were as follows: (1) the study included patients who underwent PCI with stent implantation, (2) the study was a randomized controlled trial comparing P2Y12 inhibitor monotherapy to standard 12-month dual antiplatelet therapy, (3) the study had followed up on patients’ clinical outcomes for at least 12 months after PCI, and (4) the study had reported the primary efficacy and safety outcomes of interest. The search terms used included: “P2Y12 inhibitor monotherapy”, “dual antiplatelet therapy”, “randomized trial”, “percutaneous coronary intervention”, “Outcome”, and “diabetes mellitus”. The exclusion criteria included: (1) non-randomized controlled trial and (2) studies that had not reported the data of patients with DM and non-DM. No language restriction was enforced, and studies not available in full-text were excluded. The detailed search strategies are shown in Tables S1–S3. Multiple reviewers examined all the retrieved articles and data using a predetermined form. The quality of each study was evaluated by the first and second authors (Wen-Han Feng and Yong-Chieh Chang) by using the Cochrane Collaboration tool. Discrepancies between the reviewers were solved by discussions with the corresponding author. 2.2. Data Extraction and Main Outcomes The baseline characteristics of included studies were extracted by the first two authors, and the discrepancy was resolved through negotiation. The primary efficacy outcome was major adverse cardiovascular and cerebrovascular events (MACCE); a composite of all-cause mortality, myocardial infarction, stent thrombosis, and stroke. The primary safety outcome was major or minor bleeding events. The secondary endpoint was net adverse clinical events (NACE); defined as a composite of major bleeding and adverse cardiac and cerebrovascular events. 2.3. Statistical Analysis All data were pooled to calculate the hazard ratios and 95% confidence intervals by using a random-effects model. Between-trial heterogeneity was assessed by using an I2 test, and if the value was >50% it was regarded as having considerable heterogeneity. Potential publication bias was examined via the visual inspection of funnel plots, Egger’s test, and Begg’s test. Statistical significance is defined as a p-value < 0.05. All analyses were performed using Comprehensive Meta-Analysis (CMA) software, version 3 (Biostat, Englewood, NJ, USA). 3. Results 3.1. Search Results and Study Characteristics The results of the literature searches and study selections are shown in Figure 1. A total of 2180 records were identified from PubMed, Embase, Cochrane library database, and three other websites (www.escardio.org, www.acc.org/cardiosourceplus, www.tctmd.com, accessed on 23 February 2022). Of these, 28 full-text articles were reviewed, and 24 of them were excluded due to failure to meet the pre-specified inclusion criteria. The STOP-DAPT 2 trial was excluded because there was no available reported data on patients with or without DM to perform the analysis. Finally, four randomized controlled trials were included in this systematic review and meta-analysis. The main characteristics of included trials are summarized in Table 1. A total of 29,136 patients were available for the primary analysis. There were 8615 patients with diabetes mellitus and 20,507 patients without DM. The ischemic and bleeding events of DM patients and non-DM patients in each trial are summarized in Table 2. 3.2. The Primary Efficacy and Safety Outcomes In overall enrolled patients, the quantitative analysis is demonstrated in Figure 2. There was no increased risk of MACCE in patients treated with P2Y12 inhibitor monotherapy compared to standard 12-month DAPT (OR: 0.96, 95% CI: 0.85–1.09, p = 0.50, I2 = 7%, PHeterogeneity = 0.36), but bleeding events were significantly reduced (OR: 0.68, 95% CI: 0.46–0.99, p = 0.04, I2 = 89%, PHeterogeneity < 0.001). One study that produced heterogeneity was identified via sensitivity analysis, and the heterogeneity was reduced after excluding the results of this trial (I2 = 0%, PHeterogeneity = 0.81). The net adverse clinical events (a composite endpoint of bleeding and ischemic events) were significantly lower in the patients treated with P2Y12 inhibitor monotherapy (OR: 0.84, 95% CI: 0.72–0.97, p = 0.019, I2 = 40%, PHeterogeneity = 0.17). The primary efficacy outcomes (a composite of MACCE) of patients with DM and without DM are shown in Figure 3. In DM patients, P2Y12 inhibitor monotherapy significantly lowered the risk of MACCE compared to standard 12-month DAPT (OR: 0.85, 95% CI: 0.74–0.98, p = 0.02, I2 = 0%, PHeterogeneity = 0.62). In non-DM patients, P2Y12 inhibitor monotherapy had a similar risk of MACCE compared to DAPT (OR: 0.99, 95% CI: 0.82–1.19, p = 0.89, I2 = 27%, PHeterogeneity = 0.25). The primary safety outcomes of patients with DM and without DM are shown in Figure 4. In DM patients, P2Y12 inhibitor monotherapy was associated with a favorable reduction in bleeding events (OR: 0.80, 95% CI: 0.64–1.05, p = 0.107, I2 = 22%, PHeterogeneity = 0.28). In non-DM patients, P2Y12 inhibitor monotherapy showed a great reduction in bleeding events (OR: 0.58, 95% CI: 0.38–0.88, p = 0.01, I2 = 78%, PHeterogeneity = 0.004). 3.3. Quality Assessment and Publication Bias The detailed quality assessment and risk of bias assessment for each study can be found in Table S4. The overall risk of bias in selection, detection, and reporting bias was low. All studies in this meta-analysis were randomized controlled trials, but only TWILIGHT was double-blinded. There was no publication bias in all outcomes. The outcomes of included trials are distributed symmetrically in the funnel plot (Figure S1), and the p-value of the Begg’s and Egger’s tests were more than 0.05 in all outcomes (Table S5). Heterogeneity was low in all outcomes. 4. Discussion Conventionally, it is recommended that DAPT should be continued for at least 6 months (in stable coronary artery disease) or 12 months (in acute coronary syndrome) unless contraindications occur [12]. Early suspension of antiplatelet therapy would increase the risk of recurrent ischemic events and stent thrombosis. This concept was changed because of the advent of safer, newer-generation DES and the awareness of increased bleeding risk caused by prolonged DAPT. Based on these reasons, a new treatment strategy of using a very short period of DAPT followed by a potent P2Y12 inhibitor monotherapy was proposed. The results from this systematic review and meta-analysis of 29136 patients from four randomized controlled trials indicate that the P2Y12 inhibitor monotherapy could significantly lower the risk of bleeding complications without increasing the risk of ischemic events compared with standard DAPT in patients without DM. Surprisingly, P2Y12 inhibitor monotherapy significantly reduced the risk of ischemic events in patients with DM, but not the risk of bleeding complications. Currently, there is no clear biological rationale to explain this clinical finding. However, ticagrelor seemed to have better clinical effects when combined with a lower dose of aspirin in a PLATO study [13]. One possible hypothesis is that aspirin reduces not only the release of thromboxane A2, but also the release of prostacyclin [14]. The therapeutic effect of ticagrelor may be attenuated when endogenous prostacyclin production is inhibited [15,16]. It is possible that ticagrelor works better in monotherapy than in combination therapy with aspirin. Further investigations are needed to elucidate the complex interactions between these two drugs. These findings challenge contemporary practice guideline recommendations for DAPT as the standard treatment for post-PCI care. Other meta-analyses have been published on P2Y12 inhibitor monotherapy after PCI [17,18,19]. However, our meta-analysis is unique in focusing on DM patients. This distinction is important given the growing prevalence of diabetic patients and the different prognostic nature of these patients. The TWILIGHT DM substudy was the very first randomized study to show that P2Y12 inhibitor monotherapy could have better outcomes compared to standard DAPT [10]. However, the case number was relatively small. Our study is the first meta-analysis to show a decrease in the risk of ischemic events with P2Y12 inhibitor monotherapy in patients with DM compared to standard DAPT. One of the salient findings in our study is that patients with DM indeed had a significantly higher risk of ischemic events after PCI in the modern era. The rate of ischemic events in DM and non-DM patients treated with standard DAPT was 18.7% vs. 12.8% in the GLOBAL LEADERS trial, 3.8% vs. 1.7% in the SMART-CHOICE trial, 5.9% vs. 2.8% in the TWILIGHT trial, and 5.1% vs. 2.7% in the TICO trial. All four clinical trials had the same findings. However, the risk of bleeding complications was only slightly higher in DM patients than in non-DM patients. There are several possible explanations for these findings. First, platelet reactivity was higher in DM patients than in non-DM patients [20]. Second, the turnover rate and the number of reticulated platelets were both higher in DM patients, resulting in more endothelial cell adhesion [21]. Third, DM patients tend to be more resistant to antiplatelet agents [22]. Based on these factors, we could assume that the bleeding risk of DM patients treated with antiplatelet agents would be similar or even lower than non-DM patients. Therefore, DM patients should be treated with a different antiplatelet regimen than non-DM patients [23]. DM also leads to endothelial dysfunction (one of the main pathophysiologic mechanisms associated with cardiovascular disease) and is described as an independent determinant of ischemic heart disease and acute coronary syndrome [24]. Several biochemical pathways have been described to demonstrate the association between endothelial dysfunction and platelet activation, such as nitric oxide (NO) and prostacyclin (PGI2) [25]. NO, a well-known atheroprotective and vasodilating substance, may also attenuate platelet activation. Blood vessels of patients with DM have diminished NO production, enhanced NO degradation, and decreased sensitivity to NO. PGI2 is another important regulator produced by endothelial cells that inhibits platelet activation through binding to the prostacyclin receptor on platelets. DM is associated with lower levels of prostacyclin synthase in subcutaneous arteries and possibly leads to impaired formation of PGI2 [26]. Together, endothelial dysfunction and platelet hyperactivity make DM patients much more susceptible to cardiovascular disease than non-DM patients [27]. The success of P2Y12 inhibitor monotherapy was not a coincidence. Many in vitro and ex vivo investigations have shown that aspirin provided very limited additional platelet inhibition and anti-thrombotic effect to a potent P2Y12 inhibitor [28,29,30]. Of note, most of the patients treated with P2Y12 inhibitor monotherapy were using ticagrelor. Ticagrelor is a more potent P2Y12 inhibitor than clopidogrel and may improve endothelial function and blood viscosity [31,32]. In addition, clopidogrel is a prodrug that requires metabolism to transform into an active form. DM patients have a greater prevalence of being unresponsive to clopidogrel than non-DM patients [33]. Impaired drug metabolism, metabolic disorders, and competition for CYP3A4 with other drugs (e.g., statins) are possible mechanisms leading to a lower concentration of clopidogrel’s active metabolite and insufficient antiplatelet effects [34,35]. Therefore, it is possible the benefit of P2Y12 inhibitor monotherapy in patients who underwent PCI belongs to ticagrelor alone. In our previous real-world observational study, ticagrelor monotherapy resulted in substantially lower cardiovascular risk compared to clopidogrel monotherapy in patients with acute coronary syndrome (ACS) undergoing PCI [36]. 5. Limitations There are several limitations in our study. First, most patients enrolled in these included trials were implanted with newer-generation DESs. Although it is widely used in our daily practice, our findings may not apply to first-generation DESs or bare-metal stents. Second, there were some differences in baseline characteristics and the indications for PCI in included trials. Moreover, only one trial was double-blinded, and others were open-label. Third, there are heterogeneities in the definition of bleeding complications. Although BARC (Bleeding Academic Research Consortium) 3 or 5 is similar to TIMI (Thrombolysis in Myocardial Infarction) minor or major bleeding, they are not identical. Fourth, most of the patients treated with P2Y12 inhibitor monotherapy were using ticagrelor. The outcomes may not apply to other P2Y12 inhibitors. 6. Conclusions Based on this systematic review and meta-analysis, P2Y12 inhibitor monotherapy followed by a short duration of dual antiplatelet therapy could significantly decrease the risk of bleeding events without increasing the risk of stent thrombosis or myocardial infarction in the general population. The benefit of reducing bleeding events was much more significant in non-DM patients than in DM patients. Surprisingly, P2Y12 inhibitor monotherapy could lower the risk of MACCE in DM patients but not in non-DM patients. These findings support that P2Y12 inhibitor monotherapy is a feasible, alternative choice of medical treatment for patients with or without diabetes mellitus undergoing percutaneous intervention with stent implantation in the modern era. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23094549/s1. Click here for additional data file. Author Contributions W.-H.F.: study design, data collection, data analysis, and manuscript; Y.-C.C.: data collection, data analysis, and validation; Y.-H.L.: data collection and manuscript editing; H.-L.C.: data collection and data analysis; H.-M.C.: manuscript reviewing and editing; C.-S.C.: supervision and scientific revision of the manuscript. All authors have read and agreed to the published version of the manuscript. Funding The research was funded by Kaohsiung Municipal Ta-Tung Hospital (kmtth-109-006) and Regeneration Medicine and Cell Therapy Research Center in Kaohsiung Medical University (KMU-TC111A02-0 and KMU-TC111A02-1). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Data available in a publicly accessible repository. The data presented in this study are openly available in PubMed, Embase, Cochrane library database, ClinicalTrials.gov (accessed on 23 February 2022), and websites including www.escardio.org, www.acc.org/cardiosourceplus, www.tctmd.com (accessed on 23 February 2022). Conflicts of Interest The authors declare no conflict of interest. Figure 1 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram for the searching and identification of included studies. Figure 2 The primary efficacy and safety of P2Y12 inhibitor monotherapy in patients undergoing PCI compared to 12-month DAPT. (A) MACCE; (B) bleeding events; (C) NACE. Figure 3 The primary efficacy outcomes (a composite of major adverse cardiovascular and cerebrovascular events) of P2Y12 inhibitor monotherapy compared to 12-month DAPT. (A) Patients with DM; (B) patients without DM. Figure 4 The primary safety outcomes (major or minor bleeding events) of P2Y12 inhibitor monotherapy compared to 12-month DAPT. (A) Patients with DM; (B) patients without DM. ijms-23-04549-t001_Table 1 Table 1 Clinical characteristics and outcomes of included randomized trials. Clinical Trials Global Leaders [2] Global Leaders [2] Smart-Choice [3] Smart- Choice [3] Twilight [4] Twilight [4] Tico [5] Tico [5] Year 2018 2018 2019 2019 2019 2019 2020 2020 Study population PCI PCI PCI PCI High-risk, PCI High-risk, PCI ACS, PCI ACS, PCI Arm DAPT 1 m, then mono DAPT DAPT 3 m, then mono DAPT DAPT 3 m, then mono DAPT DAPT 3 m, then mono DAPT P2Y12 inhibitor Ticagrelor Ticagrelor or clopidogrel Clopidogrel (77%) Clopidogrel (77%) Ticagrelor Ticagrelor Ticagrelor Ticagrelor Patients number 7980 7988 1495 1498 3555 3564 1527 1529 Age (mean) 64.5 64.6 64.6 64.4 65.2 65.1 61 61 ACS (%) 3750 (47.0) 3737 (46.8) 870 (58.2) 873 (58.2) 2273 (63.9) 2341 (65.7) 1527 (100) 1529 (100) STEMI (%) 1062 (13.3) 1030 (12.9) 164 (11.0) 150 (10.0) Excluded Excluded 546 (35.7) 557 (36.4) NSTEMI (%) 1684 (21.1) 1689 (21.1) 239 (16.0) 230 (15.4) 1024 (28.8) 1096 (30.8) 539 (35.3) 488 (31.9) DM (%) 2049 (25.7) 1989 (24.9) 570 (38.2) 552 (36.8) 1319 (37.1) 1301 (36.5) 418 (27.4) 417 (27.2) Follow-up time 24 m 24 m 12 m 12 m 12 m 12 m 12 m 12 m Primary endpoint Death, new Q-wave MI Death, new Q-wave MI death, MI, stroke death, MI, stroke Bleeding Bleeding NACE NACE MACCE (%) 407 (5.10) 421 (5.27) 42 (2.9) 36 (2.5) 135 (3.9) 137 (3.9) 35 (2.3) 51 (3.4) All-cause death at 12 m (%) 108 (1.35) 131 (1.64) 21 (1.4) 18 (1.2) 34 (1.0) 45 (1.3) 16 (1.1) 23 (1.5) CV death at 12 m (%) N/A N/A 11 (0.8) 13 (0.9) 26 (0.8) 37 (1.1) 7 (0.5) 12 (0.8) MI at 12 m (%) 179 (2.24) 158 (1.98) 11 (0.8) 17 (1.2) 95 (2.7) 95 (2.7) 6 (0.4) 11 (0.7) Stroke (%) 52 (0.65) 49 (0.61) 11 (0.8) 5 (0.3) 16 (0.5) 8 (0.2) 8 (0.5) 11 (0.7) Stent thrombosis ‡ 53 (0.66) 41 (0.51) 3 (0.2) 2 (0.1) 14 (0.4) 19 (0.6) 6 (0.4) 4 (0.3) Major or minor bleeding # 529 (6.63) 532 (6.66) 28 (2.0) 49 (3.4) 141 (4.0) 250 (7.1) 53 (3.6) 83 (5.5) Major bleeding # 117 (1.47) 136 (1.70) 12 (0.8) 14 (1.0) 34 (1.0) 69 (2.0) 25 (1.7) 45 (3.0) NACE 616 (7.72) 653 (8.17) 65 (4.5) 81 (5.6) 163 (4.6) 196 (5.5) 59 (3.9) 89 (5.9) ‡ Stent thrombosis was defined as definite or probable thrombosis, according to the Academic Research Consortium. # The bleeding outcome was defined according to TIMI criteria in TICO study, and BARC criteria in GLOBAL LEADERS, SMART-CHOICE, and TWILIGHT study. Major bleeding was defined as BARC type 3-5 bleeding, and major or minor bleeding was BARC type 2-5 bleeding. Values are n(%) unless otherwise indicated. ACS: acute coronary syndrome; BARC: Bleeding Academic Research Consortium; CV: cardiovascular; DAPT: dual antiplatelet therapy; DM: diabetes mellitus; m: month; MACCE: major adverse cardiovascular and cerebrovascular events; MI: myocardial infarction; NACE: net adverse clinical events; N/A: not applicable; NSTEMI: non-ST-elevation myocardial infarction; PCI: percutaneous coronary intervention; STEMI: ST-elevation myocardial infarction; TIMI, thrombolysis in myocardial infarction. ijms-23-04549-t002_Table 2 Table 2 The efficacy and safety outcomes of P2Y12 inhibitor monotherapy in patients with and without diabetes mellitus of the included randomized studies. DM Patients Non-DM Patients P2Y12i Monotherapy DAPT Hazard Ratio (95% CI) p-Value P2Y12i Monotherapy DAPT Hazard Ratio (95% CI) p-Value GLOBAL LEADERS n = 4038 n = 11,919 MACE 338 (16.7) 369 (18.7) 0.87 (0.74–1.02) 0.09 711 (12.2) 761 (12.8) 0.94 (0.84–1.05) 0.25 Bleeding 52 (2.6) 47 (2.4) 1.08 (0.72–1.60) 0.72 111 (1.9) 122 (2.1) 0.92 (0.71–1.19) 0.52 SMART-CHOICE n = 1122 n = 1868 MACE 23 (4.1) 20 (3.8) 1.12 (0.61–2.06) 0.72 19 (2.1) 16 (1.7) 1.22 (0.63–2.29) 0.56 Bleeding 14 (2.6) 16 (3.0) 0.84 (0.41–1.75) 0.65 14 (1.6) 33 (3.6) 0.43 (0.23–0.80) 0.01 TWILIGHT n = 2620 n = 4499 MACE 59 (4.6) 75 (5.9) 0.76 (0.54–1.08) 0.13 76 (3.5) 62 (2.8) 1.24 (0.88–1.75) 0.21 Bleeding 58 (4.5) 86 (6.7) 0.65 (0.46–0.91) 0.01 83 (3.8) 164 (7.3) 0.50 (0.39–0.66) <0.01 TICO n = 835 n = 2221 MACE 14 (3.4) 21 (5.1) 0.65 (0.33–1.30) 0.23 21 (1.9) 30 (2.7) 0.70 (0.40–1.22) 0.21 Bleeding 12 (2.9) 18 (4.5) 0.66 (0.31–1.38) 0.26 13 (1.2) 27 (2.4) 0.48 (0.24–0.93) 0.03 Values are n (%) unless otherwise indicated. MACE: Major adverse cardiovascular events; P2Y12i: P2Y12 inhibitor. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Levine G.N. Bates E.R. Bittl J.A. Brindis R.G. Fihn S.D. Fleisher L.A. Granger C.B. Lange R.A. Mack M.J. Mauri L. 2016 acc/aha guideline focused update on duration of dual antiplatelet therapy in patients with coronary artery disease: A report of the american college of cardiology/american heart association task force on clinical practice guidelines J. Am. Coll. Cardiol. 2016 68 1082 1115 10.1016/j.jacc.2016.03.513 27036918 2. Vranckx P. Valgimigli M. Jüni P. Hamm C. Steg P.G. Heg D. van Es G.A. McFadden E.P. Onuma Y. van Meijeren C. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093170 sensors-22-03170 Article Dynamic Seat Assessment for Enabled Restlessness of Children with Learning Difficulties https://orcid.org/0000-0002-7515-5440 Stanić Valentina 1* Žnidarič Taja 2† Repovš Grega 2 https://orcid.org/0000-0002-1161-9197 Geršak Gregor 1 Spinsante Susanna Academic Editor 1 Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia; gregor.gersak@fe.uni-lj.si 2 Department of Psychology, Faculty of Arts, University of Ljubljana, 1000 Ljubljana, Slovenia; taja.znidaric@ir-rs.si (T.Ž.); grega.repovs@ff.uni-lj.si (G.R.) * Correspondence: valentina.stanic@fe.uni-lj.si † Current address: University Rehabilitation Institute, Republic of Slovenia, 1000 Ljubljana, Slovenia. 21 4 2022 5 2022 22 9 317015 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Children with Attention-Deficit/Hyperactivity Disorder (ADHD) face a range of learning difficulties in the school environment, thus several strategies have been developed to enhance or optimise their performance in school. One possible way is to actively enable appropriate restlessness using dynamic seats. In this paper, an assessment of the efficacy of a dynamic seat while solving school task is presented and compared to classic chair and therapy ball. To test the effectiveness of active seat, a study that examined task solving performance while observing the intensity of movement, in-seat behaviour and psychophysiological responses (electrodermal activity, facial temperature) was designed. A total of 23 school-aged children participated in the study, 11 children with a combined type of ADHD and 12 children without disorders. Children with ADHD achieved the best results when sitting in the active seat, where the most intense movement and best in-seat behaviour was observed. At the same time, psychophysiological parameters indicate that when performing better at the task children with ADHD were not too challenged and were consequently less agitated. Results have suggested that for a better cognitive performance of children with ADHD, it is crucial to provide a comfortable and pleasant workspace that enables them the right amount of restlessness. ADHD electrodermal activity motion cognition facial temperature thermal imaging inertial measurement unit dynamic seat therapy ball ==== Body pmc1. Introduction Neurological developmental disorder Attention-Deficit/Hyperactivity Disorder (ADHD) is expressed in children as difficulty maintaining attention, inhibiting behaviour (impulsivity), and managing excessive motor and speech activity [1,2]. Start of schooling coincides with the period of typically the most intense symptoms of ADHD [3]. At the same time, most diagnoses of ADHD are made during the early school years due to the occurrence of learning difficulties and disruptive behaviour during class [4]. ADHD is denoted with weaker executive functions such as inhibition, working memory, planning, and task switching [5,6,7]. Problems with reading and reading comprehension in children with ADHD are also explained by the higher incidence of specific learning disabilities [8], with as many as 25–40% of children with ADHD meeting the criteria for dyslexia [9]. In the school environment, this manifests itself as carelessness at school tasks, disorganization, interrupting the teacher (and classmates), poor listening and following instructions, speaking without permission, avoiding mentally demanding tasks, forgetfulness, restlessness, and poor in-seat behaviour [10,11]. Although all children occasionally exhibit such behaviour, it is so common and intense in children with ADHD that it prevents them from functioning normally in the school environment and in everyday life [2]. It is recommended to combine several measures to manage behaviour in children with ADHD at school, such as adjusted classroom arrangement (grid arrangement of desks, sitting close to the teacher and away from windows, lower lighting, larger workspace for a child), smaller classes, interesting and short explanations (treatment of material in small sets with regular breaks, multi-sensory presentations, e.g., use of visual aids, enrichment of the lesson with novelties, e.g., quizzes and films), sensible distribution and composition of tasks (shorter and original tasks, start of the class with easier tasks, which become more difficult over time), encouraging and rewarding good work habits (writing notes, daily report cards, raising the hand before talking), and preventing inappropriate behavior with a calm but firm warning [10,11,12,13]. Problems with the regulation of psychological arousal are also often attributed to ADHD [14]. Psychological arousal encompasses behavioural and physiological mechanisms for regulating the state of mindfulness and attention [14]. According to Yerkes–Dodson law [15], good cognitive functioning requires an optimal level of psychological arousal, determined by interactions between the central and autonomic nervous systems. To better understand the atypical functioning of the autonomic nervous system and the cognitive activity of children with ADHD, physiological parameters such as electrodermal activity and skin temperature can be measured. Electrodermal activity (EDA) can be observed by measuring changes in the electrical properties of the skin due to the activity of the sweat glands [16,17]. Although the main function of sweating is the body thermoregulation, sweat glands are active also during psychological or emotional arousal and stressful situations [16,18]. Using the EDA signal, we can determine the level of psychological arousal by observing skin conductance level (SCL) and skin conductance response (SCR). SCL determines the level of psychological arousal and the baseline [16]. Current skin conduction values are described by SCRs, the density of which indicates the degree of psychological arousal (for example, more than 20 SCRs/min means high psychological arousal) [16]. SCRs represent values that exceed a certain threshold within the selected time frame [16]. Usually, SCR occurs 2–9 s after the start of the stimulus, when the amplitude of the SCR signal exceeds a threshold with a typical value between 0.01 µS and 0.05 µS [16]. The type of electrodes and the place of their installation depend on the purpose of the measuring device. The highest density of sweat glands is on the palms [16], so silver wet electrodes with a thin silver/silver chloride (Ag/AgCl) layer placed on the posterior joints of the index finger and middle finger are usually used [16,17]. A gel containing the electrolyte is required for optimal operation of such electrodes [16,17]. In the case of wearable sensors, the emphasis is on ergonomic design and ease of use, so dry stainless steel electrodes mounted on the upper arm are often used [17]. This site is suitable for measuring EDA, as all sweat glands are active in psychological sweating, and differences in the amount of sweat occur only due to the density of their distribution [19]. Body temperature depends on environmental conditions, both on biological conditions (adaptation to ambient temperature, overcoming a virus) and on emotional responses (social interactions, fight-or-flight) [20]. In the latter, the autonomic nervous system controls temperature through narrowing (vasoconstriction) and widening of subcutaneous vessels (vasodilation) and psychological sweating [20,21]. Vasoconstriction of peripheral facial vessels and increased cognitive load redirects blood flow from the face to the brain and thus affects the temperature image of the entire face [22,23]. The most stable temperature is in the forehead area and the most variable at the tip of the nose; therefore, these areas are often the subject of research [24,25,26]. There is also a very dense distribution of sweat glands on the forehead, so in case of stressful situations, psychological sweating is noticeable [19], which (negatively) affects the measurement of forehead temperature in the case of water film formation [27]. Enabling Restlessness in School Hyperactivity is a unique feature of ADHD [28], but its role is not entirely clear. One explanation is given by the theory of optimal stimulation [29], according to which hyperactivity is a mechanism that compensates for the lack of psychological arousal with an additional visual and kinesthetic contribution. Thus, increased activity occurs only in low-stimulation environments [29]. This theory is also supported by the model of functional working memory [30], which attributes to hyperactivity the role of stimulating the activity of the prefrontal cortex in demanding cognitive tests. At the same time, increased activity enables the avoidance of environmental requirements or tasks that are too demanding and overloading for their less developed working memory [30]. The model is supported by more research showing that children with ADHD move more intensely than typically developed children in more cognitively demanding tasks involving working memory load [31,32,33]. In contrast, in children without ADHD, increased exercise results in poorer functioning of their working memory [33]. Increased movement in children with ADHD may reflect the use of multiple cognitive resources [31]. With more intense physical activity, it is possible to strengthen the functioning of the cognitive control in children with ADHD [33]. The hyperactivity characteristic of children with ADHD therefore plays a functional role in their neurocognitive functioning [30,31,32,33]. The ability to direct and shift attention plays a key role in controlling movement [34]. Movement control is more effective when the individual has as many sources of attention as possible and at the same time as few distractions as possible [35]. Children with ADHD have shorter attention span [36]; therefore, movement control, imposed by any seating that does not allow spontaneous movement, can be an important consumer of attention. The positive link between hyperactivity, appropriate behaviour and problem-solving efficiency is also supported by other research [31,33], resulting in an idea of allowing for restlessness during class. Possible strategies are the use of dynamic seats (therapy ball, balance pillow, one-legged chair, standing desks), inclusion of physical activity in lessons (active games, moving furniture, carrying books, distributing papers to classmates, cleaning the board), holding classes outside and using classroom-friendly fidget toys [10,12,13]. Increasing physical activity and allowing for restlessness during class can also reduce the risk of many health problems resulting from prolonged sedentary position in school [37,38,39,40,41,42]. A commonly used alternative to the standard school chair is the therapy ball. Research on the impact of sitting on a therapy ball has divided opinions. While some confirm the positive effects of the therapy ball on in-seat behaviour and improving attention span in children with ADHD [43,44,45], recent research has not seen improvements in behaviour and productivity [46,47]. The potential benefits of the therapy ball have most likely not been revealed because it allows too much movement and overloads children with ADHD who have difficulties with self-control [47]. Despite the disagreement about the impact of the therapy ball on behaviour, there is no doubt about its popularity among children, as they labelled it as very comfortable and that it helped them improve concentration in solving tasks [43,46]. There are also many other kinaesthetic seats available that encourage active sitting in a unique way. This study evaluates an active seat that does not restrict legs movement and at the same time stabilizes the torso, thus enabling writing and solving tasks despite increased activity. The chair’s advantage is also in the adjustable height and shape of the seat, so it can be perfectly adapted to the child. This eliminates many of the potential negative consequences of an oversized or undersized seat [48,49,50]. This chair is still in the prototype phase, so it is necessary to check its actual effect on the cognitive performance of children with ADHD. To our knowledge, this study represents the first investigation of children’s cognitive performance using a series of cognitive tasks with simultaneous observation of the observed child’s movement and psychophysiology. 2. Materials and Methods To comprehensively address the impact of seating type on student performance, an experiment was conducted that focused on cognitive tests, observation of movement and psychophysiological response of children. The study examined the effectiveness of an active seat, a therapy ball, and a standard school chair. The main focus was on whether changing the seat could improve the performance of children with ADHD in solving school assignments, what would be the most appropriate solution for children with ADHD in the school environment and whether the change of seat in any way also affects the effectiveness of children without ADHD. 2.1. Participants The study included 23 children, 11 children with a combined type of ADHD (8 boys and 3 girls aged between 9 and 11) and 12 children without ADHD (6 boys and 6 girls aged between 8 and 10). Children had to be medication-free for at least 24 h before the start of the experiment. Children with ADHD also had various comorbid disorders listed in Table 1. The parents or legal representatives of all children signed a consent form to participate in the research, which was approved by the Ethics Committee of the Faculty of Arts, University of Ljubljana (application number: 167-2019). 2.2. Instrumentation 2.2.1. Seats Three seats (Figure 1) were used in the experiment: a classic school chair with a typical design—a wooden frame with a backrest and no armrests, a standard therapy ball of two sizes (ball diameter: 45 or 55 cm), where the appropriate size was chosen according to the height of the participant, and an active seat. The design of the latter consists of a metal frame with a flexible seat and backrest as well as an adjustable footrest that mimics a swing. The seat, backrest and footrest are softly padded. Depending on the height of the individual, the seat is raised and adjusted enabling feet to move freely. The chair also has armrests and a removable plate that substitutes the desk. 2.2.2. Inertial Measurement Unit The intensity of movement of the lower limbs was measured using accelerometers in seven inertial measurement units (IMU), which were placed on the instep of the left and right foot, the front of the left and right shank, the front of the left and right thigh, and the sacrum (Figure 2). The estimated attachment error was 2 cm. The measuring range of the accelerometer was set to 2 g. The sensor part of each IMU consists of a three-axial accelerometer STM LIS331DLH, a three-axial gyroscope Invernsense IMU-3000 and a three-axial magnetometer Honeywell HMC5883 [51]. IMU has a built-in 3.7 V battery with a capacity of 250 mAh, microcontroller and transmitter. The used user interface was built in the MATLAB/Simulink R2019b environment to capture and transfer IMU data [52]. IMU signals were sampled at a frequency of 80 Hz. 2.2.3. SenseWear Sensor To measure skin conductance level, the BodyMedia SenseWear AB155 was used, which was placed on the upper arm of a non-dominant hand, as shown in Figure 2. The device does not have a power button, but turns on automatically within 10 min of skin contact. To ensure the operation of the device during the tests, it is necessary to dedicate 10–15 min to the preparations [53]. The sampling frequency of the skin conductance was set to 32 Hz, and for energy consumption 1 Hz. 2.2.4. Thermal Images and Cameras Facial skin surface temperature was monitored using the FLIR T650sc thermal imaging camera, which captures 24 bit images with a thermal sensor resolution of 640 × 480 with 50 fps. The emissivity factor was set to 0.98 and the distance to 3 m (Figure 2). Two video cameras, the Sony HXR-MC50E and the Sony Handycam HDR-CX6, were also part of the measurement system. The first captured the side view of the participant and the second the front view (Figure 2), together providing a complete overview of the participant’s movement. 2.2.5. Cognitive Tests To observe (allegedly) weaker executive functions three equivalent sets of five cognitive tests were prepared. The estimated capacity of the spatial working memory (SWM) [54] was measured by a computer test in the PsychoPy 1.90.3 software. Participants had to renew the sequence of coloured squares that appeared on the screen, where the sequence lengthened with each attempt. The task was terminated after two incorrect answers. The number of correct answers represented the estimated capacity of the SWM. Estimated verbal working memory (VWM) capacity [54] was assessed with three variants of the numerical range: numerical range forward (participant repeats the heard string of numbers in the same sequence, e.g., 3, 2, 6 renews as 3, 2, 6), numerical range backwards (participant restores the heard string of numbers in reverse order, from last to first, e.g., 3, 2, 6 renews as 6, 2, 3) and editing the numeric range by size (participant edits the heard string of numbers from smallest to largest, e.g., 3, 2, 6 renews as 2, 3, 6). The shortest string consisted of three numbers, and with every correct answer, the string lengthened. In case of a wrong answer, another string of the same length was given. If the latter could not be restored, the task was terminated. Numbers 0–9 were included. The average number of correct answers of all three variants represented the estimated capacity of the VWM. The ability to switch tasks was assessed using the adapted Trail Making Test (TMT-AD) [55]. Participants had to correctly connect the circled numbers and letters, without lifting the pen from the desk and crossing links with each other. The TMT-AD consisted of three parts: The first part included circles with numbers 1–25. Participants had to connect the circles in ascending order (1, 2, 3, 4, …, 25). The second part included circles with numbers 1–12 and letters A-L. Participants had to connect the circles in ascending (numerical and alphabetical) order, while exchanging numbers and letters (1, A, 2, B, 3, C, 4, D, …, 12, L). The third part consisted of empty circles, connected with marked links. The participant had to connect the circles by following the links. Participants were able to correct any errors and proceed with the task solving, which only extended the overall time. The average solving time of all three parts was chosen for the representative value. The Reading Comprehension (RC) task consisted of text and questions about the read text. The tasks were taken from the national test of Slovene langugage for the 6th grade of primary school. As participants belonged to a younger age group, they could ask the researcher for an explanation in case of incomprehension of the instructions or words. RC score was the proportion of correct answers. The planning capabilities were evaluated using an adapted computer version of the Tower of London test (ToL-AD) [56]. The share of correct estimates of the participant to get the required arrangement of balls in a figure with the smallest number of moves was considered. The task consisted of 20 cases where 2 min was available for each. 2.3. Measures 2.3.1. Movement Intensity To measure the movement intensity of the participants, seven IMU with a built-in accelerometer that measures accelerations in all three axes of the local coordinate system were used. The intensity of the movement is represented by the dynamic component of the acceleration, so the contribution of gravity was mathematically removed. 2.3.2. In-Seat Behaviour The in- and out-of-seat behaviour was observed. In this study, the in-seat behaviour is defined as proper, when it did not hinder writing and solving tasks, as it allowed for less torso activity. The main information was determined by the IMU on the sacrum (IMU1), because the intensity of torso movement can be inferred directly through measurements. For example, a child who heavily bounces on the ball (high IMU1 value) finds it very difficult to write. In addition, the average of the representative IMU values on both legs (from IMU2 to IMU7) was calculated and compared to the IMU1 value. Higher leg activity in comparison to the sacrum can be interpreted as a more proper in-seat behaviour. 2.3.3. Cognitive Tests Solving Performance With specific cognitive tests from Section 2.2.5 the estimated capacity of spatial and verbal working memory, the ability to switch tasks, reading comprehension and planning skills were assessed. 2.3.4. Electrodermal Activity Psychological arousal of children was observed by measuring skin conductance. The average of measurements within each cognitive test was calculated, which can be thought of as the skin conductance level (SCL) of the test in question. Electrodermal activity is represented by the relative skin conductance (SC-R), which is determined by the ratio between the average of all measurements of a particular test and the baseline. The latter is defined as an average of all measurements during 1 min resting period, ending 2 min before the beginning of the first cognitive test. SC-R can also be interpreted as a deviation from the baseline, both in the positive and negative direction. 2.3.5. Facial Temperature Images of key measurement moments were extracted from the thermal imaging camera videos for each cognitive test, thus presenting temporal changes in temperature during the measurement. Similar to other research [24,25,26], the forehead and the tip of the nose were chosen as region of interest (ROI) as presented in Figure 3. The size of each ROI was 3 × 3 pixels. For each cognitive test, thermal images at key measurement moments were obtained: at the moment of the explanation of the task, as well at the beginning and the end of task solving. 2.3.6. Subjective Assessment Participants chose the most likeable seat and task as well as evaluated the intrusiveness of the measurement system. The latter answers were divided into three categories: intrusive measurement system—the participant was constantly aware of the measurement system and was also burdened by it, less intrusive measurement system—the participant was aware of the measurement system, but eventually got used to it and did not deal with it during the measurement, and non-intrusive measurement system—the participant completely forgot about the measurement system during the measurement. 2.3.7. Statistical Analysis In the study, participants sat in three seats; therefore, in addition to descriptive statistics, a two-way analysis of variance (ANOVA) for mixed plans, which evaluated the differences in intensity of movement, in-seat behaviour, performance at cognitive tests and SC-R values both between groups (children with and without ADHD) and within an individual group, was performed. The unrepeatable factor was Group (children with and without ADHD) and the repeatable factor was Seating (active seat, therapy ball, school chair). The limit value for statistical significance was set at p=0.05. Two-way ANOVA was not used at facial temperature due to deficient measurement values. For assessing differences between the children with and without ADHD in choosing the most likeable seat and task as well as the evaluation of the intrusiveness of the measurement system the Fisher’s exact test was used, where p≤0.05 determined statistically significant differences. 2.3.8. Protocol In order to minimise the measurement anxiety of the ADHD participants, the experiment was performed at the counselling centre, where the participants with ADHD receive counselling and therapeutic services. The measuring protocol consisted of preparation for measurement, installation of measuring equipment, solving five cognitive tests, and a short discussion of tasks and measuring instrumentation. A simplified diagram of the measurement protocol is shown in Figure 4. In designing the protocol and order of cognitive tests, the emphasis was on the exchange of more and less popular tasks in order to reduce the impact of the measurement on the result. The RC and the VWM test were evaluated as less pleasant; therefore, the cognitive tests were performed in the following order: SWM test, VWM test, TMT-AD, RC, and ToL-AD. For optimal efficiency of children, all measurements were performed in the morning, thus reducing the possibility of the occurrence of exciting events before the measurement, such as an exam or physical education class. To ensure constant illuminance, air temperature and humidity of the room the blinds were closed and lights turned on. The movement and emotional state of the children were monitored with a sensory system that covered three areas of interest: intensity of movement, in-seat behaviour and psychophysiological responses. It was taken into consideration that children with ADHD have difficulty maintaining attention; therefore, highly intrusive measurement instrumentation was avoided [57]. The measurement began with greeting the participant and attaching the measuring equipment. To ensure the operation of the SenseWear AB155 sensor and the thermal adjustment of the facial temperature, 10–15 min were spent on the attaching of the measuring system and the preparation of the seat. First, the SenseWear AB155 sensor was atteched and then all the IMUs. Next, the seat was adjusted to the size of the participant. During the installation of the seat and measuring instruments, the researcher talked to the participant to relax them as much as possible and to decrease potential measurement anxiety. Afterwards, the participant began to solve cognitive tests, which were always in the same sequence. After each test, a short break (maximum 2 min) was possible at the request of the participant. At the end, a short conversation followed, where the participant assessed the perceived disturbance of the measuring system and the most likeable chair. Each participant took three measurements, each time sitting on a different seat. The three seats allow for six combinations, so for ensuring the randomness of the experiment, the order of the seats was determined by classifying the children into six groups, as shown in Table 2. In each group, there were children with and without ADHD. 3. Results Results are presented with mean value (M) and standard deviation (s) as well as median value (Me). Statistical representation of movement intensity in all seats is shown in Figure 5. A higher value represents a higher intensity. The figure shows that the average movement intensity of children with ADHD is higher than children without ADHD regardless of the seat, with the highest scatter in the active seat. Children with ADHD in all seats moved more intensely compared to the children without ADHD. On average, children with ADHD moved most intensively in the active seat (M=0.50 g, s=0.36 g, Me=0.57 g) and least in the school chair (M=0.17 g, s=0.12 g, Me=0.12 g). Children without ADHD were also, on average, most lively in the active seat (M=0.22 g, s=0.23 g), where the largest difference between children with and without ADHD was observed. Compared to the Me values, the smallest difference in activity between children with and without ADHD is on the therapy ball, where the Me of the children without ADHD is the highest (Me=0.17 g). In-seat behaviour can have a negative value, which indicates a more active IMU on the sacrum compared to the average activity of all IMU on the legs and, consequently, less proper in-seat behaviour. The statistical representation of the in-seat behaviour in all seats is shown in Figure 5. The figure shows that children with and without ADHD exhibited the least proper in-seat behaviour while sitting on the therapy ball (children with ADHD: M=0.12, s=0.29, Me=0.03; children without ADHD: M=−0.04, s=0.24, Me=−0.05) and the most proper while sitting in the active seat (children with ADHD: M=1.61, s=0.96, Me=1.33; children without ADHD: M=0.86, s=0.86, Me=0.53). The statistical representation of the cognitive tests solving performance on each chair is shown in Figure 5. Children with ADHD performed worse compared to the children without ADHD, regardless of the seat. On average, children with ADHD performed best while sitting on the therapy ball (M=55.5%, s=12.0%) and worst while sitting in the school chair (M=53.8%, s=13.0%). However, according to the median values, the opposite is true—children with ADHD achieved best results while sitting in the school chair (Me=53.6%) and worst results on the therapy ball (Me=50.8%). In addition to the achieved result, it is also beneficially to look at the time spent on solving the task. Table 3 shows that both children with and without ADHD in most cases spent more time on task solving while sitting in the active seat than on the therapy ball or school chair. At the same time, children with ADHD solved the VWM and RC tasks faster than children without ADHD. The statistical representation of the electrodermal activity in all seats is shown in Figure 5. According to the results, the SC-R of children with ADHD was highest on the therapy ball (M=20.76, s=29.07, Me=13.05) and lowest in the active seat (M=10.78, s=10.44, Me=9.62). The average nose and forehead temperatures of children with and without ADHD are shown in Figure 6 and Table 4. The forehead temperature of children with and without ADHD was more stable compared to the temperature of the nose. The forehead and nose temperatures of children with ADHD were lower in all three seats compared to the temperatures of children without ADHD. Average forehead and nose temperatures of both children with and without ADHD were highest while sitting in the active seat and lowest in the school chair. The smallest difference between the forehead and nose temperatures of children with ADHD was on the active seat, while the most prominent difference was on the therapy ball. Table 5 shows the results of the two-way ANOVA for mixed plans. It can be seen that the seat had a significant effect on movement intensity. Further analysis of simple contrasts revealed that participants were moving significantly more intense while sitting in the active seat compared to the therapy ball, F1,21=5.062, p=0.035, ηp2=0.194, and the school chair, F1,21=15.490, p=0.001, ηp2=0.425. Furthermore, simple contrasts analysis showed significant more intense movement on the therapy ball compared to the school chair, F1,21=16.162, p=0.001, ηp2=0.435. Results in Table 5 also report significant effect of the seat on the in-seat behaviour of participants. Further simple contrasts analysis revealed significantly less proper in-seat behaviour on the therapy ball compared to the active seat, F1,21=42.922, p≤ 0.001, ηp2=0.671, and the school chair, F1,21=7.200, p=0.014, ηp2=0.255. No factor had a significant effect on test solving performance and electrodermal activity. The most likeable cognitive test among children with ADHD after sitting in the active seat were SWM and ToL-AD (27.3%) and the least favourite RC (72.7%). Similarly, after sitting on the therapy ball children with ADHD most preffered SWM test (38.5%) and least RC (63.6%). After solving tasks on the school chair most children with ADHD chose ToL-AD as their favourite task (50.0%) and again RC as the least favourite task (72.7%). Fisher’s exact test revealed no significant difference between children with ADHD and children without ADHD in choosing the most and the least favourite task regardless of the seat. Children with and without ADHD liked active seat the most (children with ADHD: 66.7%; children without ADHD: 75.0%) and school chair the least (children with ADHD: 0.0%; children without ADHD: 8.3%). The similarity of responses between children with and without ADHD was also confirmed by Fisher’s exact test, which showed that there was no significant difference in the seat preference among the participants (p=0.640). None of the participants judged the measurement system to be intrusive. Fisher’s exact test showed that there were no significant differences between children with and without ADHD in the assessment of intrusiveness of the measuring system after measurement in the active seat (p=0.285), therapy ball (p=0.392), nor after measurement in the school chair (p=0.618). 4. Discussion The exact cause of ADHD is not yet known, so several theories have been developed based on the atypical brain function and structure. As a result, there is no single solution to alleviate the symptoms of ADHD, but the measures depend on numerous factors, such as the age of the child and the intensity of the symptoms. The structured school environment uncover problems managing excessive physical activity and maintaining attention; therefore, teachers and parents try to help children with professional treatments, promoting positive work habits and a healthy lifestyle, adapting the workplace at home and school, implementing appropriate class structure as well as various strategies to encourage motor activity during learning and solving school tasks. The latter include the use of dynamic seats, such as a therapy ball and an active seat, which children usually like more than a standard school chair. This study evaluated whether a change of seat improves the cognitive performance of children with the neurological developmental disorder ADHD by observing their movement patterns and psychophysiological responses when solving various cognitive tests. The results show that, on average, children with ADHD performed worse in all seats than children without ADHD, which is in line with generally accepted assumptions about impaired executive functions [5,58,59,60]. As seen in Figure 7, children with ADHD were generally moving more intensively in all three seats, compared to the children without ADHD. Similar results are found in other studies [31,33] that regard hyperactivity in children with ADHD as a mechanism to compensate for low arousal and short attention span. This aspect is also confirmed by the theory of optimal stimulation [29] and the model of functional working memory [30], which associate the higher intensity of movement of children with ADHD with a more demanding cognitive tasks. Thus, we can conclude that in children with ADHD, more intense movement indicates a more successful test solving performance. Furthermore, in children with very intense excessive movement, it was observed that the active seat did not keep them in the workplace. This was most evident during the unpopular reading comprehension test, where hyperactive children calmed down only after standing up. Here, an advantage in the height of the work surface in the active seat was shown, as they could read and write without awkwardly leaning over the table. Standing desks are an interesting proposal for further studies, as they are adaptable and much easier to implement in classrooms for all children [61,62,63]. Studies [43,44,46,47] emphasize that with dynamic seats, it is not only the intensity that is important, but also the in-seat behaviour. Both children with and without ADHD exhibited better in-seat behaviour while achieving better results in cognitive tasks (Figure 7). Although the therapy ball allows for intense movement, the movement can be excessive and thus negatively affect effectiveness of the children in tackling cognitive tasks. This also explains why some research reports positive effects of the therapy ball on the behaviour and productivity of children with ADHD [43,44,45], while others have not seen any improvement [46,47]. Consequently, we can predict that in children with ADHD, more proper in-seat behaviour correlates with better performance on tests. It should be noted here that the improper in-seat behaviour characterise behaviour that hinders children from solving tasks and is disturbing to the surroundings (e.g., children frequently leaving the seat). Figure 7 shows that in children with and without ADHD, the SC-R value increased with each test. In children with ADHD, the increase in mean SC-R was less pronounced with better performance on the test, while in children without ADHD, the opposite was observed. A higher SC-R level indicates a greater cognitive load [64], but in a more difficult test, the level of SC-R may be lower if the children perceive it as easy or boring [65] and if they were very impatient or enthusiastic during the explanation. Elevated SC-R levels are also characteristic of various emotional states such as anxiety, disgust, excitement, and joy [66]. Due to the wide range of probable emotions, it is not possible to properly distinguish between them by observing SC-R alone [67], so it is necessary to consider additional, even redundant parameter of the autonomic nervous system, such as facial skin temperature. Nevertheless, less intense and more subdued emotions are characterized by a less pronounced rise in SC-R levels [68]. Therefore, it can be concluded that children with ADHD were less agitated and (probably) less cognitively burdened during more successful task solving, while children without ADHD responded in exactly the opposite way. Children with ADHD had similar average nasal tip temperature and higher average forehead temperature compared to children without ADHD (Figure 7). The nasal tip temperature for children with and for children without ADHD generally decreased with more successful test solving performance, while forehead temperature increased. The study [22] associates this type of facial temperature picture with a more demanding cognitive task. Cooling of the nose and warming of the forehead may indicate joy, stress, anger, or anxiety [20,66,69,70]. By considering the level of SC-R, a moderate feeling of anxiety or agitation can be attributed to the children with ADHD during the more successful test solving performance, and the children without ADHD were feeling more stressed. The forehead temperature was more stable compared to nasal temperature, which is consistent with other studies [24,26,71]. Based on the results of facial temperature, we can conclude that children with ADHD were more cognitively burdened and less agitated when they performed better on tests. From Figure 6, no prominent change in temperature between the explanation and the beginning of solving the task can also be observed. Therefore, it can be concluded that adequate explanation prepares children for the task and increase their engagement in the problem solving. Subjective assessments report that children preferred the test where they achieved better results, and conversely, the test where they performed the worst caused them the most problems and was the least popular with them. In addition, the seat where they were more successful in task solving was more liked by all children irrespectful of ADHD. Participants predominantly performed best in the active seat, which was also the most popular choice. Children with ADHD mostly achieved the worst results in the school chair, and children without ADHD on the therapy ball. Thus, we can conclude that active seat is the best choice for both groups of children, but its effects are not sufficiently pronounced to significantly contribute to better cognitive performance of children. Limitations Certain limitations of the research also need to be addressed. The selected sample of children was not representative or large enough. Given that the intensity of ADHD symptoms may decrease with age, an age-homogeneous sample should be selected. It could also include only children without prescribed treatment with medications, thus excluding its potential impact on the cognitive performance and behaviour of children. Perhaps the effect of the active seat would be expressed in children with predominantly inattentive or hyperactive/impulsive type of ADHD disorder. To better understand the performance and behaviour of children, their school grades, IQ, and personality traits could also be considered. The children were not bothered by the measuring system, so the choice of less intrusive and non-contact measuring devices was appropriate. Nevertheless, improvements to the measurement system are possible. The IMU signal could be used to calculate the kinematic model, thus obtaining accurate information on the trajectory of motion as well as the actual contribution of gravity. The thermographic camera could be closer to the face, as this would allow for more appropriate ROI determination. During the measurement, the children occasionally touched the face, which potentially affected the measured temperature. For a more reliable assessment of the psychological state, the sensory system could be supplemented by the temperature of the corners of the eyes and the measurement of an additional parameter regulated by the autonomic nervous system, such as heart rate. 5. Conclusions Dynamic seats can be distinguished according to the movement they allow. Versatile and arbitrary movement is possible on an affordable therapeutic ball, but it can also be overwhelming. Children, especially those who are severely over-active, can be hindered from writing, listening, and completing school assignments by such a flexible seat. As a result, a number of dynamic seats that try to find a balance between allowing movement and ensuring stability while sitting are available on the market. The study of enabling sedentary restlessness included cognitive tests to address performance in school children with and without ADHD, and the observation of movement intensity, in-seat behaviour, electrodermal activity, and facial temperature. This study revealed best perfomance of children with ADHD in solving tasks in an active seat. In doing so, they moved very intensely with mostly proper in-seat behaviour, as they predominantly waved their legs and not the trunk, and accoriding to the psychophysiological parameters they were not too challenged or anxious about the task. The active seat failed to retain children with severe hyperactivity in the seat. However, the height of the workspace, similar to the standing desks, enabled children comfortable writing surface while standing and consequently help them to mantain attention on the task. This research shows that active seat in these circumstances does not have a significant effect on the learning difficulties of children with ADHD, but it does have certain positive qualities that can be a starting point for further work. It turns out that children with ADHD need a comfortable and pleasant work space that allows them the right amount of restlessness, such as swinging their legs, squeezing a ball and standing while working. The research also sheds light on the problem of differences in intensity of the symptoms of ADHD, which makes it difficult to find a unique solution for the learning disabilities of children with this neurological disorder. Author Contributions Conceptualization, V.S., T.Ž., G.R. and G.G.; methodology, V.S., T.Ž., G.R. and G.G.; investigation, V.S. and T.Ž.; data curation, V.S. and T.Ž.; writing—original draft preparation, V.S.; writing—review and editing, V.S., T.Ž. and G.G.; visualization, V.S.; supervision, G.G.; funding acquisition, G.G. All authors have read and agreed to the published version of the manuscript. Funding The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0225). Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Faculty of Arts, University of Ljubljana (protocol code 167-2019; date of approval: 27 September 2019). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 From left to right: school chair, therapy ball, active seat. Figure 2 Measuring system with measuring instruments: 1—IMU, 2—SenseWear sensor, 3—thermal imaging camera, 4—(RGB) video camera. Figure 3 ROI on forehead (red frame) and nose (green frame) of one participant during different tasks. Figure 4 Measurement protocol. Figure 5 (a) Intensity of movement, (b) in-seat behaviour, (c) cognitive tests solving performance, (d) SC-R. Figure 6 Average forehead and nose temperature of children with and without ADHD while solving cognitive test in active seat, therapy ball and school chair. Moments of interest are marked as 1—explanation of the task, 2—start of task solving, 3—end of task solving. Figure 7 Average (a) movement intensity, (b) in-seat behaviour, (c) SC-R, (d) nose and forehead temperature difference, (e) nose temperature and (f) forehead temperature of children with and without ADHD while solving cognitive tests, with trendlines. The cognitive tests are marked as 1—SWM, 2—VWM, 3—TMT-AD, 4—RC and 5—ToL-AD. The seats are marked as A—active seat, B—therapy ball and C—school chair. For each cognitive test best average result with corresponding seat is presented (e.g., 4-B is best solved RC, which happened to be on the therapy ball). sensors-22-03170-t001_Table 1 Table 1 Comorbid disorders of children with ADHD. Participant Comorbid Disorders ES01 / ES02 Specific learning disabilities ES03 Dyslexia ES04 Dyslexia ES05 Dyslexia ES06 Specific learning disabilities ES07 Speech and language disorder, Specific learning disabilities ES08 Developmental co-ordination disorder (dyspraxia) ES09 / ES10 Dyslexia ES11 Specific learning disabilities sensors-22-03170-t002_Table 2 Table 2 Seating order for participants with ADHD (ES) and without ADHD (KS). The seats are marked as A—active seat, B—therapy ball and C—school chair. Seating Order Participants A-B-C ES01, KS01, ES07, KS07 A-C-B ES02, KS02, ES08, KS08 B-A-C ES03, KS03, ES09, KS09 B-C-A ES04, KS04, ES10, KS10 C-A-B ES05, KS05, ES11, KS11 C-B-A ES06, KS06, KS12 sensors-22-03170-t003_Table 3 Table 3 Mean value of the task solving times for children with and without ADHD. The seats are marked as A—active seat, B—therapy ball and C—school chair. Children with ADHD [s] Children without ADHD [s] A 88.0 92.1 SWM B 93.5 88.9 C 59.6 97.6 A 193.3 224.5 VWM B 177.3 206.0 C 174.8 201.2 A 189.8 136.1 TMT-AD B 184.0 135.6 C 153.9 130.4 A 743.1 852.8 RC B 707.3 825.7 C 680.6 840.3 A 315.0 261.2 ToL-AD B 265.3 251.1 C 256.4 245.1 sensors-22-03170-t004_Table 4 Table 4 Average values of nose and forehead temperatures for children with and without ADHD in all three seats. The seats are marked as A—active seat, B—therapy ball and C—school chair. A [°C] B [°C] C [°C] Forehead 34.0 33.9 33.4 Children with ADHD Nose 32.1 30.5 30.5 Difference 1.9 3.4 2.9 Forehead 34.5 34.2 34.0 Children without ADHD Nose 32.8 32.2 30.8 Difference 1.8 2.0 3.2 sensors-22-03170-t005_Table 5 Table 5 Two-way ANOVA for factors Group and Seat. Source of Variation SS df MS F p ηp2 Movement Intensity Group 0.441 1 0.441 4.049 0.057 0.162 Error(Group) 2.286 21 0.109 Seat 0.576 2 0.288 12.014 <0.001 0.364 Seat × Group 0.137 2 0.068 2.850 0.069 0.120 Error(Seat) 1.007 42 0.024 In-seat Behaviour Group 2.396 1 2.396 1.688 0.208 0.074 Error(Group) 29.813 21 1.420 Seat 1 16.899 1.600 10.562 11.838 <0.001 0.361 Seat × Group 1 1.213 1.600 0.758 0.850 0.414 0.039 Error(Seat) 1 29.977 33.600 0.892 Cognitive Tests Solving Performance Group 656.628 1 656.628 1.587 0.222 0.070 Error(Group) 8688.643 21 413.745 Seat 12.238 2 6.119 0.123 0.885 0.006 Seat × Group 27.126 2 13.563 0.273 0.763 0.013 Error(Seat) 2088.831 42 49.734 Electrodermal Activity Group 565.688 1 565.688 1.622 0.218 0.079 Error(Group) 6628.284 19 348.857 Seat 93.566 2 46.783 0.190 0.828 0.010 Seat × Group 742.876 2 371.438 1.505 0.235 0.073 Error(Seat) 9378.676 38 246.807 1 Greenhouse-Geisser correction. 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==== Front Plants (Basel) Plants (Basel) plants Plants 2223-7747 MDPI 10.3390/plants11091159 plants-11-01159 Article Distribution Patterns of Essential Oil Terpenes in Native and Invasive Solidago Species and Their Comparative Assessment https://orcid.org/0000-0003-0310-1678 Radušienė Jolita 1 https://orcid.org/0000-0001-5620-5775 Karpavičienė Birutė 1 https://orcid.org/0000-0001-6934-9524 Marksa Mindaugas 2 https://orcid.org/0000-0001-5390-2161 Ivanauskas Liudas 2 https://orcid.org/0000-0002-7324-0043 Raudonė Lina 34* Sgorbini Barbara Academic Editor 1 Laboratory of Economic Botany, Nature Research Center, Akademijos Str. 2, 08412 Vilnius, Lithuania; joilta.radusiene@gamtc.lt (J.R.); birute.karpaviciene@gamtc.lt (B.K.) 2 Department of Analytical and Toxicological Chemistry, Lithuanian University of Health Sciences, Sukileliu Av. 13, 50162 Kaunas, Lithuania; mindaugas.marksa@lsmuni.lt (M.M.); liudas.ivanauskas@lsmuni.lt (L.I.) 3 Department of Pharmacognosy, Lithuanian University of Health Sciences, Sukileliu Av. 13, 50162 Kaunas, Lithuania 4 Laboratory of Biopharmaceutical Research, Institute of Pharmaceutical Technologies, Lithuanian University of Health Sciences, Sukileliu Av. 13, 50162 Kaunas, Lithuania * Correspondence: lina.raudone@lsmuni.lt 25 4 2022 5 2022 11 9 115904 4 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The importance of invasive Solidago L. species to the environment creates a new approach to controlling their spread through the use of potentially high value raw materials. The aim of this study was to assess the distribution patterns of volatile compounds in the four Solidago spp., by identifying common and species-specific compounds with their potentials, and to confirm the origin of the spontaneous hybrid Solidago × niederederi on the basis of comparative assessment of essential oil (EO) profiles. Plant material in the flowering phase was collected in mixed populations from six different sites. The EOs were isolated separately from the leaf and the inflorescence samples by hydrodistillation for 3 h. The chemical analysis was performed by gas chromatography—mass spectrometry. Multivariate data analysis was employed to explain the interspecies relationships among Solidago spp. The results revealed the similarity among Solidago spp. EO profiles, which were dominated by monoterpenes and oxygenated compound fractions. Solidago spp. differed in species distinctive terpenes and their distribution between accessions and plant parts. Volatile compound patterns confirmed the origin of Solidago × niederederi between Solidago canadensis and Solidago virgaurea, with the higher contribution of alien species than native ones. Correct taxonomic identification of species is highly essential for the targeted collection of raw material from the wild for different applications. Solidago spp. can be considered to be underutilized sources of bioactive secondary metabolites. invasive species distinctive terpenes interspecific diversity Solidago × niederederi underutilized resources ==== Body pmc1. Introduction The use of plant products has grown remarkably in recent years, and research into natural products such as volatile terpenoids has become an important task for future human and animal well-being [1]. Natural products are generally easy to prepare and, due to their natural origin, are environmentally friendly and not financially challenging. In this respect, invasive species are of growing interest as a potential resource for obtaining high value-added products. Solidago canadensis L. (Canadian goldenrod) and Solidago gigantea Aiton (Giant goldenrod), native to North America, are considered to be one of the most aggressive plant invaders, which were introduced to Europe as garden ornamentals in the middle of the 18th century and began to spread in the 19th century [2]. Abandoned previously cultivated and disturbed areas contribute to a rapid and successful invasion of goldenrods. Both species form pure dense stands due to the clonal growth of their re-sprouting rhizome system, which provides a strong competitive ability to eliminate other grassland species while reducing biodiversity [3]. Propagation of the Solidago species easily by wind distributed seeds guarantees their distance dispersal and the occupation of new disturbed areas creating homogenized landscape [4]. On the other hand, spontaneous hybridization gives rise to new hybrids, such as sexually reproducing Solidago × niederederi Khek., which was first recorded in Austria more than a century ago [5]. The hybrid has been described as a new alien species between the invasive S. canadensis and native Solidago virgaurea L. (European goldenrod), which is spreading rapidly along with the parental species, increasing the negative impact on the native flora [6,7]. In addition, phytotoxicity of alien species has been often invoked as a significant factor negatively influencing the native species and composition of plant communities [8,9]. The fastest and cheapest result to eradicate or limit the invasion of goldenrods is the use of herbicides [10]. However, the application of herbicides has a negative impact on the environment and their use is limited. Based on the importance of goldenrods to the surrounding environment, a new approach has recently been developed so that invasive species can be a potential source of high value-added products, instead of eliminating them by labor consumption and environmentally unfriendly ways [11]. The high biomass produced by exotic goldenrods is a promising source of renewable energy that can be exploited in rural households as an alternative to expensive firewood and that do not compete with crops for food or animal feed [12]. Late-flowering goldenrods attract pollinators and are honey bee plants that are considered to be superior to crops treated with pesticides [13]. Solidaginis virgaureae herba is included in the ESCOP Monographs with therapeutic indications for the treatment of urinary tract and genital disorders [14]. The European Medicines Agency in a finalized community monograph of Solidago virgaurea confirms traditional use of this plant material for the treatment of minor urinary tract complaints [15]. Furthermore, S. gigantea, S. canadensis, and their hybrids, as well as S. virgaurea, are included in European Pharmacopoeia [16]. A wide range of specialized metabolites have been reported in goldenrod raw materials, of which phenolic compounds and EOs were considered to be the most valuable [17,18,19]. The comparative evaluation of phenolic compounds in Solidago spp. has been presented in our previous studies [20]. Essential oils (EOs), due to the structural diversity in their constituents, expose a wide range of biological effects and are of great interest as a source of functional ingredients for agriculture, food, cosmetics, and pharmaceuticals [21]. Numerous studies have reported the potential use of EOs for integrated weed and pest control as an environmentally friendly approach [22]. Significant antifungal activity of S. canadensis EO was found against Botrytis cinerea, which reduced fruit rot and successfully controlled gray mold in inoculated strawberries [21]. Elshafie et al. [23] has also demonstrated antimicrobial activities of S. canadensis EO in vitro against some other phytopathogens. In this way, EOs of alien Solidago spp. are a promising source for the development of organic pesticides and can meet the high demand for their production. In addition, the allelopathic activity of S. virgaurea is considered as the herbicidal potential of EOs for environmentally acceptable weed control in organic farming [24]. The EO of S. canadensis has been shown to exhibit significant cytotoxic and antiproliferative activities against human tumor cell lines, correlating with the terpene compounds [25,26]. The major constituents of EO usually contribute to the principal role of the biological activity of the mixture, so their efficiency can be predicted to some extent from the complex of components [27]. On the other hand, even minor components have been shown to play a significant role in different biological activities due to their complementary and synergistic effects [28]. Consequently, it is important to know the distribution of phytochemical compounds in species and their populations due to the targeted selection of raw materials for their possible multifunctional use. The study hypothesized that the screening of Solidago spp. growing in the same area and in their mixed populations could provide reasonable comparative information on the volatile profiles and their chemotaxonomic relationships and confirm the origin of the S. × niederederi taxon. The objectives of the study were: (1) to assess the distribution of volatile constituents in the populations of native and alien Solidago spp.; (2) to identify intraspecific and interspecific diversity in Solidago spp.; (3) to identify common and species distinctive volatiles; and (4) to confirm the origin of the spontaneous hybrid S. × niederederi on the basis of a multivariate comparative analysis of volatile profiles. 2. Results 2.1. Essential Oil Content of Solidago Species Inflorescences took priority over the leaves in EO content in all the evaluated Solidago species. The highest yield of EO was obtained from inflorescences of S. canadensis (0.19–0.26%), followed by S. gigantea (0.16–0.23%), S. × niederederi (0.14–0.20%), and S. virgaurea (0.15–0.18%). Meanwhile, the leaves of S. gigantea accumulated the highest EO content (0.16–0.20%), followed by S. canadensis (0.14–0.18%), S. × niederederi (0.13–0.15%), and S. virgaurea (0.10–0.15%). Previous studies reported the similar range of EO yield in S. canadensis (0.18–0.27%) [23,29] and S. gigantea (0.15–0.16%) [29]. Kalemba [30] reported higher levels of EO contents in S. virgaurea (0.32–0.38%) than levels found in this study. 2.2. Chemical Profiles of Essential Oils The EOs of four Solidago spp. were dominated by monoterpenes, with an average of 43.9–74.6% of the total composition of EOs in inflorescences and 39.7–69.1% in leaves. The mean percentage of sesquiterpenes ranged from 15.0 to 35.4% in inflorescences and from 25.5 to 38.2% in leaf EOs. Chemical profiles for EOs of Solidago spp. varied according to the contents of individual compounds and their distribution among accessions and plant parts. Examples of fingerprint profiles for inflorescence EOs are shown in Figure 1. 2.2.1. Solidago gigantea Inflorescence EOs were dominated by monoterpene hydrocarbons (25.4%) and oxygenated sesquiterpenes (19.7%) followed by oxygenated monoterpenes (18.5%) and sesquiterpene hydrocarbons (15.3%). Meanwhile, oxygenated sesquiterpenes (23.3%) and oxygenated monoterpenes (23.1%) were the major chemical fractions in leaf EOs. The principal compounds in all inflorescence and leaf EOs were α-pinene, bornyl acetate, spathulenol, isospathulenol, and caryophyllene oxide. Nevertheless, there were high differences for other compound prevalences and their concentrations between samples and plant parts. Germacrene D was a major common component in inflorescence oils (3.4–18.3%), whereas this compound was detected in less than half of the leaf samples (1.6–12.7%). One of the major components in the inflorescences was o-cymene (6.8–18.4%), while among the principal compounds in the leaves was β-cubebene (17.3–19.6%), but these compounds were found in only a few EO samples. Additionally, trans-pinocarveol (0.6–1.1%), cis-verbenol (0.3–1.0%), trans-verbenol (1.2–3.0%), and γ-muurolene (0.5–1.1%) were found in minor concentrations in all inflorescence EOs. Meanwhile, camphene (1.54–3.9%), β-pinene (1.0–3.4%), o-cymene (0.8–4.9%), limonene (0.6–1.3%), and γ-cadinene (0.6–1.5%) were common in all leaf EOs. In agreement with our results, α-pinene, bornyl acetate, germacrene D, and spathulenol have been previously reported to be major components of S. gigantea EOs [29,31,32,33]. In addition, cyclocolorenone, α- and γ-gurjunene, khusinol, and/or ledol and selina-3,11-dien-6-α-ol were also reported as the predominant constituents [31,33], although these sesquiterpenoids were not detected in the S. gigantea EOs tested. According to Gruľová et al. [34], S. gigantea EOs were dominated by sesquiterpene hydrocarbons such as δ-cadinene, γ-muurolene, α-cubebene, and γ-cadinene, and two of them, γ-muurolene and γ-cadinene, were common in the inflorescence or leaf EOs studied. 2.2.2. Solidago canadensis Monoterpenoids were predominant in S. canadensis EOs, with an average of 46.6.0% in inflorescences and 26.4% in leaves, followed by monoterpenes (28.0%) in inflorescences and sesquiterpenes (24.9%) in leaves. The first or second principal components in most of inflorescence EOs were α-pinene (0.1–36.1), trans-verbenol (5.2–21.7%), and bornyl acetate (3.8–19.8%). Among the most abundant constituents in leaf and inflorescence EOs were α-pinene (1.3–21.8) and bornyl acetate (6.5–20.4%). Leaf EOs were dominated by sesquiterpene hydrocarbons as β-cubebene (6.1–33.6%) and germacrene D (4.0–45.2%), and their concentrations exceeded those in inflorescence oils (0.8–13.1% and 1.9–11.4%, respectively). The other main constituent common for all leaf EOs was isospathulenol (0.7–10.2%). In addition, carvacrol was present as a major compound in two leaf EOs (22.83 and 23.7%) and was not detected in the remaining samples. Other components with noteworthy values in both inflorescence and leaf EOs were limonene (0.3–16.2% and 0.8–10.9%, respectively) and caryophyllene oxide (1.6–10.0% and 1.4–10.4%, respectively). The data revealed that cis-verbenol (0.6–3.7%), pinocarvone (0.9–2.7%), myrtenal (0.8–3.4%), and verbenone (1.0–5.2%) were detected in all inflorescence EOs in highly variable concentrations. Meanwhile, camphene (0.4–2.2%), β-pinene (0.7–2.7%), β-caryophyllene (0.8–6.9%), trans-verbenol (0.5–5.4%), β-elemene (0.6–5.9%), epoxyazulene (0.7–6.47%), and spathulenol (0.4–3.8%) were found in all or most of the leaf EOs. The presented results are in agreement with previous studies that confirmed α-pinene, limonene, bornyl acetate, germacrene D, β-cubebene, and caryophyllene oxide among the predominant compounds in S. canadensis EOs [19,23,25,33,35]. In addition, studies from different countries have shown that γ-cadinene and myrcene [36,37] sabinene [36], cyclocolorenone [29], or thymol [34] were among the major compounds in S. canadensis EOs. Meanwhile, our results showed a low concentration or frequency of these compounds in the samples tested. 2.2.3. Solidago × niederederi Solidago × niederederi inflorescence and leaf EO profiles were characterized by oxygenated monoterpenes (41.5 and 35.7%, respectively), followed by monoterpenes (26.4 and 17.7%, respectively), sesquiterpenoids (17.0 and 16.9%, respectively), and sesquiterpenes (6.0 and 15.0%, respectively). The principal constituents in the inflorescence EOs were α-pinene and trans-verbenol, which were the first or second major compounds in seven EOs, accounting for 22.3–31.7% and 13.4–22.9%, respectively. Caryophyllene oxide was among the major constituents in four (7.9–25.3%) and bornyl acetate in three (12.8–21.7%) EOs. Meanwhile, limonene and humulene epoxide II were present in most of the samples, however, dominated only in two (12.2 and 16.3%) and one (28.4%) samples, respectively. All inflorescence EOs contained varied levels of α-campholenal (0.9–6.2%), trans-pinocarveol (1.6–5.4%), and verbenone (0.6–5.1%). Other compounds with a mean content of 1 to 5.0% were β-pinene, camphene, cis-verbenol, pinocarvone, myrtenal, myrtenol, trans-carveol, germacrene D, epoxyazulene, and spathulenol. Solidago × niederederi leaf EOs contained seven compounds in concentrations above 10% in at least one sample and were considered as principal compounds. The major compounds such as α-pinene (1.1–25.8%), trans-verbenol (2.2–20.2%), bornyl acetate (4.2–20.2%), and caryophyllene oxide (2.1–38.1%) were found in all leaves in highly different concentrations. Verbenone and germacrene D were present in most EOs, averaging 5.2 and 8.8%, respectively, with the exception of the two EOs in which these compounds were predominant, accounting for 20.2 and 18.0%, respectively. Meanwhile, β-cubebene was detected only in two oils (16.4 and 23.9%) in which it was the first or second major compound. One leaf EO contained noteworthy concentrations of thymol (6.4%) and carvacrol (9.5%). In addition, 21 compounds were detected with a mean content of 1 to 5.0%, the most prominent of which were sesquiterpenes such as β-copaene, β-bisabolene, epoxyazulene, humulene epoxide II, and isospathulenol. Similar results for the composition of the major compounds in S. × niederederi EO have been recently published [26]. However, the results of only one plant accession were reported, making the comparison insufficient as the prevalence and number of compounds varied among samples. 2.2.4. Solidago virgaurea Solidago virgaurea inflorescence and leaf EOs were dominated by monoterpenes fraction, which accounted for an average of 35.9 and 49.0% of the total EO composition, respectively, followed by sesquiterpenes (20.7 and 16.9%, respectively). The main compounds in inflorescence EOs were α-pinene (18.8–36.3%), β-copaene (5.3–21.2%), and caryophyllene oxide (6.7–11.4%). All leaves were predominated by trans-verbenol (10.2–49.0%), two samples prevailed by α-pinene (22.4 and 23.3%) and caryophyllene oxide (10.1 and 14.7%), and one by verbenone (16.9%). Inflorescence and leaf EOs contained 22 and 29 compounds, respectively, with a mean percentage greater than 1% and less or equal to 5%. Among them, the most prominent were β-pinene, limonene, verbenone, α-campholenal, trans-pinocarveol, pinocarvone, bornyl acetate, α-copaene, germacrene D, cubebol, α-muurolene, δ-cadinene, and spathulenol, which were common to all inflorescence and/or leaf EOs. Similar to our identification, previous studies have confirmed the dominance of monoterpene and sesquiterpene fractions in S. virgaurea EOs [30,32]. Monoterpenes such as α-pinene, myrcene, β-pinene, and limonene together with sesquiterpene germacrene D have been reported as the major constituents in S. virgaurea. In addition, oxygenated sesquiterpenes, humulene epoxide II, spathulenol, selina-3,11-dien-6-α-ol, and caryophyllene oxide have been also considered as major compounds in S. virgaurea EOs [26,33]. Meanwhile, in this study, only α-pinene and caryophyllene oxide were found among the predominant compounds in all EOs tested, and the contents of the other mentioned compounds differed among samples. Meanwhile, selina-3,11-dien-6-α-ol was not detected at all in the presented EOs. 2.3. Interspecific Differences The results presented revealed similarities and differences in the frequency of distribution and contents of EO constituents among the four Solidago spp. Significant differences in monoterpene and sesquiterpene fractions were found between the inflorescences of the species studied (Table 1). Meanwhile, the chemical groups of compounds in leaves did not differ significantly between the four Solidago spp. (Table 2). Oxygenated monoterpenes predominated in the EOs of S. canadensis and S. × niederederi inflorescences and S. virgaurea and S. × niederederi leaves. The highest proportion of sesquiterpenoids among the species was found in S. gigantea inflorescences and leaves. Mono- and sesquiterpenes prevailed in the S. virgaurea inflorescence EOs; however, no significant differences were found between the species for monoterpenes. The common principal constituents of the inflorescence and leaf EOs of all four Solidago species were α-pinene, bornyl acetate, and caryophyllene oxide. Species showed significant differences (p ≤ 0.05) in the accumulation of bornyl acetate and caryophyllene oxide in leaf EOs, but no differences in α-pinene were observed between species (Table 1 and Table 2). The highest mean concentration of bornyl acetate was found in inflorescence and leaf EOs of both S. gigantea and S. canadensis, while caryophyllene oxide prevailed in S. × niederederi and S. virgaurea. The other major compound trans-verbenol was prevalent in all inflorescence EOs with the highest level (p ≤ 0.001) in S. canadensis and S. × niederederi. Meanwhile, the leaves of S. virgaurea and S. × niederederi had priority over the other two species in accumulation of trans-verbenol. Inflorescences of S. gigantea accumulated the highest (p ≤ 0.001) level of germacrene D compared to other species, but this compound did not differ significantly (p > 0.05) in the leaves between species. The inflorescences and leaves of S. gigantea were in priority to other species in accumulation of oxygenated sesquiterpenes as epoxyazulene, spathulenol, and isospathulenol. In addition, γ-muurolene, differently other species, was a common compound in all inflorescence EOs of S. gigantea, while other monoterpene hydrocarbons such as camphene and o-cymene were common in all leaf EOs of this species. The inflorescence EOs of all species differed significantly in the mean concentrations of oxygenated monoterpenes, such as α-campholenal, trans-pinocarveol, cis-verbenol, pinocarvone, verbenone, and trans-carveol, with the highest levels and frequency found in S. canadensis and S. × niederederi, followed by S. virgaurea. The same compounds, with the exception of trans-carveol, differed significantly in leaf EOs, with the highest concentrations in S. virgaurea and S. × niederederi leaves. Meanwhile, trans-carveol was detected in small amounts only in S. canadensis and S. × niederederi leaves. In addition, S. virgaurea inflorescence EOs differed from other species in the highest levels and frequency of distribution in α- and β-copaene, cubebol, α-muurolene, and δ-cadinene. As a consequence, quantitative rather than qualitative differences were observed between the species EOs. However, 10 compounds common in more than 30% of all studied inflorescence and/or leaf EOs did not differ significantly between Solidago spp. (Table 1 and Table 2). Among them, the most abundant were α- and β-pinene, limonene, bornyl acetate, germacrene D, and caryophyllene oxide. 2.4. Principal Component Analysis (PCA) PCA was employed to explain the phytochemical relationships arising due to inter- and intraspecific differences between the four Solidago spp., using the selected EOs compounds. A scree plot criterion was applied to reduce the number of PCs for explaining the variance in the selected variables. A two-dimensional PCA square matrix model explained more than 53.3% of the total variance and was used to visualize the available patterns of Solidago spp. EOs profiles (Figure 2). PC3 explained only 6.8% of the total variance and had no significant effect on scores differentiation, so results were not presented. PC1 accounted for 29.4% of the total data set variance and showed high negative correlation with α-campholenal, trans-pinocarveol, cis- and trans-verbenol, pinocarvone, myrtenal, myrtenol, verbenone, and trans-carveol and positive with carvone, germacrene D, γ-muurolene, γ-cadinene, spathulenol, and isospathulenol in inflorescences, and camphene, o-cymene, epoxyazulene, and (E)-nerolidol in leaves (Figure 2a). PC2 explained 23.6% of the total data set variance and was highly associated with positive loadings of α- and β-copaene, α-cubebene, cubebol, α-muurolene, and δ-cadinene in inflorescences and with α-campholenal, trans-pinocarveol, cis-verbenol, and pinocarvone in leaves, as well as with negative loadings of bornyl acetate and isospathulenol in leaves. The PCA score plot model showed the arrangement of 40 EOs into two separate and two overlapping ellipses, each with a 95% confidence interval limit (Figure 2b). The group on the right-hand plot combined all S. gigantea EOs along the positive PC1. Variables with high PC1 loadings contributed the highest impact on the grouping of S. gigantea samples were germacrene D, γ-cadinene, γ-muurolene, and spathulenol in inflorescences, and camphene, o-cymene, epoxyazulene, (E)-nerolidol, and spathulenol in leaves. These compounds were shared among all S. gigantea EOs in the highest amounts compared to the other species studied. Conversely, variables with high PC2 loadings had a weak contribution on the grouping of EOs and were found in minor quantities in S. gigantea. Solidago virgaurea EOs were clustered into a separate group on the upper positive side of the score plot, in distance from all other samples, indicating differences in their composition. The location of the samples can be explained by the same position of variables, which have a significant positive contribution to PC2. Variables with unit vectors close to each other were positively correlated, and their impact on the position of the samples was similar. Thus, α- and β-copaene, α-cubebene, cubebol, δ-cadinene, and α-muurolene in inflorescences, and α-campholenal, cis-verbenol, trans-verbenol, pinocarvone, and trans-pinocarveol in leaves, were common in S. virgaurea EOs and found in significantly higher amounts than in other species analyzed. Meanwhile, S. canadensis and S. × niederederi EOs were clustered into two partially overlapping ellipses, mainly in the left-hand score plot, showing the similarity of the volatile compound patterns. The arrangement of inflorescence EOs for both species coincided with a significant correlation of α-campholenal, trans-pinocarveol, pinocarvone, verbenone, cis-verbenol, trans-verbenol, myrtenal with PC1. In addition, the clustering of S. canadensis was influenced by myrtenol, trans-carveol, and carvone. Variables with high PC2 loading had no significant impact on EOs’ arrangement, except for trans-verbenol in S. × niederederi leaves, showing similarity of this taxon to S. virgaurea. Meanwhile, a previous study of phenolic compounds showed greater chemical similarity of S. × niederederi to S. virgaurea than to S. canadensis [20]. Consequently, the phytochemical patterns complemented the evidence of S. × niederederi origin between native S. virgaurea and invasive S. canadensis, with the higher contribution of alien species than that of native ones. In addition, S. × niederederi EOs were much more scattered on the PCs space, indicating higher diversity than other species, suggesting that S. × niederederi is a continuously evolving taxon. Consequently, Solidago spp. EOs differed significantly in the presence of terpenes that could be considered as species distinctive components. The inflorescence EOs of S. gigantea differed from other species by γ-cadinene, γ-muurolene, and spathulenol, and the leaves by camphene, o-cymene, epoxyazulene, (E)-nerolidol, and spathulenol. The inflorescences of S. canadensis and S. × niederederi differed significantly from the other species by the accumulation of oxygenated monoterpenes, such as α-campholenal, trans-pinocarveol, pinocarvone, verbenone, cis-verbenol, trans-verbenol, and myrtenal. In addition, S. canadensis inflorescence EOs were characterized by the prevalence of myrtenol, trans-carveol, and carvone, while S. × niederederi leaves were prominent by trans-verbenol. The species distinctive volatiles in S. virgaurea inflorescence EOs were α- and β-copaene, α-cubebene, cubebol, δ-cadinene, and α-muurolene, and in the leaves—α-campholenal, cis-verbenol, trans-verbenol, pinocarvone, and trans-pinocarveol. Consequently, multivariate data analysis allowed for an explanation in the intra- and interspecific diversity in four Solidago taxa according to the differences in EO volatiles. 3. Discussion Alien goldenrods are morphologically and phylogenetically close to each other, but differ in their ploidy level; S. gigantea is tetraploid (2n = 36), while S. canadensis, and S. × niederederi together with native S. virgaurea are diploids (2n = 18) [3,38]. The close relationships between Solidago spp. were reflected in the similarity of their phytochemical profiles. A comprehensive metabolomics approach to the different species indicated that the successful alien species had higher total number and more unique composition of secondary metabolites than their native congeners [39]. A comparison of the current and previous reports showed that our results are in agreement with previous reports for higher proportions of sesquiterpenoids in S. gigantea and hydrodrocarbons in S. virgaurea EOs [26,29,32,33]. Similar to our identification, the most abundant common compounds detected in the present study were also observed in previous studies on different species. Thus, the volatiles commonly found in various plant species have a high potential to accumulate in Solidago spp. EOs as well. On the other hand, there were compounds such as thymol and carvacrol, sporadically high levels of which were found in only a few S. × niederederi and S. canadensis EOs. Populations rich in thymol and carvacrol, compounds with a broad spectrum of biological activity [40,41,42], can be considered as a source of high potential raw material. In addition, Solidago spp. EOs differed significantly in some of the terpenes that can be considered as volatiles with great potential in chemophenetic studies of species. The first comparative study on terpenes as species distinctive compounds confirmed the origin of S. × niederederi as an interspecific taxon between S. canadensis and S. virgaurea. According to Orians [43], parental phytochemicals in hybrids tend to mostly express as either intermediate or similar to one of the parent’s compositions. The composition of S. × niederederi EOs was close to S. canadensis, one of parental species. The chromosome number may provide information about the hybrid origin of the species when it display allopolyploidy, but S. × niederederi exhibit a homoploid condition compared to its parental species and may backcross toward parental species. In this way, hybridization can increase the invasive capacity of goldenrods through gene introgression and significantly alter the ecosystems in which they grow [44]. However, species–specific compounds and chromosome number are not the main tools for hybrid identification. DNA fingerprinting techniques are the most reliable tools, but the use of additional phytochemical markers can provide insight into the ecological performance of hybrids and their further applications [45]. More often, the lower proportion of monoterpenes compared to sesquiterpenes [46] accounted for a higher proportion in the Solidago spp. EOs tested. Oxygenated monoterpenes have been proven to be the main phytotoxic active compounds in different plant EOs and have been highlighted as predictors of potential bioherbicides [47]. In addition, a tendency has been suggested that the monoterpene-rich EOs to promote higher phytotoxicity than sesquiterpene-rich EOs [48]. Comparative studies on the toxic activity of oxygenated monoterpenes revealed that the most active were alcohols, myrtenol and trans-pinocarveol, and ketones, verbenone and pinocarvone, which can be classified as predictors of the herbicidal activity of EOs [49,50,51]. Thus, S. canadensis and S. × niederederi, whose EOs differed from other species in some of oxygenated monoterpenes, suggested the potential of their raw materials for herbicidal activity. Meanwhile, terpene hydrocarbons have been found to be low phytotoxic EOs compounds [40,52]. On the other hand, Lawson et al. [36] reported that monoterpenes such as α- and β-pinene, limonene, or myrcene showed weak antifungal activity. Recent findings reported that (E)-nerolidol and spathulenol, which were presented as species–specific sesquiterpenoids in S. gigantea EOs, revealed effective allelopathic and insecticidal effects with potential for the developing a new natural pesticide [53,54,55]. Many findings have demonstrated the biological activity of caryophyllene oxide [53,55,56,57,58] that was a common compound in the presented Solidago spp. EOs. The potential biological activity of EOs is associated with the presence of high oxygenated compounds, as confirmed by a systematic review of phytotoxicity studies [59]. In this context, S. canadensis and S. × niederederi EOs, are of greatest interest in the development of new and safe bioproducts. Considering the previous and presented results for Solidago spp. EOs, the prevalence of predominant and other compounds varied across different studies, and their comparison is not informative enough. Reports often provide single sample data that are difficult to summarize as a species–specific composition of volatiles. According to Zidorn [60], correct taxonomic identification, geographical location, plant harvesting season, plant parts, and other indirect factors are crucial in phytochemical studies, which often receive little attention. Exogenous factors or environmental regulated factors such as light, precipitation, growing site and soil are often considered to be the most important factors modifying the qualitative/quantitative composition of EOs [61]. Experiments have shown that plants exposed to drought stress increased the concentration of monoterpenes to protect plant cells from ROS damage [62]. Meanwhile, Caser et al. [63] found that drought increased the production of sesquiterpenoids and decreased monoterpenoids. According to Paulsen and Selmar [64], increased terpene synthesis is not supported by carbon allocation theory, but is attributed to changes in biomass production. In addition, differences in the chemical profiles of EOs are often explained in the context of the interaction of the metabolic inversion of the ratio of oxygen-free to oxygen-containing terpenes with the surrounding environment. Sesquiterpenes have been observed to be predominate during the dry season, while higher concentrations of sesquiterpenoids were found during the wet season [65]. The water scarcity increased the production of monoterpenoids and monoterpenes, while the opposite trend was observed for sesquiterpenes [66]. On the other hand, Tsusaka et al. [67], investigating the influence of genetic and environmental factors on sesquiterpenoids in Atractylodes lancea, (Thunb.) DC. found that the genotype had a greater effect on EO compounds than the conditions of the plant cultivation year. The volatiles were stable despite the changing growing conditions, but the absolute values of terpenoids were induced by the site of cultivation. Tardugno et al. [68] determined that the composition of Thymus vulgaris L. essential oils were highly influenced by the cultivating techniques. Over time, local environment leads to differences in metabolomics and the formation of ecotypes and chemotypes within a species [69]. The intraspecific differences in Solidago spp. volatile compounds observed in the present study can be explained by genotypic differences, as the plants grew under close conditions. Sexual reproduction helps maintain a high level of genetic and phenotypic diversity in goldenrods. Our previous study showed a high morphological diversity in Solidago spp. both between populations and between individual genets [3]. Similarly, high variability in volatiles and morphological characters was observed in wild Mentha longifolia L. accessions growing in the same field [70]. According to Zhao et al. [71], high genetic variations are characteristic within invasive and native areas of S. canadensis populations. Considerations suggested that the study of local populations makes it possible to identify intraspecific diversity that potentially reflects local genetic changes rather than the controversial dependence of terpenes synthesis under changing environmental conditions. The outstanding diversity in the goldenrods studied allows the selection of accessions in terms of the desired composition of EO volatiles. Phytochemical profiling of plant raw materials is an informative tool to learn about their potential for further development of new natural products. 4. Material and Methods 4.1. Plant Material Plant material of four Solidago spp. in the flowering phase was collected from six different sites in Vilnius district, Lithuania, in August 2018. Eighteen accessions of S. canadensis, seven of S. gigantea, nine of S. × niederederi, and three accessions of S. virgaurea were collected at least one kilometre apart from each other in abandoned dry grasslands and disturbed farmlands (Table 3). The vegetation of the collection sites was characterized as semi-ruderal dry grassland dominated by plant communities of Agropyretea intermedii-repentis and Artemisietea vulgaris. The EOs in habitats were sand or sandy loam, with low to moderate humus content (1.8–2.7%), rich in phosphorus (126–260 P2O5 mg kg−1) and potassium (146–205 K2O mg kg−1), pHKCl varied from 5.8 to 7.1. The harvested plant material consisted of shoots of a single genet derived from a single seed. Individual genets were identified by phenological and morphological characteristics and by rhizome connections. The accessions of the same species were collected at least five meters apart from each other if more than one accession was collected from the same site. The plant material was dissected into inflorescences and leaves and dried separately at 25 °C. The botanical identification of species was based on morphological diagnostic characters such as the shape and size of inflorescences and ray flowers, stem color, and stem hairiness by Birutė Karpavičienė and Jolita Radušienė [3]. The specimens of evaluated Solidago spp. were deposited in the Herbarium of the Institute of Botany of Nature Research Centre (BILAS), Vilnius, Lithuania. 4.2. Isolation of Essential Oils The plant material from 30 g of air-dried leaves and inflorescences was hydrodistilled separately for three hours using a Clevenger type apparatus. Each sample of yellowish EO was dried over anhydrous sodium sulphate and stored in a sealed vial at 4 °C until analysis. A sample preparation for chemical analysis included 1.0 μL of EO added into 1.0 mL of n-hexane following previous studies [23,33]. The essential oil content was calculated as relative percentages per 100 g of dry plant material. 4.3. Analysis of Essential Oils The EOs analysis was performed using the GCMS-QP 2010 Ultra system equipped with a Shimazu autoinjector AOC-5000 (Shimadzu, Europa GmbH). A capillary column RXi-5MS (30 m × 0.25 mm i.d. × 0.25 film thickness µm) (Restek, Bellefonte, PA, USA) was used. The sample injection volume was 1 µL, a split ratio was 1:60 (v:v) and the split injector temperature was 260 °C. Helium was used as carrier gas with flow rate of 1.22 mL min−1. The initial column temperature was 50 °C, held for 5 min and raised to 200 °C at the rate of 2 °C min−1, then raised from 200 to 315 °C at the rate of 15 °C min−1 and held for 5 min. The detector ion source and interface temperatures were 200 °C and 280 °C, respectively. Mass spectra were acquired at an ionization voltage of 70 eV, a scan rate of 2500 m/z within the range of 29–500 m/z and a scan time of 0.2 s. The chromatographic analysis was run in triplicate. 4.4. Identification and Quantification of Components The linear retention indices (LRI) of compounds were calculated using a homologous series of n-alkanes C8–C26 (Sigma-Aldrich, UK, purity >99.2%) injected at the beginning of the analysis and comparing the retention times of the eluted peaks with those of the alkanes [72]. Chromatographic data were analyzed using GC–MS solution software (Shimadzu, Europa GmbH). The EO constituents were identified by comparing the unique mass spectral fragmentation patterns of each peak with the mass spectral computer library database and those presented as standards in the NIST 14, FFNSC, WR10, and WR10R libraries, as well as comparing the obtained LRI with presented in NIST 14 datasets and reference [73] corresponding to the conditions for dimethylsilicone stationary phase with 5% phenyl groups. The relative percentages of analytes as the mean of the three runs were calculated from their peak areas in the chromatographic profiles without the use of correction factors corresponding to the conditions for the stationary phase of dimethylsilicone with 5% phenyl groups. The repeatability and intermediate precision of analysis, expressed as relative standard deviation (RSD), was evaluated by performing the retention time and peak area values of five analytes in the same S. canadensis EO extract for intra- and inter- daily tests (Table 4). Repeatability was determined in five consecutive injections of EO in the same day. The RSD for the retention time ranged from 0.22 to 0.32% and for relative peak area from 0.33 to 0.45%. Intermediate precision was assessed by five injections over two different days, with RSD values ranging from 0.54 to 0.92% for retention time and 0.98 to 2.21% for peak areas. The accuracy of the quantification was satisfactory, as RSD value within the 3% range is generally considered as acceptable. 4.5. Data Analysis Multivariate statistical analysis was performed using software package Statistica 10.0 (StatSoft Inc.). The Kruskal–Wallis ANOVA was used to determine the differences between species. Significant differences were specified by two-tailed test at p ≤ 0.05. Principal component analysis (PCA) was used to identify the similarities and differences between the EOs analyzed using statistically independent variables. The PCA was based on 24 inflorescence and 16-leaf standardized variables that different significantly between species and that represented constituents detected in 30% or more of EO samples in at least one species. Leaf and inflorescence data sets were pooled and used in PCA, resulting in more convincing results than separate leaf and inflorescence PCAs. 5. Conclusions The frequency and percentage of distribution in the volatile constituents of Solidago spp. varied depending on the species, accessions, and plant parts. The principal compounds common to all Solidago spp. inflorescence and leaf EOs were α- and β-pinene, limonene, bornyl acetate, germacrene D, spathulenol, and caryophyllene oxide. Solidago spp. differed significantly in some of the distinctive terpenes that can be considered as compounds with high potential for chemophenetic and taxonomic studies of the genus. A comparison of volatile profiles for Solidago spp. confirmed the interspecific origin of S. × niederederi between S. canadensis and S. virgaurea with a higher metabolic contribution of alien species than native ones. The findings provide the bioprospecting of Solidago spp. as a source for specified composition of volatiles. The vast resources of invasive goldenrods are of great interest as a convenient and readily acceptable, underutilized source of natural bioactive compounds that can be used for different applications. The combination of fingerprint and multivariate data analysis demonstrated a simplified assessment of the quality of wild plant materials. Correct species identification is essential for the development of raw material quality control protocols for the targeted collection and assessment of raw materials from wild populations. The screening of a relatively large number of plant accessions from mixed populations of different species allows for a more reasonable comparison of their volatile profiles and enables prediction of the most likely quality of raw materials harvested from the wild. Acknowledgments The authors thank to Open Access Centre for the Advanced Pharmaceutical and Health Technologies (Lithuanian University of Health Sciences) for providing the opportunity to use infrastructure for experiments. Author Contributions Conceptualization, J.R. and B.K.; methodology, L.R. and M.M., software, B.K.; validation, M.M. and L.I.; formal analysis, L.I. and M.M.; investigation, J.R., M.M. and B.K.; resources, L.I. and J.R.; data curation, M.M., J.R. and B.K.; writing—original draft preparation, J.R.; writing—review and editing, J.R. and L.R.; visualization, B.K. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Data Availability Statement All data generated during this study are included in this article. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Examples of fingerprint profiles for S. × niederederi, S. virgaurea, S. canadensis, and S. gigantea inflorescence EOs performed by GC–MS using the GC–MS-QP2010 Ultra Gas system. The peak numbers correspond to the number of EO compounds listed in the Table 1. Figure 2 PCA model representing the accumulation of terpenes in inflorescences and leaves of four Solidago species: (a) loading plot of the variables contributing to PC1 and PC2; (b) scores plot for the tested EOs with a 95% ellipses confidence limit for each species. The numbers of the variables correspond to the numbers of EO compounds listed in the Table 1 and Table 2. Inflorescence variable numbers were marked in brown; leaf variable numbers were marked in green. plants-11-01159-t001_Table 1 Table 1 The frequency of distribution (F, %) of the compounds detected in more than 30% of the inflorescence EO samples in at least one of the four Solidago species; their mean relative percentage (M) with SD and significance (p) of interspecies differences according to the Kruskal–Wallis two-tailed test. No Compounds LRI Exp. LRI Ref. S. gigantea (n = 7) S. canadensis (n = 18) S. × niederederi (n = 9) S. virgaurea (n = 3) p F M SD F M SD F M SD F M SD 1 α-pinene 930 930 100 10.7 8.31 100 18.1 13.84 100 17.4 12.84 100 28.6 8.96 0.196 2 camphene 945 948 71.4 1.8 0.77 72.2 1.7 0.69 77.8 1.2 0.34 100 1.0 0.35 0.713 3 thuja-2,4(10)-diene 950 957 28.6 0.6 0.35 66.7 0.5 0.16 44.4 0.7 0.32 66.7 0.6 0.13 0.504 4 sabinene 971 976 71.4 2.5 2.07 66.7 1.1 0.79 66.7 1.3 1.79 33.3 0.6 – 0.258 5 β-pinene 974 980 71.4 3.0 0.90 83.3 2.3 1.40 88.9 2.2 1.71 100 3.6 1.51 0.536 6 β-myrcene 992 991 42.9 1.6 0.34 55.6 0.8 0.60 33.3 0.6 0.15 66.7 1.5 0.23 0.564 7 p-cymene 1018 1014 42.9 2.5 1.42 5.6 0.5 – 0 0 – 0 0 – 0.021 8 o-cymene 1022 1009–1076 42.9 10.9 6.57 55.6 0.5 0.42 33.3 0.5 0.30 0 0 – 0.338 9 limonene 1023 10270 71.4 1.1 0.19 100 5.2 4.48 88.9 5.8 5.05 100 1.1 0.57 0.032 10 linalool 1090 1098 0 0 – 16.7 0.1 0.07 44.4 0.5 0.10 33.3 0.8 – 0.105 11 1 α-campholenal 1109 1105 85.7 1.3 a 0.58 100 4.1 b 1.45 100 3.6 b 1.61 66.7 3.1 1.35 0.004 12 trans-pinocarveol 1135 1139 100 0.8 a 0.20 88.9 2.9 b 1.18 100 3.0 b 1.28 100 1.6 0.93 0.005 13 trans-sabinol 1139 1140 42.9 0.3 0.16 0 0 – 0 0 – 0 0 – 0.004 14 trans-verbenol 1143 1144 100 2.9 a 1.12 100 13.1 b 5.39 100 13.6 b 5.79 100 6.6 3.43 0.001 15 cis-verbenol 1145 1142 100 0.5 a 0.27 100 1.9 b 0.88 100 1.8 b 0.88 66.7 1.2 0.23 0.001 16 pinocarvone 1158 1160 85.7 0.4 a 0.04 100 1.6 b 0.50 88.9 1.8 b 0.62 66.7 1.4 0.34 0.003 17 p-mentha-1,5-dien-8-ol 1164 1166 0 0 – 5.6 0.4 0.31 44.4 0.7 0.39 0 0 – 0.016 18 α-phellandrene-8-ol 1165 1166 0 0 a – 77.8 0.9 b 0.32 0 0 a – 0 0 – <0.001 19 terpinen-4-ol 1174 1175 42.9 1.0 0.51 16.7 0.3 0.03 22.2 0.4 0.06 0 0 – 0.241 20 myrtenal 1192 1193 85.7 0.5 a 0.13 100 1.9 b 0.63 88.9 1.7 b 0.57 66.7 1.2 0.24 0.001 21 myrtenol 1193 1194 85.7 0.7 a 0.35 100 1.5 b 0.63 88.9 1.3 0.54 33.3 1.1 – 0.014 22 verbenone 1205 1205 42.9 0.4 a 0.29 100 2.7 b 1.19 100 2.7 b 1.38 100 1.2 0.67 <0.001 23 trans-carveol 1219 1215 14.3 0.6 a – 94.4 1.9 b 0.69 88.9 1.6 0.60 33.3 0.8 – <0.003 24 carvone 1241 1242 0 0 a – 94.4 1.3 b 0.18 77.8 1.1 0.52 0 0 – <0.001 25 bornyl acetate 1287 1285 100 10.4 4.89 100 11.9 4.74 100 9.3 5.73 100 4.1 2.81 0.098 26 α-cubebene 1347 1345 0 0 – 0 0 a – 0 0 – 100 1.0 b 0.40 <0.001 27 α-copaene 1372 1376 0 0 – 11.1 0.2 a 0.01 0 0 – 100 2.6 b 1.34 <0.001 28 β-cubebene 1380 1389 0 0 a – 77.8 4.9 b 3.21 11.1 6.0 a 0.04 0 0 – <0.001 29 β-elemene 1389 1391 42.9 0.3 0.08 55.6 0.8 0.46 55.6 0.7 0.28 0 0 – 0.243 30 (E)-β-caryophyllene 1414 1419 71.4 0.9 0.31 88.9 0.6 0.16 44.4 0.8 0.44 66.7 0.7 0.04 0.425 31 β-copaene 1424 1432 14.3 0.4 – 22.2 0.2 a 0.13 33.3 3.6 3.80 100 12.2 8.15 0.006 32 α-humulene 1448 1452 42.9 0.4 0.04 38.9 0.2 0.21 22.2 1.3 1.34 0 0 – 0.588 33 γ-muurolene 1475 1477 100 0.8 a 0.23 0 0 b – 0 0 b – 33.3 0.7 – <0.001 34 germacrene D 1480 1480 100 11.2 a 4.80 22.2 8.1 b 4.21 88.9 2.5 1.65 100 1.5 0.20 <0.001 35 β-selinene 1481 1486 57.1 0.4 0.01 16.7 0.3 0.08 22.2 0.7 0.01 0 0 – 0.155 36 epi-cubebol 1489 1493 14.3 0.7 – 0 0 – 11.1 1.0 – 66.7 1.6 1.24 0.007 37 α-muurolene 1499 1499 28.6 0.4 0.03 0 0 a – 22.2 1.1 0.16 100 1.4 b 0.78 <0.001 38 γ-cadinene 1507 1513 85.7 0.7 a 0.26 16.7 0.4 b 0.09 0 0 b – 0 0 – <0.001 39 cubebol 1514 1515 0 0 – 0 0 a – 22.2 1.5 0.04 100 2.8 b 2.08 <0.001 40 δ-cadinene 1520 1524 14.3 0.5 – 0 0 a – 22.2 1.1 0.16 100 1.3 b 0.90 <0.001 41 epoxyazulene 1561 1554 71.4 6.4 2.98 55.6 1.9 1.22 88.9 1.6 1.35 0 0 – 0.054 42 (E)-nerolidol 1565 1564 71.4 0.7 0.28 11.1 0.4 0.13 11.1 0.4 – 0 0 – 0.002 43 spathulenol 1575 1576 100 5.0 a 2.97 38.9 0.9 b 0.74 66.7 2.8 3.96 100 1.3 0.20 <0.001 44 caryophyllene oxide 1578 1581 100 3.6 2.44 100 3.6 2.17 100 8.4 8.26 100 8.5 2.48 0.083 45 viridiflorol 1586 1590 0 0 – 0 0 – 0 0 – 33.3 1.2 – 0.010 46 humulene epoxide II 1607 1606 0 0 – 0 0 a – 77.8 4.5 b 8.60 0 0 – <0.001 47 isospathulenol 1627 1630 100 6.1 a 6.47 83.3 2.3 1.52 66.7 2.1 0.73 33.3 1.3 b – 0.018 Monoterpene hydrocarbons 100 25.4 16.52 100 28.0 20.88 100 26.4 18.09 100 35.9 11.86 0.608 Oxygenated monoterpenes 100 18.5 a 6.04 100 46.6 b 15.42 100 41.5 b 16.35 100 19.2 7.60 <0.001 Sesquiterpene hydrocarbons 100 15.3 b 6.63 100 7.9 6.00 100 6.0 4.85 100 20.7 a 10.64 0.007 Oxygenated sesquiterpenes 100 19.7 a 8.85 100 7.1 b 3.48 100 17.0 15.54 100 14.7 5.25 <0.001 Oxygenated diterpenes 14.3 0.1 – 11.1 0.9 0.19 22.2 1.0 0.51 0 0 – 0.479 1 Compounds in bold were included in the PCA. Compounds are listed in order of their linear retention indices (LRI exp.) calculated using homologous series of n-alkanes (C8–C26). LRI ref.—linear retention indices from NIST 14 data basis and reference. The mean values of the compounds marked with the letters (a, b) differ significantly at p ≤ 0.05 between species according to the Kruskal–Wallis test. n—number of distilled accessions per species. plants-11-01159-t002_Table 2 Table 2 The frequency of distribution (F, %) of the compounds detected in more than 30% of the leaf EO samples in at least one of the four Solidago species; their mean relative percentage (M) with SD and significance (p) of interspecies differences according to the Kruskal–Wallis two-tailed test. No Compounds a LRI Exp. b LRI Ref. S. gigantea (n = 7) S. canadensis (n = 18) S. × niederederi (n = 9) S. virgaurea (n = 3) p F M SD F M SD F M SD F M SD 1 α-pinene 930 930 100 7.8 2.18 100 8.6 5.69 100 11.9 10.37 100 16.0 11.77 0.856 2 1 camphene 945 948 100 2.8 a 0.76 100 1.2 b 0.40 77.8 1.1 b 0.45 66.7 0.5 b 0.07 <0.001 3 thuja-2,4(10)-diene 950 957 0 0 – 11.1 0.2 0.13 33.3 0.6 0.13 33.3 0.3 – 0.187 4 sabinene 971 976 42.9 1.1 0.61 44.4 0.6 0.42 33.3 1.1 1.21 66.7 0.3 0.01 0.926 5 β-pinene 974 980 100 2.2 0.88 94.4 1.4 0.61 88.9 1.9 1.16 66.7 3.5 0.64 0.193 6 β-myrcene 992 988 28.6 1.2 0.12 5.6 1.0 – 22.2 0.8 0.49 33.3 0.4 – 0.372 7 o-cymene 1022 1009–1076 100 2.1 a 1.40 50.0 0.5 b 0.46 22.2 0.4 b 0.06 0 0 b – <0.001 8 limonene 1023 1027 100 1.1 a 0.16 100 2.5 b 2.17 100 2.2 1.56 66.7 1.1 0.35 0.006 9 α-campholenal 1109 1105 14.3 0.6 a – 77.8 0.8 0.72 77.8 2.6 1.68 100 2.8 b 0.52 0.007 10 trans-pinocarveol 1135 1139 42.9 0.3 a 0.16 66.7 0.9 a 0.50 77.8 1.6 0.75 100 3.6 b 1.83 0.003 11 trans-verbenol 1143 1144 42.9 0.5 a 0.23 100 2.7 b 1.81 100 8.9 b 6.85 100 24.6 b 21.21 <0.001 12 cis-verbenol 1145 1142 0 0 a – 27.8 0.4 0.27 44.4 1.6 0.39 100 1.5 b 0.82 0.004 13 pinocarvone 1158 1160 0 0 a – 22.2 0.6 a 0.31 66.7 1.1 0.51 100 1.8 b 0.04 <0.001 14 myrtenal 1192 1193 0 0 – 72.2 0.6 0.29 55.6 1.6 0.48 66.7 1.6 0.02 0.024 15 myrtenol 1193 1194 0 0 – 55.6 0.3 0.25 55.6 1.0 0.33 66.7 1.2 0.03 0.038 16 verbenone 1205 1206 42.9 0.3 a 0.16 88.9 1.5 1.35 77.8 5.2 b 6.77 100 7.3 b 8.37 0.008 17 cis-carveol 1208 1206 0 0 – 5.6 1.1 – 0 0 – 33.3 0.7 – 0.168 18 trans-carveol 1219 1215 0 0 – 16.7 0.2 0.07 44.4 0.9 0.21 33.3 0.8 – 0.066 19 bornyl acetate 1287 1285 100 22.4 a 5.37 100 18.7 a 7.55 100 14.0 5.53 100 2.6 b 0.46 0.004 20 thymol 1295 1297 0 0 – 5.6 0.5 – 11.1 9.5 – 33.3 3.8 – 0.330 21 carvacrol 1306 1308 0 0 – 11.1 23.3 0.63 11.1 6.4 – 66.7 3.2 0.42 0.079 22 α-copaene 1372 1376 0 0 – 22.2 0.3 0.09 0 0 – 66.7 0.5 0.03 0.059 23 β-bourbonene 1378 1385 85.7 0.9 0.45 50.0 0.5 0.46 22.2 2.0 2.27 33.3 1.8 – 0.056 24 β-cubebene 1380 1389 57.1 14.1 8.41 38.9 21.3 10.25 22.2 20.1 5.25 0 0 – 0.467 25 β-elemene 1389 1391 28.6 0.8 a 0.04 83.3 1.7 b 1.45 66.7 0.8 0.57 33.3 0.4 – 0.005 26 (E)-β-caryophyllene 1414 1419 85.7 1.0 0.27 61.1 2.5 2.14 66.7 2.0 1.67 66.7 1.5 1.42 0.985 27 β-copaene 1424 1432 14.3 0.5 – 50.0 0.6 0.19 22.2 3.8 4.56 66.7 3.2 0.79 0.161 28 α-humulene 1448 1452 42.9 0.5 0.08 61.1 0.8 0.54 33.3 1.9 1.89 33.3 0.4 – 0.380 29 γ-muurolene 1475 1477 100 0.8 a 0.23 0 0 b – 0 0 b – 33.3 0.7 – <0.001 30 germacrene D 1480 1480 42.9 5.9 5.95 50.0 19.6 15.22 66.7 8.8 5.22 66.7 5.2 6.46 0.751 31 β-selinene 1481 1486 14.3 0.6 – 38.9 0.5 0.47 0 0 – 0 0 – 0.103 32 epi-cubebol 1489 1493 0 0 – 0 0 – 0 0 – 66.7 1.4 0.31 0.012 33 α-muurolene 1499 1499 0 0 – 0 0 – 0 0 – 33.3 0.3 – 0.222 34 β-bisabolene 1505 1509 0 0 – 11.1 5.0 1.05 11.1 4.7 – 33.3 1.8 – 0.569 35 γ-cadinene 1507 1513 100 1.0 0.32 5.6 0.7 – 0 0 – 0 0 – <0.001 36 cubebol 1514 1515 0 0 – 0 0 – 0 0 – 66.7 0.7 0.10 0.014 37 δ-cadinene 1520 1524 0 0 – 5.6 0.7 – 11.1 0.9 – 33.3 0.4 – 0.384 38 epoxyazulene 1561 1554 100 6.1 a 3.22 100 2.7 1.43 66.7 2.9 b 2.49 33.3 0.3 b – 0.001 39 (E)-nerolidol 1565 1564 85.7 0.9 a 0.20 38.9 0.6 0.20 0 0 b – 0 0 – 0.001 40 spathulenol 1575 1576 100 6.7 a 6.25 88.9 1.2 b 0.98 66.7 2.1 b 1.94 100 3.1 1.30 0.003 41 caryophyllene oxide 1578 1581 100 3.1 a 0.77 77.8 3.9 a 2.24 100 10.4 b 11.18 100 9.0 6.36 0.004 42 humulene epoxide II 1607 1606 57.1 2.4 1.26 38.9 2.7 1.04 22.2 3.2 0.92 66.7 5.0 1.44 0.276 43 isospathulenol 1627 1630 100 6.3 a 3.11 100 4.8 a 2.54 66.7 3.7 2.26 0 0 b – 0.004 Monoterpene hydrocarbons 100 16.6 4.08 100 14.4 9.42 100 17.7 10.92 100 20.1 15.28 0.634 Oxygenated monoterpenes 100 23.1 5.64 100 27.9 11.2 100 35.7 13.07 100 49.0 27.81 0.072 Sesquiterpene hydrocarbons 100 14.9 7.85 94.4 23.3 15.3 100 15.0 11.32 66.7 8.6 10.93 0.252 Oxygenated sesquiterpenes 100 23.3 10.03 100 13.3 6.07 100 16.9 12.56 100 16.9 7.63 0.181 Oxygenated diterpenes 0 0 – 5.6 0.7 0.26 11.1 0.1 0.33 0 0 – 0.375 1 Compounds in bold were included in the PCA. Compounds are listed in order of their linear retention indices (LRI exp.) calculated using homologous series of n-alkanes (C8–C26). LRI ref.—linear retention indices from NIST 14 data basis and reference. The mean values of the compounds marked with the letters (a, b) differ significantly at p ≤ 0.05 between species according to the Kruskal–Wallis test. n—number of distilled accessions per species. plants-11-01159-t003_Table 3 Table 3 Collection sites data on Solidago gigantea (SG), S. canadensis (SC), S. × niederederi (SN), and S. virgaurea (SV). Collection Site Altitude, m Latitude N Longitude E Number of Accessions SG SC SN SV Pavilnys, Vilnius distr. 215 54°40′35″ 25°23′01″ 3 3 1 – Didieji Pupojai, Vilnius 206 54°42′38″ 25°23′29″ 1 3 3 2 Rokantiškės, Vilnius 203 54°40′03″ 25°22′58″ 3 2 1 – Raudondvaris, Vilnius distr. 149 54°52′32″ 25°31′08″ – 4 – – Dvariškės, Vilnius distr. 149 54°49′17″ 25°16′23″ – 3 3 1 Karklinė, Vilnius distr. 164 54°54′26″ 25°33′40″ – 3 1 – plants-11-01159-t004_Table 4 Table 4 Repeatability and intermediate precision on the relative retention time (tR,) and peak area (A) of the five analytes in S. canadensis EOs expressed as relative standard deviation (RSD, %). Analytes Repeatability (Run-to-Run) Intermediate Precision (Day-to-Day) tR, min RSD, % A, % RSD, % tR, min RSD, % A, % RSD, % α-pinene 11.85 0.23 24.57 0.45 11.92 0.82 24.83 1.51 limonene 15.24 0.29 9.07 0.33 15.31 0.73 9.17 1.42 trans-verbenol 23.20 0.22 10.36 0.41 23.36 0.92 10.54 2.21 bornyl acetate 32.70 0.32 10.21 0.43 32.77 0.54 10.26 0.98 β-cubebene 44.59 0.26 2.82 0.38 44.74 0.60 2.84 1.02 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Franz C.M. Essential oil research: Past, present and future Flavour. Fragr. J. 2010 25 112 113 10.1002/ffj.1983 2. Weber E. Current and potential ranges of three exotic goldenrods (Solidago) in Europe Conserv. Biol. 2001 15 122 128 10.1111/j.1523-1739.2001.99424.x 3. Karpavičienė B. Radušienė J. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093124 sensors-22-03124 Article Twisted Few-Mode Optical Fiber with Improved Height of Quasi-Step Refractive Index Profile https://orcid.org/0000-0001-8737-5486 Bourdine Anton V. 1234 Demidov Vladimir V. 2 https://orcid.org/0000-0003-0276-0874 Kuznetsov Artem A. 5* https://orcid.org/0000-0003-1845-6260 Vasilets Alexander A. 56 Ter-Nersesyants Egishe V. 2 Khokhlov Alexander V. 2 https://orcid.org/0000-0001-8594-741X Matrosova Alexandra S. 27 Pchelkin Grigori A. 28 https://orcid.org/0000-0002-3919-4151 Dashkov Michael V. 1 Zaitseva Elena S. 1 https://orcid.org/0000-0002-0753-0608 Gizatulin Azat R. 9 Meshkov Ivan K. 9 https://orcid.org/0000-0002-0713-7806 Sakhabutdinov Airat Zh. 5 Dmitriev Eugeniy V. 10 https://orcid.org/0000-0003-4779-4656 Morozov Oleg G. 5 https://orcid.org/0000-0003-1723-9168 Burdin Vladimir A. 1 Dukelskii Konstantin V. 247 Ismail Yaseera 11 https://orcid.org/0000-0002-8604-0913 Petruccione Francesco 1112 https://orcid.org/0000-0002-0833-6815 Singh Ghanshyam 13 Tiwari Manish 14 Yin Juan 15 Fienga Francesco Academic Editor 1 Department of Communication Lines, Povolzhskiy State University of Telecommunications and Informatics, 23, Lev Tolstoy Street, Samara 443010, Russia; bourdine@yandex.ru (A.V.B.); mvd.srttc@gmail.com (M.V.D.); zaytzewa@inbox.ru (E.S.Z.); burdin@psati.ru (V.A.B.) 2 JSC “Scientific Production Association State Optical Institute Named after Vavilov S.I.”, 36/1, Babushkin Street, St. Petersburg 192171, Russia; demidov@goi.ru (V.V.D.); ter@goi.ru (E.V.T.-N.); khokhlov@goi.ru (A.V.K.); a.pasishnik@gmail.com (A.S.M.); beegrig@mail.ru (G.A.P.); kdukel@mail.ru (K.V.D.) 3 “OptoFiber Lab” LLC, Skolkovo Innovation Center, 7, Nobel Street, Moscow 143026, Russia 4 Department of Photonics and Communication Links, Saint Petersburg State University of Telecommunications Named after M.A. Bonch-Bruevich, 22, Bolshevikov Avenue, St. Petersburg 193232, Russia 5 Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A.N. Tupolev-KAI, 10, Karl Marx Street, Kazan 420111, Russia; a.vasilets@mail.ru (A.A.V.); azhsakhabutdinov@kai.ru (A.Z.S.); microoil@mail.ru (O.G.M.) 6 Faculty of Management and Engineering Business, Kazan Innovative University Named after V.G. Timiryasov (IEML), 42, Moskovskaya Street, Kazan 420111, Russia 7 Faculty of Photonics and Optical Information, School of Photonics, ITMO University, Bldg. A, 49, Kronverksky Alley, St. Petersburg 197101, Russia 8 Institute of Physics, Nanotechnology and Telecommunications, Peter the Great St. Petersburg Polytechnic University, Bldg. II, 29, Politekhnicheskaya Street, St. Petersburg 194064, Russia 9 Department of Telecommunication Systems, Ufa State Aviation Technical University, 12, Karl Marx Street, Ufa 450000, Russia; azat_poincare@mail.ru (A.R.G.); mik.ivan@bk.ru (I.K.M.) 10 Department of Quantum Communication Systems Research, Radio Research and Development Institute, 16, Kazakova Street, Moscow 105064, Russia; dmitriev@niir.ru 11 Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Durban 4001, South Africa; ismaily@ukzn.ac.za (Y.I.); petruccione@ukzn.ac.za (F.P.) 12 National Institute for Theoretical Physics, University of KwaZulu-Natal, Durban 4001, South Africa 13 Department of Electronics and Communication Engineering, Malaviya National Institute of Technology (MNIT) Jaipur, J.L.N Road, Jaipur 302017, Rajasthan, India; gsingh.ece@mnit.ac.in 14 Department of Electronics and Communication Engineering, School of Electrical and Electronics & Commu-nication Engineering, Manipal University Jaipur, Jaipur-Ajmer Expressway, Jaipur 303007, Rajasthan, India; manish.tiwari@jaipur.manipal.edu 15 Division of Quantum Physics and Quantum Information, University of Science and Technology of China, 99, Xiupu Road, Pudong District, Shanghai 200093, China; yinjuan@ustc.edu.cn * Correspondence: artem.a.kuznetsov@bk.ru; Tel.: +7-919-6425689 19 4 2022 5 2022 22 9 312414 2 2022 13 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). This work presents designed and fabricated silica few-mode optical fiber (FMF) with induced twisting 10 and 66 revolutions per meter, core diameter 11 µm, typical “telecommunication” cladding diameter 125 µm, improved height of quasi-step refractive index profile and numerical aperture 0.22. Proposed FMF supports 4 guided modes over “C”-band. We discussed selection of specified optical fiber parameters to provide desired limited mode number over mentioned wavelength range. Some results of tests, performed with pilot samples of manufactured FMF, are represented, including experimentally measured spectral responses of laser-excited optical signals, that comprise researches and analysis of few-mode effects, occurring after fiber Bragg grating writing. twisted optical fiber laser beam profile differential mode delay laser-based few-mode optical signal transmission fiber Bragg grating few-mode effects ==== Body pmc1. Introduction Twisted optical fibers have been known since the early 1980s: here, the concept of fiber spinning was firstly originally introduced in the work [1]. The fabrication technique of twisted optical fibers is based on rotation of preform during the fiber drawing [1] or directly spinning of the drawn optical fiber [2]. Twisted single mode optical fibers are usually considered as fibers with reduced polarization mode dispersion (PMD) [1,2,3,4], while induced chirality (including twisting) over multimode optical fibers is declared as the method for differential mode delay (DMD), decreasing with total bandwidth improvement [5,6]. Nowadays, twisted optical fibers (both with typical coaxial geometry (core, bounded by intermediate and/or outer solid cladding) and microstructured/photonic crystal optical fibers) are considered as alternative unique fiber optic elements with great potentiality for various applications in fiber optic sensors [7,8,9,10]. At the same time, many recently published works demonstrated new effects, occurring in fiber Bragg gratings (FBGs), written in FMFs as well as in multimode optical fibers (MMFs), under laser-source excitation. A lot of many recently published works demonstrated few-mode operation of these conventional, tilted or slanted FBGs on MMFs and FMFs in vibration, temperature, deformation, displacement, bending, etc., fiber optic sensors [11,12,13,14,15,16,17,18,19,20]. A few-mode regime adds a new other dimension to the space of parameters: it is associated with guided modes of particular order, in which a limited number (from two to a few dozen) transfer the most part of optical signal power over tested optical fiber. We suppose that twisted FMF with recorded FBG can be considered as a new complicated fiber optic element with unique features and great potentiality for application in fiber optic sensors. This work is focused on design and fabrication of twisted few-mode optical fiber (FMF) with specified limited (3…4) guided modes, supporting over “C”-band. Therefore, at the first stage, by using commercially available software with rigorous numerical finite element method, technological/geometrical parameters were specified to provide the desired few-mode regime operation of designed FMF. We performed adaptation of the conventional technique for drawing optical fibers to fabricate designed FMF with induced twisting under small as well as large number of revolutions per meter. Some results of theoretical and experimental researches, performed for pilot samples of manufactured FMF, are represented, including experimentally measured spectral responses of laser-excited optical signals, that comprise researches and analysis of few-mode effects, occurring after fiber Bragg grating recording. 2. Design of FMF: Selection of Geometrical Parameters to Provide Desired Limited Number of Guided Modes At the first stage, we utilized rigorous numerical finite element method, used in COMSOL Multiphysics software, to analyze a preliminary designed set of specified step-index optical fibers with the same typical “telecommunication” cladding diameter 125 µm, but differing by combination of core diameter and numerical aperture (e.g., height of refractive index profile). Here, for each designed optical fiber sample, guided modes (which satisfy to the cut-off condition) were defined, and their effective refractive indexes were computed. The main criterion was focused on providing limited (3…4) transversal guided modes′ propagation over FMF under laser source excitation at the wavelength λ = 1550 nm. We performed analysis of FMF by an earlier developed and successfully verified method [21,22,23], based on the numerical solution of linear Maxwell equation system, written for a homogeneous isotropic dielectric in the absence of free charges and currents and reduced to wave equations for the vectors of electric (E) and magnetic (B) fields [24]:(1) ∇×1μ∇×E−k02εE=0, where k0 is wave number (k0 = 2π/λ); ε is the dielectric permeability (ε = n2, n is refractive index); μ is magnetic permeability. By taking into account satisfaction to the perfectly matched layer (PML) conditions, Equation (1) was transformed to the following form [25]:(2) ∇×1μ′∇×1SE−k02ε′1SE=0, where ε′ and μ′ are modified dielectric and magnetic permeability; [S] is matrix of PML layer coefficients. Solution of Equation (2) is equation of electromagnetic wave, propagating along z-axis of optical fiber [24]:(3) Ez, t=E0expjωt−ωcneffz, where E0 is amplitude of electric field strength; ω is circular frequency; c is light speed in vacuum; t is time. Effective refractive index neff is defined by numerical solution of Equation (3) and related transversal mode is identified (in terms of linear polarized modes LPlm, e.g., azimuthal and radial orders l and m) by comparison computed radial mode field distribution with pre-defined field patterns of known order modes LPlm, which are exact solutions for scalar wave equation, written for model optical fiber with ideal step index or unbounded parabolic refractive index profiles and described by Bessel or Laguerre-Gaussian functions [26,27]. Table 1 shows results of optical fiber analysis, performed by a rigorous finite element numerical method in COMSOL Multiphysics software under wavelength λ = 1550 nm. We considered ideal step-index refractive index profile, the same cladding diameter 125 μm (that corresponds to conventional telecommunication optical fibers), three various core diameters (8.3, 10.0 and 11.0 μm) and six values of numerical aperture NA (0.14, 0.16, 0.18, 0.20, 0.22 and 0.24—it corresponds to approximately the difference between core and cladding refractive indexes 0.02). We start from the core diameter 8.3 μm as the typical value for standard single mode optical fibers (SMFs) of ITU-T Rec. G.652 [28]. It was supposed that even the weak improvement of refractive index profile height, in comparison with ratified SMF, may provide desired few-mode regime with 3…4 transversal guided mode propagation at λ = 1550 nm. However, results of computation showed that following increasing both core diameter and numerical aperture (e.g., refractive index profile height) is required). For example, combination of the SMF “nominal” core diameter 8.3 μm and maximal (from the researched range) numerical aperture value NA = 0.24 provides technical satisfaction of the cut-off condition for desired 4 modes—the fundamental LP01 and higher order modes LP02, LP11, LP21. However, the last two modes LP02 and LP21 are unacceptably instable to propagation over long distances due to their field concentration in the cladding: here, optical confinement factor Pco (e.g., mode power, transferred over core) for both aforementioned modes is inadmissibly low (Pco < 0.5). Therefore, we conclude that none of the researched combinations of core diameter 8.3 μm and 6 tested numerical aperture values NA = 0.14…0.24 do not provide desired 4-mode operation at wavelength λ = 1550 nm. The same matter corresponds to core diameter 10.0 μm and NA = 0.20: here also, higher-order modes LP02 and LP21 satisfy cut-off condition under unacceptable low optical confinement factor Pco < 0.5, while desired 4-mode operation is achieved for numerical aperture range NA = 0.22…0.24. Following improvement of core diameter up to 11 μm showed the best results for NA = 0.20 and NA = 0.22: all 4 modes satisfy the cut-off condition under the required optical confinement factor Pco > 0.5. Lower NA = 0.18 led to inappropriate low Pco < 0.5 for the same last two higher-order modes, while increased NA = 0.24 provides satisfaction of the cut-off condition for 5th mode LP31. Therefore, according to computation results, we selected the following configuration for fabricated FMF: core diameter 11 µm, typical “telecommunication” cladding diameter 125 µm, numerical aperture NA = 0.22. 3. Pilot FMF 11/125 with Improved Height of Quasi-Step Refractive Index Profile and Induced Twisting According to the aforementioned technological parameters, a preform of the desired FMF 11/125 with the numerical aperture NA = 0.22 was prepared by conventional modified chemical vapor deposition (MCVD) method [29]. Figure 1 presents measured refractive index profile with improved height of FMF fabricated preform. The general form of fabricated preform refractive index profile is quasi-step. Moreover, there is a dip of refractive index in the core center, which is typical for MCVD technique: it is caused by highly volatile GeO2 dopant diffusion during support tube collapse. Here, the absolute height of the profile reaches ~0.27, while dip is ~0.08. As a result, to correctly evaluate refractive index profile height, we computed the area of the central (core) part and further estimated the effective height of the profile. For researched prepared FMF 11/125 preform, this parameter was ~0.018, that is equivalent to numerical aperture NA = 0.22. We performed some modifications of the drawing tower to induce twisting on FMF during its drawing. The detailed description of modification is represented in the earlier published work [30]. Usually, preform is fixed in a mechanical chuck of the feed unit, which inputs preform to the heat space of a high temperature furnace. Preform is kept in a stationary position and redrawn without rotation. To induce desired twisting over manufactured FMF, we integrated the stepper motor to the feed unit, which continuously rotates preform under the set speed and adds a new rotation function to the drawing system. The minimal motor rotation speed is 20 revolutions per minute, while the maximal is 200. Therefore, under slow drawing speed 2…3 m/min (that is usually used for manufacturing special or experimental optical fibers), it induces twisting with 10 and 66 revolutions per meter (rpm), respectively. Figure 2 shows an image of the end-face of fabricated pilot sample of twisted FMF 11/125 with numerical aperture NA = 0.22, drawn from the aforementioned manufactured preform. Figure 3 presents near field laser beam profile (operating wavelength λ = 1550 nm) after propagation over the fabricated FMF 11/125 by CCD camera. We measured both 10 rpm and 66 rpm pilot sample 50 m length FMF 11/125 attenuation α(λ) by cutback method over wavelength band λ = 900–1700 nm by using a halogen lamp (OSRAM 64642 HLX) as a light source, programmable monochromator (ANDO), germanium photodiode (wavelength range 900…1700 nm), optical amplifier (eLockIn) and optical power meter (and ANDO AQ-1135E). Measured attenuation curves α(λ) contain typical resonance “water” peaks with strong loss due to simplified and rapid technique for FMF preform fabrication without hydroxyl (OH–) dopants extraction: here, we just focused on twisted FMF pilot sample length manufacturing with specified geometry parameters, which should provide desired few-mode operation by low widening core diameter and strong improvement of refractive profile height and did not pay attention to attenuation reduction. In the same way, increased attenuation (in comparison with commercially available silica optical fibers [28]) at the central regions of the “C”- and “O”-bands, which reaches almost α = 7…8 dB/km, was expected due to intentional excluding (to reduce reagent consumption and also to simplify preform fabrication process) of typical operation of Fluorine (F) doping to the core region, which helps to decrease GeO2 dopant unwanted influence on attenuation increasing. Figure 4 demonstrates that attenuation curve α(λ) over “flat” regions between the resonance peaks for FMF with twisting 66 rpm being lower in comparison with 10 rpm twisted FMF. It may be explained by more smoothing of refractive index profile typical MCVD technological defects under more rapid twisting of preform during optical fiber drawing. 4. Dispersion Parameters of Guided Modes, Propagating in the Pilot Sample FMF 11/125 with Improved Height of Quasi-Step Refractive Index Profile During the next stage, we computed spectral characteristics of dispersion parameters of guided modes, satisfying the cut-off condition for fabricated pilot sample of FMF 11/125. For this purpose, it was proposed to utilize an earlier on developed simple and fast approximate method, which is a modification of the Gaussian approximation, extended to the case for estimation of the transmission parameters of arbitrary order modes, propagating in a weakly guiding optical fiber with an arbitrary axially symmetric refractive index profile [31], with following, optionally (in appropriate case), accuracy improvement by rigorous numerical method of mixed finite elements [32]. This extended modification of the Gaussian approximation (EMGA) is based on combination of the stratification method [26] and “classical” Gaussian approximation [27]. Stratification method provides ability to represent complicated form of researched optical fiber refractive index profile with high detailing and corresponding technological defects (including local refractive index fluctuations), in spite of the most approximate methods, which typically utilize one or a set of smooth functions. Proposed approach significantly reduces computational error during direct calculation of transmission parameters of guided modes in optical fiber with large core diameter (in comparison with single mode optical fibers) and complicated form of refractive index profile [31,32]. Here, only one variational parameter—normalized mode field radius R0—should be determined as a result of characteristic equation solution, while R0 within the “classical” Gaussian approximation is the basis and it completely defines all desired guided mode transmission parameters. According to Gaussian approximation, radial mode field distribution is represented by a well-known approximating expression, based on Laguerre-Gaussian functions [27], that corresponds to exact solution of scalar wave equation, written for weakly guiding optical fiber with an ideal inbounded parabolic refractive index profile. This permits to derive and write analytical expressions for variational expression and characteristic equation in the form of finite nested sums, and further, their first and second derivations—mode delay and chromatic dispersion parameter. Therefore, developed approximate method EMGA does not require high computational resources (even during higher-order mode dispersion parameter estimation) and provides low (less than 1% [26,27]) computational error. Figure 5 shows an equivalent quasi-step refractive index profile of the analyzed FMF 11/125 with a numerical aperture NA = 0.22, restored by report of measurements, performed for drawn optical fiber. At the first stage, we computed optical confinement factor for modes, propagating in a mentioned above FMF 11/125, over wavelength range λ = 700…1700 nm. The results are presented in the form of a diagram in Figure 6. According to computational results, desired 4-mode optical signal transmission is provided by researched FMF 11/125 over band λ = 1450…1700 nm. Generally, 38 LPlm modes with l = 0…7 azimuthal and m = 1…9 radial orders nominally satisfy the cut-off condition at the least researched wavelength range bound λ = 700 nm. However, only for 19 modes with also l = 0…7, but m = 1…4, orders optical confinement factor as more Pco ≥ 0.5 for the same wavelength. At the central region of the “O”-band (λ = 1300 nm), researched FMF 11/125 supports 6 guided modes that satisfy the cut-off condition under the optical confinement factor value more Pco ≥ 0.5: they are listed in Section 2—LP01, LP11, LP21, LP02 modes and two additional higher-order modes LP12, LP31. We computed spectral curves of dispersion parameters for those 6 aforementioned guided modes. Results are represented in Figure 7a with spectral characteristics of mode delay and Figure 7b with chromatic dispersion coefficient. Analysis of mode delay curves show that DMD reaches 18.35 ns/km over λ = 1300 nm wavelength region, while near λ = 1550 nm DMD decreases down to 14.93 ns/km due to “suppression” of two higher-order modes LP12 and LP31. By comparing spectral characteristics of the chromatic dispersion coefficient for the fundamental and higher-order modes, computed curves are generally similar to spectral characteristic of chromatic dispersion coefficient for standard telecommunication single mode optical fiber (SMF) of ITU-T Rec. G.652 [28]. Here, zero dispersion wavelength of both the fundamental and higher-order guided modes corresponds to wavelength range λ = 1300…1350 nm. Maximal deviation of this parameter D between higher-order guided modes was 27.09 ps/(nm·km) at λ = 1300 nm and 4.97 ps/(nm·km) at λ = 1550 nm. 5. Experimental Research of Spectral Responses of FBG, Written over Twisted FMF 11/125 with Improved Height of Quasi-Step Refractive Index Profile Two FBG samples were written on the short (less 1.5 m) segments of SMF (Rec. ITU-T G.652 [28]) and fabricated pilot sample of twisted FMF 11/125 by Lloyd ineterferometric setup workstation under the same mask (with the same grating period), providing expected Bragg wavelength about λB ≈ 1550 nm. We performed preliminary measurements of both FBG spectral responses under propagation of optical signal, generated by continuous emission (CE) wideband laser diode (LD) with operating wavelength λ = 1550 nm and pigtailed by SMFs. Conventional setup was utilized for FBG spectral response measurement by optical spectrum analyzer (OSA) with fiber optic circulator (CIR), also pigtailed by SMFs. The described above scheme for testing of FBG, written on FMF, is shown in Figure 8. Both tested FBGs were jointed to SMF pigtail by fusion splicer and further connected to circulator via corresponding fiber optic adapter. Results of measurements—OSA software screenshots—are represented in Figure 9a,b. Comparison of two measured spectral responses show that detected Bragg wavelength of FBG, written on FMF 11/125, is higher up to 16.46 nm (λB = 1567.50 nm), than for FBG on SMF (λB = 1551.04 nm). This suggests that effective refractive index for the fundamental mode LP01 of FMF is somewhat higher, in comparison with the fundamental mode LP01 of SMF, in approximately 1%. Spectral response of FBG on FMF contains main and periphery peaks. It may be considered as superposition of several modes, corresponding to transversal mode components, that led to response widening and confirms desired few-mode regime of FMF operation at the central wavelength of “C”-band (λ = 1550 nm). The next set of tests was concerned with research of FBG Bragg wavelength λB shifting sensitivity to the temperature action with the following comparison. We placed sequentially both FBGs to the thermostat and discretely varied temperature from +40 °C up to +120 °C with a step of 20 °C. Here, Bragg wavelength λB under the least bound temperature +40 °C was considered as the reference value for the following estimation of λB shifting under the temperature increasing. Results are represented in Figure 10. Both dependences are highly linear, while the slope for FBG on FMF is somewhat higher (approximately on 5%). By analogy with the previous measurements, we performed test series which focused on research and comparison sensitivity of FBGs on SMF and FMF to mechanical action. For these researches, we placed FBGs to the precision translation stage, which provides tensile and following with precision particular elongation of researched optical fiber segment with written FBG over the range 100…250 µm with a step of 50 µm. Here, Bragg wavelength under the unstrained state was considered as the reference value for the following estimation λB shifting Δλ under the described mechanical action. Results are represented in Figure 11. Both dependences are also highly linear, while the slope for FBG on FMF is somewhat higher (approximately on 2.5%). The next test series was concerned with researches of few-mode effects, occurring during laser-excited optical signal propagation over FBG, written in FMF 11/125, under some various stress actions. Here, we utilized a “direct” FBG spectral response measurement scheme without fiber optic circulator (Figure 12). We tested sample FBG on FMF which was pigtailed by using fusion splicer by short SMF pigtails with length not more than 140 mm to avoid conversion of higher-order FMF guided modes to leakage/cladding modes in SMF pigtail [33]. Spectral response, measured for FBG on FMF 11/125 in an unperturbed state (that would be further considered as the reference) is presented in Figure 13. Here, 3 peaks (1 main peak (1567.24 nm) and 2 periphery peaks (1566.65 nm and 1567.96)) could be seen quite distinctly. During the next tests, we measured spectral responses under forming FMF loop with radius 15 mm before, on and after FBG. Results are shown in Figure 14. As expected, in all cases, Bragg wavelength shifting was detected. However, response smoothing as well as periphery peak dropout were noticed. Here, λB shifted down to Δλ = 0.24 nm under loop before and after FBG, while the loop on FBG λB became longer up to 0.08 nm in comparison with the reference response main peak value. Second test series was concerned with spectral response measurements after placing loops with radius 86 and 63 mm over researched segment of FMF 11/125 with written FBG. Results are demonstrated in Figure 15. Here again, response smoothing and periphery peak dropout are noticed under the same Bragg wavelength shifting down to 0.16 and 0.20 nm. 6. Conclusions This work is devoted to the design and fabrication, as well as experimental and theoretical researches of the parameters of FMF 11/125 with induced twisting and improved height of a quasi-step refractive index profile, which provides 4-mode operation over “C”-band. Based on the series of simulation of described optical fiber, we selected specified technological parameters to support the desired 4 guided modes over the mentioned above “C”-band: core diameter 11 μm, cladding diameter 125 μm and numerical aperture NA = 0.22. Successfully fabricated pilot sample lengths of the described above FMF 11/125 with induced twisting of 10 and 66 rpm are presented. Results of measured attenuation showed expected increased loss α = 7…8 dB/km over “C”- and “O”-bands, explained by an intentionally simplified technique for FMF preform manufacturing by excluding typical F doping to optical fiber preform core region, which helps to decrease GeO2 dopant unwanted influence on attenuation increasing. We performed analysis of designed and fabricated FMF 11/125: here, data from the measurement report were utilized to restore the real form of quasi-step refractive index profile. Orders of guided modes, satisfying the cut-off condition, were defined over researched wavelength band λ = 700…1700 nm (4…6 guided modes were localized over “C”- and “O”-band, respectively). Spectral characteristics of dispersion parameters (mode delay and chromatic dispersion coefficient) for defined guided modes were computed for the mentioned above researched wavelength range. Analysis of the mode delay curves showed that at the wavelength λ = 1300 nm, DMD reaches 18.35 ns/km, while at the wavelength λ = 1550 nm, it reduces down to 14.93 ns/km. By comparing spectral characteristics of the chromatic dispersion coefficient for the fundamental and higher-order modes, computed curves are generally similar to spectral characteristics of chromatic dispersion coefficient for conventional SMF (ITU-T Rec. G.652): here, zero dispersion wavelength of both the fundamental and higher-order guided modes corresponds to wavelength range λ = 1300…1350 nm. FBG was written in the sample of fabricated FMF 11/125 segment and test series were performed to research a few-mode effects, occurring during laser-excited optical signal propagation over FMF with written FBG, both unperturbed and under the temperature or mechanical actions. Main and periphery peaks were localized on the spectral response of unperturbed FMF FBG, while under the stress besides the expected Bragg wavelength shifting, spectral response smoothed and periphery peaks dropped out. Results of performed theoretical and experimental researches showed a good potentiality for utilization of designed and fabricated twisted FMF 11/125 in various applications of selected order guided mode management as well as in fiber optic sensors. Author Contributions Conceptualization, A.V.B., V.V.D., G.S., M.T. and F.P.; methodology, A.V.B., V.A.B., K.V.D., Y.I., O.G.M., F.P. and J.Y.; development of technique for twisted optical fiber fabrication, modification of drawing tower, twisted optical fiber fabrication, V.V.D., K.V.D., A.V.K., A.S.M., G.A.P. and E.V.T.-N.; measurements of geometry parameters, laser beam profile, attenuation, V.V.D., A.V.K., A.S.M., G.A.P. and E.V.T.-N.; writing FBG, measurements of FBG spectral responses, A.V.B., M.V.D., A.A.K., A.Z.S., E.S.Z., A.R.G., I.K.M. and E.V.D.; writing—review and editing, A.V.B., V.V.D., A.A.V. and A.A.K.; visualization, A.V.B., V.V.D., M.V.D., A.A.K., A.A.V., E.V.D., A.R.G. and I.K.M.; supervision, A.V.B.; project administration, A.V.B. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by RFBR, DST, NSFC and NRF according to the research project 19-57-80016 BRICS_t. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Refractive index profile of pilot FMF preform (measured by refractometer Photon Kinetics P101). Figure 2 Image of the end-face of fabricated pilot 4-mode FMF 11/125 with numerical aperture NA = 0.22 (high-resolution optical microscope Nikon Eclipse N-U). Figure 3 Near field laser beam profile (operating wavelength λ = 1550 nm), measured after propagation over pilot sample of FMF 11/125 by CCD camera DataRay WinCamD-LCM-C-TE. Figure 4 Attenuation of manufactured 50 m length FMF 11/125 samples with induced twisting 10 and 66 rpm. Figure 5 Equivalent quasi-step refractive index profile with improved height, restored by measurement report data. Figure 6 Diagram of the optical confinement factor distribution between modes of FMF 11/125 over the wavelength range λ = 700…1700 nm. Figure 7 Spectral characteristics of guided mode dispersion parameters: (a) mode delay; (b) chromatic dispersion coefficient. Figure 8 Conventional setup for reflected FBG spectral response measurement: testing of FBG, written on FMF under laser-based few-mode operation. Figure 9 Spectral responses of FBG, excited by laser-source (CE LD, λ = 1550 nm): (a) FBG on SMF; (b) FBG on FMF 11/125. Figure 10 FBG Bragg wavelength λB shifting sensitivity to the temperature action: (a) FBG on SMF; (b) FBG on FMF 11/125. Figure 11 FBG Bragg wavelength λB shifting sensitivity to the mechanical action: (a) FBG on SMF; (b) FBG on FMF 11/125. Figure 12 Conventional setup for direct FBG spectral response measurement: testing of FBG, written on FMF under laser-based few-mode operation. Figure 13 Reference spectral response of unperturbed FBG, written on FMF 11/125. Figure 14 Spectral responses under 15 mm loop before, after and on the FBG, written on FMF 11/125. Figure 15 Spectral responses under 86 and 63 mm loops, placed over FMF 11/125 with written FBG. sensors-22-03124-t001_Table 1 Table 1 Results of optical fiber analysis, performed by rigorous numerical method: step-index optical fibers under various combinations of core diameter and numerical aperture (λ = 1550 nm). № Core Diameter, μm Cladding Diameter, μm Numerical Aperture NA Mode Composition n eff Δneff 1 8.3 125 0.14 LP 01 1.460478 – 2 8.3 125 0.16 LP 01 LP 11 1.462210 1.457688 0.004522 3 8.3 125 0.18 LP 01 LP 11 1.464263 1.459082 0.005181 4 8.3 125 0.20 LP 01 LP 11 1.466624 1.460940 0.005684 5 8.3 125 0.22 LP 01 LP 11 1.469284 1.463199 0.006085 6 8.3 125 0.24 LP 01 LP 11 LP 21 LP 02 1.472237 1.465821 1.458136 1.457159 0.006416 0.014101 0.015078 7 10 125 0.14 LP 01 LP 11 1.461181 1.457847 0.003334 8 10 125 0.16 LP 01 LP 11 1.463027 1.459219 0.003808 9 10 125 0.18 LP 01 LP 11 1.465177 1.461012 0.004165 10 10 125 0.20 LP 01 LP 11 LP 21 LP 02 1.467622 1.463176 1.457854 1.457139 0.004446 0.009768 0.010483 11 10 125 0.22 LP 01 LP 11 LP 21 LP 02 1.470355 1.465682 1.459875 1.458498 0.004673 0.010480 0.011857 12 10 125 0.24 LP 01 LP 11 LP 21 LP 02 1.473371 1.468510 1.462343 1.460621 0.004861 0.011028 0.012750 13 11 125 0.14 LP 01 LP 11 1.461499 1.458455 0.003044 14 11 125 0.16 LP 01 LP 11 1.463387 1.459994 0.003393 15 11 125 0.18 LP 01 LP 11 LP 21 LP 02 1.465572 1.461914 1.457563 1.457061 0.003658 0.008009 0.008511 16 11 125 0.20 LP 01 LP 11 LP 21 LP 02 1.468048 1.464179 1.459378 1.458241 0.003869 0.008670 0.009807 17 11 125 0.22 LP 01 LP 11 LP 21 LP 02 1.470808 1.466767 1.461642 1.460198 0.004041 0.009166 0.010610 18 11 125 0.24 LP 01 LP 11 LP 21 LP 02 LP 31 1.473847 1.469664 1.464288 1.462650 1.458078 0.004183 0.009559 0.011197 0.015769 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Cells Cells cells Cells 2073-4409 MDPI 10.3390/cells11091488 cells-11-01488 Article Sarco/Endoplasmic Reticulum Ca2+ ATPase 2 Activator Ameliorates Endothelial Dysfunction; Insulin Resistance in Diabetic Mice https://orcid.org/0000-0002-1347-5115 Kimura Toyokazu Kagami Kazuki Sato Atsushi Osaki Ayumu Ito Kei Horii Shunpei https://orcid.org/0000-0002-4681-2798 Toya Takumi Masaki Nobuyuki Yasuda Risako https://orcid.org/0000-0002-3430-4614 Nagatomo Yuji * Adachi Takeshi * Secondo Agnese Academic Editor Department of Internal Medicine I, Division of Cardiovascular Medicine, National Defense Medical College, 3-2 Namiki, Tokorozawa 359-8513, Japan; oyotikuuyakim@gmail.com (T.K.); mirror.1028k@gmail.com (K.K.); atsushi19821005@yahoo.co.jp (A.S.); ayumu.osaki@gmail.com (A.O.); saintsilvesta@yahoo.co.jp (K.I.); shumhorii@outlook.jp (S.H.); con367@ndmc.ac.jp (T.T.); con320@ndmc.ac.jp (N.M.); cln302@ndmc.ac.jp (R.Y.) * Correspondence: con401@ndmc.ac.jp (Y.N.); kenada@ndmc.ac.jp (T.A.) 28 4 2022 5 2022 11 9 148829 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Background: Sarco/endoplasmic reticulum Ca2+-ATPase2 (SERCA2) is impaired in various organs in animal models of diabetes. The purpose of this study was to test the effects of an allosteric SERCA2 activator (CDN1163) on glucose intolerance, hepatosteatosis, skeletal muscle function, and endothelial dysfunction in diabetic (db/db) mice. Methods: Either CDN1163 or vehicle was injected intraperitoneally into 16-week-old male control and db/db mice for 5 consecutive days. Results: SERCA2 protein expression was decreased in the aorta of db/db mice. In isometric tension measurements of aortic rings from db/db mice treated with CDN1163, acetylcholine (ACh)-induced relaxation was improved. In vivo intraperitoneal administrations of CDN 1163 also increased ACh-induced relaxation. Moreover, CDN1163 significantly decreased blood glucose in db/db mice at 60 and 120 min during a glucose tolerance test; it also decreased serum insulin levels, hepatosteatosis, and oxygen consumption in skeletal muscle during the early period of exercise in db/db mice. Conclusions: CDN1163 directly improved aortic endothelial dysfunction in db/db mice. Moreover, CDN1163 improved hepatosteatosis, skeletal muscle function, and insulin resistance in db/db mice. The activation of SERCA2 might be a strategy for the all the tissue expressed SERCA2a improvement of endothelial dysfunction and the target for the organs related to insulin resistance. type 2 diabetes mellitus sarco/endoplasmic reticulum Ca2+-ATPase2 endothelial function skeletal muscle function endoplasmic reticulum stress Japanese Ministry of Defense and a MEXT/JSPS KAKENHI Grant-in-Aid for Scientific ResearchJP 17K09596, 17K09565, and JP18H02815 This research was funded by grants from the Japanese Ministry of Defense and a MEXT/JSPS KAKENHI Grant-in-Aid for Scientific Research (Number JP 17K09596, 17K09565, and JP18H02815). ==== Body pmc1. Introduction Diabetes mellitus is considered a globally emergent disease because of the gradual increase in the number of patients who have it, and it is associated with a range of complications such as blindness, renal failure, and cardiovascular disease. In particular, the number of patients with type 2 diabetes mellitus (T2D) is constantly increasing, resulting in a considerable financial burden. According to the International Diabetes Federation Diabetes Atlas (8th edition), as of 2017, there are 425 million people with diabetes mellitus worldwide, which will rise to 629 million by 2045 [1]. T2D is characterized by chronic hyperglycemia, dyslipidemia, and insulin resistance [2,3]. The major problem caused by T2D is vascular complications, which are usually classified into two categories: microvascular (i.e., retinopathy, nephropathy, and neuropathy) and macrovascular complications (i.e., coronary heart disease, peripheral artery disease, and stroke). Clinical trials have shown that the intensive treatment of hyperglycemia is associated with a reduction in the development of microvascular complications [4,5]. On the other hand, clinical trials of T2D for anti-diabetic drugs were unsatisfied for macrovascular diseases. [5,6,7]. Lowering glucose levels might cause adverse events associated with hypoglycemia [8,9,10]. However, other factors, such as dyslipidemia, hepatosteatosis, and insulin resistance, might contribute to the development of macrovascular complications. Thus, new drug targets for insulin resistance for T2D have emerged as a strategy to improve insulin resistance and macrovascular diseases. Insulin resistance deteriorates glucose/lipid metabolisms, which induce endothelial dysfunction. Recently, we reported that endothelial insulin resistance impaired nitric oxide (NO) bioactivity resulting in vascular/heart failure [11]. Since insulin resistance is closely related to endothelial function, a new target for improving NO bioactivity might be a candidate. Sarco/endoplasmic reticulum Ca2+-ATPase 2 (SERCA2), which is a membrane protein located in the endoplasmic reticulum (ER), uptakes Ca2+ from the cytoplasm into the ER lumen and maintains intracellular Ca2+ homeostasis [12]. SERCA2 dysfunction can increase intracellular Ca2+ levels and induce ER stress, which can lead to the development of T2D [13,14]. In recent years, SERCA2 has been found to be associated with the pathogenesis and cardiovascular complications of T2D [15,16,17]. Previously, we found that NO directly activated SERCA2 with S-glutathiolation at Cys674, leading to a decrease in intracellular calcium and arterial tonus [18,19]. Insulin resistance, oxidative stress, inflammation, and post-translational modifications, which are induced in T2D, can impair the expression and/or function of SERCA2 [13,17,20,21]. SERCA2 dysfunction has been observed in the progression of atherosclerosis, endothelial dysfunction, smooth muscle migration, dysfunction of angiogenesis, and impairment of heart function [18], which occur frequently in patients with diabetes. Recently, it was reported that an allosteric SERCA2 activator, CDN1163 [22], ameliorated glucose metabolism and hepatosteatosis in obese (ob/ob) mice, a typical model of metabolic syndrome, by offsetting ER stress [23]. The data suggested that CDN1163 might be a strong candidate for the treatment of insulin resistance and glucose metabolism in patients with T2D. In this study, we investigated whether CDN1163 ameliorated glucose metabolism and vascular dysfunction in diabetic (db/db) mice. 2. Materials and Methods 2.1. Animals Male control mice (db/+) [24] or db/db mice aged less than 12 weeks were obtained from Oriental Yeast (Tokyo, Japan) and fed a normal rodent diet (CLEA Japan, Inc., Tokyo, Japan). When aged 14–16 weeks, the control and db/db mice were injected intraperitoneally with either vehicle (10% DMSO, 10% Tween 80 in 0.9% NaCl (Wako Pure Chemical Corporation, Osaka, Japan)) or CDN1163 (100 mg/kg) for 5 consecutive days (days 0–4). CDN1163 was purchased from Neurodon (Crown Point, IN, USA). These mice were referred to as control+veh (control mice, vehicle), control+CDN (control mice, CDN1163), db/db+veh (db/db mice, vehicle), and db/db+CDN (db/db mice, CDN1163). The animals were maintained in a temperature-controlled facility on a 12-h light and 12-h dark cycle. This study was approved by the National Defense Medical College Institutional Animal Care and Use Committee (approval number: 15,074; number of mice used: 114). 2.2. Measurement of Body Weight and Food Intake The body weight values were measured on day 0 before injection and day 7 in a non-fasted state. Their twenty-four-hour food intake was measured on days 7–9 by placing the animals in a clear cage with a food tray (Oxymax; Columbus Instruments, Columbus, OH, USA), and the daily mean value was defined as the food intake. 2.3. Measurement of Metabolites The blood glucose levels were measured with a glucose-detection kit. Serum insulin levels were measured with an enzyme-linked immunosorbent assay kit, and the serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), total cholesterol (TC), and triglyceride (TG) were measured with enzymatic assays. All kits and assays were obtained from Wako Pure Chemical Corporation (Osaka, Japan). An oral glucose tolerance test (OGTT) was performed after fasting the mice for 14 h. Blood was collected from the tail vein at 0, 15, 30, 60, and 120 min after 1.5 mg/g body weight of D-glucose was administered orally to the mice [25]. Serum insulin levels were measured using blood samples at 0 and 120 min of the OGTT, and insulin secretion was measured at 0 and 120 min. 2.4. Western Blotting Analysis Tissues were excised and homogenized on ice in a homogenization buffer (20 mmol/L Tris-HCl, 150 mmol/L NaCl, 1 mmol/L Na2EDTA, 1 mmol/L EGTA, 1% NP-40, 2.5 mmol/L sodium pyrophosphate, 1 mmol/L monoglycerophosphate, 1 mmol/L Na2VO4, pH 7.4) containing a 1 mmol/L phenylmethylsulfonyl fluoride and protease inhibitor cocktail. All reagents were obtained from Wako Pure Chemical Corporation (Osaka, Japan). The homogenates were centrifuged at 13,000× g for 20 min. A Bradford protein assay (Bio-Rad laboratories, CA, USA) was performed to measure protein concentration with bovine serum albumin as a standard [26]. For the Western Blotting of SERCA2, the samples were not incubated at 95 °C to avoid SERCA2 protein aggregation. The samples were run on a sodium dodecyl sulfate-polyacrylamide gel and transferred onto a polyvinylidene difluoride membrane. After blocking, the gel was incubated overnight at 4 °C with a primary anti-SERCA2 antibody (Santa Cruz, Dallas, TX, USA and Thermo Fisher Scientific, Waltham, MA, USA). 2.5. Hematoxylin and Eosin Staining The mice were sacrificed with triple anesthesia containing 0.3 mg/kg medetomidine, 4.0 mg/kg midazolam, and 5.0 mg/kg butorphanol [27] and perfused with 0.9% saline followed by 4% paraformaldehyde. The livers were excised and fixed in 10% formalin overnight, embedded in paraffin, and sectioned (3 μm thick). All samples were stained with hematoxylin and eosin. Images were obtained with BZ-X710 (Keyence, Osaka, Japan). The lipid droplet area divided by the tissue area excluding the luminal structures was evaluated. 2.6. Metabolic Chamber Experiments The metabolic parameters were measured using an indirect open-circuit calorimeter (Oxymax; Columbus Instruments, Columbus, OH, USA), as described previously, with some modifications [28]. The mice were placed in clear chambers and could move freely for 24 h in a 12 h light and 12 h dark cycle. A water bottle was connected to each chamber, and a food tray was also placed there. The flow rate of room air through the chamber was 0.5 L/min. Exhaled gases were passed through the O2 and CO2 sensors and measured every 10 min to calculate oxygen consumption (VO2) and carbon dioxide production (VCO2). The gas sensors were calibrated using a standard gas with known concentrations of N2, O2, and CO2 (Koatsu Gas Kogyo, Shiga, Japan) before each experiment. The respiratory exchange ratio (RER) was calculated as VCO2 divided by VO2. The mice were placed in the chamber for 2 days for acclimation before the experiments. 2.7. Exercise Capacity with a Treadmill Test A treadmill test was conducted using a Metabolic Modular Treadmill (Columbus Instruments, Columbus, OH, USA), as described previously, with some modifications [29]. For acclimation to the exercise, the mice were allowed to practice for 2 days before the experiment by being placed in the chamber with the treadmill at speed 0 m/min for the first 5 min, then the shock grid was activated. After that, the mice walked at a speed of 4 m/min for the next 10 min. In the treadmill test, after being placed on the stationary treadmill for 5 min, the speed of the treadmill was increased to 2 m/min for 5 min. Then, the speed was increased to 3 m/min for 2 min, after which the speed was increased by 1 m/min every 2 min thereafter. The test was continued until the mice remained in contact with the shock grid for more than 5 s. We measured running distance, VO2, and VCO2, and also calculated oxygen consumption at the modified ventilatory threshold (mVT), VO2mVT. VT is generally defined as the exercise intensity at which VCO2 increases sharply during exercise. VO2 continues to increase in proportion to the load, but VCO2 increases rapidly at one point. However, in our db/db mice, VO2 did not increase continually. Therefore, mVT was defined as the exercise intensity at which VCO2/VO2 (i.e., RER) increased sharply during exercise. We plotted three graphs with time as the horizontal axis and VO2, VCO2, or RER as the vertical axis; VO2mVT was calculated using these graphs. 2.8. Isometric Tension Measurement with an Organ Chamber Isometric tension measurement was performed as described previously [30]. The mice were sacrificed with triple anesthesia containing 0.3 mg/kg medetomidine, 4.0 mg/kg midazolam, and 5.0 mg/kg butorphanol [27]. The thoracic aorta was excised, and adherent fat was removed carefully to avoid damaging the endothelium and then cut into 2.5–3 mm rings. The aorta was mounted in an organ bath filled with Krebs–Ringer bicarbonate solution (118.3 mmol/L NaCl, 4.7 mmol/L KCl, 2.5 mmol/L CaCl2, 1.2 mmol/L MgSO4, 1.2 mmol/L KH2PO4, 25 mmol/L NaHCO3, 5.5 mmol/L D-glucose) aerated with 95% O2 and 5% CO2 at 37 °C. The aorta was attached to a force transducer, and isometric tension was recorded. Initially, a 1 g pre-tension was applied to all rings for 1 h. All rings were immersed in a 60 mmol/L KCl solution to confirm contraction. After washout, L-phenylephrine (10−6.5 mol/L) was added to each chamber to contract all rings, and, after the contraction curve reached a plateau, the rings were exposed to increasing concentrations of ACh (10−9 to 10−5 mol/L) or sodium nitroprusside (SNP; 10−9 to 10−5 mol/L). In ex vivo experiments, the rings were pretreated with either DMSO (5 μL) or CDN1163 (1 μmol/L) for 15 min before precontraction with L-phenylephrine. After this pretreatment, we performed the same procedures as in the in vivo experiments. 2.9. Statistical Analysis Data are expressed as the mean ± standard error of the mean (SEM). The OGTT and vascular relaxation were evaluated by two-way ANOVA with repeated measures followed by a post hoc test with Bonferroni’s correction for multiple comparisons. The treadmill test was evaluated by two-way ANOVA with repeated measures, and a t test was used to compare individual time points [31]. The other data were evaluated either by a one-way ANOVA followed by a post hoc test with Bonferroni’s or Tukey’s correction or by the Mann–Whitney U test. All statistical analysis were performed with GraphPad Prism Software version 7.03 (GraphPad Software, La Jolla, CA, USA). A value of p < 0.05 was considered to be statistically significant. 3. Results 3.1. Decreased SERCA2 Expression in the Aorta of db/db Mice First, we examined SERCA2 protein expression in homogenates from the liver, heart, aorta, and soleus muscle of db/db mice. The soleus muscle is slow-twitch muscle and contains more SERCA2 proteins compared with fast-twitch muscles. There was no significant difference in the expression of SERCA2 protein in the liver and soleus muscle, it had a tendency to decrease in the heart and was significantly decreased in the aorta from db/db mice (Figure 1). 3.2. Decreased Body Weight and Ameliorated OGTT with CDN1163 in db/db Mice We measured the body weight of the mice before and at seven days after the administration of CDN1163 or vehicle. There was significant weight loss in the db/db+CDN group compared with the db/db+veh group (Figure 2A); however, there was no significant difference in food intake (Figure 2B). CDN1163 did not decrease the body weight of the control mice. To evaluate glucose metabolism, an OGTT was performed. There was a significant decrease in the blood glucose levels of the db/db+CDN group at 60 and 120 min after the oral administration of D-glucose compared with the db/db+veh group, and there was no significant difference between the control+veh and control+CDN groups (Figure 3A,B). We also measured the serum insulin concentration at 0 and 120 min after D-glucose administration, which decreased significantly at 120 min in the db/db+CDN group compared with the db/db+veh group (Figure 3C). These data indicate that CDN1163 improved glucose tolerance by improving insulin sensitivity, not by increasing insulin secretion. 3.3. Amelioration of Hepatosteatosis with CDN1163 Although there was no difference in the expression of SERCA2 protein in the liver between the control+veh and db/db+veh groups, we evaluated hepatic function and the lipid profile using histology and by measuring AST, ALT, fasting TG, and TC, which are essential for glucose metabolism. Histologically, the percentage of lipid droplet area increased significantly in the db/db+veh group compared with the control+veh group, and its percentage decreased significantly in the db/db+CDN group compared with the db/db+veh group (Figure 4A,B). There was no significant difference in AST levels between the db/db+veh and db/db+CDN groups, whereas ALT levels were lower in the db/db+CDN group compared with the db/db+veh group (Figure 4C,D). TC levels were lower in the db/db+CDN group compared with the db/db+veh group, but fasting TG levels were not affected (Figure 4E,F). 3.4. Increased VO2 during Non-Exercise Load and VO2mVT with CDN1163 in the Treadmill Test of db/db Mice As described previously, the expression of SERCA2 protein in the soleus muscle did not decrease in db/db mice compared with control mice. However, the skeletal muscle is well known as a major tissue for extracting glucose from the blood, especially during exercise. Thus, we evaluated the effect of CDN1163 on skeletal muscle function with metabolic chamber experiments. During non-exercise stress, there was no significant difference in VO2 and VCO2 between the db/db+veh and db/db+CDN groups (data not shown). RER increased considerably in the db/db+CDN group compared with the db/db+veh group, although the difference did not reach statistical significance (p = 0.057, data not shown). This implies that CDN1163 might increase the utilization of glucose in db/db mice. In the treadmill test, there was no significant difference in VCO2, RER, and running distance between the db/db+veh and db/db+CDN groups (Figure 5B–D). However, CDN1163 significantly increased VO2 at the early time point after starting exercise and VO2mVT in db/db mice (Figure 5A,E). These data suggest that CDN1163 improved mitochondrial respiration during modest intensity exercise with a higher endurance capacity in aerobic metabolism. 3.5. Restored Endothelium-Dependent Relaxation of Aortic Rings from db/db mice with CDN1163 In general, exercise stress increases blood flow considerably in the skeletal muscle by shear stress-induced endothelium-dependent relaxation. Endothelial dysfunction in the aortic rings of db/db mice has been reported previously [32]. SERCA2 plays a crucial role in NO-induced relaxation; thus, we hypothesized that the increase in skeletal muscle metabolism by CDN1163 might be due to an improvement of NO-induced relaxation. Next, we performed organ chamber experiments with ACh to evaluate if CDN1163 might improve the endothelium-dependent relaxation of the aortic rings of db/db mice. First, we tested the effects of in vivo treatment with CDN1163 on the ACh-induced relaxation of the aortic rings. ACh-induced relaxation was impaired in aortic rings from the db/db+veh group compared with the control+veh group; however, CDN1163 restored ACh-induced relaxation in the aortic rings of the db/db mice, although it had little effect on the aortic rings of the control mice (Figure 6A,B). Furthermore, CDN1163 increased the SNP-induced relaxation of the aortic rings of db/db mice (Figure 6C,D). These data indicate that in vivo treatment with CDN1163 increased endothelium-dependent relaxation and ameliorated smooth muscle response to NO donor from db/db mice. To assess the direct effect of CDN1163 on the aorta, we performed organ chamber experiments with ex vivo treatment of CDN1163. CDN1163 was added to the Krebs–Ringer solution in the organ chamber for 15 min before contraction with L-phenylephrine. Pretreatment with CDN1163 also increased the endothelium-dependent relaxation of the aortic rings of db/db mice, although it had no significant effect on the aortic rings of control mice (Figure 7A,B). In this protocol, pretreatment with CDN1163 had no effect on the SNP-induced relaxation of the aortic rings of db/db and control mice (Figure 7C,D). The data suggest that CDN1163 has a direct effect on endothelium-dependent relaxation in db/db mice. 4. Discussion T2D is characterized by glucose intolerance, dyslipidemia, and insulin resistance. Although various glucose-lowering drugs have been developed, there are a limited number of drugs that are effective for macrovascular complications. In the present study, we employed db/db mice, a typical murine model of T2D, and investigated the effects of an allosteric SERCA2 activator, CDN1163, on metabolism and endothelial function. Initially, we examined the metabolic effects of CDN1163. In spite of it having no effect on food intake, CDN1163 modestly decreased the body weight of the db/db mice, which was not observed in the control mice. CDN1163 did not change either the blood glucose or the insulin levels after fasting and before the oral administration of D-glucose in an OGTT. However, CDN1163 decreased the blood glucose levels at 60 and 120 min in the OGTT, and the insulin levels were decreased at 120 min in db/db mice. Fasting blood glucose levels are mostly dependent on gluconeogenesis, mainly from the liver [33]. On the other hand, lowering blood glucose levels in the latter phase is dependent on glucose uptake, which is mainly dependent on the function of the skeletal muscle and increased blood flow through arterioles by insulin [34,35]. Therefore, these data indicate that CDN1163 had a minimal effect on gluconeogenesis but increased the uptake of glucose by skeletal muscle. CDN1163 also decreased serum insulin levels at 120 min only in db/db mice. From these data, we considered that CDN1163 ameliorated glucose metabolism not by improving the secretion of insulin from β-cells, but by improving the insulin sensitivity of peripheral organs such as skeletal muscle. Among the large number of anti-diabetic drugs, metformin, which improves insulin resistance, is the current first choice drug for the treatment of T2D, because it can improve the outcome of macrovascular complications [36]. The insulin-sensitizing effects of SERCA2 are fascinating as a drug target for diabetes with the aim of improving insulin sensitivity. Kang et al. reported the beneficial effects of CDN1163 in ob/ob mice, which is a typical murine model of obesity [23]. CDN1163 improved glucose tolerance and decreased blood glucose levels in the absence of a reduction in food intake in ob/ob mice. On the other hand, a decrease in blood glucose was not observed in lean control mice. These effects were compatible with our results, indicating that the effect of CDN1163 could be seen exclusively in ob/ob or db/db mice, and that CDN1163 was unlikely to trigger hypoglycemia. We observed favorable effects of CDN1163 on hepatosteatosis in db/db mice. Although AST levels were not decreased, ALT levels, which are considered to be the more dominant enzyme in hepatosteatosis, were decreased by CDN1163 in db/db mice. TC levels decreased slightly, but fasting TG levels were not changed by CDN1163 in db/db mice. These data suggest that the short-term administration of CDN1163 contributed to a modest amelioration of hepatosteatosis. Considering that fasting blood glucose levels were not decreased by CDN1163, its effect might be smaller in db/db mice than in ob/ob mice [23]. In general, hepatosteatosis is more prominent in obesity than in T2D; thus, we might observe a difference in drug potency on hepatosteatosis simply due to the different models. Compared with ob/ob mice, endothelial and muscle dysfunction is more prominent in db/db mice. We reported that liver-specific ERK2 KO mice deteriorated hepatosteatosis by ER stress with decreases in hepatic SERCA2 expression, resulting in worsening insulin resistance and endothelial dysfunction [30]. We prepared to improve NO bioactivity by genetic modification in the endothelium and observed whether insulin resistance and hepatosteatosis were improved (ongoing study). We have interest in the close relationship among insulin resistance, hepatosteatosis, and endothelial dysfunction in diabetes. Our results from the OGTT also indicated that increased blood flow was related to the favorable effects of CDN1163. To study the effects of CDN1163 on muscle function, we originally investigated the physiological effects of CDN1163 on skeletal muscle and endothelial function, which are generally impaired in diabetes. In this study, we examined how the expression of the SERCA2 protein changes in the liver, heart, aorta, and soleus muscle of db/db mice compared with control mice. Shah and Brownlee showed that hyperglycemia provided uridine diphosphate-N-acetylglucosamine, which is a substrate for the enzyme O-linked N-acetylglucosamine transferase, and O-linked N-acetylglucosamine transferase modified transcription complex factors regulating the expression of SERCA2, thereby reducing its levels [17]. Thus, hyperglycemia in diabetes can reduce the expression of the SERCA2 protein. In db/db mice, we observed that the reduced expression of the SERCA2 protein in the aorta had tendency to be decreased in the heart and not in the liver and soleus muscle compared with control mice of a similar age (Figure 1). All the tissue expressed SERCA2a and SERCA2b. SERCA2a was predominantly expressed in the heart and the skeletal muscle, and SERCA2b was predominantly expressed in the aorta and liver [37]. Thus, subtypes could not be reasons for the different expression in each tissue. Oxidative stress, such as hydrogen peroxide and peroxynitrite, which is produced in diabetes, can also decrease the expression and function of SERCA2 [38,39]. Further studies are required to clarify the complex regulations of SERCA2 expression in diabetes. As mentioned above, insulin resistance in the latter phase of the OGTT is dependent on skeletal muscle function and blood flow regulated by endothelial function. Hence, we examined how CDN1163 affected skeletal muscle and endothelial function. We focused on changes in oxygen consumption by CDN1163, which is mainly represented as the mitochondrial function of the muscles, especially during exercise [40]. In the non-exercise state, CDN1163 did not change VO2 or RER of db/db mice compared with vehicle-treated mice (p = 0.057, data not shown). RER increases if the glucose metabolism is accelerated compared with lipid metabolism as an energy source. Due to the sedentary nature of db/db mice, we applied exercise to these animals to increase mitochondrial respiration in the muscle. In the treadmill test, CDN1163 significantly increased VO2 during the early phase of exercise and VO2mVT in db/db mice. Previous studies reported that the O2 uptake response during low to moderate exercise was impaired in T2D, which might be due to the impairment of blood flow in the skeletal muscle [41,42]. The increase of VO2 in the early phase indicated that CDN1163 improved mitochondrial O2 uptake in db/db mice. When the exercise load is low, there is sufficient ATP to produce energy by utilizing glucose or fat with oxygen (aerobic metabolism) in mitochondria. In this phase, CDN1163 might increase the consumption of ATP by SERCA2 and improve mitochondrial oxygen consumption in the skeletal muscle, resulting in increased VO2. On the other hand, as the load increases during exercise, the consumption of ATP will further increase and be exhausted. In this state, the production of ATP without oxygen will be activated, the level of lactic acid is elevated (anaerobic metabolism), and the production of CO2 is increased by metabolizing lactic acid. This state is represented by VT. CDN1163 increased the VO2mVT of db/db mice, indicating that CDN1163 increased oxygen consumption at the point when aerobic metabolism switched to anaerobic metabolism. These data suggested that CDN1163 improved skeletal muscle function and mitochondrial metabolism, which was impaired in db/db mice. Skeletal muscle metabolism and function was relied on by the blood flow during exercise. Sheer stress in exercise evoked endothelial-dependent, NO-induced relaxation in arteriole and augment blood flow. Thus, the enhanced NO-induced relaxation with CDN1163 could improve the skeletal muscle function. A recent report showed that CDN1163 directly improved myotube metabolism and function [43]. Thus, CDN1163 improved the skeletal muscle function by these two mechanisms. Next, we tested the effects of CDN1163 on endothelial function. Our analysis with isometric tension measurement revealed that CDN1163 restored ACh-induced relaxation in db/db mice with in vivo and ex vivo treatment. In addition, CDN1163 also ameliorated SNP-induced relaxation in db/db mice with in vivo treatment, although such an effect was not seen with ex vivo treatment. These results indicate that CDN1163 had favorable effects on endothelium-dependent relaxation. NO released from endothelial NO synthase mainly activates guanylate cyclase and increases the levels of cyclic guanosine monophosphate (cGMP). cGMP activates cGMP-dependent protein kinase G, regulates myosin light chain phosphatase, and relaxes vessels [44]. On the other hand, NO activates SERCA2 in aortic smooth muscle in a partially cGMP-independent manner [19,20], which decreases intracellular Ca2+ and vessel relaxation. This mechanism has a role in S-glutathiolation at Cys674 on SERCA2 and atherosclerosis, aging, and heart failure, while, in diabetes, the cysteine residue is oxidized and impairs the mechanism [45]. The regulation of SERCA2 by NO in endothelial cells is also important for endothelial function and angiogenesis [46]. Many diseases decreased the aortic SERCA2 activity and NO-induced relaxation. Antioxidants improved SERCA2 activity and increased NO-induced relaxation [18,19]. Thus, we considered that the activation of SERCA2 with CDN1163 could improve NO-induced relaxation in db/db mice. We observed that in vivo treatment with CDN1163 restored SNP-induced vessel relaxation in db/db mice, suggesting that CDN1163 improved not only the endothelial function, but also the smooth muscle response to NO donor. We did not observe effects on the smooth muscle in our ex vivo experiments, potentially due to the period of administration or different sensitivity between the endothelium and smooth muscle. The restoration of the endothelial function observed following the systemic or direct administration of CDN1163 to the aortic rings might at least partially explain its favorable metabolic effects and the improvement of muscle function. These results indicate the important relationship between a decreased endothelial function and the SERCA2 function in diabetes. As endothelial dysfunction and insulin resistance are closely associated with macrovascular complications in patients with T2D [47], their improvement with a SERCA2 activator is an appealing strategy for the treatment of vascular complications. Insulin resistance is characterized by the impairment of the insulin receptor substrate/phosphatidylinositol 3-kinase/protein kinase B (Akt) pathway. In endothelial cells, Akt phosphorylates endothelial NO synthase at Ser1177 [48], and endothelial NO synthase can also be activated through the intracellular Ca2+ binding Ca2+/calmodulin complex. As CDN1163 improved endothelial function and insulin sensitivity, the activation of SERCA2 may improve both of them, which are impaired in T2D. It is well known that ER stress is one of the causes of T2D [49,50], and SERCA2 dysfunction triggers ER stress. Due to the induction of ER stress, various organs or tissues may fail to work normally. ER stress is associated with hepatosteatosis in the liver [51,52], endothelial dysfunction in the aorta [32], reduced insulin secretion from β-cells [53], reduced oxygen consumption in skeletal muscle [54], left ventricular dysfunction in the heart [55], accelerated platelet aggregation [56], and brain cell apoptosis [57].In fact, ER stress has been considered to be a major cause of dopaminergic neuron loss in Parkinson’s disease, and Dahl showed that CDN1163 protected neurons from ER stress-induced cell death in vitro [57]. It has also been recognized that ER stress has an important role in inducing β-cell apoptosis. An adequate store of Ca2+ in the ER is essential for maintaining ER function, and the activation of SERCA2 is essential to maintain it. A SERCA2 activator might also improve ER stress, which is induced in T2D (Figure 8). SERCA2 protein expression decreased in various diseases including heart failure. The tissue-targeted overexpression of SERCA2 is a next strategy for the various diseases. SERCA2 activity was regulated with associated proteins (phospholamban, salcolipin, etc.), micro-RNA or post-translational modifications (phosphorylation, SUMOnylation, S-glutathiolation, etc.), which might also be next targets to improve SERCA2 activity [37]. In the present study, there are some limitations and points that should be improved. Firstly, we could not show decreased SERCA2 expression in the liver, heart, and soleus muscle of db/db mice compared to control mice. In general, db/db mice are considered a model of systemic ER stress [32,53,58], and SERCA2 dysfunction can be observed in almost all organs. The absence of SERCA2 dysfunction in the liver, heart, and soleus muscle in our study could be due to the relatively young age of the mice. The non-significant difference of VO2 in the non-exercise-state experiments might also be due to the relatively young age of the mice. Secondly, though we validated the pharmacological impact of CDN1163 on the skeletal muscle and vascular function, we did not explore the precise molecular mechanism by which SERCA2 activation affected them. We determined the concentration and duration for the administration of CDN1163 according to a previous study [23] and a preliminary study for metabolic effects; however, we did not evaluate dose dependency and long-term effects. Thirdly, we only tested one animal model of diabetes and did not examine the presence of side effects. As CDN1163 has hydrophobic properties, other activators of SERCA2 should be considered for drug development. Further study is required for the assessment of the beneficial effects of SERCA2 activators for the treatment of diabetes. 5. Conclusions An allosteric SERCA2 activator, CDN1163, improved vascular endothelial function, skeletal muscle function, hepatosteatosis, and glucose metabolism in db/db mice. The activation of SERCA2 is an appealing strategy for the treatment of cardiovascular complications in patients with type II diabetes. Acknowledgments The authors thank all the members of the Center for Laboratory Animal Science and the Central Research Laboratory of the National Defense Medical College for their assistance with animal care and pathological assistance. The authors also thank Azusa Onodera for technical support. A part of the experiments was presented in the meeting of the 81st Japanese Circulation Society. Author Contributions Conceptualization, T.K. and T.A.; methodology, T.K., K.K., A.S., A.O., K.I., S.H. and T.A.; validation, Y.N. and T.A.; formal analysis, T.K.; investigation, T.K., K.K., A.S., A.O., K.I. and S.H.; resources, T.K., K.K., A.S., A.O., K.I. and S.H.; data curation, T.K., T.T., N.M., Y.N. and T.A.; writing—original draft preparation, T.K.; writing—review and editing, N.M., R.Y., Y.N. and T.A.; visualization, T.K., K.K., Y.N. and T.A.; supervision, T.T., N.M., R.Y., Y.N. and T.A.; project administration, Y.N. and T.A.; funding acquisition, T.A. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The animal study protocol was approved by the Institutional Review Board (or Ethics Committee) of the National Defense Medical College (protocol code 15074, 16055, dates of approval 2 September 2016, 3 June 2016). Informed Consent Statement Not applicable. Data Availability Statement Data supporting reported results can be obtained from the corresponding author under reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 SERCA2 expression in the tissues of db/db mice compared with control mice. (A) SERCA2 expression in liver, heart, aorta, and soleus muscle by western blotting. (B) Relative quantification of SERCA2 expression to control mice (n = 4 in heart and soleus muscle and n = 6 in liver and aorta for each group). Error bars represent SEM. * p < 0.05 vs. control. SERCA2, sarco/endoplasmic reticulum Ca2+-ATPase 2; GAPDH, glyveraldehehyde-3-phosphate. Figure 2 CDN1163 decreases the body weight of db/db mice. (A) Change of body weight between day 0 and day 7 (n = 11 for each group). (B) Twenty-four-hour food intake was measured using a food tray in a clear cage in which the mice were placed individually (control+veh, n = 8; control+CDN, n = 7; db/db+veh, n = 5; db/db+CDN, n = 6). Error bars represent SEM. ** p < 0.01 vs. db/db+veh. CDN indicates CDN1163; veh, vehicle; NS, not significant. Figure 3 CDN1163 ameliorates glucose tolerance in db/db mice. (A,B) OGTT and OGTT-AUC for glucose levels. OGTT results are expressed as mean blood glucose concentration ± SEM (control+veh, n = 7; control+CDN, n = 5; db/db+veh, n = 7; db/db+CDN, n = 5). (C) Serum insulin levels were measured at 0 min and 120 min after glucose administration (n = 10 for each group). Error bars represent SEM. ** p < 0.01, **** p < 0.0001 vs. db/db+veh. AUC, area under the curve; CDN, CDN1163; NS, not significant; OGTT, oral glucose tolerance test; veh, vehicle. Figure 4 CDN1163 improves hepatosteatosis in db/db mice. (A) Hematoxylin and eosin staining of liver sections for control and db/db mice with either vehicle or CDM1163. Scale bars, 100 μm. (B) Percentage of lipid droplet area. The value was calculated as lipid droplet area divided by tissue area excluding luminal structures (n = 5 for each group). (C) Serum AST levels (n = 9 for each group). (D) Serum ALT levels (n = 9 for each group). (E) Serum fasted TG levels (Ctrl+veh, n = 4; Ctrl+CDN, n = 4; Db+veh, n = 5; Db+CDN, n = 4). (F) Serum TC levels (n = 9 for each group). Error bars represent SEM. * p < 0.05, ** p < 0.01, **** p < 0.0001 vs. Db+veh. AST, aspartate aminotransferase; ALT, alanine aminotransferase; Db, db/db mice; TG, triglyceride; TC, total cholesterol; NS, not significant. Figure 5 CDN1163 increases VO2 of db/db mice during the early phase of the treadmill test. (A–C) Measurement of VO2 (A), VCO2 (B), and RER (C) during 5 min of rest (−5–0 min) and the first 7 min of exercise (0–7 min). RER was calculated using VO2 and VCO2 (n = 11 for each). Two-way analysis of variance was used for the comparison of VO2, VCO2, and RER in both groups during the entire period, and the t-test was used for the comparison of individual time points. (D) Measurement of running distance (n = 11 for each). (E) Measurement of VO2mVT (n = 11 for each). Modified VT (mVT) was defined as exercise intensity at which VCO2/VO2 (RER) started to increase sharply during exercise, and VO2mVT was defined as VO2 at the mVT. Error bars represent SEM. * p < 0.05 vs. Db+veh. CDN, CDN1163; Db, db/db mice; NS, not significant; RER, respiratory exchange ratio; VCO2, carbon dioxide production; veh, vehicle; VO2, oxygen consumption; VO2mVT, oxygen consumption at modified VT; VT, ventilatory threshold. Figure 6 CDN1163 increases endothelium-dependent and endothelium-independent relaxation of db/db mice in in vivo experiments. (A,B) Vascular relaxation of aortic rings isolated from control mice (A) (Ctrl+veh, n = 11; Ctrl+CDN, n = 9) or db/db mice (B) (Db+veh, n = 10; Db+CDN, n = 11) with ACh. (C,D) Vascular relaxation of aortic rings isolated from control mice (C) (n = 9 for each group) or db/db mice (D) (n = 9 for each group) with SNP. Results of relaxation are expressed as percentage changes in the steady-state level of contraction with 10−6.5 mol/L L-phenylephrine. Error bars represent SEM. * p < 0.05 vs. Db+veh. CDN, CDN1163; Ctrl, control mice; Db, db/db mice; veh, vehicle; SNP, sodium nitroprusside. Figure 7 CDN1163 increases endothelium-dependent function in db/db mice in ex vivo experiments. All experiments were conducted on the condition that either DMSO (5 μL) or CDN1163 (1 μM, 5 μL) was added to the bath for 15 min before L-phenylephrine precontraction. (A,B) Vascular relaxation of aortic rings isolated from control mice (A) (n = 8 for each group) or db/db mice (B) (n = 12 for each group) with ACh. (C,D) Vascular relaxation of aortic rings isolated from control mice (C) (n = 8 for each group) or db/db mice (D) (n = 12 for each group) with SNP. Results of relaxation are expressed as percentage changes in the steady-state level of contraction with 10−6.5 mol/L L-phenylephrine. Error bars represent SEM. * p < 0.05 vs. Db+DMSO. CDN, CDN1163; Ctrl, control mice; Db, db/db mice; veh, vehicle; SNP, sodium nitroprusside. Figure 8 Mechanisms by which SERCA2 activator (CDN1163) improves the function of the liver, vascular tissue, and skeletal muscle. Hyperglycemia and oxidative stress induced in type 2 diabetes decrease SERCA2 expression and activity. CDN1163 ameliorates impaired glucose lipid metabolism, insulin resistance, and ER stress, then improves hepatosteatosis in the liver, endothelial dysfunction and NO bioactivity in vessels, and oxygen consumption and mitochondrial dysfunction in skeletal muscle. ER, endoplasmic reticulum; SERCA2, sarco/endoplasmic reticulum Ca2+-ATPase 2; NO, nitric oxide. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. International Diabetes Federation IDF Diabetes Atlas-8th Edition Available online: http://www.diabetesatlas.org/ (accessed on 15 August 2018) 2. Pandey A. Chawla S. Guchhait P. Type-2 diabetes: Current understanding and future perspectives IUBMB Life 2015 67 506 513 10.1002/iub.1396 26177573 3. Natarajan R. Nadler J.L. Lipid inflammatory mediators in diabetic vascular disease Arter. Thromb Vasc. Biol. 2004 24 1542 1548 10.1161/01.ATV.0000133606.69732.4c 15166011 4. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095622 ijerph-19-05622 Article Access to City Center: Automobile vs. Public Transit https://orcid.org/0000-0001-6337-8041 Liu Linlin Zheng Bohong * Luo Chen Bedra Komi Bernard https://orcid.org/0000-0001-8912-3477 Masrabaye Francis Tchounwou Paul B. Academic Editor School of Architecture and Art, Central South University, Changsha 410018, China; divine@csu.edu.cn (L.L.); luochenlc@csu.edu.cn (C.L.); komibedra@csu.edu.cn (K.B.B.); mfrancis@csu.edu.cn (F.M.) * Correspondence: zhengbohong@csu.edu.cn 05 5 2022 5 2022 19 9 562204 4 2022 03 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). For current territory development planning in China, city center accessibility (CCA) has gained increasing attention for evaluating the expansion of urban areas. How should CCA and its differences between the automobile and public transit (PT) modes be measured? We analyzed CCA from travel time and travel cost perspectives using the travel data obtained from the Baidu Map at a 100 m × 100 m resolution. The GWR was then examined to explore the correlation between the explanatory variables and the CCA differences. Automobile-based CCA shows a concentric structure and varies with time, while PT-based CCA has an apparent linear expansion along the metro lines and fluctuates less. When measuring by travel cost instead of travel time, CCA gaps between the two modes are lessened, and the automobile’s advantage is no longer evident. The distance from the metro stations has a significant positive effect on CCA differences, and the positive effect concentrates in the 3.6 km range (measured by travel time) and 2.8 km range (measured by travel cost) around the metro stations. Our study highlights the importance of multiple perspectives when comparing the accessibility of different transport modes, and the results also provide implications for policy-makers. city center accessibility online map isochrone maps cumulative opportunities GWR Central Universities of Central South University2019zzts840 This research was funded by Fundamental Research Funds for the Central Universities of Central South University, grant number 2019zzts840. ==== Body pmc1. Introduction China’s State Council released the “National Main-function Area Plan” in 2010, which is the first territory development plan in China. In shifting from urban and rural planning to territory development planning, the government has issued various guidelines to guide the construction and control of land use. Within these guidelines, city center accessibility (CCA) gains more and more attention as an important indicator for evaluating the development of urban space and land use. Since Hansen [1] proposed the concept of accessibility, it has been defined in different ways, as accessibility is studied in various fields, such as socioeconomics, transportation, and urban planning [2]. Some researchers defined accessibility as “the ease and the ability to reach opportunities, activities, and services using a transport mode or a combination of modes” [3,4,5] to explain the interaction between land use and transport systems. CCA can be delineated as the ease of reaching the city center and measured by the time or the cost incurred in travel from the origin to the city center. The ease of reaching city centers has a significant impact on urban residents’ lives in China because they are important commercial hubs, major destinations for business, consumption, and entertainment. With the acceleration of urbanization and the expansion of urban areas, the residential land spreads outwards, and the disparities of CCA among residents at different locations also increase. Public transport (PT) is considered to play a critical role in reducing private car usage, alleviating traffic pressure, ensuring social equity and sustainable development because of its higher energy efficiency and its lower cost for users [6,7]. Nowadays, more and more people advocate green travel, and urban public transport infrastructure is becoming perfected, further changing CCA’s distribution disparities. Equitable PT distribution can reduce the accessibility gap between different groups [8], because disadvantaged social groups have limited travel options and tend to rely more on PT to access opportunities [9]. According to Changsha’s 2018 resident travel survey report, the PT mode share is 54%, and this proportion is still increasing as more metro lines are being opened. One way to evaluate PT service equity is to understand the accessibility difference between PT and automobile. The comparison between the two modes provides information about the efficiency of PT, as better PT accessibility can affect PT travel choice, then generate higher PT mode share [10]. Increasing accessibility to urban services is crucial for transport policy and urban planning [11]. Accessibility difference between transport modes is not a new concept. In early studies comparing the accessibility of PT and automobile, PT was mainly focused on the bus mode. These studies point out that automobiles have better accessibility than PT [12,13,14]. Nowadays, the improvement of urban metro systems has brought new changes to the accessibility difference between PT and automobile. Related studies should include bus, metro, and a combination of both. However, when referring to accessibility compared between different transport modes, most accessibility measures only use time (or distance) and neglect the travel cost [15]. Such an analysis may be disadvantageous for PT and tends to underestimate its accessibility, as it has a more significant advantage in travel cost instead of travel time than automobile. In this paper, we convert travel time to its monetary value and add the actual travel fare to get the total travel cost, which reflects the monetary and non-monetary costs, to explore the following questions from travel time and travel cost perspectives. What are the differences between PT-based CCA and automobile-based CCA? How should these differences be measured? What are the variables that may influence these differences? To answer the first two questions, by using the real-time travel data obtained from the online map, we calculate and visualize PT-based and automobile-based CCA separately, then the PT-based and automobile-based CCA ratio for each unit to describe the difference between the two modes. As for the third question, we examine the ordinary least squares (OLS) regression and the geographically weighted regression (GWR). This study aims to measure automobile-based and PT-based CCA under real transport networks without the tedious building process of road networks, trying to get more accurate and reasonable results. Because of the real-time characteristics of the acquired travel data, we can analyze CCA not only in spatial dimensions but also capture variations along temporal dimensions. Our research findings could aid in making policies that reduce socio-spatial inequalities. The remainder of this article starts with a review of related research in Section 2. Section 3 describes the study area, data assembled and methods adopted. Then we present the results and analyses in Section 4, and conclusions are given in Section 5. 2. Literature Review Since accessibility was proposed, various measures have been developed, such as gravity-based measures [1], the cumulative opportunities method [16,17], and the two-step floating catchment area method [18]. Accessibility is often measured by counting the number of opportunities reached within a given constraint, such as time or distance, usually combined with isochrone maps. Time measures can be more sensitive than distance measures when mirroring demographic, social, economic, and cultural constraints [19]. Therefore, accurate travel time estimation is crucial for measuring spatial accessibility [20]. In many studies, the travel time is often achieved with the help of network analysis tools in ArcGIS. Often, researchers first build a road network and set appropriate speeds for different levels of roads, then get the travel time of each road by dividing the road length by the speed [21,22]. Travel time calculated by this method is too ideal, and the considerable spatial variations are concealed. It is also challenging to embody the dynamic characteristics of accessibility for seldom considering the actual traffic status. Accessibility is not only a spatial concept but also contains temporal characteristics [23]. It varies by time of day [24,25] and varies by different levels of congestion [26,27]. Taking dynamic features into account can enhance the accuracy and predictability of accessibility [28,29]. Navigation datasets can be obtained through application programming interface (API) services of online maps based on location-based services (LBS). The datasets contain abundant road information, such as road networks, speed limits, PT stations, traffic status, etc., and can estimate travel times for different transport modes [30]. These datasets are often validated through on-site surveys, making the data more up-to-date and reliable than traditional network analysis [31]. The real-time route planning service API provided by online maps enables estimating various and dynamic travel times by different transport modes under time-variant traffic conditions. Travel data obtained through the online map API is based on the latest road network rather than a self-built road network, which has the advantage of reflecting the real-time traffic status, compared with the traditional network analysis [32]. Recently, numerous studies in terms of traffic accessibility based on online map APIs have emerged. Niu et al. [33] used the Amap API to obtain travel times by walking, driving, and PT to evaluate the accessibility of parks in Wuhan city. Su et al. [34] calculated hospital accessibility at different periods using real-time traffic navigation data obtained by online map API. Chen et al. [35] studied the accessibility of fire stations in Nanjing City using the car travel mode of the Amap API to obtain real-time travel time from fire stations to fire events. Wang et al. [36] analyzed the accessibility of 56 scenic spots in Xi’an City via car and public travel modes using the real-time travel function of the Baidu Map API. These studies have proved that online mapping services can provide objective and accurate travel data. To date, however, current studies are mainly concerned with the accessibility of various public services within the city; the application of online map API is still scarce in the studies on CCA. In accessibility studies, suitable spatial units are usually selected first for the study area to collect and analyze data. The following two approaches are commonly used to specify spatial units. One approach is to use administrative units such as sub-district [37], census tract [31], community [38], and block [39] as the basic spatial unit, taking their centroids as the origins. The other is to divide the study area into an equal-sized raster, for instance, 1 km × 1 km [40], 500 m × 500 m [33,34,41], 400 m × 400 m [42], with the centroid of each grid as the origin. The latter approach is often used when obtaining travel data using online map APIs. Some scholars [33,37] have highlighted the fact that smaller spatial units can generate better results. When using the online map API to obtain data, higher resolution data can be collected by dividing the study area into grids with smaller units, improving the accuracy of transit accessibility measurements [43]. In this paper, the study area was divided into 100 m × 100 m grids containing 97,605 units in total. 3. Data and Methodology In this section, we introduce the information about the study area relevant to our research and the process of data acquisition. The methods used in the analysis procedure and why we chose GWR are also shown here. 3.1. Study Area Changsha is the capital of Hunan Province, in the south-central part of China, with a total population of up to 10 million in 2020, governing 6 districts and 1 county. In Changsha, there are 6 metro lines, and 102 metro stations, and the average daily metro passenger volume is 1.21 million. There are also 291 bus lines, nearly 5000 bus stops, and the average daily bus passenger volume is 1.3 million. The daytime heat map of Changsha City provided by Baidu Map shows that most population activities are concentrated within the third ring road. Therefore, the area shown in Figure 1 is selected as the study area. Bus lines, metro lines, and ring roads are also plotted in Figure 1 based on data from the Open Street Map. According to the overall plan of Changsha City, Wuyi Square is the center of the city, with a large number of commercial and business lands clustered around it. Meanwhile, Wuyi Square Station is the first transfer station of Changsha Metro, marking the formation of the cross pattern of Changsha Metro. 3.2. Travel Data and Population Information Dividing the study area into grids can be easily combined with grid-based population data to facilitate accessibility modelling, calculation, and evaluation. We divided the study area into 100 m × 100 m grids with 97,605 units (excluding the water system) and obtained the real-time travel data from Baidu Map (https://lbsyun.baidu.com/, accessed on 8 October 2021), one of the biggest online maps in China. The center of each unit is the origin, and the destination is Wuyi square. The route navigation module of Baidu Web Mapping API can provide accurate travel data by PT and automobile from each grid to Wuyi Square based on actual real-time traffic status. A python algorithm program that can access the route navigation module has been used to acquire the necessary data. For an origin-destination trip, the travel data for each mode includes the shortest travel time from the origin to the destination and the corresponding route. The data also contains the actual fare incurred for the PT route and the taxi fare driving along the automobile route. There is no uniform charge for automobiles, and it’s hard to find a proper standard to quantify the cost of using automobiles. Different types (powered by electric, gas, or hybrid) and different driving habits will lead to great variations in driving costs. The cost of purchase, maintenance, insurance, and depreciation for automobiles should also be counted in. Taxi fare is seen as a standardization of the monetary cost of driving to the city center. Morning and evening peak hours are when traffic congestion on roads and crowding on PT are at their highest. And the frequency of PT service is usually higher during peak hours. To compare the CCA among peak and off-peak hours, we selected three time periods for the study: morning peak (AM), mid-day rest (MD), and evening peak (PM). The data were obtained during the period 11 October 2021–7 November 2021 (four weeks in total) and comprised three hours of a day: AM (8–9 a.m.), MD (1–2 p.m.), PM (6–7 p.m.). Since the travel times obtained are based on real-time road conditions, each origin-destination travel time at different hours is the hour’s average during four weeks to avoid randomness. The demographic data used in this study is the 100 m precision Chinese population data in 2020 provided by WorldPop (https://www.worldpop.org/, accessed on 12 July 2021) and validated with China’s seventh national census data. The population in each spatial unit is obtained by performing a bilinear resampling method in ArcGIS 10.2. All data are carefully checked to avoid abnormal values. 3.3. Methods Our methods are developed as follows. First, utilizing the Baidu online mapping API, we collect the travel time and travel fare from each grid to the city center by automobile and PT. Then we use the travel time to draw the isochrone maps to the city center at AM, MD, and PM for the two transport modes. According to the cumulative opportunity method, we draw the cumulative accessible land and population percentages as travel time increases. Then, we convert the travel time into an equivalent monetary cost based on the average wage in Changsha in 2020 and add it to the actual travel fare to get the travel cost required. Following the method mentioned above, we draw the iso-cost maps and the cumulative curves as travel cost increases. After that, for each unit, the ratio of travel time (time-ratio) and the ratio of travel cost (cost-ratio) for the two transport modes are calculated and visualized. Finally, the variables influencing time-ratio and cost-ratio are explored using the OLS regression and the GWR. (1) Isochrone map and iso-cost map Isochrone map provides a visual indication of the area where the city center can be reached within a given travel time. Based on the travel data obtained from the Baidu Map and after the above transformation, each unit in the study area has attributes including travel time and travel cost. And the isochrone map and iso-cost map are generated in ArcGIS 10.2 using the inverse distance weighting (IDW) method with intervals of 10-min and $5. Both isochrone maps and iso-cost maps are drawn for the two travel modes at the three periods (AM, MD, PM), a total of 6 scenarios. (2) Cumulative opportunities For the 6 scenarios mentioned above, based on each unit’s travel time or travel cost, we count the land located within a given travel time or cost i, then plot the cumulative growth curves of the land percentage P(li) as the travel time or the cost increases. Combined with each unit’s population information, the cumulative growth curves of the population percentage P(oi) are plotted. (1) P(li)=liL×100% (2) P(oi)=oiO×100% In Equation (1), li is the land where the city center is accessible within travel time or cost i, L is the total land of the study area, P(li) indicates li as a percentage of L, while oi, O, P(oi) in Equation (2) indicate the corresponding population-related indicators. (3) Regression analysis For each unit, the ratio of travel time (time-ratio) and travel cost (cost-ratio) to the city center by the two transport modes are calculated and visualized to depict the CCA differences between the two modes. Time-ratio and cost-ratio are used as dependent variables, and possible explanatory variables are analyzed. (3) time-ratio=travel time by PTtravel time by automobile (4) cost-ratio=travel cost by PTtravel cost by automobile (5) Y=Xβ+ε (6) Y=Xβ+ε,ε=λWε+μ,μ∼N[0,σ2I] (7) Y=ρWY+Xβ+ε,ε∼N[0,σ2I] (8) yi=βi0+∑k=1pβikxik+εi For an OLS model, the relationship between the dependent variable Y and explanatory variables X can be formulated as Equation (5), where β is the coefficient and ε is the random error. The spatial error model and spatial lag model write as Equations (6) and (7). In Equation (6), Y is an N × 1 vector of dependent observations, X is an N × K matrix of exogenous explanatory variables, β is a vector of coefficients for X, λ is the coefficients of spatial error terms Wε, and μ is the random error. In Equation (7), ρ is the coefficient of lagged dependent observations WY. The regression models that underlie GWR can be written as Equation (8). For the unit i, βi0 is the constant term of the statistical regression, βik is the regression coefficient of the explanatory variable xik. The GWR model is an extension of the OLS model, and it adds spatial geographic information of the data to the regression parameters. By using ArcGIS10.2, first, we started fitting our data with OLS (Equation (5)) regression while the error term (ε) is assumed to be at least independent and identically distributed. Cluster and outlier analysis (Anselin Local Moran I) are performed on the residuals of OLS models. Significant positive Moran’s I for the residuals in all models are found, which means the unexplained error term (ε) in OLS models is unlikely independent. The regression analysis of our data should consider the spatial correlation. Then we fit our models with the spatial error model and spatial lag model using GeoDa. However, the results show that the two models are hard to explain the correlation between variables. When using the spatial error model, the coefficient λ (Equation (6)) of the spatial error term reaches above 0.99; when using the spatial lag model, the coefficient ρ (Equation (7)) of the lagged term also reaches above 0.99, and the residuals of these models are still highly spatially correlated. The spatial error model and spatial lag model are techniques introduced to deal with spatial dependence based on general regression analysis. Although different independent variables have different degrees of influence on the dependent variables, the contribution of individual independent variables is still the same in different areas. Essentially, the two models belong to the global model. In contrast, the GWR can estimate the coefficients for different independent variables at different locations, and the differences in the relationships between variables due to different geographical locations can be detected intuitively. For a spatial phenomenon such as accessibility, it is possible to investigate better the influence of each variable on the spatial variation of accessibility, so we finally choose the GWR. 4. Results In this section, we visualize automobile-based and PT-based CCA by isochrone maps, iso-cost maps, and cumulative curves during the three time periods: morning peak (AM), mid-day rest (MD), and evening peak (PM). Then we present the regression analysis results, cooperating with some reasonable explanation. 4.1. Travel Time Measures In terms of spatial distribution (Figure 2), the PT-based isochrone maps show apparent linear expansion along the metro lines, reflecting the shape of the metro network, while the automobile-based maps are more circular, showing a concentric structure that gradually spreads from center to outside. And the “center-edge” gap in PT-based maps is more significant than in automobile-based maps. PT-based isochrone maps waver less with time, while automobile-based maps vary widely between peak and off-peak hours and demonstrate significantly better CCA during MD than the other two periods. The statistics of travel time by the two modes at the three time periods are shown in Table 1. Both modes exhibit better CCA during MD and show not much difference between morning and evening peak hours; the two modes’ difference is most remarkable during MD. For the entire study area, the mean travel time to access the city center by automobile is 39.5 min, while PT is 80.4 min. The mean time by automobile differs significantly between peak and off-peak hours, with the value being 59.6 min and 57.7 min during peak hours, while 35.3 min during MD, with a decrease of more than 20 min. Travel time by PT varies less among the three time periods. This result is understandable since travel time by PT is less affected by congestion. The bus has scheduled frequency and stops, and its driving speed can not exceed its speed limit even if in good traffic conditions. The bus must stop at each stop, whether there are passengers or not, coupled with the short distance between bus stops (most of the bus stops in the study area are about 500 m apart), making it unlikely to travel at a very high speed. In addition, in the travel routes by PT, part of the journey may require taking the metro, which passes underground and is not affected by traffic congestion. Table 1 also shows the mean time-ratio in the study area for the three time periods. The time-ratio equals PT-based travel time divided by automobile-based travel time for each unit (Equation (3)). A unit with time-ratio smaller than one means that it takes less time by PT than by automobile to reach the city center; time-ratio greater than one means the opposite. The time-ratio during MD is higher than during AM and PM. Because the automobile-based travel time decreases significantly at MD, while the PT-based travel time is relatively stable, leading to the time-ratio increases during MD. However, it should be noted that the mean time-ratio during all three periods is greater than one, implying that PT-based CCA is overall inferior to automobile-based CCA from the travel time perspective. The spatial distributions of time-ratio during three time periods are shown in Figure 3. It can be seen that there are fewer areas with time-ratio less than one during MD, and most of them are distributed around metro stations. The more extreme case, where the PT-based travel time is more than double the automobile-based (time-ratio > two), is concentrated in the northeast and southwest of the study area, where bus lines are scarce and there are no metro stations. At peak hours, traffic congestion causes automobile-based travel time to increase in some areas, and more areas appear with time-ratio less than one, but it can still be found that these areas are relevant to metro lines and stations. According to the cumulative opportunities method, the number or percentage of a kind of element that can be accessed in a given time can be used to assess accessibility. The cumulative CCA can be indicated by the percentage of land or population which can access the city center. Figure 4 compares PT-based and automobile-based cumulative CCA growth curves for three time periods, showing that all curves exhibit like S-curve. Overall, automobile users have relatively better cumulative CCA than PT users in terms of travel time. In other words, when traveling by automobile, the city center is accessible to a larger proportion of the population and land within a given travel time. The cumulative CCA differences between the two modes are more significant during MD when it seems rather inequitable that the city center is within a 60 min drive by automobile for almost the whole land (99.21%) and (96.12%) population, while just about 30% (28.06%) of the land and 60% (60.83%) of the population within a 60 min drive by PT. However, it should be recognized that even during MD, when there is little congestion, the cumulative CCA is poor for both modes and worse for PT. We can see that the city center is within 15 min drive by PT for just 0.05% land and 0.23% population of the study area, and within 15 min drive by automobile, there are 2.43% land and 8.8% population. And if travel time by PT could cover a similar percentage as the automobile’s 15 min drive, it would take almost 30 min (2.3% land and 8.99% population), double the automobile travel time. Also, the curves of both modes show that the percentage of population which can access the city center increases faster than that of land when travel time increases. It also refers to that both modes can make the city center accessible for a larger percentage of the population than land within a given travel time. It is a result of the fact that population and land are not uniformly distributed within urban areas. For urban areas, population density tends to decrease gradually from the city center to the suburbs. A certain percentage of land close to the city center carries more than that percentage of the population. Supposing we use the percentage of population served as an indicator to evaluate the cumulative CCA, we will get better results than using the percentage of land, no matter which transport mode is chosen. 4.2. Travel Cost Measures The advantages of PT are also known to be its low fares and relatively stable travel time, and it is biased to use travel time alone to compare the two modes. Therefore, we try to use travel cost to compare the CCA of both modes, considering both travel fare and travel time. (9) travel cost=(travel time×8.42)+travel fare In equating travel time to monetary value, some use the minimum wage [44], and some use the average wage [15], the latter is used in this study. The time value of Changsha citizens is about 53.44 yuan ($8.42)/h, calculated based on Changsha’s total employee wages in 2020. The travel time is transferred to monetary value and summed with the actual travel fare as the travel cost required for each unit to reach the city center (Equation (9)). As mentioned in 3.2, the travel fare of PT is the actual fare incurred during the trip, while the travel fare of the automobile is measured using the taxi fare on the same route at the same time. Iso-cost maps are drawn using a $5 interval to observe the spatiotemporal distribution of CCA for the two modes (Figure 5). It can be observed that the spatial distribution of the iso-cost maps is similar to the isochrone maps, i.e., PT-based iso-cost maps expand along the metro lines, and automobile-based show concentric structure. Table 2 shows the two modes’ mean travel costs to the city center and the cost-ratio (Equation (4)). We can find the travel costs of both modes are lower during MD. And the mean cost by automobile is closer to PT during MD but significantly higher than PT during AM and PM since automobile-based cost increases during peak hours. The mean cost-ratio is higher during MD, but it is less than one for all the three time periods, i.e., the mean travel cost by PT is lower than that by automobile during peak and off-peak hours. Measured from the mean travel cost perspective, traveling by PT has better CCA. The spatial distribution of cost-ratio (Figure 6) shows that the automobile no longer prevails when travel cost is the CCA measure, especially at peak hours. Cost-ratio in most areas is less than 1, which means at peak hours, there are many areas where the travel cost reaching the city center by PT is lower than that by automobile. Moreover, values of cost-ratio show a trend of gradually decreasing from the center to the periphery, meaning the travel cost advantage of PT is gradually evident from the center to the periphery, and the surroundings of metro stations always have a smaller cost-ratio. During MD, the areas with cost-ratio less than one decrease compared to peak hours, indicating the advantage of PT declines compared with peak hours. Similarly, we draw the growth curves of cumulative population and land percentages when travel cost increases (Figure 7). The cumulative curves still show the characteristics of the S-curve. Disparities between population and land still exist in both modes. Unlike the cumulative CCA curves measured by travel time, when using travel cost as the X-axis, the PT-based cumulative CCA is mostly better than automobile-based, both in population and land. And differences between the two modes are greater during peak hours and very similar during MD. This finding suggests, to some extent, that there is not a huge accessibility deficit between PT and automobile in Changsha in terms of travel cost when road conditions are good. 4.3. Regression Analysis Clarifying the impact of influencing factors on CCA differences between the two modes can provide suggestions for the construction and layout of PT. Time-ratio and cost-ratio are the ratios of the two modes concerning travel time and travel cost. From the spatial distribution of the two indicators, both in travel time and travel cost, the automobile-based CCA of each unit shows a concentric structure centered on the city center, and the PT-based CCA tends to expand along transport lines and stations. It can be inferred that automobile-based CCA is correlated with the distance from the city center, and PT-based CCA is related to the distance from the transport station, as the transport station is a critical node of accessibility. To make the magnitude of each variable approximate, use “km” as the unit. To minimize the effect of traffic congestion, we choose the time-ratio and cost-ratio during MD as dependent variables. And the distance from the city center (center-dist), bus stops (bus-dist), and metro stations (metro-dist) are used as explanatory variables to investigate the relationship between them. First, the data are fitted using the OLS regression, and the results shown in Table 3 indicate that the OLS regression can only explain a tiny fraction of the data. Moreover, in an OLS model, the error term is assumed to be at least independent and identically distributed. Moran’s I of the residuals in the two models are 0.86 and 0.84, respectively, so regressions of the data should take spatial correlation into account. As mentioned in Section 3.3, we used the GWR to analyze the relationship between the dependent and explanatory variables. When choosing a travel route, people often walk to it if the metro station is nearby. If the metro station is far, people often walk to a nearby bus stop to take a bus first to reach a nearby metro station. Considering the bus stop spacing is about 500 m, a distance beyond 500 m may cause people to choose different bus stops and thus different travel routes. Two GWR models are constructed with time-ratio and cost-ratio as dependent variables, respectively, with fixed kernel type and fixed bandwidth of 500 m in model construction. The GWR model with time-ratio as the dependent variable has an adjustedR2 of 0.82 and an AICc of −10,576.64. In the other model with cost-ratio as the dependent variable, the adjustedR2 is 0.81, and the AICc is −90,258.68. It can be seen that the GWR can explain the two models better. The effect of the explanatory variables on the dependent variables in a GWR model is spatially heterogeneous, i.e., the regression coefficient of the explanatory variables varies in different areas. Based on the regression results of the above two GWR models, we draw probability density distributions and positive-negative effects of the regression coefficients for each variable, as shown below (Figure 8 and Table 4). As shown in Figure 8, the coefficient distribution of center-dist shows a relatively standard normal distribution. The bus-dist and metro-dist coefficients show right-skewed patterns, and the right-skewed is more prominent especially when time-ratio is used as the dependent variable. Table 4 shows that center-dist has comparable positive and negative effects on the dependent variables, while bus-dist and metro-dist have mostly positive effects. The farther the unit is from a bus stop or metro station, the larger the time-ratio and cost-ratio may be. Alternatively, PT-based CCA is more likely to be similar or even superior to automobile-based CCA for units near bus stops and subway stations. In the time-ratio and cost-ratio models, each unit has different regression coefficients for the explanatory variables: center-dist, metro-dist, and bus-dist. Figure 9 shows the spatial distribution of the regression coefficients of each variable (according to ArcGIS official documentation, the small blank area is due to the multicollinearity of the explanatory variables in this area, which leads to unreliable results). Compared with the time-ratio model, when cost-ratio is the dependent variable, each explanatory variable’s positive and negative effects become weaker. It reflects the correlation between the explanatory variables and time-ratio is more robust than that with cost-ratio. Table 4 and Figure 9 show that the center-dist’s positive and negative effects are roughly equivalent, and its spatial distribution of positive and negative effects is also random. And the center-dist’s correlation coefficients are small, indicating that the center-dist has weak correlations with time-ratio and cost-ratio. The effect of bus-dist within the second ring road is more complex, with both positive and negative effects distributed and to a high degree, while between the second ring and the third ring road is dominated by positive effects with a moderated degree. Outside the third ring road, there are highly positive and high negative correlation areas. When referring to the metro-dist, the positive effect is concentrated around metro stations. Both time-ratio and cost-ratio models show this trend, and the difference is that the degree and scope of the positive effect around metro stations are smaller in the cost-ratio model. Concerning the scope of effects, we counted the correlation coefficients of the units within the same distance range from metro stations. For units with the same range of metro-dist (100 m interval), their mean correlation coefficients of metro-dist are calculated, as shown in Figure 10. We can find that when time-ratio and cost-ratio are used as the dependent variables, the correlation coefficient decreases with increasing metro-dist, and the closer the distance to metro stations, the stronger the positive correlation. In which, when time-ratio is the dependent variable, the mean value of the correlation coefficient drops below zero when metro-dist exceeds 3.6 km. In other words, the positive effect of metro-dist on time-ratio is mainly concentrated in the area within 3.6 km of metro stations. Although some areas have negative effects within this scope, the positive effect is dominant overall. The corresponding distance is 2.8 km when cost-ratio is the dependent variable, i.e., the mean value of the correlation coefficient drops below zero when metro-dist exceeds 2.8 km. 5. Discussion and Conclusions This paper uses the real-time travel data captured via the Baidu Map API to visualize the automobile-based and PT-based spatiotemporal CCA at a 100 m × 100 m spatial resolution and compare them from travel time and cost perspectives. We focused on the CCA differences between the two modes, quantified the differences, and explored the possible influencing factors. Findings from our analysis are outlined and discussed as follows. Firstly, we obtain the travel time and the actual travel fare of each unit in the study area to reach the city center and calculate the equivalent travel cost of each unit by converting the travel time into its monetary value using the average time value. Based on this, isochrone maps and iso-cost maps are made for the two modes at different hours. The results show that automobile-based CCA has significant temporal characteristics, with it being better during MD than peak hours, while PT-based CCA differences between peak and off-peak hours are pretty slight. Automobile-based CCA shows a concentric structure centered on Wuyi Square, and PT-based CCA has noticeable expansion along the metro lines, and these spatial characteristics exist at peak and off-peak hours. This indicates that automobile-based CCA is greatly influenced by road conditions. Due to bus and metro operating rules, PT-based CCA is little affected by traffic congestion. According to the metro scheduling rules in the study area, the metro departure interval is even shortened during the morning and evening peak hours to reduce the waiting time of passengers and transport more users. All these together lead to a slight difference between peak and off-peak hours in the PT-based CCA and a greater spatial dependence on the distribution of transport stations. And this is in line with what many researchers have pointed out: transport stations are virtual nodes in the study of PT accessibility. Secondly, to consider the fare advantage of PT and to avoid the one-sidedness of using only travel time to measure accessibility, this study uses both travel time and travel cost to measure CCA. In summary, the CCA gaps between the two modes will be lessened, and the advantage of the automobile will no longer be evident if using the travel cost instead of travel time as the measurement. When measured by travel time, automobile-based CCA exhibits better, and the CCA difference between the two modes is more significant at MD hour. When measured by travel cost, PT shows better CCA, and the differences differ greater at peak hours when the travel cost of PT is lower than that of the automobile in most areas. The cumulative CCA of the two modes is measured by the cumulative accessible land and population and exhibits similar results to CCA. The automobile always makes the city center accessible for more population and greater land for a given travel time. In comparison, PT is the one that brings more population and land for a given travel cost. An attractive observation is that the two modes’ cumulative CCA at MD hour are very similar when measured by travel cost, indicating that PT is relatively well developed from the travel cost perspective. And there is no huge travel cost inequality between it and the automobile during off-peak hours. Finally, we quantified the CCA differences between the two modes, and considering accessibility is a spatial phenomenon, we chose the GWR to analyze the factors influencing the CCA differences. According to the spatial distribution characteristics of automobile-based CCA and PT-based CCA, the relevant factors affecting the CCA differences are summarized: center-dist, bus-dist, and metro-dist. The time-ratio and cost-ratio at MD hour (reducing the interference of traffic congestion) are used as dependent variables. Each unit’s center-dist, bus-dist, and metro-dist are explanatory variables. We find that the center-dist of each unit has a negligible effect on the CCA differences between the two modes. The positive effects of bus-dist and metro-dist on CCA differences are mainly observed, and the distribution of bus-dist’s positive effects does not show prominent spatial characteristics. The positive effects of metro-dist are mainly distributed around the metro stations, concentrated within a 3.6 km scope in the time-ratio model and a 2.8 km scope in the cost-ratio model. And in these scopes, the average correlation coefficient decreases with the increase of metro-dist. It suggests that the closer the unit to the metro station, the more likely it has a better PT-based CCA. When developing strategies to improve PT, it may be more efficient to appropriately increase construction density and population or job density around metro stations than to build new transportation infrastructures, considering that the building process is costly and time-consuming. Measuring spatiotemporal accessibility via an online map API is rapidly evolving and can be expected to be more widely used in the future. The detailed information contained in the real-time travel data also provides the basis for a multi-dimensional assessment of accessibility. This paper is a meaningful attempt to use dynamic data for urban analysis. Compared to using road network data and road average speed for accessibility evaluation, real-time data has a natural advantage in analyzing temporal characteristics. This paper focuses on the comparison of CCA of PT and automobile. At the same time, the online map API also provides route planning for other modes of travel, such as cycling and walking, which can also provide support for the comparison of more travel modes. The approach adopted in this paper can quantify the accessibility of any other valued destinations using other transport modes in the urban area, depending on the research purpose. Moreover, some studies [45,46] underline that geospatially objective accessibility and perceived subjective accessibility do not exactly match, and individuals’ perspectives should be considered in further research. This study does not capture the traveler preferences, nor does it consider the proportion of users who choose driving or using PT. Also, in general, the time value of the PT user should be inconsistent with the automobile user, which may affect the results of accessibility assessed by travel cost. These questions need to be further explored in future studies. Acknowledgments We acknowledge Baidu Map for offering public web mapping APIs. We thank Huan Xu for his assistance in python algorithm programming. We are grateful to the editors and reviewers for their valuable comments and help. Author Contributions Conceptualization, L.L. and B.Z.; methodology, L.L. and B.Z.; software, L.L.; validation, L.L. and C.L.; formal analysis, L.L.; data curation, L.L. and C.L.; writing—original draft preparation, L.L.; writing—review and editing, L.L., B.Z. and K.B.B.; visualization, L.L., K.B.B. and F.M. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Study area. Figure 2 Isochrone maps of automobile and PT for the three time periods. Figure 3 Time-ratio distribution for the three time periods. Figure 4 Growth curves of cumulative land and population percentages as travel time increases: (a) during AM; (b) during MD; (c) during PM. Figure 5 Iso-cost maps of automobile and PT for the three time periods. Figure 6 Cost-ratio distribution for the three time periods. Figure 7 Growth curves of cumulative land and population percentages as travel cost increases: (a) during AM; (b) during MD; (c) during PM. Figure 8 Probability density distribution of the correlation coefficient of each explanatory variable. Figure 9 Spatial distribution of the regression coefficients for each explanatory variable. Figure 10 Mean coefficient of metro-dist for units within the same range from the metro stations. ijerph-19-05622-t001_Table 1 Table 1 Travel time by two modes and time-ratio for the three time periods. Time by Automobile (Min) Time by PT (Min) Time-Ratio AM MD PM AM MD PM AM MD PM mean 59.6 39.5 57.7 83.1 80.4 82.5 1.41 2.07 1.44 std 15.4 12.4 15.1 33.9 32.3 33.5 0.61 0.88 0.78 ijerph-19-05622-t002_Table 2 Table 2 Travel cost by two modes and cost-ratio for the three time periods. Cost by Automobile (USD) Cost by PT (USD) Cost-Ratio AM MD PM AM MD PM AM MD PM mean 15.6 12.8 15.4 12.5 12.1 12.3 0.80 0.97 0.81 std 5.0 4.9 5.1 4.9 4.7 4.9 0.20 0.22 0.19 ijerph-19-05622-t003_Table 3 Table 3 Results of the OLS regression. DV Time-Ratio Cost-Ratio Coef StdError Prob Coef StdError Prob (Intercept) 1.819 0.0046 **** 0.995 0.002 **** Bus-dist 0.055 0.0013 **** 0.005 0.0006 **** Metro-dist 0.056 0.0009 **** 0.002 0.0004 **** Center-dist −0.001 0.0004 **** −0.003 0.0001 **** AdjR2 0.134 0.005 AICc 145,012.807 −25,578.091 Significance: **** < 0.001. Corrected Akaike information criterion (AICc) is a way of selecting a model from a set of models, and a lower AICc means a better model. ijerph-19-05622-t004_Table 4 Table 4 The percentage of positive and negative effects of each explanatory variable. Time-Ratio Cost-Ratio Center-Dist Bus-Dist Metro-Dist Center-Dist Bus-Dist Metro-Dist Positive effect (%) 51.11 69.84 65.10 49.62 71.27 61.81 Negative effect (%) 48.89 30.16 34.90 50.38 28.73 38.19 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Hansen W.G. How Accessibility Shapes Land Use J. Am. Inst. Plan. 1959 25 73 76 10.1080/01944365908978307 2. Doi K. Kii M. Nakanishi H. An Integrated Evaluation Method of Accessibility, Quality of Life, and Social Interaction Environ. Plan. B Plan. Des. 2008 35 1098 1116 10.1068/b3315t 3. Geurs K.T. Van Wee B. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092086 cancers-14-02086 Article Cancers and COVID-19 Risk: A Mendelian Randomization Study https://orcid.org/0000-0002-0237-4458 Li Zengbin 1 Wei Yudong 1 Zhu Guixian 1 Wang Mengjie 1 https://orcid.org/0000-0003-2343-084X Zhang Lei 1234* D’Orazi Gabriella Academic Editor Cirone Mara Academic Editor 1 China-Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Centre, Xi’an 710061, China; zengbinli@stu.xjtu.edu.cn (Z.L.); weiyudong@stu.xjtu.edu.cn (Y.W.); xianxianshell@stu.xjtu.edu.cn (G.Z.); mjwang0211@stu.xjtu.edu.cn (M.W.) 2 Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3053, Australia 3 Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3800, Australia 4 Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China * Correspondence: lei.zhang1@monash.edu; Tel.: +86-29-8265-5135 22 4 2022 5 2022 14 9 208610 3 2022 13 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary During the COVID-19 pandemic, cancer patients are regarded as a highly vulnerable population. Given the unavoidable bias and unmeasured confounders in observational studies, the causal effects of cancers on COVID-19 outcomes are largely unknown. In the study, we tried to evaluate the causal effects of cancers on COVID-19 outcomes using the Mendelian randomization (MR) approach. No strong evidence was observed to support a causal role of cancer in COVID-19 development. Previous observational correlations between cancers and COVID-19 outcomes were likely confounded. Large and well-conducted epidemiological studies are required to determine whether cancers causally contribute to increased risk of COVID-19. Abstract Observational studies have shown increased COVID-19 risk among cancer patients, but the causality has not been proven yet. Mendelian randomization analysis can use the genetic variants, independently of confounders, to obtain causal estimates which are considerably less confounded. We aimed to investigate the causal associations of cancers with COVID-19 outcomes using the MR analysis. The inverse-variance weighted (IVW) method was employed as the primary analysis. Sensitivity analyses and multivariable MR analyses were conducted. Notably, IVW analysis of univariable MR revealed that overall cancer and twelve site-specific cancers had no causal association with COVID-19 severity, hospitalization or susceptibility. The corresponding p-values for the casual associations were all statistically insignificant: overall cancer (p = 0.34; p = 0.42; p = 0.69), lung cancer (p = 0.60; p = 0.37; p = 0.96), breast cancer (p = 0.43; p = 0.74; p = 0.43), endometrial cancer (p = 0.79; p = 0.24; p = 0.83), prostate cancer (p = 0.54; p = 0.17; p = 0.58), thyroid cancer (p = 0.70; p = 0.80; p = 0.28), ovarian cancer (p = 0.62; p = 0.96; p = 0.93), melanoma (p = 0.79; p = 0.45; p = 0.82), small bowel cancer (p = 0.09; p = 0.08; p = 0.19), colorectal cancer (p = 0.85; p = 0.79; p = 0.30), oropharyngeal cancer (p = 0.31; not applicable, NA; p = 0.80), lymphoma (p = 0.51; NA; p = 0.37) and cervical cancer (p = 0.25; p = 0.32; p = 0.68). Sensitivity analyses and multivariable MR analyses yielded similar results. In conclusion, cancers might have no causal effect on increasing COVID-19 risk. Further large-scale population studies are needed to validate our findings. cancer COVID-19 SARS-CoV-2 causal association Mendelian randomization ==== Body pmc1. Introduction Coronavirus disease 2019 (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. As of April 2022, the cumulative cases and deaths of COVID-19 have reached over 500 million and 6 million, respectively [2]. Notably, COVID-19 individuals are mostly presented with mild and moderate infection, but can progress rapidly from asymptomatic to acute respiratory distress syndrome, multiple organ dysfunction syndrome and even death [3,4]. Therefore, identifying the potential risk factors for COVID-19 will be of significant value for public health and health policy. Cancer patients are a vulnerable population during the COVID-19 pandemic [5,6]. Cancer represents a severe public health problem and is the second leading cause of death worldwide [7]. The Global Cancer Observatory estimated 19.3 million new cancer diagnoses and roughly 10.0 million cancer-associated deaths globally in 2020 [8]. Previous studies suggested that cancer patients showed higher prevalence, severe illness incidence, and mortality rate of COVID-19 compared with the non-cancer population [9,10,11]. However, a prospective cohort of 0.5 million people indicated that confounders—including socioeconomic status, age, and ethnicity—might interfere with the associations between COVID-19 and risk factors [12]. It was unclear whether the positive correlations between cancers and COVID-19 outcomes resulted from confounders or biases [13]. Furthermore, associations are correlative only; they do not imply causality. Mendelian randomization, an epidemiological method, has been widely applied to assess the potential causal association between exposure and outcome [14,15]. According to Mendel’s law, genetic variants are randomly allocated at meiosis [16]. MR analysis, using genetic variants as instrumental variables (IVs), can minimize the influence of confounders or reverse causations [14]. Given the limitations of current research, we tried to evaluate the potential impact and the causal associations of cancers with COVID-19 outcomes using the MR method. 2. Materials and Methods 2.1. Study Design Figure 1 outlines the overall design of investigating the causal associations between cancers and COVID-19 outcomes through MR study. Briefly, the MR method comprises two main steps: first, randomizing participants on the basis of IVs; then, assessing the causal associations between cancers and COVID-19 outcomes [14,17]. IVs should meet three key assumptions: (1) the IVs are robustly associated with cancers; (2) the IVs are not associated with confounders; and (3) the IVs should affect the outcomes of COVID-19 only through cancers, not via alternative pathways [17]. Previous MR studies have shown that some single nucleotide polymorphisms (SNPs) for cancers might be associated with confounders between cancers and COVID-19, such as educational attainment [18,19], body mass index (BMI) [20], income [18], alcohol consumption [21] and smoking [22,23]. Thus, we performed multivariable MR analyses to limit the effects of potential confounders. 2.2. Data Sources The summary statistics in the genome-wide association studies (GWASs) for COVID-19 were sourced from the COVID-19 Host Genetics Initiative V5 [24], which excluded “23andMe” data. The COVID-19 GWAS data has been adjusted for age, gender, age2, age × gender, principal components and study-specific covariates by the original GWAS researchers. The COVID-19 outcomes included 1,683,768 participants (38,984 infection cases and 1,644,784 controls) for susceptibility, 1,887,658 participants (9986 hospitalized patients and 1,877,672 controls) for hospitalization, and 1,388,342 participants (5101 very serious respiratory confirmed patients and 1,383,241 controls) for severity, respectively. The uninfected individuals served as the controls. All cases were confirmed by laboratory, self-reported, or physician diagnosis. The severe cases were defined as patients who died or required respiratory support with COVID-19 infection [24]. The summary statistics of the GWASs for cancers were obtained from the UK biobank [25], International Lung Cancer Consortium (ILCCO) [26], Breast Cancer Association Consortium (BCAC) [27], Ovarian Cancer Association Consortium (OCAC) [28], Endometrial Cancer Association Consortium (ECAC) [29], Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome Consortium (PRACTICALC) [30] and the thyroid cancer study of Kohler et al. [31]. Overall cancer and 12 site-specific cancers were included: 336,272 participants for overall cancer, 27,209 participants for lung cancer, 18,313 participants for squamous cell lung cancer, 228,951 participants for breast cancer, 175,475 participants for estrogen receptor-positive (ER+) breast cancer, 127,442 participants for ER- breast cancer, 66,450 participants for ovarian cancer, 121,885 participants for endometrial cancer, 140,254 participants for prostate cancer, 1080 participants for thyroid cancer, 375,767 participants for melanoma, 337,159 participants for small bowel cancer, 377,673 participants for colorectal cancer, 372,510 participants for oropharyngeal cancer, 361,194 participants for lymphoma and 463,010 participants for cervical cancer. Covariates for multivariable MR analyses were included: BMI (681,275 participants) [32], educational attainment (766,345 participants) [33], intelligence (269,867 participants) [34], average total household income before tax (income, 397,751 participants) [25], cigarettes per day (smoking, 337,334 participants) [35] and alcoholic drinks per week (alcohol consumption, 335,394 participants) [35]. All data came from the European population. Detailed information on data can be found in Table 1. At the beginning of our study design, 24 site-specific cancers were considered. Overall cancer and 12 site-specific cancers were included, but another 12 types of cancer were not included due to insufficient SNPs (stomach cancer, pancreatic cancer, esophagus cancer, kidney cancer, liver cancer, biliary tract cancer, head and neck cancer, bladder cancer, testis cancer, brain cancer, multiple myeloma and bone cancer). In Table S1, we provide detailed information on cancers that failed to perform the MR study. 2.3. Selection of Instrumental Variables Appropriate SNPs used as IVs must be robustly associated with cancers (p < 5 × 10−8). To ensure independence, SNPs were restricted by low linkage disequilibrium (LD, r2 < 0.001, window size = 10,000 kb) using clumping [14,36]. We excluded palindromic SNPs whose minor allele frequency (MAF) was less than 0.42. In addition, we calculated F-statistics for SNPs to measure instrumental strength. SNPs with an F-statistic less than 10 were removed [37]. Detailed information on selected SNPs can be found in Table S2. One SNP (rs11571818) of squamous cell lung cancer was removed (F-statistic: 7.94). 2.4. Statistical Analysis In the univariable MR analysis, the IVW analysis was chosen as the primary approach to estimate the causal effects of cancers on COVID-19 outcomes [15,38]. We added the MR-Egger regression [39], weighted median [40], weighted mode [41] and MR pleiotropy residual sum and outlier (MR-PRESSO) [42] methods as supplements to sensitivity analyses. The third assumption (that IVs cannot affect the outcomes of COVID-19 through alternative pathways) was defined as independence from pleiotropy [14]. When performing MR analysis, results may be inaccurate due to the pleiotropy of these SNPs [36]. Therefore, we evaluated the potential pleiotropy via the MR-PRESSO approach. The MR-PRESSO approach could identify and correct possible outliers and estimate causal effects [42]. We evaluated the heterogeneity by Cochran’s Q test. The fixed-effect model was used if no heterogeneity was observed (p < 0.1); otherwise, a random-effect model was applied. In addition, we used the “leave-one-out” validation to determine whether a single SNP had a significant independent influence on the MR estimation. We applied the random-effect IVW method to assess the causal effects of cancers on COVID-19 outcomes for the multivariable MR analyses, after controlling BMI, educational attainment, intelligence, smoking and alcohol consumption. Given the number of cancers and COVID-19 outcomes considered, a two-sided p-value using the Bonferroni correction (0.0033, 0.05/15 cancers) was used. 0.0033 < p < 0.05 was regarded as suggestive evidence for a potential association. The β (β = lnOR; OR, odds ratio) and its SE (standard error) were calculated to reflect effect sizes. All statistical analyses were conducted in R v4.0.1 (R Foundation, Vienna, Austria) with the packages “TwoSampleMR” and “MRPRESSO” [42,43]. 3. Results 3.1. Cancers and COVID-19 Severity A total of 1,388,342 participants (5101 very serious respiratory confirmed patients and 1,383,241 controls) were included for COVID-19 severity. Severe COVID-19 cases were defined as patients who died or required respiratory support with COVID-19 infection. The effects of each SNP in cancers on COVID-19 severity can be found in Figure S1. There was significant heterogeneity in the IVW analyses of prostate cancer (p = 0.07), ovarian cancer (p < 0.001), melanoma (p = 0.01) and cervical cancer (p = 0.08) (Table 2). Hence, we performed the random-effect model in their IVW analyses. IVW analysis suggested no causal effect of overall cancer (p = 0.34), lung cancer (p = 0.60), squamous cell lung cancer (p = 0.66), breast cancer (p = 0.43), ER+ breast cancer (p = 0.79), ER− breast cancer (p = 0.66), endometrial cancer (p = 0.79), prostate cancer (p = 0.54), thyroid cancer (p = 0.70), ovarian cancer (p = 0.62), melanoma (p = 0.79), small bowel cancer (p = 0.09), colorectal cancer (p = 0.85), oropharyngeal cancer (p = 0.31), lymphoma (p = 0.51) or cervical cancer (p = 0.25) on the COVID-19 severity (Table 2). In the sensitivity analyses, the MR-PRESSO test indicated significant horizontal pleiotropy in the analyses of prostate cancer (p = 0.03) and ovarian cancer (p = 0.01) (Table 2). After removing the horizontal pleiotropy SNPs (rs12139208 for prostate cancer; rs115478735 for ovarian cancer), MR-PRESSO analysis suggested that prostate cancer and ovarian cancer had no causal association with COVID-19 severity (p = 0.60; p = 0.96). Although horizontal pleiotropy was observed in melanoma (p = 0.02), it showed no significant outlier. We conducted the “leave-one-out” analysis and found no potential SNP significantly biasing the results (Figure S4). Taken together, sensitivity analyses (MR-Egger, weighted median, weighted mode and MR-PRESSO) revealed that cancers had no causal association with COVID-19 severity (Table 2). Results of multivariable MR analyses also supported our findings (Table S3). 3.2. Cancers and COVID-19 Hospitalization COVID-19 hospitalization analysis contained 1,887,658 participants (9986 hospitalization patients and 1,877,672 controls). Figure S2 represents the effects of each SNP in cancers on COVID-19 hospitalization. Significant heterogeneity was observed in the analyses of thyroid cancer (p = 0.06), ovarian cancer (p < 0.001) and cervical cancer (p = 0.04) (Table 3). The random-effect model was subsequently applied. IVW analysis revealed no causal effect of overall cancer (p = 0.42), lung cancer (p = 0.37), squamous cell lung cancer (p = 0.66), breast cancer (p = 0.74), ER+ breast cancer (p = 0.51), ER− breast cancer (p = 0.93), endometrial cancer (p = 0.24), prostate cancer (p = 0.17), thyroid cancer (p = 0.80), ovarian cancer (p = 0.96), melanoma (p = 0.45), small bowel cancer (p = 0.08), colorectal cancer (p = 0.79) or cervical cancer (p = 0.32) on COVID-19 hospitalization (Table 3). In the sensitivity analyses, the MR-PRESSO test indicated significant horizontal pleiotropy in the analysis of ovarian cancer (p < 0.001; Table 3). After removing the horizontal pleiotropy SNPs (rs115478735 and rs71238846), ovarian cancer was still not significantly associated with COVID-19 hospitalization in the MR-PRESSO analysis (p = 0.78). The “leave-one-out” analysis showed no outliers (Figure S5). Although MR-Egger test indicated a potential association of thyroid cancer with COVID-19 hospitalization (p = 0.04), estimates in the three analyses (weighted median, weighted mode and MR-PRESSO; Table 3) directionally matched the result of IVW analysis. In the multivariable MR analyses (Table S4), potential association with COVID-19 hospitalization was observed in overall cancer (p = 0.01) and prostate cancer (p = 0.046) when adjusting for education attainment. A significant association was also found in small bowel cancer (p = 0.047) when adjusting for smoking. However, the associations of overall cancer, prostate cancer and small bowel cancer with COVID-19 hospitalization could not be replicated when intelligence (p = 0.18; p = 0.10; p = 0.23), income (p = 0.28; p = 0.06; p = 0.28) and alcohol consumption (p = 0.58; p = 0.11; p = 0.43) were adjusted (Table S4). Therefore, there was no strong evidence for a causal association of overall cancer, prostate cancer, or small bowel cancer with COVID-19 hospitalization. 3.3. Cancers and COVID-19 Susceptibility A total of 1,683,768 participants (38,984 infection patients and 1,644,784 controls) were included for COVID-19 susceptibility. Figure S3 shows the effects of each SNP in cancers on COVID-19 susceptibility. There was significant heterogeneity in the IVW analyses of ER+ breast cancer (p = 0.02), prostate cancer (p = 0.06), thyroid cancer (p = 0.06), ovarian cancer (p < 0.001), melanoma (p = 0.05) and cervical cancer (p < 0.001) (Table 4). Thus, we performed the random-effect model for their IVW analyses. IVW analysis suggested no causal effect of overall cancer (p = 0.69), lung cancer (p = 0.96), squamous cell lung cancer (p = 0.08), breast cancer (p = 0.43), ER+ breast cancer (p = 0.30), ER− breast cancer (p = 0.18), endometrial cancer (p = 0.83), prostate cancer (p = 0.58), thyroid cancer (p = 0.28), ovarian cancer (p = 0.93), melanoma (p = 0.82), small bowel cancer (p = 0.19), colorectal cancer (p = 0.30), oropharyngeal cancer (p = 0.80), lymphoma (p = 0.37) or cervical cancer (p = 0.68) on COVID-19 susceptibility (Table 4). In the sensitivity analyses, the MR-PRESSO test indicated significant horizontal pleiotropy in the analyses of ER+ breast cancer (p = 0.02) and ovarian cancer (p < 0.001) (Table 4). After removing the horizontal pleiotropy SNPs (rs4971059 for ER+ breast cancer, rs115478735 and rs71238846 for ovarian cancer), MR-PRESSO analysis suggested that ER+ breast cancer and ovarian cancer still had no causal association with COVID-19 susceptibility (p = 0.50; p = 0.39). The “leave-one-out” plot showed one potential instrumental outlier (rs6983267) for colorectal cancer (Figure S6M). However, results of multivariable MR analyses (Table S5) supported colorectal cancer having no significant causal effect on COVID-19 susceptibility. In summary, there was no strong evidence for a causal association of overall cancer or twelve site-specific cancers with COVID-19 susceptibility. 4. Discussion During the COVID-19 pandemic, healthcare resources are extremely scarce, and there is an urgent need to allocate healthcare resources rationally [44]. Identifying individuals who are vulnerable to SARS-CoV-2 and those who are prone to severe illness is of great significance for optimizing the allocation of healthcare resources. Epidemiological studies have suggested that cancer is an independent adverse prognostic factor on COVID-19 outcomes [10,45], but causality has not been assessed. We used the MR analysis to evaluate the causal effects of overall cancer and twelve site-specific cancers (lung cancer, breast cancer, endometrial cancer, prostate cancer, thyroid cancer, ovarian cancer, melanoma, small bowel cancer, colorectal cancer, oropharyngeal cancer, lymphoma and cervical cancer) on COVID-19 outcomes (severity, hospitalization and susceptibility). The MR study on extensive international genetic consortia provided no strong evidence to support the causal role of cancer in COVID-19 development. MR leverages the random allocation of genetic variants at conception, independently of confounders, to identify the causal effects that are substantially less confounded and not vulnerable to reverse causation [14,15]. We used SNPs as instrumental variables to conduct the MR study. Five analyses (IVW, MR-Egger, weighted median, weighted mode and MR-PRESSO) suggested no causal effect of overall cancer or twelve site-specific cancers on COVID-19 outcomes. Multivariate MR estimates (adjusted for BMI, education attainment, intelligence, income, smoking and alcohol consumption) were consistent with the results of five analyses. Besides UK biobank, we introduced other data to verify the results of this study (Table S6). Taken together, we concluded that cancers might have no causal effect on increasing COVID-19 risk, and these results were robust. Although many studies have generally shown positive correlations of cancers with the risk of COVID-19 [10,45,46,47], some subsequent findings are inconsistent with previous studies. No statistically significant difference was found between the severe and non-severe COVID-19 group of cancer among non-Asian patients [48]. A meta-analysis involving 46,499 patients revealed that cancer was not a risk factor for COVID-19 death in elderly patients [49]. Moreover, another meta-analysis showed that colorectal cancer patients are not significantly susceptible to SARS-CoV-2 in the global population [50]. Interestingly, a recent meta-analysis suggested that no significantly increased risk of severe illness of COVID-19 was observed in patients with lung or stage IV cancer [51]. The conflicting results indicated that cancers might not be causally associated with COVID-19 outcomes. Risk factors may be correlated with COVID-19 outcomes, but not as a causal association. Previous MR studies have shown that many traditional risk factors have no causal association with COVID-19 outcomes, such as decreased lung function, chronic obstructive pulmonary disease, blood pressure, type 2 diabetes, chronic kidney disease, coronary artery disease, stroke and nonalcoholic fatty liver disease [52,53,54,55]. However, BMI has been robustly correlated and causally associated with COVID-19 outcomes [54]. In fact, many studies have shown that there is a significant difference in the age distribution between cancer and non-cancer patients infected with COVID-19 [46,47,56]. In addition, the mortality rate of COVID-19 in cancer patients appears to be mainly determined by age, gender and comorbidities [57,58]. Therefore, the reported correlations of risk factors with COVID-19 outcomes might be confounded in observational studies, possibly due to confounders including BMI, age and gender. Notably, cancer patients should remain a key focus during the COVID-19 pandemic. Risk factors were clinically helpful in identifying critically ill patients of COVID-19, even without a causal association. MR design is less confounding than observational study, but limitations of this MR study need to be acknowledged. First, some types of cancer—such as stomach cancer, pancreatic cancer, liver cancer and brain cancer—were not included in the study because of insufficient SNPs (Table S1). Potential causality for COVID-19 outcomes might be observed in other types of cancer. Second, our results are primarily based on participants of European descent, to reduce racial influence. The findings of our MR study might not apply to other ethnic groups. With racial minorities disproportionately affected by the pandemic [59,60], reliable research on non-European ancestry is urgently needed. Third, gender-specific cancers (breast cancer, endometrial cancer, prostate cancer, ovarian cancer and cervical cancer) were included. Although the original researchers have adjusted the COVID-19 GWAS data for gender, it might have a confounded impact. Lastly, data were extracted from vast genetic epidemiological networks, but our study failed to detect minimal effects. 5. Conclusions Overall, we used MR analysis to evaluate the causal effects of overall cancer and twelve site-specific cancers on COVID-19 severity, hospitalization and susceptibility. Results of the MR study did not suggest strong evidence to support the causal associations of any examined cancer with COVID-19 outcomes. Previous observational correlations of cancers with COVID-19 outcomes were likely confounded. More large-scale epidemiological studies are needed to validate our findings. Acknowledgments We thank all institutions for providing the publically available GWAS summary-level data used in this study. Supplementary Materials The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cancers14092086/s1. Figure S1: The effects of each SNP in cancers on COVID-19 severity; Figure S2: The effects of each SNP in cancers on COVID-19 hospitalization; Figure S3: The effects of each SNP in cancers on COVID-19 susceptibility; Figure S4: The leave-one-out sensitivity analysis of the causal effects of cancers on COVID-19 severity; Figure S5: The leave-one-out sensitivity analysis of the causal effects of cancers on COVID-19 hospitalization; Figure S6: The leave-one-out sensitivity analysis of the causal effects of cancers on COVID-19 susceptibility; Table S1: Excluded cancers with insufficient SNPs; Table S2: Selected SNPs of included cancers; Table S3: Causal effects of cancers on COVID-19 severity estimated by multivariable Mendelian randomization; Table S4: Causal effects of cancers on COVID-19 hospitalization estimated by multivariable Mendelian randomization; Table S5: Causal effects of cancers on COVID-19 susceptibility estimated by multivariable Mendelian randomization; Table S6: Verification of the causal effects of cancers on COVID-19 outcomes. Click here for additional data file. Author Contributions Conceptualization, Z.L. and L.Z.; methodology, Z.L.; validation, formal analysis, and investigation, Z.L.; writing—original draft and visualization, Z.L., Y.W., G.Z., M.W. and L.Z.; writing—review and editing, Z.L. and L.Z.; supervision and project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. Funding This research was supported by the Bill and Melinda Gates Foundation (INV-006104), National Natural Science Foundation of China (Grant number: 81950410639), Outstanding Young Scholars Support Program (Grant number: 3111500001), Xi’an Jiaotong University Basic Research and Profession Grant (Grant number: xtr022019003, xzy032020032), Epidemiology modeling and risk assessment grant (Grant number: 20200344), and Xi’an Jiaotong University Young Scholar Support Grant (Grant number: YX6J004). Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board was waived because this was a retrospective study. Informed Consent Statement Participant consent was waived because this was a retrospective study. Data Availability Statement All relevant data are within the paper. Conflicts of Interest The authors declare no conflict of interest. Figure 1 The overall design of the Mendelian randomization study. cancers-14-02086-t001_Table 1 Table 1 Summary of the included data. Variable Cases Controls Sample Size Year GWAS ID COVID-19 COVID-19 susceptibility 38,984 1,644,784 1,683,768 2021 - COVID-19 hospitalization 9986 1,877,672 1,887,658 2021 - COVID-19 severity 5101 1,383,241 1,388,342 2021 - Cancer Overall cancer 26,576 309,696 336,272 2017 ukb-a-307 Lung cancer 11,348 15,861 27,209 2014 ieu-a-966 Squamous cell lung cancer 3275 15,038 18,313 2014 ieu-a-967 Breast cancer 122,977 105,974 228,951 2017 ieu-a-1126 ER+ Breast cancer 69,501 105,974 175,475 2017 ieu-a-1127 ER− Breast cancer 21,468 105,974 127,442 2017 ieu-a-1128 Ovarian cancer 25,509 40,941 66,450 2017 ieu-a-1120 Endometrial cancer 12,906 108,979 121,885 2018 ebi-a-GCST006464 Prostate cancer 79,148 61,106 140,254 2018 ieu-b-85 Thyroid cancer 649 431 1080 2013 ieu-a-1082 Melanoma 3751 372,016 375,767 2021 ieu-b-4969 Small bowel cancer 156 337,003 337,159 2017 ukb-a-56 Colorectal cancer 5657 372,016 377,673 2021 ieu-b-4965 Oropharyngeal cancer 494 372,016 372,510 2021 ieu-b-4968 Lymphoma 1752 359,442 361,194 2018 ukb-d-C_LYMPHOMA Cervical cancer 3175 459,835 463,010 2018 ukb-b-918 Covariates BMI - - 681,275 2018 ieu-b-40 Educational attainment - - 766,345 2018 ieu-a-1239 Intelligence - - 269,867 2018 ebi-a-GCST006250 Income - - 397,751 2018 ukb-b-7408 Smoking - - 337,334 2019 ieu-b-25 Alcohol consumption - - 335,394 2019 ieu-b-73 cancers-14-02086-t002_Table 2 Table 2 Causal effects of cancers on COVID-19 severity estimated by univariable Mendelian randomization. Cancer Types No. of SNPs IVW MR-Egger Weighted Median Weighted Mode MR-PRESSO Heterogeneity Pleiotropy β SE p β SE p β SE p β SE p β SE p p p Overall cancer 4 −3.44 3.61 0.34 112.35 104.87 0.40 −1.63 4.25 0.70 0.77 6.26 0.91 −3.44 4.11 0.46 0.27 0.33 Lung cancer 5 0.03 0.07 0.60 0.16 0.25 0.57 0.06 0.08 0.45 0.08 0.08 0.38 0.03 0.06 0.59 0.53 0.58 Squamous cell lung cancer 2 −0.05 0.12 0.66 - - - - - - - - - - - - - - Breast cancer 109 0.04 0.05 0.43 0.05 0.11 0.61 0.07 0.08 0.39 0.05 0.09 0.56 0.05 0.05 0.31 0.35 0.23 ER+ Breast cancer 81 −0.01 0.05 0.79 0.04 0.11 0.70 0.09 0.07 0.20 0.10 0.08 0.24 0.0001 0.05 1.00 0.13 0.10 ER− Breast cancer 27 0.03 0.06 0.66 −0.20 0.17 0.25 −0.03 0.09 0.73 −0.07 0.11 0.56 0.03 0.06 0.63 0.29 0.35 Endometrial cancer 12 0.02 0.09 0.79 −0.08 0.36 0.84 0.01 0.13 0.96 0.31 0.26 0.26 0.02 0.09 0.80 0.36 0.38 Prostate cancer 91 −0.02 0.04 0.54 −0.15 0.09 0.11 −0.02 0.07 0.74 −0.07 0.07 0.30 −0.02 0.04 0.60 0.07 * 0.03 $ Thyroid cancer 249 −0.001 0.002 0.70 −0.003 0.003 0.29 −0.003 0.003 0.32 −0.004 0.004 0.27 −0.001 0.002 0.70 0.33 0.33 Ovarian cancer 9 0.08 0.16 0.62 −0.08 0.41 0.84 0.06 0.11 0.57 0.11 0.12 0.40 0.004 0.10 0.96 <0.001 * 0.01 $ Melanoma 6 −3.45 12.83 0.79 −6.56 39.71 0.88 −10.76 10.13 0.29 −17.50 11.33 0.18 −3.45 12.83 0.80 0.01 * 0.02 $ Small bowel cancer 5 84.03 48.79 0.09 −11.30 156.92 0.95 43.10 61.67 0.48 40.60 79.89 0.64 84.03 31.59 0.06 0.79 0.76 Colorectal cancer 7 −1.05 5.44 0.85 1.45 19.05 0.94 −1.15 6.94 0.87 −1.76 8.85 0.85 −1.05 3.46 0.77 0.88 0.89 Oropharyngeal cancer 2 −52.19 51.79 0.31 - - - - - - - - - - - - 0.95 - Lymphoma 2 −15.04 22.77 0.51 - - - - - - - - - - - - 0.95 - Cervical cancer 2 −28.58 24.70 0.25 - - - - - - - - - - - - 0.08 * - * Significant heterogeneity (p < 0.1); $ significant horizontal pleiotropy (p < 0.05). cancers-14-02086-t003_Table 3 Table 3 Causal effects of cancers on COVID-19 hospitalization estimated by univariable Mendelian randomization. Cancer Types No. of SNPs IVW MR-Egger Weighted Median Weighted Mode MR-PRESSO Heterogeneity Pleiotropy β SE p β SE p β SE p β SE p β SE p p p Overall cancer 4 −1.86 2.32 0.42 22.70 61.39 0.75 −2.32 2.73 0.40 −2.83 3.81 0.51 −1.86 1.65 0.34 0.68 0.71 Lung cancer 4 0.04 0.05 0.37 0.29 0.20 0.29 0.06 0.05 0.23 0.07 0.06 0.31 0.04 0.03 0.31 0.66 0.63 Squamous cell lung cancer 2 −0.04 0.08 0.66 - - - - - - - - - - - - 0.99 - Breast cancer 106 0.01 0.03 0.74 -0.003 0.07 0.97 0.01 0.05 0.80 0.02 0.06 0.70 0.02 0.03 0.60 0.20 0.13 ER+ Breast cancer 79 −0.02 0.03 0.51 0.04 0.07 0.56 −0.02 0.05 0.71 0.02 0.05 0.77 −0.01 0.03 0.70 0.36 0.22 ER− Breast cancer 25 −0.004 0.04 0.93 0.03 0.12 0.83 −0.03 0.06 0.63 −0.05 0.09 0.59 -0.005 0.04 0.90 0.58 0.60 Endometrial cancer 12 0.06 0.05 0.24 0.43 0.21 0.07 0.07 0.08 0.34 0.03 0.11 0.77 0.06 0.06 0.28 0.36 0.37 Prostate cancer 90 0.04 0.03 0.17 −0.01 0.06 0.85 0.06 0.04 0.15 0.05 0.05 0.28 0.04 0.03 0.16 0.12 0.07 Thyroid cancer 246 −0.0003 0.001 0.80 −0.004 0.002 0.04 # −0.0005 0.002 0.79 0.0002 0.002 0.94 0.0003 0.0005 0.63 0.06 * 0.08 Ovarian cancer 9 0.01 0.11 0.96 −0.20 0.28 0.52 0.01 0.08 0.94 0.01 0.08 0.85 0.01 0.04 0.78 <0.001 * <0.001 $ Melanoma 6 −3.52 4.62 0.45 −3.62 16.93 0.84 −10.08 6.00 0.09 −11.14 7.97 0.22 −3.52 5.60 0.56 0.20 0.26 Small bowel cancer 2 78.90 45.54 0.08 - - - - - - - - - - - - 0.83 - Colorectal cancer 7 0.99 3.70 0.79 7.67 13.80 0.60 2.91 4.62 0.53 4.04 5.77 0.51 0.99 2.48 0.70 0.85 0.85 Oropharyngeal cancer - - - - - - - - - - - - - - - - - - Lymphoma - - - - - - - - - - - - - - - - - - Cervical cancer 2 −19.95 20.17 0.32 - - - - - - - - - - - - 0.04 * - * Significant heterogeneity (p < 0.1); $ significant horizontal pleiotropy (p < 0.05); # potential association (p < 0.05). cancers-14-02086-t004_Table 4 Table 4 Causal effects of cancers on COVID-19 susceptibility estimated by univariable Mendelian randomization. Cancer Types No. of SNPs IVW MR-Egger Weighted Median Weighted Mode MR-PRESSO Heterogeneity Pleiotropy β SE p β SE p β SE p β SE p β SE p p p Overall cancer 4 0.47 1.15 0.69 −3.29 42.93 0.95 −0.62 1.38 0.65 −0.78 1.95 0.72 0.47 1.32 0.75 0.27 0.35 Lung cancer 5 0.001 0.02 0.96 0.03 0.08 0.77 0.02 0.03 0.54 0.02 0.03 0.56 0.00 0.02 0.95 0.54 0.59 Squamous cell lung cancer 2 −0.07 0.04 0.08 - - - - - - - - - - - - 0.77 - Breast cancer 109 −0.01 0.02 0.43 −0.01 0.04 0.85 −0.02 0.03 0.44 −0.03 0.03 0.43 −0.01 0.02 0.57 0.26 0.23 ER+ Breast cancer 81 −0.02 0.02 0.30 0.01 0.04 0.78 −0.02 0.02 0.38 −0.03 0.03 0.36 −0.01 0.02 0.50 0.02 * 0.02 $ ER− Breast cancer 27 −0.03 0.02 0.18 −0.08 0.06 0.19 −0.03 0.03 0.27 −0.05 0.04 0.24 −0.03 0.02 0.22 0.39 0.46 Endometrial cancer 12 −0.01 0.03 0.83 0.04 0.11 0.69 −0.01 0.03 0.69 −0.02 0.05 0.69 −0.01 0.02 0.76 0.92 0.91 Prostate cancer 91 −0.01 0.01 0.58 −0.04 0.03 0.16 −0.03 0.02 0.16 −0.03 0.02 0.17 −0.01 0.01 0.67 0.06 * 0.05 Thyroid cancer 248 0.001 0.0006 0.28 −0.000001 0.001 1.00 0.0001 0.0009 0.94 0.0004 0.001 0.71 0.0006 0.0006 0.28 0.06 * 0.05 Ovarian cancer 9 −0.01 0.09 0.93 −0.14 0.22 0.54 −0.01 0.04 0.74 0.02 0.04 0.68 −0.03 0.03 0.39 <0.001 * <0.001 $ Melanoma 6 0.76 3.28 0.82 9.84 8.38 0.31 −1.10 2.97 0.71 −1.11 3.78 0.78 0.76 3.28 0.83 0.05 * 0.05 Small bowel cancer 4 23.09 17.71 0.19 148.10 80.98 0.21 5.85 20.89 0.78 −2.59 29.45 0.94 23.09 19.36 0.32 0.31 0.37 Colorectal cancer 7 1.96 1.88 0.30 8.71 8.86 0.37 1.85 2.64 0.48 6.56 6.09 0.32 1.96 2.31 0.43 0.17 0.16 Oropharyngeal cancer 2 −3.82 15.14 0.80 - - - - - - - - - - - - 0.77 - Lymphoma 2 −6.05 6.79 0.37 - - - - - - - - - - - - 0.47 - Cervical cancer 2 −7.01 17.21 0.68 - - - - - - - - - - - - <0.001 * - * Significant heterogeneity (p < 0.1); $ significant horizontal pleiotropy (p < 0.05). 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093110 sensors-22-03110 Article RAFI: Robust Authentication Framework for IoT-Based RFID Infrastructure Kumar Vikas 1 Kumar Rahul 1 https://orcid.org/0000-0001-7525-8864 Khan Akber Ali 2 https://orcid.org/0000-0002-2939-1100 Kumar Vinod 3 https://orcid.org/0000-0002-5577-0016 Chen Yu-Chi 45* Chang Chin-Chieh 6 Chen Chien-Ming Academic Editor Wu Mu-En Academic Editor 1 Department of Mathematics, SSV College, Hapur 245101, Uttar Pradesh, India; vikas.chaudhary26@gmail.com (V.K.); ujjwalrahul@gmail.com (R.K.) 2 B. S. Anangpuria Institute of Technology and Management, Faridabad 121004, Haryana, India; cs.akberkhan@gmail.com or 3 Department of Mathematics, PGDAV College, University of Delhi, New Delhi 110065, Delhi, India; vinod.iitkgp13@gmail.com or 4 Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan 5 Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan 6 Department of Accounting Information, National Taipei University of Business, Taipei 100, Taiwan; ccchang@ntub.edu.tw * Correspondence: wycchen@saturn.yzu.edu.tw 19 4 2022 5 2022 22 9 311022 2 2022 14 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The Internet of Things (IoT) is a future trend that uses the Internet to connect a variety of physical things with the cyber world. IoT technology is rapidly evolving, and it will soon have a significant impact on our daily lives. While the growing number of linked IoT devices makes our daily lives easier, it also puts our personal data at risk. In IoT applications, Radio Frequency Identification (RFID) helps in the automatic identification of linked devices, and the dataflow of the system forms a symmetry in communication between the tags and the readers. However, the security and privacy of RFID-tag-connected devices are the key concerns. The communication link is thought to be wireless or insecure, making the RFID system open to several known threats. In order to address these security issues, we propose a robust authentication framework for IoT-based RFID infrastructure. We use formal security analysis in the random oracle model, as well as information analysis to support the claim of secure communication. Regarding the desirable performance characteristics, we describe and analyze the proposed framework’s performance and compare it to similar systems. According to our findings, the proposed framework satisfies all security requirements while also improving the communication. IoT RFID security authentication random oracle model ==== Body pmc1. Introduction An RFID infrastructure has a symmetric nature. The RFID system is a wireless technology that is used to identify remote objects that have RFID tags embedded in them. RFID technology is utilized in a variety of applications, including transportation, supply chain management, livestock management, e-passport, e-payment, and patient healthcare [1,2,3]. Backend readers, servers, and tags are all a part of a conventional RFID system whose architecture is symmetric, since the dataflow is in one direction from the tag, reader to server, and then, the inverse Table 5. The lack of physical contact between the reader and the tags is a crucial element of RFID systems, and the following are some of the benefits of using them: RFID tags are small and inexpensive, and radio frequency communication can recognize large numbers of RFID tags at the same time [4,5]. RFID systems, on the other hand, are exposed to a variety of security attacks and privacy exposure concerns due to their use of wireless communication and signal broadcasting techniques. It is difficult to apply a comprehensive cryptographic algorithm to an RFID system due to the strictly limited calculation resources, tiny storage capacity, and weak power supply of low-cost tags, and these issues are impeding the rapid development of this technology [6]. RFID security is fundamentally concerned with authentication and privacy issues. A secure protocol running RFID tags and readers can provide authentication. If a tag contains unique secret information and the RFID reader and RFID tag can convince the RFID reader that they both have that information, the tagged product is considered to be authentic and the person has access to it. Tag anonymity is one of the most important features that any RFID-based authentication technique aspires to attain, and tag untraceability, which ensures the privacy of the tag or the mobility of a user wearing an RFID tag, is a more satisfactory property of tag anonymity. To achieve this attribute, a tag must encode its original identity using a cryptographic primitive such as a one-way secure collision-resistant hash function in existing state-of-the-art authentication protocols. RFID is the simplest form of pervasive sensor network and is widely used for object identification [7]. RFID systems are made up of a tag with a transceiver that sends and receives radio signals from connected devices [8,9]. The RFID reader is another device that acts as an access point and can receive and deliver messages to transceivers. The reader is also in charge of ensuring that tag information is available at the application level [10]. IoT-based RFID tags can be of the passive or active type. The differences between these tags are summarized in Table 1. 1.1. Related Work In recent years, numerous exciting anonymous IoT-based RFID authentication and key agreement frameworks have been proposed, which can be classified into Public Key Cryptosystem- (PKC) and Non-Public Key Cryptosystem- (NPKC) based authenticated schemes. These approaches are unsuitable for tiny powered tags due to the modular exponential operations. Hash-based RFID systems, on the other hand, would be the best choice among NPKCs because of their low computational overhead [7,11,12,13]. Yang et al. [11] introduced an authentication mechanism based on a one-way secure collision-resistant hash function and exclusive-OR, claiming that it addressed all of the security vulnerabilities that occur in RFID systems. Unfortunately, the protocol is vulnerable to many attacks, including “man-in-the-middle”, forgeries, and loss of untraceability [14]. Cho et al. [13] developed a secure hash-based authentication framework, claiming that it addresses all of the security, privacy, and forgery difficulties that exist in RFID communication systems. However, Safkhani et al. [15] recently demonstrated that the protocol does not meet the authors’ security promises. In their paper, they cryptanalyzed Cho et al.’s [13] protocol and concluded that it is vulnerable to “de-synchronization or DoS attacks, tag impersonation attacks, and reader impersonation attacks”. Furthermore, they showed in their paper that all proposed lightweight authentication techniques based on one-way hash functions and exclusive-OR are impracticable [11,12,13,16,17]. Ayaz et al. [18] suggested another mutual authentication approach for secure RFID communication systems utilizing only symmetric key cryptography operations. In this framework, an authentication is accomplished on the basis of user biometrics’ verification in their protocol. Liu et al. [19] proposed an authentication protocol for an RFID system by using hash and XoR operations. The correctness of the protocol was proven by using “Burrows–Abadi–Needham (BAN)” logic analysis. Mansoor et al. [20] proposed a securing IoT-based authentication protocol for RFID systems by using a symmetric cryptography approach. Unfortunately, we studied their protocol and found the security weaknesses of their protocol. Furthermore, Mansoor et al. [20] showed that the protocol proposed by Gope et al. [21] is vulnerable to collision attacks, DoS attacks, and stolen verifier attacks. In 2022, Gao and Lu pretested a new ultra-lightweight RFID authentication protocol in passive RFID systems [22]. The proposed protocol, they claimed, prevents numerous known attacks, beats several existing ultra-lightweight protocols in terms of computational cost, storage requirements, and communication costs, and is efficient in terms of the computational cost, storage requirements, and communication costs. Wang et al. suggested a protocol [23] for which they had formal and informal discussions about security and privacy. Xiaomei et al. discussed [24] the RFID logic of an event-based authentication framework for secure communication. Shariq et al. proposed an RFID-based anonymous and secure framework for deployment in IVs [25]. Wei et al. proposed an improved security authentication protocol for lightweight RFID based on ECC [26]. Arslan and Bingöl presented the security and privacy analysis of recently proposed ECC-based RFID authentication schemes [27]. 1.2. Adversary Model Our adversary model is based on the threat model of [28], which is well-known and widely recognized. By altering, monitoring, deciding on, and introducing information into the communications channel, the attacker can not only see the communications channel, but also capture session keys, confidential documents, and private keys stored in the contributor memory through explicit attacks. Many assaults, such as replay attacks, man-in-the-middle attacks, impersonation attacks, etc., are now possible in the RFID system due to the utilization of public communication networks and wireless communication networks. As a result, the privacy and security issues are major concerns in RFID frameworks. Thus, an authentication and key management mechanism is required to validate the legitimacy of specified entities. 1.3. Security Requirements for an IoT-Based RFID Communication System As far as we know and based on the available literature, many authentication protocols for RFID communication systems have been presented during the last few years. In RFID systems, authentication and key agreement are the best approaches to make them suitable for a wide range of applications. During the transmission of messages between RFID tags and RFID readers, many types of security attacks may occur. We outline various security needs in light of these issues, such as forward security, mutual authentication, anonymity, scalability, confidentiality, untraceability,“ man-in-the-middle attack, insider attack, replay attack, impersonation attack”, etc., to provide secure communication for the RFID system. Such requirements are utilized as the criteria for assessing the RFID system in order to provide a secure and efficient authentication protocol. The following security criteria should be met by any authentication scheme that attempts to secure a practical RFID-based system:Mutual authentication: This is the most important aspect of any authentication mechanism. Furthermore, mutual authentication must be achieved in the presence of all three RFID system participants. The authentication process takes place between the backend database server and the RFID tag. Messages are sent between the tag, reader, and server over an unsecured communication channel. Tag anonymity: To minimize forgery and ensure security, this is the most important and necessary security requirement. Furthermore, if an opponent is unable to trace an RFID tag during message delivery over a public channel, the RFID authentication system maintains its anonymity. Anonymity can be divided into two categories: strong anonymity and weak anonymity. Furthermore, in IoT communication, the participants involved do not disclose their real identity in order to defend their security and privacy. Message authentication: In Internet operations, this maintains the integrity of message communication. Untraceability: In the RFID communication system, untraceability means that no one can trace the behavior patterns of the participants involved and their forwarded messages. Session key agreement: Following the successful implementation of the proposed protocol, a session key agreement will be established between users with their mobile devices and the network control center for future communication. Confidentiality: Encrypting shared secrets on the public channel ensures the security of RFID communications between the tag and reader. Perfect forward secrecy: Perfect forward secrecy is a technique that should be used in the authentication protocol design to give secrecy to previously communicated messages, where an opponent who discovers the entities private and public keys will be unable to derive a past session key. Scalability: The approach is not scalable if the server conducts an extensive search to verify a tag. Worse, an opponent may conduct a timing attack [29] against the protocol, which can identify a tag based on how long it took the server to authenticate it. To maintain scalability, an authentication strategy should avoid any exhaustive search operations. Availability: In an RFID system, the authentication and key agreement procedure runs all the time between the RFID tag and RFID backend database server. In most authentication methods, the shared secret information between the RFID tag and RFID backend database server must be updated to achieve the attribute of accessibility. However, security risks such as Denial-Of-Service (DoS) or de-synchronization attacks may disrupt this process. The RFID system’s efficiency may be harmed as a result of these concerns. Thus, when designing an authentication protocol, this issue should be considered. Impersonation attack: An adversary could try to mimic legitimate protocol participants (such as the cloud database server, RFID reader, or RFID tag) by replaying a message captured from the channels. Any impersonation should be avoided at all costs. Replay attack: An outsider attempts to confuse other certified participants by restating intercepted data in this attack. This attack targets a user whose information is intercepted by an uncertified third party. Man-in-the-middle attack: An adversary listens in on transmitted data and then attempts to delete or manipulate the contents of the data sent to receivers in this attack. Insider attack: Any insider can play the role of adversary in the RFID communication system. De-synchronization attack: An adversary may generate desynchronization problems if a protocol authentication is based on shared values. The server may be unable to verify the tag in the future if the shared data are updated by the server, but the tag is not. De-synchronization attempts should be avoided. 1.4. Motivation and Contribution Many authentication and key agreement frameworks for RFID systems have been presented during the last few decades, as far as we know and based on the existing literature [13,16,17,19,20,21]. However, a suitable authenticated key agreement protocol for RFID systems that is secure and efficient for RFID systems is missing. RFID systems require an authenticated key agreement scheme because of their varying computing capabilities and privacy requirements. Thus, we propose an authenticated key agreement protocol for RFID communication systems. Table 2 shows the comparative study of the advantages and disadvantages of other protocols with respect to our suggested protocol. The following are some notable characteristics of the proposed framework:- We propose a robust authentication protocol that supports key agreement between RFID tags and the database server for IoT-based RFID infrastructure. - We give a thorough explanation of the informal security study, proving that the suggested protocol can resist a variety of well-known security attacks. - The proposed protocol security is formally demonstrated using a random oracle model. - The proposed the RAFI has desirable security features that make the proposed protocol robust and efficient, according to the proof of security. - The results of the performance evaluation and comparison show that the proposed RAFI has desirable performance features. sensors-22-03110-t002_Table 2 Table 2 Merits and demerits of the existing authentication protocols in RFID environments. Protocols Approach Used Published Year Merits Demerits Tan et al. [16] Hash function 2008 Provides backward and forward secrecy Susceptible to replay attack, insider attack, and de-synchronization DoS attack, and tag anonymity problem Cai et al. [17] Hash function 2009 Provides a mutual authentication and Vulnerable to impersonation attack, anonymity and secure against stolen verifier attack insider attack, and DoS attack Cho et al. [13] Hash function 2015 Provides a mutual authentication and tag untraceability Prone to insider attack, man-in-the-middle attack and secure against stolen verifier attacks and impersonation attack Gope and Hwang [21] Hash function 2015 Prevents replay attacks, de-synchronization, Vulnerable to collision attacks, and man-in-the-middle attack DoS attacks, and impersonation attack Liu et al. [19] Hash function 2018 Provides mutual authentication, Susceptible to stolen verifier attacks, tag untraceability, and tag anonymity collision attacks, and DoS attacks Mansoor et al. [20] Hash function 2019 Attains mutual authentication, scalability, Vulnerable to impersonation attack, man-in- and data confidentiality the-middle attack, collision attack, and replay attack 1.5. Organization of the Paper The remainder of the proposed framework is organized as follows: Section 2 covers the fundamentals of the mathematics. The proposed framework is discussed in Section 3. In Section 4, the proposed framework security is evaluated. Section 5 includes a performance study of the proposed framework. Finally, the findings are summarized in the Section 5.4. 2. Mathematical Preliminaries The notations and terminology used in the RAFI are defined in this section. 2.1. Notations As shown in Table 3, the following notations are utilized. 2.2. Cryptography Materials Here, various cryptographic primitives that are used to design the proposed security protocol are discussed. In this regard, we make use of lightweight cryptographic primitives to ensure security and computational efficiency. 2.2.1. Cryptographic Hash Function The hash operation takes a variable-length message (M) as the input and outputs a fixed string result H(M), which is known as the message digest. In practice, reversing this process is nearly impossible. As a result, this function is referred to as a collision-resistant one-way hash function. Following that, our system integrity will be protected using the Secure Hash Algorithm (SHA-256). The one-way collision-resistant h:{0,1}*→{0,1}n hash function [30,31,32] takes an input x∈{0,1}* and returns an output h(x)∈{0,1}n of definite length n of a message. The advantage of any A for calculating the collision is as follows: Advantage AdvAHASH(t)=Pr[(x1,x2)⇐RA:x1≠x2, and h(x1)=h(x2)] and (x1,x2)⇐RA represent the set of (x1,x2) computedby attacker A. The probability of this advantage is thus calculated across the random choice values made by A with the run duration t. Hash function h(.) is collision-resistant if AdvAHASH(t)≤ϵ, where ϵ>0. 2.2.2. XoR Cipher In cryptography, the XoR operation includes some postulates: P⊕(Q⊕R)=(P⊕Q)⊕R, P⊕P=0, P⊕0=P, and (Q⊕P)⊕P=Q⊕0=Q. 3. The Proposed Protocol The steps in the proposed framework are as follows: “ registration phase of RFID with database server” and “login and authentication phase”. The architecture of the proposed protocol given the Figure 1. 3.1. Registration Phase The following are the instructions for registering the RFID tag with the database server. The detailed of this phase also mentioned in Table 4. Step AK1: To register with database server S, tag Ti inputs IDTi and, then, Ti⇒S:MRi1={IDTi} via a secure channel. Step AK2: Upon receiving Mi1, it generates sequence number SNi for Ti and computes S1=IDS⊕h(IDTi∥SNi∥xS) where xS is private key for S. Furthermore, the data server computes S2=h(S1∥IDTi)⊕IDTi. Finally, S stores S1,S2,SNi in the database and sends MR2i2={S1,S2,SNi,h(.)} towards the tag via a secure medium. Step AK3: Upon receiving MR2i2, the RFID tag stores parameters {S1,S2,SNi} in the database for further communication via a secure medium. 3.2. Login and Authentication Phase Ti successfully registers with S, and when she/he wants to use the service, she/he makes an access request to S. The following is a description of the procedure in steps. Further, The detailed of this phase also mentioned in Table 5. Step MA1: Ti generates random value r and computes the following values r1=r⊕(S1⊕S2), H1=h(IDTi∥S1∥S2), H11=H1⊕S2. Furthermore, Ti→Rj:M1={r1,H11,T1}. Step MA2: Upon receiving M1, RFID reader Rj verifies T2−T1≤▵T and Rj→S:M2={r1,H11,T3}. Step MA3: Upon receiving M2, S verifies T4−T3≤▵T. Then, S computes H1*=H1⊕S2 and verifies H1*=?H11; if this condition does not hold, then it terminates the process; otherwise, S computes r*=r1⊕(S1⊕S2), generates a random value r2, computes the link of computations SKS=h(IDS∥IDTi∥r*∥r2∥SNi∥S1∥S2∥T5), H2=h(S1∥S2∥r*), H22=H2⊕(r*⊕S2), K1=IDTi⊕h(r*∥SNi∥H1*), and encrypts E1=EK1(H22,r2,IDS,T5). Finally, S→Rj:M3={E1,T5}. Step MA4: Upon receiving M3, Rj verifies T6−T5≤▵T. Furthermore, Rj→Ti:M4={M3,T7}. Step MA5: Upon receiving M4, Ti verifies T8−T7≤▵T and decrypts (H22,r2,IDS,T5)=DK2(E1) with the help of computed key K2=IDTi⊕h(r∥SNi∥H1). Furthermore, it computes H2*=H22⊕(r⊕S2) and verifies H2*=?H2. Finally, Tag sets the session key for furter communication as SKT=h(IDS∥IDTi∥r∥r2∥SNi∥S1∥S2∥T5). Hence, session key agreement SK=SKT=SKS. 4. Security Analysis The security analysis of the proposed protocol is conducted by a formal method and an informal method as follows. 4.1. Informal Security Analysis The following is an informal security analysis of the proposed protocol. 4.1.1. Key Freshness In the proposed protocol, the session key contains the timestamp and a freshly generated random number. Furthermore, in the authentication procedure, the timestamp and random number are distinct for each session. The uniqueness of these parameters confirms the session’s unique key. Thus, the unique key for each session confirms the key freshness property of the proposed protocol. 4.1.2. Untraceability If a cryptographic scheme has two features, it is untraceable. A is unable to distinguish between users’ initial identities; A is unable to determine whether two distinct sessions starting at different times belong to the same user. Thus, it is intended that both properties be maintained. 4.1.3. Session Key Agreement In the proposed scheme, the database server calculates SKS=h(IDS∥IDTi∥r*∥r2∥SNi∥S1∥S2∥T5) and the RFID tag computes SKT=h(IDS∥IDTi∥r∥r2∥SNi∥S1∥S2∥T5). Thus, SKS=SKT. Thus, the proposed protocol maintains the said cryptographic property. 4.1.4. Session Key Verification The RFID tag verifies its session key in our proposed system as H2*=?H2, where H2*=H22⊕(r⊕S2) and H22=H2⊕(r*⊕S2), embedded with many secret credentials. Therefore, the proposed technique allows for the verification of session keys. 4.1.5. Scalability In the proposed protocol for the RFID system, the RFID server S does not perform an exhaustive process to authenticate each RFID tag. The RFID server S, on the other hand, validates the RFID tag and reacts immediately to it. This increases the scalability of the proposed protocol. 4.1.6. Forward Secrecy Given that the proposed protocol only uses symmetric key cryptography, i.e., the secure collision-resistant hash function, and we do not update the shared parameters per session, it is not possible to give this property, similar to any other protocol in this context. It should be emphasized that if the protocol employs a public key primitive, this attribute can be simply provided. 4.1.7. Traceability and Anonymity In the proposed protocol, the exchanged messages are M1 and M2. In these messages, excluding Ti and Tj, which are the timestamps and cannot be connected to any identity to trace or compromise its anonymity, the rest of the information is encrypted values or the output of the one-way hash function and from one session to another session is randomized by fresh nonce values. Hence, the exchanged messages do not reveal any information to trace the tag or server or compromise their anonymity. 4.1.8. Replay Attack Random numbers and timestamps are common countermeasures in replay attacks. However, in the proposed protocol, both of them are present. The timestamp condition checks Ti−Tj≤△T, where △T is the valid period, and a,b∈Zq*, where a, b are fresh random numbers and q is a large prime number. 4.1.9. Privileged Insider Attack In the proposed protocol, interacting participants and a third party do not maintain any verifier repository. The authentication procedure is performed by participants using their unique secret keys. Thus, the proposed protocol resiststhe stolen verifier and insider threats. 4.1.10. Man-in-the-Middle Attack The protocol is secure against the man-in-the-middle attack. The adversary is not successful in obtaining the key and pseudonym value. Furthermore, hash functions ensure message integrity, and timestamps control the session time; therefore, any message modification or unexpected delay by a “man-in-the-middle attack” will be detected with a high probability. In the proposed protocol, we verify conditions on both sides, H1*=?H1 and H2*=?H2. As a result, the proposed protocol is protected from the “man-in-the-middle attack”. 4.1.11. Impersonation Attack To impersonate the RFID tag, the attacker should either perform a replay attack or generate a valid M1. However, the replay attack is not feasible in this proposed protocol, and the attacker also has no chance to compute a valid M1, because it does not have access to SKi. The same logic can be applied to an impersonating server. Hence, the proposed framework is safe from impersonation attacks. 4.1.12. De-Synchronization Attack There is no secret sharing between the RFID tags and the RFIF backend server in the proposed protocol. Furthermore, no value needs to be updated in each authentication session. Thus, our suggested protocol is resistant to the de-synchronization attack. 4.1.13. Parallel Session Attack When an A reprocesses past messages in an insecure channel to compose a new request, this is known as a parallel session attack. To retrieve the key, A impersonates the user tag Ti. The secret credentials, which are used to compute the content, must be known by A before user Ti may compute a valid login request or execute the session key. It is apparent from the preceding study that A is unable to obtain the session key. Hence, the proposed framework protects against the parallel session attack. 4.2. Formal Security Analysis In this section, the random oracle model is deployed to demonstrate that the beacons exchanged in the proposed protocol are robust against any form of eavesdropping, and hence, the communicating entities can trust each other as they communicate over insecure channels. 4.2.1. Handshake Model The handshake stage is used to exchange information and perform device synchronization amongst the participants. This is also the point at which the server takes control of the process and maintains it until the user is authenticated. At this level, the input is in the form of a classical medium, but the output is in the form of a quantum medium. The handshake stage is used to exchange information and perform device synchronization amongst the participants. This is also the point at which the server takes control of the process and maintains it until the user is authenticated. At this level, the input is in the form of a classical medium, but the output is in the form of a quantum medium. The handshake authentication model for the proposed RFID protocol shown in the Table 6. 4.2.2. Formal Security Model The formal model for the propose framework, which is based on the random oracle model, is discussed in this section [33,34]. We made some changes to the original to make it work with the proposed framework. We employed three participants to demonstrate our proof, T,R, and S as the RFID tag, the RFID reader, and the database server. IDTi is the identity of T. Similarly, IDS is the identity of S. N is the identities’ dictionary. More information about this model may be found in [35]. 4.2.3. Formal Security Proof In this part, we show the proposed framework’s formal security using a model [28] based on the random oracle model [33,34]. In this model, an adversary A can interact with framework entities, say Ω, which is a server. Theorem 1. Suppose that A is a polynomial-time attacker attempting to compromise the protocol semantic security and close to the QH hash query, Qe execute query, Qs send query, AdvEKSE(A) is the advantage of A, and |D| is the set of uniformly distributed cardinality. Thus, the advantage of A in the proposed protocol is given by Advrfid(A)≤(QH2+QS)2L−1+(QS+QE)2p+2QS|D|+2AdvEKSE(A) Proof.  For the proof of this theorem, we introduce the game of series, initially with GM0 the real attack, and stop with GM5 where A has no advantage. The details of these are explained as below in GM0 to GM5. Further, the simulation queries based on this random oracle model are ginen in Table 7. GM0: The execution of Game GM0 is the same as the real attack in the oracle model. We have (1) Advrfid(A)=|2Pr[Succ0]−1|. GM1: Different queries are conducted in GM1, and the results of the queries are kept in the oracle lists, making it impossible for an attacker to distinguish between the two oracle games. As a result, we have (2) Pr[Succ1]=Pr[Succ0]. GM2: The execution of GM2 is like GM1, except that GM2 stops when a collision is present in the hash function and information messages. Therefore, the birth day paradox, the probability of collision in the transcript is (QS+QE)22p at most [36], and the success probability of secure hash function collision is at most QH22L+1. Hence, we have (3) |Pr[Succ2]−Pr[Succ1]|≤QH22L+1+(QS+QE)22p. GM3: The simulation of GM3 is identical to that of GM2, with the exception that GM3 will be terminated if A guesses the verifier operations without knowing the random oracle. Until the server grid fails in a legitimate authentication request, GM3 and the preceding game are different. As a result, we have (4) |Pr[Succ3]−Pr[Succ2]|≤QS2L GM4: GM4 is the same as GM3, except that only the test inquiry of GM4 stops when adversary A discloses a TestID to obtain the real identity IDi or sends a query to obtain the password information. Therefore, we conclude that (5) |Pr[Succ5]−Pr[Succ4]|≤QS|D|+AdvEKSE(A). GM5: The execution of GM5 is the same as GM4, except that only TestSK of GM5 will stop when adversary A publishes a secure hash inquiry with h(IDS∥IDTi∥r∥r2∥SNi∥S1∥S2∥T5), because A by utilizing the secure hash inquiry obtains the SK with success probability QH2/2L+1. Therefore, we have (6) |Pr[Succ6]−Pr[Succ5]|≤QH22L+1           Thus, A does not contain a favorable advantage in perceiving the actual SK from an arbitrary random one without making a hash query with the true input, Pr[Succ6]=1/2. Adding every one of these probabilities, we can conclude that the theorem is proven. □ sensors-22-03110-t007_Table 7 Table 7 Simulation of oracles. Simulation Queries Hash queries hn(m), n = 0, 1, 2, 3, 4, 5. If (m,hvn) exists in the index list of Lhn, the value hvn will be returned. Otherwise, the generated random value will be added to the index list Lhn. Computes r1=r⊕(S1⊕S2) Computes H1=h(IDTiS1∥S2) Computes H11=H1⊕S2 Then, it answers with M1={r1,H11,T1} For the send(V,{r1,H11,T1} query, the G oracle simulates the following steps: Verifies T2−T1≤▵T Then, it answers with M2={r1,H11,T3} For send(G,{r1,H11,T3} query, the V oracle simulates the following steps: Computes H1*=H1⊕S2 Verifies H1*=?H1 Computes r*=r1⊕(S1⊕S2) Generates random value r2 Computes SKS=h(IDS∥IDTi∥r*∥r2∥SNi∥S1∥S2∥T5) Computes H22=H2⊕(r*⊕S2) Computes K1=IDTih(r*∥SNi∥H1*) Encrypts E1=EK1(H22,r2,IDS,TS) Then, it answers with M3={E1,T5} For the send(V,{E1,T5} query, the oracle simulate the following steps Verifies T6−T5≤▵T Then, it answer with M4={M3,T7} For send(G,{M3,T7} query, the T oracle simulates the following steps: Verifies T8−T7≤▵T Computes K2=IDTih(r∥SNi∥H1) Decrypts (H22,r2,IDS,TS)=DK2(E1) Computes H2*=H22⊕(r⊕S2) Verifies H2*=?H2 Computes SKT=h(IDS∥IDTi∥r∥r2∥SNi∥S1∥S2∥T5) For an Execute (Ti,Rt,Sj) query, all Send queries are consecutively completed. Massage (M1,M2,M3,M4) is the output. For a Reveal(IK) query, if the chance IK has been settled and provided a safe session key, output SKT or SKS; otherwise, ⊥ is the response. For a Corrupt(IK) query, all the information of IK is returned. For a Test(IK) query, if IK is not fresh, return ⊥; otherwise, a coin γ is tossed. If γ=0, the output is a random value with length l. If γ=1, the conclusion is the appropriate session key. 5. Performance Analysis The performance analysis of the proposed framework compared to related frameworks [13,16,17,19,20,21] is given in three subsections: comparison of the security and functionality features and the computational and communication cost comparisons. The conclusion of the performance analysis demonstrates that the proposed framework has better efficiency and security in RFID communication systems. 5.1. Comparison of the Security and Functionality Features The features that an authentication protocol is supposed to have are known as security requirements. These properties or needs must be guaranteed by every authentication protocol. The suggested protocol was compared to current protocols based on these requirements. The features/requirements examined for the comparison analysis are listed below. In Table 8, we summarize the security properties of the proposed framework and those schemes that are available in literature [13,16,17,19,20,21]. The related schemes can be seen with different security shortcomings against various security attacks. 5.2. Comparison of the Computational Cost We calculated the computational cost of the RAFI and compared it to other frameworks [13,16,17,19,20,21], which is illustrated in Table 9. The computation time of the execution of hash operation (Th) was 0.0023 ms, while the computation time of the execution of the encryption and decryption (TE/D) was 0.0046 ms. The experiment was conducted on an Ubuntu system with a 2.20 GHz Intel dual-core Pentium CPU with a 2048 MB processor and RAM [20,37]. The protocol presented in [16] incurred 2Th, 2Th, and 3Th for each RFID tag, RFID reader, and database server, respectively, and the total computational cost in their protocol was 4Th≈0.0161. In the same way, the protocols’ computational cost was provided in [17] to be 4Th, 2Th, and 6Th for each RFID tag, RFID reader, and database server, respectively, for each participant, totaling 12Th≈0.0276. The computational cost presented in [13] was 3Th, 2Th, and 5Th for each participant, totaling 10Th≈0.023. The computational cost in [21] was 5Th for the RFID tag, 2Th for the reader, and 7Th for the database serve; therefore, the total computational cost in their framework was 14Th≈0.0322. The computational cost in [19] for the RFID tag was 2Th, for the RFID reader was 2Th, and for the database server was 4Th; therefore, the total computational cost in their framework was 8Th≈0.0184. The protocol presented in [20] required 2Th, 2Th, and 4Th+2TE/D for each RFID tag, RFID reader, and database server, respectively, and its total computational cost was 8Th+2TE/D≈0.0276. Furthermore, we computed the computational cost of the proposed framework, which required 2Th+TE/D for the RFID tag and for the database side 2Th+TE/D; thus, the total computational cost of the operations of the proposed framework was 4Th+2TE/D≈0.0184. The results based on the comparison given in Table 9 are also visualized in Figure 2. 5.3. Communication Cost Comparison In Table 10, we compute the communication cost of our proposed protocol and other existing protocols [13,16,17,19,20,21]. After that, in Figure 3, we compare the communication costs of the proposed framework to those of different frameworks in the same environment. This demonstrates that the suggested framework has less communication cost than alternative frameworks [13,16,17,19,20,21]. Furthermore, we computed the communication cost of every framework as under a random number, timestamp, and identity taking 64 bits. Here, we used 160 bits for the hash function message digest (SHA-1) and 256 bits for symmetric key encryption/decryption (AES-256). 5.4. Conclusions In this paper, we proposed a unique hash-based lightweight authentication framework for IoT-based RFID communication environments, after a thorough examination of the various types of RFID authentication and key agreement protocols and their benefits and drawbacks. For secure authentication between valid participants, the protocol uses a hash function and the XoR operations mechanism. We were able to minimize the computational cost of the authentication process by using this technique. When we compared it to other current protocols, our proposed protocol provided improved security while consuming less communication, computational, and storage resources. In the future, the suggested framework could be used in IoT applications such as medical privacy protection, the Internet of Vehicles (IoV), smart city environments, and healthcare systems. Author Contributions Conceptualization, methodology, visualization, V.K. (Vikas Kumar), R.K., A.A.K. and V.K. (Vinod Kumar); software, validation, formal analysis, investigation, data curation, writing—original draft preparation, V.K. (Vikas Kumar), V.K. (Vinod Kumar), A.A.K. and Y.-C.C.; resources, writing—review and editing, supervision, V.K. (Vinod Kumar), A.A.K. and Y.-C.C.; project administration, funding acquisition, Y.-C.C. and C.-C.C. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Taiwan Ministry of Science and Technology, Grant Number 109-2628-E-155-001-MY3. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Architecture of the RAFI. Figure 2 Comparison of the computational cost. Figure 3 Comparison of the computation cost. sensors-22-03110-t001_Table 1 Table 1 IoT-based RFID tag features’ comparison. Features Active Tags Passive Tags Data Storage 128 bytes 128 bytes Tag Battery Yes No Range Up to 100 M Up to 3–5 M Multiple Tag Reading More then 1000 tags recognized up to 100 mph Less than a thousand tags within 3 M of the reader’s range Signal Strength Required to Tag Very low Very high Tag Power Internal source to tag Energy transferred through radio frequency from the reader Availability of Source Power Continuous Only in range of radar sensors-22-03110-t003_Table 3 Table 3 Notations. Symbol Description Ti ith RFID tag Rj jth RFID reader ⊕ Bitwise XoR operation h(·) Cryptographic one-way hash function xS Secret key of S S Database server ▵T Maximum time delay in communication ‖ Concatenation operation SKij(.) Session key agreement between entities i and j i=?j Whether i equals j A Adversary ≈ Approximate value IDTi The identity of the ith tag i···⇒j:{M} i sends message M to j via a secure channel i···→j:{M} i sends message M to j via a public channel sensors-22-03110-t004_Table 4 Table 4 Registration phase of RFID tag. Tag Ti   Database Server S Inputs IDTi Sends MRi1={IDTi} ················⇒ Generates sequence number SNi for Ti Computes S1=IDS⊕h(IDTi∥SNi∥xS) Where xS is the private key of S Computes S2=h(S1∥IDTi)⊕IDTi Stores S1,S2,SNi in the database Sends MR2i2={S1,S2,SNi,h(.)} upon receiving MR2i2 ⇐················ Stores {S1,S2,SNi} in the database sensors-22-03110-t005_Table 5 Table 5 Login and authentication phase of RFID. RFID Tag Ti RFID Reader Rj Database Server S Generates random value r Computes r1=r⊕(S1⊕S2) Computes H1=h(IDTi∥S1∥S2) Computes H11=H1⊕S2 Sends M1={r1,H11,T1} ················→ Verifies T2−T1≤▵T Sends M2={r1,H11,T3} ················→ Verifies T4−T3≤▵T Computes H1*=H1⊕S2 Verifies H1*=?H11 Computes r*=r1⊕(S1⊕S2) Generates random value r2 Computes SKS=h(IDS∥IDTi∥r*∥ r2∥SNi∥S1∥S2∥T5) Computes H2=h(S1∥S2∥r*) Computes H22=H2⊕(r*⊕S2) Computes K1=IDTi⊕h(r*∥SNi∥H1*) Encrypts E1=EK1(H22,r2,IDS,T5) Sends M3={E1,T5} ←················ Verifies T6−T5≤▵T Sends M4={M3,T7} ←················ Verifies T8−T7≤▵T Computes K2=IDTi⊕h(r∥SNi∥H1) Decrypts (H22,r2,IDS,T5)=DK2(E1) Computes H2*=H22⊕(r⊕S2) Verifies H2*=?H2 Computes SKT=h(IDS∥IDTi∥r∥r2∥SNi∥S1∥S2∥T5) sensors-22-03110-t006_Table 6 Table 6 Challenge: handshake authentication for the RAFI. RFID Tag Ti RFID Reader Rj Database Server S Challenge ················→ Challenge ················→ Response ←················ Success then Response ←················ Success sensors-22-03110-t008_Table 8 Table 8 Comparison security and functionality features. Security Features  [16]  [17]  [13]  [21]  [19]  [20] Proposed RAFI1 × ✓ ✓ ✓ ✓ ✓ ✓ RAFI2 × × ✓ ✓ ✓ × ✓ RAFI3 × ✓ × ✓ ✓ × ✓ RAFI4 ✓ × ✓ ✓ × × ✓ RAFI5 × × × ✓ × ✓ ✓ RAFI6 × × ✓ × × × ✓ RAFI7 × × ✓ × × × ✓ RAFI8 × × × ✓ ✓ × ✓ RAFI9 ✓ ✓ ✓ × × ✓ ✓ RAFI10 ✓ × ✓ ✓ ✓ ✓ ✓ RAFI11 × × × ✓ ✓ × ✓ RAFI12 ✓ × × × ✓ × ✓ RAFI13 × ✓ ✓ ✓ ✓ × ✓ RAFI14 × × × ✓ × ✓ ✓ RAFI15 × × × ✓ ✓ × ✓ Note ⇒ ×: not secure against the attack; ✓: secure against the attack; “RAFI1: mutual authentication; RAFI2: tag untraceability; RAFI3: tag anonymity; RAFI4: backward/forward secrecy; RAFI5: scalability; RAFI6: collision attacks; RAFI7: dos attacks; RAFI8: replay attacks; RAFI9: stolen verifier attacks; RAFI10: de-synchronization attacks; RAFI11: man-in-the-middle attack; RAFI12: impersonation attack; RAFI13: message authentication; RAFI14: data confidentiality; RAFI15: insider attack”. sensors-22-03110-t009_Table 9 Table 9 Comparison of the computational cost. Tag Reader Server Total Operations Execution Cost (ms) [16] 2∗Th 2∗Th 3∗Th 4∗Th 0.0161 [17] 4∗Th 2∗Th 6∗Th 12∗Th 0.0276 [13] 3∗Th 2∗Th 5∗Th 10∗Th 0.023 [21] 5∗Th 2∗Th 7∗Th 14∗Th 0.0322 [19] 2∗Th 2∗Th 4∗Th 8∗Th 0.0184 [20] 2∗Th 2∗Th 4∗Th+2∗TE/D 8∗Th+2∗TE/D 0.0276 Proposed 2∗Th+TE/D − 2∗Th+TE/D 4∗Th+2∗TE/D 0.0184 sensors-22-03110-t010_Table 10 Table 10 Communication cost comparison with relevant frameworks. Communication Costs in Bits No. of Messages [16] 2432 4 [17] 1056 5 [13] 1280 5 [21] 1408 4 [19] 896 4 [20] 1792 4 Proposed 832 4 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Finkenzeller K. RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication John Wiley & Sons Hoboken, NJ, USA 2010 2. Want R. An introduction to RFID technology IEEE Pervasive Comput. 2006 5 25 33 10.1109/MPRV.2006.2 3. Hajipour V. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094773 ijms-23-04773 Article Pancreatic Ductal Cell-Derived Extracellular Vesicles Are Effective Drug Carriers to Enhance Paclitaxel’s Efficacy in Pancreatic Cancer Cells through Clathrin-Mediated Endocytosis https://orcid.org/0000-0002-0150-1339 Sun Haoyao 12 https://orcid.org/0000-0003-4826-207X Bhandari Kritisha 2 Burrola Stephanie 2 Wu Jinchang 1* https://orcid.org/0000-0001-7358-3700 Ding Wei-Qun 2* Stefanachi Angela Academic Editor 1 Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215001, China; sun464209459@gmail.com 2 Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; kritisha-bhandari@ouhsc.edu (K.B.); stephaine-burrola@ouhsc.edu (S.B.) * Correspondence: wjinchang@sina.com (J.W.); weiqun-ding@ouhsc.edu (W.-Q.D.); Tel.: +86-1377-604-8328 (J.W.); +1-405-271-1605 (W.-Q.D.) 26 4 2022 5 2022 23 9 477306 4 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Chemo-resistance challenges the clinical management of pancreatic ductal adenocarcinoma (PDAC). A limited admittance of chemotherapeutics to PDAC tissues is a key obstacle in chemotherapy of the malignancy. An enhanced uptake of drugs into PDAC cells is required for a more effective treatment. Extracellular vesicles (EVs), especially small EVs (sEVs), have emerged as drug carriers for delivering chemotherapeutics due to their low immunogenicity and propensity for homing toward tumor cells. The present study evaluated sEVs derived from six different human cell lines as carriers for paclitaxel (PTX). The encapsulation of the chemotherapeutics was achieved using incubation, sonication and electroporation. The cytotoxicity of the EV drugs was evaluated by MTS assay. While sonication led to a higher efficiency of drug loading than incubation and electroporation, PTX loaded through incubation with HPNE-derived sEVs (HI-PTX) was the most efficacious in killing PDAC cells. Furthermore, HI-PTX was taken up by PDAC cells more efficiently than other EV drugs, implying that the efficacy of HI-PTX is associated with its efficient uptake. This was supported by the observation that the cytotoxicity and uptake of HI-PTX is mediated via the clathrin-dependent endocytosis. Our results indicate that the hTERT-HPNE cell-derived EVs are effective drug carriers to enhance paclitaxel’s efficacy in PDAC cells. extracellular vesicles pancreatic cancer paclitaxel clathrin endocytosis ==== Body pmc1. Introduction Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease, with its 5-year overall survival rate being less than 10% [1]. The high mortality rate of PDAC is related to the fact that the majority of pancreatic cancer patients have their tumor already metastasized at the time of diagnosis [2], making systemic therapy the mainstay of treatment. For those with advanced or metastasized tumors, chemotherapy is one of the most effective regimens recommended by the National Comprehensive Cancer Network guidelines [3]. Traditional chemotherapeutics are mainly small-molecule cytotoxic drugs that have distinct pharmacological profiles [4]. Upon intravenous administration, these drugs are passively distributed in the body via the bloodstream. Most of the drugs will gather in the liver and few can reach the tumor sites. Furthermore, drug distribution in the body mainly depends on the passive diffusion of the concentration gradient in the tumor microenvironment. It has been reported in the three-dimensional cell model that the penetration rate of small-molecule anti-tumor drugs in tumor tissue is only about 5% [5]. The limited admittance of chemotherapeutics to PDAC tissues in vivo is even more evident due to the unique stroma composition of PDAC that is histologically manifested as desmoplasia [6]. This pharmacological kinetic feature of chemotherapeutics causes traditional chemotherapy drugs to have serious side effects with limited potential of dose elevation in PDAC patients. To overcome this challenge, new chemo drug carriers have been developed to improve the pharmacokinetics, increase tumor-targeting efficacy and reduce the side effects of chemotherapeutics [7,8,9,10,11]. One of the recently studied natural Nano drug-carriers are extracellular vesicles (EVs), which are small lipid bilayer-delimited particles generated through various cellular processes and released from all types of cells investigated. They are able to transfer genetic and cellular materials between different cell types to mediate intercellular communication [12,13]. The most attractive properties of human cell-derived EVs as drug carriers include their lower immunogenicity and higher capacity of homing toward tumor cells [14,15,16,17]. Results from 12 recent clinical trials testing small EVs (sEVs) as therapeutic carriers or potential cancer therapeutics demonstrated safe profiles of sEVs delivered in humans, supporting the development of human cell-line-derived sEVs as chemotherapeutic carriers [18]. However, questions remain to be answered as to the choice of EV sources, the strategies of drug encapsulation, and the mechanisms of the cellular uptake of the delivered EV drugs. The present study was driven by the above-mentioned questions. We compared the drug loading efficiency of sEVs derived from six different human cell lines, including the human normal pancreatic duct cell line hTERT-HPNE, the human embryonic kidney cell line HEK-293T, the cancer-associated fibroblast cell line CAF19 and three human PDAC cell lines, PANC-1, MIA PaCa-2, and BxPC-3. Direct incubation, sonication and electroporation of sEVs were applied to incorporate paclitaxel (PTX) or gemcitabine (GEM), two commonly used chemotherapeutics. The sEV-drug efficacy was tested in the PDAC cell lines PANC-1, MIA PaCa-2, and BxPC-3. Our results show that while sonication leads to a higher efficiency of drug loading than incubation and electroporation, sEV derived from hTERT-HPNE cells and incubated with PTX (HI-PTX) was the most efficacious in killing PDAC cells. By using specific endocytosis pathway inhibitors and gene manipulation techniques, we demonstrated that the increased cellular cytotoxicity of HI-PTX is associated with enhanced cellular uptake of the sEV-drug complex through clathrin-mediated endocytosis. 2. Results 2.1. Characterization of sEVs and Drug Encapsulation Small EVs were isolated from culture medium of the six human cell lines as we described [19]. Protein analysis of isolated sEVs showed that hTERT-HPNE sEV has the lowest protein concentration, while MIA PaCa-2 sEV the highest. Cancer-cell-derived sEVs seemed to have a higher EV concentration than normal-cell-derived sEVs (Figure 1a). EV particle quantity derived from the six cell lines also differed, but not as much as the protein concentrations (Figure 1b). Two sEV-positive markers, flotillin-1 and CD63, and one negative marker, calnexin, were detected by Western blot to verify the isolated sEVs (Figure 1c). All sEVs had iconic peaks around 100–200 nm, based on nanoparticle tracking analysis (Figure 1d). To quantify EV-drug concentration, both EV drugs and sEVs were analyzed by Nanodrop in the UV–Vis spectrum. Standard curves of free PTX, GEM and sEVs derived from the six cell lines were generated separately at 230 or 275 nm (Figure S1). The quantity of loaded drugs was expressed as ng of drug/μg of sEV. Our data showed that sonication leads to a higher concentration of drug loading than incubation and electroporation (Figure 1e). 2.2. HPNE sEV-PTX Derived from Incubation (HI-PTX) Is most Efficacious in Killing Pancreatic Cancer Cells To test whether the EV-PTX are cytotoxic toward cancer cells, PANC-1, MIA PaCa-2 and BxPC-3 cells were seeded in 96-well plates and treated with free or EV drugs for 24–72 h. MTS assay was applied to assess cell cytotoxicity as we previously described [20,21]. As expected, PTX and GEM suppressed cell viability in a time- and concentration-dependent manner, with IC50 values ranging from 10 to 100 nM in the three cancer cell lines (data not shown). We then treated the cells using EV drugs or free drugs with the same drug concentrations ranging from 1 to 1000 nM for 72 h. While five EV-PTX drugs showed lower IC50 values than that of free PTX, only HI-PTX showed consistent cytotoxicity in all three cancer cell lines (Figure 2a–f). In contrast to EV-PTX, by following the same protocol and procedures, no EV-GEM drugs were more cytotoxic than free GEM in PANC-1 cells (Figure 2g,h). These results indicate that the efficacy of EV-encapsulated chemotherapeutics is associated with the loading methods, EV sources, and the drug of interest. 2.3. Uptake of HI-PTX in PANC-1 Cells To understand why cytotoxicity differs among the EV drugs, we examined EV-drug uptake using the PKH67 dye under a fluorescent microscope. We found that both HPNE sEVs or HI-PTX uptake by PANC-1 cells were time-dependent, with the highest uptake at 10 h post sEV addition. Interestingly, HI-PTX uptake was more pronounced than HPNE sEV uptake at each time point (Figure 3a). To exclude the potential effects of PTX on fluorescent imaging, we compared the uptake of HPNE sEV versus HPNE sEV plus free PTX and found no differences in any of the three cancer cell lines between the two groups of sEVs (Figure 3b). To determine whether the method of drug encapsulation affects the uptake, we compared the uptake of sEVs derived from HPNE and HEK-293T cells and prepared with PTX via sonication, electroporation, and incubation. It turned out that HI-PTX had the highest uptake of the EV drugs in PANC-1 and BxPC-3 cells (Figure 3c), consistent with its more pronounced cytotoxicity. While there was also a tendency for increased uptake of HI-PTX in MIA PaCa-2 cells, statistical significance could not be reached. These results suggest a connection between cell cytotoxicity and uptake of the EV drugs. 2.4. HI-PTX’S Uptake and Cytotoxicity Is Associated with Clathrin-Mediated Endocytosis To explore the mechanism of HI-PTX uptake and cytotoxicity in pancreatic cancer cells, several endocytic pathway inhibitors were applied. The higher uptake of HI-PTX was diminished by the inhibitors Monesin, Bafilomycin A1 (BFA), and clathrin-mediated endocytosis inhibitor Pitstop2. To the contrary, the caveolin-mediated endocytosis inhibitor Genistein had no effect on HI-PTX uptake (Figure 4a). However, among the inhibitors tested, only Pitstop2 could reverse the cytotoxicity of HI-PTX in PANC-1 cells when 3 nM PTX equivalent concentration of HI-PTX was applied (Figure 4b), indicating that the clathrin-mediated endocytosis is primarily involved in HI-PTX’s uptake and cytotoxicity. To further confirm the contribution of clathrin-mediated endocytosis to HI-PTX uptake, overexpression and siRNA knockdown of clathrin light chain and caveolin was achieved in PANC-1 cells (Figure 4c,d). The most successful knockdown of clathrin light chain was obtained using Si-CLTB-93, which was used for subsequent experiments (Figure 4e,f). As shown in Figure 4e, EV uptake was associated with the expression levels of clathrin, not that of caveolin, for both HPNE sEVs and the HI-PTX. These observations support the conclusion that HI-PTX uptake is facilitated, at least in part, by clathrin-mediated endocytosis in pancreatic cancer cells. To make sure the knockdown of clathrin light chain impairs clathrin-mediated endocytosis, an RFP-tagged transferrin receptor construct (TfR-pHuji plasmid [22] (Addgene Plasmid #61505) was used to monitor cellular localization of the transferrin receptor during siRNA knockdown (Supplemental Figure S2). It confirmed that the knockdown leads to more transferrin receptor on the cell surface. This is consistent with previous reports showing that the depletion of clathrin light chain effectively inhibits the clathrin-mediated internalization of cargos such as bacteria and virus particles that are too large for conventional endocytosis [23,24] and that exosomes or small EVs share physical properties and size ranges with viral particles [25]. 3. Discussion Although the experimental evidence showing the effectiveness of EV drugs against cancer has been abundant, there has been no clear consensus regarding the choice of methods for EV encapsulation of drugs, the source of EVs as carriers, and the mechanisms of EV-drug internalization to achieve the best therapeutic effects. The results from the present study demonstrate that, while sonication leads to higher efficiency of PTX loading than incubation and electroporation, HI-PTX prepared by incubation is most efficacious in killing pancreatic cancer cells, observations in line with a recent report using EV-encapsulated doxorubicin (DOX) [26]. Furthermore, we demonstrated that the uptake and cytotoxicity of HI-PTX are associated with clathrin-mediated endocytosis in pancreatic cancer cells, implicating endocytosis pathways in EV-drug efficacy. Cellular uptake of molecules larger than one kilo Dalton, such as proteins or nanoparticles, is usually facilitated by endocytic pathways [4]. It has been reported that EV uptake is mediated through various endocytic pathways, including clathrin-dependent endocytosis and clathrin-independent endocytosis, such as caveolin-mediated uptake and lipid raft-mediated internalization. Because EV populations are often heterogeneous, more than one route of uptake is generally involved during EVs internalization into cells [27]. For example, clathrin- and caveolin-dependent endocytosis and macropinocytosis are the predominant routes of sEV-mediated communication between bone marrow stromal cells and multiple myeloma cells, and the knocking down of calveolin-1 and clathrin heavy chain in multiple myeloma cells significantly suppressed sEV uptake and chemo sensitivity to bortezomib [28]. However, the endocytosis pathways involved in the uptake of drug-loaded EVs has not been previously established. Our experiment results showed that the cellular uptake of HI-PTX (PTX encapsulated by HPNE sEVs via incubation) is enhanced when compared to the uptake of HPNE sEVs in all three pancreatic cancer cell lines. This enhanced uptake of HI-PTX is attributed to clathrin-mediated endocytosis, since modulation of this process using the inhibitor Pitstop2 or by expression manipulation of clathrin altered the uptake of HI-PTX and its cytotoxicity, whereas the caveolin-dependent endocytosis seemed to be irrelevant in this process. Our observations thus provide novel information in the understanding of EV drug uptake and efficacy in cancer cells. The higher uptake of HI-PTX, when compared to EV-PTX prepared via sonication and electroporation, may be explained by the possibility that, compared to incubation, both sonication and electroporation are likely to trigger a harsh process to sEVs that cause damage of sEV membranes, thereby compromising their ability for cellular internalization [29]. Nonetheless, the mechanisms responsible for a higher uptake of HI-PTX, when compared to the uptake of HPNE sEVs, remain to be explored in the future. Various sources of sEVs have been tested for their potential as therapeutic carriers, and each type of sEV may have pros and cons when used for drug delivery [30]. This is most likely due to their differences in the EV cargos in which each may have unique lipid, protein and RNA profiles that may directly influence sEVs’ ability to interact with receiving cells [31]. In particular, tumor-cell-derived EVs were considered drug carriers for selective targeting and enhanced immune response, yet may ironically promote tumor growth and invasion, due to their cargo compositions [30]. In this context, a strategy using exosomes coated with magnetic nanoparticles to deliver chemotherapeutics specifically to tumor cells has been described [32,33,34]. However, the best EV sources have yet to be identified in the development of EVs as therapeutic carriers. In this study, we tested sEVs derived from six human cell lines, including cancer lines and non-cancer lines. We found that the sEVs derived from the human pancreatic ductal cell line HPNE (a non-cancer line) are the most efficacious when used to encapsulate PTX via incubation, suggesting that this group of sEVs is a promising candidate for further development as cancer therapeutic carriers. In vivo testing is warranted for the efficacy and safety of HI-PTX. An interesting finding from this study was that in contrast to HI-PTX, no EV-GEM drugs showed superior cytotoxicity compared with free GEM in pancreatic cancer cells, suggesting that individual chemotherapeutics entails a different fate when encapsulated by sEVs. Specific to GEM, two research groups have reported that GEM is successfully loaded into autologous exosomes that suppress tumor growth [35,36]; however, others have also reported that GEM is inefficiently entrapped by nanoparticles due to leakage or hydrophilic property [37,38]. These inconsistent observations, along with ours, are most likely due to the nature of the sEVs used, and the procedures applied for sEV isolation and drug encapsulation. In summary, we have demonstrated that HI-PTX is the most efficacious EV-PTX in suppressing pancreatic cancer cell viability, which is primarily mediated via the clathrin-dependent endocytosis pathway. Our data indicate that the efficacy of EV-encapsulated chemotherapeutics is associated with the loading methods, EV sources, and EV uptake efficiency. 4. Materials and Methods Cell culture. The human pancreatic cancer cell lines PANC-1, MIA PaCa-2 and BxPC-3, the immortalized human pancreatic duct cell line HPNE, and the human embryonic kidney cell line HEK-293T were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). The cancer-associated fibroblast cell line CAF19 was kindly provided by Dr. Priyabrata Mukherjee, University of Oklahoma Health Sciences Center. Cells were cultured following ATCC’s instructions, and CAF19 was cultured in DMEM supplemented with 10% FBS. Exosome-depleted FBS and horse serum were prepared by pelleting the serum EVs at 100,000× g for 1.5 h at 4 °C and then filtered through a 0.22 µm PVDF centrifuge filter. Cells were routinely incubated in a humidified environment at 37 °C and 5% CO2. EV isolation and validation. EVs from 20 mL cell culture medium were isolated following the protocol we previously described, with minor modifications [19,39]. Briefly, after being pre-cleared by 10,000× g centrifugation for 30 min at 4 °C, the resulting supernatant was transferred to a 100 kDa cut-off centrifugal column (Merckmillipore, Burlington, MA, USA) and centrifuged for 15 min at 4000× g. The concentrated supernatant was then filtered through PVDF centrifuge filters (Merckmillipore, Burlington, MA, USA) as we described [39]. Small EVs (exosomes) were recovered using the Total Exosome Isolation Reagent (Thermofisher, Waltham, MA, USA) following the manufacturer’s instructions. The isolated sEVs dissolved in PBS were verified by Western blot detecting positive and negative exosome proteins and nanoparticle tracking analysis (Nanosight NS300 System, Malvern Instruments, Malvern, UK) measuring sEV sizes and concentrations. Drug encapsulation and quantification. PTX (Sigma-Aldrich, St. Louis, MO, USA, 0.01 µmol, CAS 33069-62-4) or GEM (Sigma-Aldrich, St. Louis, MO, USA, 0.1 µmol, CAS 122111-03-9) was mixed with the purified sEVs (around 50 µg) in 1 mL PBS. Three loading methods, including incubation, sonication and electroporation, were applied. For the incubation method, the sEV-drug mixture was incubated at 37 °C for 1 h. For the sonication method, the mixture was sonicated using a FB505 sonicator (Fisher Scientific, Pittsburgh, PA, USA) with the following settings: 20% amplitude, 6 cycles of 30 s on and off, followed by 2 min cooling period. After sonication, the EV-drug mixture was incubated at 37 °C for 1 h. For the electroporation method, the sEV-drug mixture was electroporated using the P3 Primary Cell 4D-NucleofectorTM X Kit L (Lonza, Basel, Switzerland) with DN-100 program of the 4D-NucleofectorTM Core Unit. After electroporation, the EV-drug mixture was also incubated at 37 °C for 1 h. The EV-drug mixture was then pelleted by the total exosome isolation reagent and dissolved in 200 μL PBS to remove unbounded drugs. UV absorbance of the EV-drug mixtures at 230 nm (PTX) or 275 nm (GEM) was recorded by Nanodrop (Denovix, Wilmington, DE, USA) to determine the drug concentrations as previously described [40,41]. A standard curve of free PTX or GEM was established, and drug concentration in the EV-drug mixture was calculated as the following: EV-drug UV absorbance minus sEV UV absorbance, and the resulting absorbance was fitting to the free drug standard curve. The drug loading efficiency was presented as drugs (ng)/sEVs (μg). Western blot analysis. Western blot was performed as we recently described [39]. Primary antibody raised against Clathrin-LC was obtained from Santa Cruz Biotechnology (Dallas, TX, USA), and those against Caveolin 1 and GAPDH were purchased from Cell Signaling Technology (Danvers, MA, USA). Antibodies used for sEV marker detection include: CD63 (Santa Cruz, Dallas, TX, USA), Flotillin-1 and Calnexin (Cell Signaling Technology, Danvers, MA, USA). The Li-Cor Odyssey® Fc Imaging System was used to visualize and image the blots. Cell proliferation (MTS) assay. Cells were seeded onto 96-well plates at a density of 6000–8000 cells/well in triplicate and drugs were added the next morning. After 72 h incubation at 37 °C with 5% CO2, the medium was replaced with 100 μL fresh medium supplemented with 20 μL CellTiter 96® AQueous One Solution (Promega, Madison, WI, USA). After 1 h of incubation, the absorbance value at 490 nm was recorded using a spectrometer (VWR SpectraMax® 190, Radnor, PA, USA). The data were expressed as percentage of the absorbance value detected in untreated control cells. EV uptake analysis. Small EVs were labelled using the PKH67 Green Fluorescent Cell Linker kit (Sigma-Aldrich, St. Louis, MO, USA) following the manufacture’s protocol. Same amount of sEV or EV-drug (around 20 μg) was added to 500 μL diluent before adding 1 μL PKH67. The mixture was incubated at room temperature for 20 min and 500 μL EV-depleted FBS was added to stop the labeling. The labeled sEVs were pelleted by ultracentrifuge at 100,000× g, 4 °C for 1.5 h. The pellet was resolved in EV-depleted medium. Cancer cells (5 × 104 cells/well) were plated on a 24-well plate. The labeled sEVs or sEV-drug mixtures (around 20 μg) were added to the cell culture 24 h post seeding and incubated for 2 to 10 h. In some experiments, endocytosis inhibitors were added 6 h prior to addition of the labeled sEVs or EV drugs. Cells were washed by PBS, fixed by paraformaldehyde and mounted using Prolong Antifade Reagents (Thermofisher, Waltham, MA, USA). Fluorescent signal in cells was detected by a fluorescent microscope (Thermofisher, Waltham, MA, USA) and analyzed using the ImageJ software [42]. Manipulation of clathrin and caveolin expression. The mCherry-clathrin-LC and mCherry-caveolin1 plasmids were purchased from Addgene (Watertown, MA, USA). The mCherry-C1 was purchased from Takara Bio USA (Mountain View, CA, USA) as control. DNA transfection to PANC-1 cells was performed using Lipofectamine 3000 (Fisher Scientific, Pittsburgh, PA, USA) and clathrin, and caveolin overexpression was verified by fluorescent microscopy and Western blot. For clathrin light chain and caveolin knockdown, three clathrin siRNA—s3191 (siCLTB91), s3192 (siCLTB92), and s3193 (siCLTB93)—one caveolin siRNA—s2446 (siCAV1)—and one negative control siRNA were synthesized (Table 1, Thermofisher, Waltham, MA, USA). Transfection of the siRNAs to PANC-1 cells was performed using Lipofectamine 3000 (Fisher Scientific, Pittsburgh, PA, USA) and the knockdown of clathrin and caveolin was verified by Western blot. Acknowledgments We thank the Department of Pathology at the University of Oklahoma Health Sciences Center for administrative support, and the core facility at the Peggy and Charles Stephenson Cancer Center for support in nanoparticle analysis. We thank Jundong Zhou at the Department of Radiation Oncology, the Affiliated Suzhou Hospital of Nanjing Medical University, for his participation in initiating this study. Supplementary Materials The following are available online at https://www.mdpi.com/article/10.3390/ijms23094773/s1. Click here for additional data file. Author Contributions Conceptualization, W.-Q.D. and J.W.; Investigation, H.S., K.B., W.-Q.D. and K.B.; Methodology, H.S. and S.B.; Resources, W.-Q.D. and J.W. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the National Cancer Institute (CA235208-01), Suzhou Science and Technology Development: applied basic research (SKJY2021118), the Presbyterian Health Foundation, the Peggy and Charles Stephenson Cancer Center, and the Affiliated Suzhou Hospital of Nanjing Medical University. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are contained within the article and Supplementary Materials. Conflicts of Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Figure 1 Validation of extracellular vesicles and quantification of EV drugs. (a) EVs were derived from 20 mL medium of 6 different human cell lines, and EV concentrations were analyzed by BCA assay; (b) EV particle analysis by NanoSight (1:1000 dilution); (c) Small EV-positive markers, CD63 and Flotillin-1, and a negative marker, Calnexin, detected by Western blot; (d) Representative EV size distribution and particle numbers analyzed by Nanosight; (e) Sonication leads to higher efficiency of EV drug loading than incubation and electroporation. **** p < 0.0001, *** p < 0.001, ** p < 0.01, one-way ANOVA (n = 3–5, comparison of Incubation, Sonication, and Electroporation). HS3: HPNE sEV; 2S3: HEK-293T sEV; CS3: CAF19 sEV; PS3: PANC-1 sEV; MS3: MIA PaCa-2 sEV; BS3: BxPC-3 sEV; PTX: Paclitaxel; GEM: Gemcitabine. Figure 2 HI-PTX is more cytotoxic than other EV drugs. (a,d) Cell vitality of PANC−1 cells treated with equivalent of 3 nM PTX and 1 to 100 nM EV−PTX for 72 h; (b,e) Cell vitality of MIA PaCa−2 cells treated with equivalent of 3 nM PTX and 1 to 100 nM EV−PTX for 72 h; (c,f) Cell vitality in BxPC−3 cells treated with equivalent of 1 nM PTX or 1 to 100 nM EV−PTX for 72 h; (g,h) Cell vitality of PANC−1 cells treated with equivalent of 100 nM GEM or 10 nM to 1 µM EV−GEM for 72 h. The cytotoxicity of all EV−GEM was lower than that of free GEM. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post-test for (a) and two-way ANOVA for (d–f). *** p < 0.001, ** p < 0.01, * p < 0.05 (data from three individual determinations). HI-PTX: Incubation of HPNE sEV with paclitaxel; HS−PTX: Sonication of HPNE sEV with paclitaxel; HE−PTX: Electroporation of HPNE sEV with paclitaxel; 2I-PTX: Incubation of HEK−293T sEV with paclitaxel; 2S−PTX: Sonication of HEK−293T sEV with paclitaxel; 2E−PTX: Electroporation of HEK−293T sEV with paclitaxel; CI-PTX: Incubation of CAF19 sEV with paclitaxel; CS−PTX: Sonication of CAF19 sEV with paclitaxel; PI−PTX: Incubation of PANC−1 sEV with paclitaxel; PS−PTX: Sonication of PANC−1 sEV with paclitaxel; MI−PTX: Incubation of MIA PaCa−2 sEV with paclitaxel; MS−PTX: Sonication of MIA PaCa−2 sEV with paclitaxel; BI−PTX: Incubation of BxPC−3 sEV with paclitaxel; BS−PTX: Sonication of BxPC−3 sEV with paclitaxel. Figure 3 HI-PTX is taken up more effectively by pancreatic cancer cells. (a) Equal amounts of HPNE-sEV and HI-PTX were labeled with PKH67 and loaded onto PANC-1 cells for 2 to 10 h. Cellular uptake of HPNE-sEV and HI-PTX was monitored by fluorescent microscopy (excitation 488 nm, detection, 510 nm; Nikon TE2000-E, 10× magnification); (b) Adding HPNE-sEV and PTX simultaneously to PANC-1, MIA PaCa-2, and BxPC-3 cells did not enhance EV uptake. The fluorescence was detected by fluorescent microscopy and the fluorescence intensity was semi quantified, and presented as relative levels; (c) The uptake of HPNE- and 293T-derived EV, when loaded with PTX by incubation, sonication and electroporation, in the three pancreatic cancer cell lines. *** p < 0.001, * p < 0.05, one-way ANOVA followed by Dunnett’s post-test (data from three individual determinations). Figure 4 HI-PTX’s uptake and cytotoxicity is associated with clathrin-mediated endocytosis. (a) The clathrin-mediated endocytosis inhibitor Pitstop2 inhibited the uptake of HI-PTX by PANC-1 cells; (b) Treatment with Pitstop2 attenuated HI-PTX’s cytotoxicity in PANC-1 cells; (c) Overexpression of clathrin and caveolin (Nikon TE2000-E, 10× magnification); (d) Knockdown of clathrin and caveolin expression in PANC-1 cells, evidenced by fluorescent images and Western blot analysis, mCherry-clathrin, 63 kDa; clathrin, 31 kDa; mCherry-caveolin, 53 kDa; caveolin, 22 kDa; (e,f) The uptake efficiency of HI-PTX was altered by overexpression or knockdown of clathrin in pancreatic cancer cells. ** p < 0.01, * p < 0.05, one-way ANOVA followed by Dunnett’s post-test (data from three individual determinations). ijms-23-04773-t001_Table 1 Table 1 siRNA sequences for knockdown of clathrin light chain and caveolin. Name Sequence Target si_CLTB91 sense: GCCCAGCUAUGUGACUUCATT antisense: UGAAGUCACAUAGCUGGGCCA Clathrin light chain si_CLTB92 sense: CCUCCUCUCAGUCUACUCATT antisense: UGAGUAGACUGAGAGGAGGCG Clathrin light chain si_CLTB93 sense: GAACAAGUAGAGAAGAACATT antisense: UGUUCUUCUCUACUUGUUCAC Clathrin light chain si_CAV1 sense: GCUUCCUGAUUGAGAUUCATT antisense: UGAAUCUCAAUCAGGAAGCTC Caveolin Statistics. Statistical analyses were performed using GraphPad Prism software (La Jolla, CA, USA). One-way ANOVA was used to determine p-values among experimental groups and a p-value of ≤0.05 was considered statistically significant. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020 CA Cancer J. Clin. 2020 70 7 30 10.3322/caac.21590 31912902 2. Tas F. Sen F. Keskin S. Kilic L. Yildiz I. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095738 ijerph-19-05738 Review Cholera Outbreaks in India, 2011–2020: A Systematic Review Muzembo Basilua Andre 1* Kitahara Kei 12 Debnath Anusuya 13 Ohno Ayumu 12 Okamoto Keinosuke 1 Miyoshi Shin-Ichi 1 Tchounwou Paul B. Academic Editor 1 Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8530, Japan; keikitahara@okayama-u.ac.jp (K.K.); anusuyadebnath@yahoo.co.in (A.D.); py386nyz@okayama-u.ac.jp (A.O.); okamot-k@okayama-u.ac.jp (K.O.); miyos-s@okayama-u.ac.jp (S.-I.M.) 2 Collaborative Research Center of Okayama University for Infectious Diseases in India, Kolkata 700010, India 3 Department of Biotechnology, Brainware University, Kolkata 700125, India * Correspondence: andersonbasilua@yahoo.fr or muzembo_andre@okayama-u.ac.jp 08 5 2022 5 2022 19 9 573801 4 2022 06 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Fecal contamination of water sources and open defecation have been linked to cholera outbreaks in India. However, a systematic review on the drivers responsible for these outbreaks has yet to be published. Here, we systematically review the published literature on cholera outbreaks in India between 2011 and 2020. We searched studies in English in three databases (MEDLINE, EMBASE, and Web of Science) and the Integrated Disease Surveillance Program that tracks cholera outbreaks throughout India. Two authors independently extracted data and assessed the quality of the included studies. Quantitative data on the modes of transmission reviewed in this study were assessed for any change over time between 2011–2015 and 2016–2020. Our search retrieved 10823 records initially, out of which 81 full-text studies were assessed for eligibility. Among these 81 studies, 20 were eligible for inclusion in this review. There were 565 reported outbreaks between 2011 and 2020 that led to 45,759 cases and 263 deaths. Outbreaks occurred throughout the year; however, they exploded with monsoons (June through September). In Tamil Nadu, a typical peak of cholera outbreaks was observed from December to January. Seventy-two percent (33,089/45,759) of outbreak-related cases were reported in five states, namely Maharashtra, West Bengal, Punjab, Karnataka, and Madhya Pradesh. Analysis of these outbreaks highlighted the main drivers of cholera including contaminated drinking water and food, inadequate sanitation and hygiene (including open defecation), and direct contact between households. The comparison between 2011–2015 and 2016–2020 showed a decreasing trend in the outbreaks that arose due to damaged water pipelines. Many Indians still struggle with open defecation, sanitation, and clean water access. These issues should be addressed critically. In addition, it is essential to interrupt cholera short-cycle transmission (mediated by households, stored drinking water and foodstuffs) during an outbreak. As cholera is associated with deprivation, socio-economic development is the only long-term solution. cholera outbreak water supply open defecation sewage household food close contact behavioral changes India Japan Initiative for Global Research Network on Infectious Diseases (J-GRID) from the Ministry of Education, Culture, Sports, Science & Technology in Japan (MEXT)Japan Agency for Medical Research and Development21wm0125004h0002 We declare that we have no conflicts of interest. This study was supported by the Japan Initiative for Global Research Network on Infectious Diseases (J-GRID) from the Ministry of Education, Culture, Sports, Science & Technology in Japan (MEXT), and the Japan Agency for Medical Research and Development (AMED; Grant Number 21wm0125004h0002). The funders had no role in its study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors had full access to all the data in the review process, and the corresponding author had the final responsibility in deciding to submit this article for publication. ==== Body pmc1. Introduction Cholera is a disease associated with destitution [1]. The heavy reliance on untreated environmental water sources for daily water needs such as drinking, bathing, cooking, and washing utensils by poverty-stricken communities increases the risk of ingesting copepods, the biotic carriers for cholera-causing bacteria Vibrio cholerae O1 or O139 (V. cholerae). Copepods soar in environmental water due to fluctuations in several climatic factors such as increased water temperature. Under such conditions, this increases the likelihood of ingesting an infective dose of V. cholerae through copepod-infested water [2]. Transmission of cholera spreads further upon contamination of drinking water sources or food with feces from infected people. Studies have demonstrated that host factors (such as age, nutrition, and blood group) also play a role in the development of cholera [3,4,5,6]. In October 2017, the WHO Global Task Force on Cholera Control (GTFCC) launched a vigorous fight against cholera. The GTFCC aims for the elimination of the disease as a public threat by 2030 in at least 20 countries with an emphasis on multiple targets including preventing the recurrence of cholera in hotspots [7]. Cholera outbreaks are relatively frequent in India. Surveillance data reveal a steady increase in reported cholera outbreaks throughout the country. From 1997 to 2006, 68 outbreaks were reported [8], while the reported outbreaks rose to 559 between 2009 and 2017 [9]. However, this is only the tip of the iceberg. The disease is grossly underreported in India [10], and despite these figures, cholera remains an under-recognized health issue in India [1,8]. Many state authorities are unaware of the disease burden and its impact on the citizens [11]. In India, cholera is endemic and occurs with marked seasonal dynamics; cholera is prevalent in the hot, humid and rainy season. In general, the seasonality of cholera outbreaks is mediated by various contributing and overlapping factors such as environmental parameters and climatic factors [2,12], waning host-immunity [13], and probably human behaviors (e.g., social gatherings) and activities also. For instance, the tribal communities (poorest and marginalized Indian communities) usually work in the paddy fields during the rainy season and became ill after drinking untreated environmental water [14]. Although access to safe drinking water and improved sanitation has been enhanced in most of the states and union territories (SUTs) by the Swachh Bharat (Clean India) Mission, substantial progress is still needed because of high inequities in distribution. For instance, only 16% of the population in rural India had access to piped water up until 2015 [15,16]. In addition, sanitation is another major hurdle to be handled. Approximately sixty percent of the world’s population who defecate in the open are in India. The overwhelming majority of individuals in rural India engage in open defecation that leads to the contamination of water bodies and heavy rainfall further worsens the situation [14,17]. As a remote driver of cholera, high rainfall raises the water level causing sewage and stagnant water to leak into damaged pipelines [14]. Several analyses of cholera outbreaks occurring in India have been documented [8,9,18,19]. However, the drivers of these outbreaks have yet to be systematically synthesized. The most recent report [9] summarized outbreaks from 2009 to 2017 and focused on antimicrobial resistance. Although this is only four years ago, data on fresh outbreaks have also become available. While it is useful and important in understanding antimicrobial resistance, the latter [9] did not address the importance of shifts in human behaviors in addition to access to clean drinking water to interrupt transmission during cholera outbreaks. Hence, both a timely update of data and a detailed synthesis of the evidence base for public health are warranted for policy recommendations. The objective of this study is to provide the trend of cholera outbreaks in India over the last ten years. In addition, we also analyzed data for potential changes in the pattern of drivers of cholera. We specifically sought to understand whether there is any decreasing trend among the key drivers of cholera outbreaks by comparing two periods: 2011–2015 and 2016–2020. As cholera outbreaks are strongly seasonal in India, this makes us question what human-behavioral practices are associated with these seasonal fluctuations. We argue that broad shifts in behaviors are central to effective outbreak control. The data reviewed here would prove useful for informing policy-makers by pinpointing the areas where efforts should be focused for better prevention measures (such as motivating people in rural areas to use toilets, providing tap water to every household in the rural areas and urban slums, along with education on health and hygiene, education on household water storage, and mass availability of oral cholera vaccine for target-oriented vaccination), enhancing advocacy for launching a National Cholera Control Program in India or at least strengthening the sentinel surveillance system for diarrheal diseases and cholera in particular. 2. Methods 2.1. Data Sources We undertook a systematic review according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) [20] to explore cholera outbreaks in India from the period 2011 to 2020. This systematic review is registered with PROSPERO (CRD42021233348). We defined a cholera outbreak as the occurrence of “at least one laboratory confirmed cholera case either by culture or polymerase chain reaction and there was evidence of local transmission in a specified geographical area or could be linked by place and time” [21]. For practical purposes, we considered a cholera outbreak as it had been defined in the studies included in this review. We searched three electronic databases (MEDLINE through PubMed, EMBASE, and Web of science) for studies that were published on cholera outbreaks from January 2011 to December 2020 in India. This period of 10 years was chosen based on a similar review conducted by Kanungo and colleagues in which they also analyzed data over a ten-year period (1997–2006) [8]. The following groups of keywords were employed for cholera: “Cholera” OR “Vibrio cholerae” OR “Vibrio cholerae O1” OR “Vibrio cholerae O139”. These keywords were combined with: “outbreak” OR “epidemiology” OR “epidemics” OR “pandemic” OR “prevalence” OR “incidence” OR “risk factors”, OR “community” OR “immunity” AND “India.” We further narrowed down our searches by including each of the 36 names of SUTs. The search was further refined by scanning the reference lists of the obtained studies and related reviews. We did not apply any language restrictions during the search. Retrieved studies were exported to the Endnote software X9 (Clarivate, PA, USA) and duplicated studies were manually removed. Anticipating a scarcity of peer-reviewed publications on cholera outbreaks in India, the searches were supplemented by the grey literature data, i.e., the epidemiology reports of the Integrated Disease Surveillance Program (IDSP) that track cholera outbreaks [22]. The last search was performed on 6 April 2021. We used population, exposure, comparison, outcomes, and study design (PECOS) as a framework for study selection. To be included, a study had to meet the following inclusion criteria:(1) Population: any group of individuals affected by a cholera outbreak in India; (2) Exposure: a study had to assess sources of exposure or potential risk factors for an outbreak; (3) Comparison: it was not considered obligatory to include a comparison group for the present analysis; (4) Outcomes: we focused on transmission routes as well as human practices that lead to cholera, the sources of the cholera outbreak and other human factors that may explain the seasonality of cholera; (5) Study design: prevalence and incidence studies were eligible. Articles were excluded for any of the following reasons: they were non-outbreak studies, reports were available in abstract form only, they investigated sporadic cholera cases, or the study failed to meet the above inclusion criteria. 2.2. Data Extraction and Analysis Two authors independently screened articles for inclusion and abstracted data from the included studies. Disagreements were discussed and resolved by consensus. We devised a standardized chart to extract data. For each study, the extracted data included the first author’s last name, year of publication, setting and geographic region, duration of the outbreak, number of cholera cases, number of deaths, attack rates, investigated risk factors, behavioral characteristics of the index case, occurrence season, V. cholerae serogroup/serotype/biotype and required data for quality assessment. We also gathered data on two particular aspects related to the setting of each study: (1) urban versus rural, and (2) SUTs. Data on antibiotic resistance were also abstracted wherever applicable because antibiotic resistance is a serious public health issue that needs novel intervention strategies. Data extracted from IDSP outbreak reports included information on setting, number of cases, number of deaths, date of onsets, and transmission vehicle. Two authors independently assessed the quality of the included studies. The risk of bias in the included studies was assessed employing a modified Downes et al. appraisal checklist for cross-sectional studies [23]. Results were presented in both textual narrative and tabular formats. In addition, the geographical distribution of outbreaks was presented in area maps. The country area maps were generated using MapChart [24]. We generated graphs using the Stata software package (version 16, StataCorp LP, College Station, TX, USA). The prevalence of laboratory-confirmed cholera was synthetized using a random-effects model in the Comprehensive meta-analysis software, version 3. Annual reports of the Central Bureau of Health Intelligence (CBHI) on the national health profile of India were used to ascertain the Indian population by SUTs [25]. Cumulative cases were expressed as cholera cases per 100,000 persons. Quantitative data on the modes of transmission reviewed in this study were assessed for any change over time between the two time periods, i.e., period 1 (from 2011 to 2015) and period 2 (from 2016 to 2020). 3. Results 3.1. Study Characteristics Overall, 10,823 records were identified initially, out of which 81 full-text studies were assessed for eligibility (Supplementary Figure S1). Among these 81 studies, only 20 met the inclusion criteria (Table A1). Most (90%; 18/20) of them were cross-sectional studies published from January 2011 to March 2021. All these 20 studies identified by our search strategy described 21 cholera outbreaks [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. Cholera outbreaks were mostly reported in Southern and Eastern India (Table A1). In most studies, cholera diagnosis was often based solely on clinical symptoms, whereas laboratory confirmation of cholera was performed only in a limited number of patients (Table A2). The proportion of laboratory-confirmed cases ranged from 4.7% to 71.4%. The pooled detection rate of laboratory-confirmed cholera among suspected cases was 30.3% (95% confidence interval, 20.4–42.3; I2 = 88.4%) based on a random-effects meta-analysis of 15 studies (Table 1). The duration of these outbreaks ranged from 4 to 60 days (Table A1). The majority (75%; 15/20) of included studies was scored as a moderate risk of bias, 25% (5/20) as a low risk of bias, and no study was deemed to have a high risk of bias (Table A3). 3.2. Geographical Distribution of Cholera Outbreaks There were 565 outbreaks reported between 2011 and 2020 resulting in approximately 45,759 cholera cases and 263 (0.6%) deaths [22]. The median annual number of outbreaks reported during period 1 (2011 to 2015) was higher than period 2 (2016 to 2020). However, the difference was not statistically significant (66 versus 31; p = 0.058) (Table 2). In addition, the crude number of cases during 2011 to 2015 (n = 22,438; 49%) was lower compared with 2016 to 2020 (n = 23,321; 51%). These outbreaks occurred in 24 of the 36 SUTs at least once between 2011 to 2020 (Figure 1). The occurrence of outbreaks varied greatly across the years. The highest number of reported outbreaks was recorded in 2016 (114/565; 20%), whereas the year 2020 had strikingly fewer (0.9%; 5/565) reported outbreaks than the previous years (Figure 2, Figure 3 and Figure 4). Five states, namely Gujarat, Karnataka, Maharashtra, Punjab, and West Bengal, reported a recurrence of cholera outbreaks every year from 2011 to 2019 (Figure 2). On the other hand, Karnataka and Maharashtra reported cholera outbreaks every year throughout the last 10 years that we have reviewed. When comparing period 1 (2011 to 2015) with period 2 (2016 to 2020), Delhi and Rajasthan reported cholera outbreaks during period 2 (2016 to 2020) but there was not a single report during period 1 (2011 to 2015) (Supplementary Figure S2). Of the 565 outbreaks, Karnataka reported the most (102 outbreaks; 18%), followed by West Bengal (97 outbreaks; 17%), Maharashtra (54 outbreaks; 10%), Gujarat (53 outbreaks; 9%), Punjab (51 outbreaks; 9%), Assam (33 outbreaks; 6%), Madhya Pradesh (30 outbreaks; 5%), Tamil Nadu (25 outbreaks; 4%), and Odisha (19 outbreaks; 3%). The rest of the 27 SUTs reported 18% (101/565) of the outbreaks. The magnitude of outbreaks varied between the SUTs (Figure 5). Five states (Maharashtra, West Bengal, Punjab, Karnataka, and Madhya Pradesh) reported more than 3000 cases, which accounted for 72% of cases (33,089/45,759). The estimated incidence of cases during outbreaks remained low across the SUTs; the cumulative incidence was found to be the highest (1.2 cases per 100,000 persons) in the state of Chandigarh (Figure 6). Cholera outbreaks affected both rural and urban areas. However, 90% (507/565) of the outbreaks affected individuals living in rural areas (Table A1 and Figure 7), denoting that resuming progress towards cholera control in India needs increased efforts both in villages and urban slums. 3.3. Seasonality Cholera outbreaks occurred throughout the year (Figure 4 and Figure 8); however, the explosion of outbreaks (61%, 345/565; Figure 9) occurred during monsoon season (June to September) in most of the SUTs and the peak was observed in July. The state of Tamil Nadu is the only exception, where the peak was observed during the winter season, from December to January. 3.4. Transmission Routes and Source of Water Contamination From the IDSP surveillance data, the proportion of outbreaks in which the routes of transmission were identified was 62% (351/565), whereas 38% (214/565) had either unknown routes of transmission or were not reported. Among the 351 outbreaks, 319 (91%) transmission routes were the consumption of contaminated drinking water or exposure to unimproved water sources, and 32 (9%) were a lack of hygiene or inadequate sanitation. In more detail, transmission routes were (i) waterborne, including leaking water pipelines; (ii) inadequate sanitation or hygiene, including open defecation; (iii) waterborne with inadequate sanitation/hygiene; and (iv) foodborne/household spread or during social gatherings (Table 2; Figure 8, Figure 10 and Figure 11). As for changes over time in these transmission routes, a decreasing trend was observed in the number of outbreaks linked to leaking water pipelines (Figure 11). The median annual number of outbreaks due to leaking water pipelines from 2011 to 2015 was higher than from 2016 to 2020 (Table 2). However, there was no change in the median annual number of cholera outbreaks linked to other transmission routes, although the absolute number was generally higher from 2011 to 2015 (n = 347) compared with 2016 to 2020 (n = 218). In some settings (Table A2), cholera outbreaks were specifically linked to the use of contaminated sources such as pond water [29,40], wells [26,28,41], pipe water [45], handpumps [32], leaky water pipelines [30,33,36,39,43,44,45], consumption of untreated municipal water [30], and unboiled water [39]. The one seasonal activity that could be linked to the cholera outbreaks was the period of paddy cultivation during which the farmers practice open defecation and consume drinking water from open wells within paddy fields [26] and from nearby rivers [35]. The spread of V. cholerae in India also benefits from the back-and-forth flow of the population for labor or trade between rural areas and peri-urban slums. For instance, when there is no work on the farms, seasonal waged labor drives rural people towards urban areas as part-time workers and thereafter these rural people return to the villages for farming during rainy seasons [35]. Several outbreaks were attributed to fecal contamination of drinking water, i.e., water samples with coliforms above the maximum permissible number [26,30,32,37,38,40,41,43]. Some outbreaks particularly arose in zones prone to natural disasters (i.e., flooding or cyclone) or during humanitarian emergencies as a result of water contamination due to overflowing toilets, canals, and drains [31], interrupted water distribution, or shortages of drinking water supply leading to the usage of unimproved water sources [34,38,39]. Shortage of drinking water during the summer also compelled people to use contaminated water [28]. However, it is sometimes challenging to isolate V. cholerae O1 or O139 from water samples. For instance, we noted that in a subset of studies, water samples were negative for V. cholerae even though patients showed typical cholera-like symptoms [32,33,35,41]. 4. Discussion In this study, we sought to understand whether there is any decreasing trend among the key drivers of cholera outbreaks in India by comparing two periods: 2011–2015 and 2016–2020. Of the reviewed modes of transmission (Table 2), only outbreaks due to damaged water pipelines showed a decreasing trend. As compared to a previous report summarizing cholera outbreaks from 1997 to 2006 [8], our review provides good evidence to substantiate the fact that access to safe water and sanitation continues to be an issue in India. A similar situation was also observed in Bangladesh, where leakages in water pipelines were the most frequent route of cholera transmission [46]. Damaged water pipelines and sanitation had also been hypothesized to spread cholera in Ghana, Guinea, and Sierra Leone [47]. Francois Jeannot recently pointed out that access to safe water and sanitation declined in Haiti from 1990 to 2015, and this issue creates a fertile ground for the spread of cholera [48]. From 2011 to 2020, we identified 565 reported cholera outbreaks that occurred every year. This is different from the African continent where outbreaks are sporadic in most African countries, except in some countries such as the Democratic Republic of the Congo (DRC) and Mozambique [49]. In 2016, cholera outbreaks were at their highest (Figure 3 and Figure 11). The reasons for this finding are unknown. While other explanations are possible, one hypothesis is that this increasing trend could have been the result of more thorough reporting of outbreaks from the affected SUTs. Another hypothesis is that India experienced its warmest year since 1901 in 2016 (ideal conditions for copepods to thrive). As a result, the amount of rain that fell during the 2016 monsoon varied, with below-normal rainfall in June and August (87%), and above-normal rainfall in July (107%; accompanied by flooding and cyclones), thus affecting water demands, especially for rural communities [50]. In this study, the overall number of cholera cases was lower (n = 45,759) compared with reported cases from 1997 to 2006 (n = 222,038) [8], but the case fatality rate was slightly higher during the period 2011–2020. The case fatality rate was 0.6% during the period 2011–2020 in contrast to 0.4% in the period from 1997 to 2006. However, the case fatality rate found in this study is within the range (0.07–0.6) reported in the previous ten-year period (1997 to 2006) [8]. Differences in the case fatality rate could be due to the current relatively improved surveillance and reporting in recent times. The picture of cholera outbreaks has also changed in terms of geographical distribution. States with high morbidity were quite different in the recent decade (2011–2020) compared to the previous decade (1997 to 2006), except the state of West Bengal which consistently falls within the cholera-prone region. In this study, 72% of outbreak-related cases were reported from five states (Maharashtra, West Bengal, Punjab, Karnataka, and Madhya Pradesh). However, during the period 1997–2006, 91% of the cases were reported in four states (West Bengal, Odisha, Chhattisgarh, and Andaman and Nicobar Islands). This means that outbreaks are not limited only to the endemic states (such as West Bengal); thus, vigilance is needed even in states that do not report outbreaks. Outbreaks were reported from 24 of the 36 SUTs. Despite having similar socio-economic difficulties in 12 no-cholera outbreak reporting states, this is a very unlikely scenario. This seems to be due to a general stigma against cholera in Indian society. This precludes the authorities from disclosing cholera outbreaks as it portrays a tarnished image of the water distribution networks and sanitation systems of their states [11]. Alternative explanations for underreporting could be attributed to the limited laboratory diagnostic resources, especially in the peripheral healthcare centers, along with constraints in cholera surveillance resources [1,8]. Only 21 cholera outbreaks were found in the peer-reviewed literature; an obvious explanation for this relatively low number of publications pertaining to the perceived notion of the investigators that this kind of outbreak reporting lacks novelty. Therefore, it is less likely to get published in a peer-reviewed journal [8]. Another explanation is that we might have missed some articles as Google scholar and Indian medical journals were not searched; we consider this to be one of the limitations of this study. Despite the efforts of the Indian government to invest in efficient programmatic water sanitation and hygiene (e.g., Swachh Bharat Mission), there are numerous challenges to cope with, such as in-house contamination of drinking water [51], inadequate water infrastructures resulting in contamination of drinking water, and a shortage of water supply compelling people to use unimproved water sources. The fact that water was found to be the major vehicle for cholera outbreaks is not surprising because 90% (507/565) of reported outbreaks occurred in rural India, where inequity in clean water distribution is a significant problem. For example, in rural India, only 16% of people used improved piped water for drinking in 2015 [15,16]. In addition, the widespread fecal contamination of drinking water is still common in the country [26,30,32,37,38,40,41,43], in part due to higher rates of open defecation across the country and decaying sewage infrastructure. Fecal contamination of the surrounding environment by persons infected with V. cholerae is frequently observed in India. This can be seen in the state of Odisha, where tribal people practice open defecation [29]. Another set of people who might be responsible for the fecal contamination is daily workers—people who move day-by-day to earn their living such as street vendors, farmers, fishermen, and traders. These people may be compelled either to practice open defecation or defecate in unimproved toilets in heavily polluted environments [52]. Therefore, a hygienic sanitation campaign for these people might serve a bigger purpose. It is also increasingly evident that exposure to V. cholerae in the country has centered overwhelmingly around some workplaces such as tea gardens, urban slums, and colonies where marginalized people of society reside due to a lack of access to basic water and sanitation services [19]. This observation is quite similar in countries where V. cholerae thrives. For instance, a fishing community in Uganda practices open defecation leading to cholera outbreaks [53] or discharges pit latrines into open drainage channels during heavy rains, contaminating well water, which also results in cholera outbreaks [54]. The studies reviewed suggest that V. cholerae can be transmitted through close person-to-person contact and also via environmental water during outbreaks In India. In contrast, V. cholerae is rarely detected in environmental water bodies of African countries (except some countries such as Mozambique). The principal mode of cholera spread was person-to-person contact in most African countries such as Uganda and Cameroon [49]. We found that contact with a patient suffering from cholera or an asymptomatic human carrier increased the risk of illness [37,40,55,56,57,58]. This may occur via fomites, food, or water (e.g., stored in-house water) contaminated with V. cholerae. Someone who touched infected fomites with V. cholerae unknowingly became a carrier, and in the absence of handwashing with soap, this carrier might, in turn, contaminate edibles or infect the person through a fecal–oral pathway. For instance, in-house fecal contamination of stored water represents a major hygiene problem in India. This issue was highlighted in one study where they found that 7% of stored water samples contained V. cholerae in the urban slums of Kolkata and 58% of samples had fecal coliforms higher than permissible limits [51]. This reminds us that we should not underestimate the basic health-promoting behavior of frequent handwashing with soap, especially in the context of India, due to two cultural habits. One of them is the habit of anal cleansing with water after defecation using hands and another one is eating with bare hands as socio-cultural norms. The lack of handwashing after anal cleansing followed by food consumption using those hands establishes an easy route for coliform intake. Households with limited access to handwashing resources (soap and running water) would not be able to often wash their hands and handwashing will less likely to be a priority and thus, the awareness about handwashing would be meaningless. Therefore, we need to develop and maintain hand-washing facilities alongside providing logistics to support hand-washing. Even the ample availability of handwashing facilities will neither automatically translate into their higher usage (high uptake) nor into effective health benefits because it requires substantial behavioral changes that might be difficult to maintain over time. Thus, we stress targeting educational efforts that would probably give desirable outcomes along with social mobilization, support for behavioral change and counselling as an alternative intervention strategy to enhance compliance in order to reduce exposure to V. cholerae. During cholera outbreaks, cooking stations, areas in close proximity to the patient’s bed, and toilet floors were found to be the most contaminated surfaces in a household [59]. The sanitization of household surfaces and drinking water with chlorine-based disinfectant not only reduces cholera transmission but can also provide room for hygiene promotion. Therefore, it would be an ideal tool for curbing the burden of cholera. However, previous attempts to use household sprays to control cholera outbreaks did not warrant whether the procedure was effective because often this is not conducted in a timely manner, i.e., when V. cholerae had already been transmitted to other healthy household members by the sick person. It should be borne in mind that there are drawbacks associated with household spraying such as stigma, and household disinfection by a response team might increase hesitation among people to report cholera cases. Hence, new research is needed to yield sufficient evidence to support the use of household spraying during cholera outbreaks. Countries such as Thailand [60] and Singapore [61] have also experienced contamination of food as the mode of cholera transmission, as with India. The consumption of contaminated food supplies remains a prominent driver of cholera outbreaks across SUTs, demonstrating that food-related transmission plays a non-negligible role in the spread of V. cholerae and we have to increasingly recognize the need to tackle this issue in order to ensure successful control of outbreaks. However, food-related cholera outbreaks have been under-explored in India with only very few published studies, which denotes a critical research gap. Any food contaminated with V. cholerae can spread the disease. In India, different foods had been incriminated in cholera outbreaks such as fermented rice, known as Pantha Bhat [40], milk products [56], and ice cream [62]. Some of these contaminated foods were from street vendors [40,62], indicating that food-related cholera outbreaks could still be a great public issue. Thus, the intervention methods targeting street food might be an effective method to prevent secondary transmission. Another critical factor is the presence of asymptomatic cholera carriers among the general population. These people, in spite of infection with V. cholerae, might remain asymptomatic but shed the bacteria in their feces and, therefore, are likely to sustain the transmission chain. This observation emphasizes the importance of targeting asymptomatic food handlers such as street food vendors by the investigators of outbreaks whenever food is suspected to be the cause of the outbreak. Especially, food handlers with diarrhea should be given advice on hygiene, and should not handle food that other people would eat. The observation of this review is consistent with the findings from a recent meta-analysis which reported that the consumption of street food was associated with a 5-fold increase in the odds of cholera [4]. These observations advocate for prevention efforts focused on tailored hygiene and cooking practices in people responsible for preparing food. In addition, advice must be given about the proper storage of cooked food and, if bound to keep food at ambient temperature because of poor resources, food must be heated before consumption. In some instances, it had been observed that uncontaminated food was unknowingly mixed with contaminated water due to a particular kind of food habit. This was illustrated in an outbreak triggered among villagers due to the consumption of fermented rice that was made using pond water that had neither been boiled nor chlorine treated. Even after villagers became ill, they said that the fermented rice tasted good only when pond water was used for its preparation [40], which further justifies the need to encourage behavioral changes as part of the prevention efforts. There are two relevant limitations in the interpretation of our findings. Firstly, the depiction of our conclusion is based on the cholera outbreak data provided by the IDSP surveillance system and peer-reviewed articles which most likely underestimate the number of cholera outbreaks that have occurred in India since 2011. One possible explanation for this probable underestimation is that many outbreaks were classified as of unknown etiology and recorded in IDSP as outbreaks of acute watery diarrhea [22] and some outbreaks could have been missed during the literature search. Secondly, there were differences in outbreak notifications over time periods or SUTs. This means that for any comparisons of trends, one needs to apply caution in interpreting the data of interferences that could influence the detection of outbreaks along with their reporting systems. Notably, the decreased number of cholera outbreaks reported in 2020 was likely due to constraints in surveillance because of the COVID-19 pandemic, and SUTs with viable IDSP infrastructure and diagnostic facilities were more likely to report more about cholera outbreaks as compared to other SUTs with rudimentary surveillance structures [9]. 5. Conclusions In conclusion, an analysis of reported cholera outbreaks in India reconfirms that cholera is indeed a disease associated with destitution which mostly affects the neglected population. Most of the outbreaks occurred in rural India, where only 16% of people used improved piped water for drinking and open defecation is a common practice. Surprisingly, outbreaks due to damaged water pipelines showed a decreasing trend when a comparison was made between the two time periods 2011–2015 and 2016–2020. Cholera outbreaks in India are likely to recur unless social and economic development (including higher education and better housing) improves dramatically along with the termination of apparently insurmountable behaviors such as doing the laundry in ponds, hygienic bathing in an environmental water source after defecation, open defecation, infrequent handwashing, and eating unhygienic street foods. The spread of V. cholerae during outbreaks should not be interrupted only through the intrusion of long-cycle transmission (mediated through the environment and water supply) but also by the interruption of short-cycle transmission of cholera mediated by unhygienic practices of households and food contamination. Previous studies investigating outbreaks in India have recommended equally important measures that can be applied to counter future outbreaks. These include targeted use of cholera vaccines, access to safe drinking water, chlorination of water sources, regular disinfection of tube wells and wells, filtering the water with a piece of silk cloth, supplying oral rehydration salts (ORS), antibiotics and bleaching powder, use of telemedicine, action research, adequate sanitation, promotion of good personal hygiene, education and awareness campaigns (e.g., regarding latrine sanitation), safe food handling, proper sewage disposal, construction of drainage water away from the water pipelines, and long-term disease surveillance. Acknowledgments We would like to thank Matthew James McLaughlin for his editing skills and Mansongi Biyela Carine for her assistance in searching for articles in the reference lists of related review articles. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19095738/s1, Supplementary Figure S1: Flow chart showing evidence search and selection of studies (Cholera outbreaks in India, 2011–2020); Supplementary Figure S2: Cholera outbreaks in selected state and union territories comparing 2011–2015 with 2016–2020. The states of Tamil Nadu and Haryana did not cholera outbreaks during 2016–2020. Click here for additional data file. Author Contributions B.A.M. and S.-I.M.: study conception and its design; B.A.M. and K.K.: data collection, analysis and interpretation; B.A.M.: wrote the first draft of the manuscript; K.K.: Commented on an early version of the manuscript; K.K., A.D., A.O., K.O. and S.-I.M.: revised the manuscript for important academic content. S.-I.M.: supervised this work. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement No applicable. Informed Consent Statement No applicable. Data Availability Statement All relevant data are within the manuscript and its supporting information files. Conflicts of Interest The authors declare no conflict of interest. Appendix A ijerph-19-05738-t0A1_Table A1 Table A1 Characteristics of included studies. Outbreak Number References Location Urban/Rural Area Study Design/Type Study Period Age (Year)/Descriptor Outbreak Duration (Days) Population at Risk Number of Cholera Cases Attack Rate (Case/100 Person) Case Fatality Ratio (Number of Death) Occurrence Month 1 Dutta, 2021 [26] Ghughri, Madhya Pradesh, Central Rural Cross-sectional 2016 27 (1–76) 30 101,115 628 0.6 2 (14/628) August 2 Jain, 2021 [27] Shahpur, Haryana, Northern Rural Cross-sectional 2019 18 (1–65) 29 2602 196 8 1 (2/196) September 3 Kale, 2020 [28] Yavatmal, Maharashtra, Western Rural Cross-sectional 2018 All - - - - - March–July 4 and 5 Nayak, 2020 [29] Odisha, Eastern Rural Cross-sectional 2018 >5 4 1387 55 4.0 0 August 2019 >5 5 500 73 14.6 1.4 April 6 Singh, 2020 [30] Bhadola, Delhi, Northern Urban Case-control 2018 Median = 14.5 56–59 7280 129 1.8 0 April-May 7 Mukhopadhyay, 2019 [31] Kolkata and vicinity, West Bengal, Eastern Urban Cross-sectional 2015 Median = 26 15 - - - 1 death August 8 Goswami, 2019 [32] Wardha, Maharashta, Western Urban Cross-sectional 2018 3–65 9 104 28 27 0 July 9 Gopalkrishna, 2019 [33] Aurangabad, Maharashta, Western Urban Cross-sectional 2017 >14 (90%) 12 16,000 7447 47 - November 10 Pal, 2019 [34] Odisha, Eastern Rural Cross-sectional 2018 All - - - - 0 May 11 Pal, 2017 [35] Narla, Kalahandi, Odisha, Eastern Urban Cross-sectional 2014 >20 60 46,236 321 0.7 0.9 July–September 12 Uthappa, 2015 [36] Medipally, Telangana, Southern Rural Case-control 2013 All 9 – 138 11.5 0.7(1 death) November 13 Bhattacharya, 2015 [37] Somanakoppa, Bagalkot, Karnataka, Southern Rural Cross-sectional 2013 - 12 – 49 3.5 – August 14 Allam, 2015 [38] Medak, Andhra Pradesh, Southern Rural Cross-sectional 2013 All (0–74) 30 281 3.3 1.4 (3 deaths) August 15 Fredrick, 2015 [39] Pondicherry, Puducherry, Southern Urban Case-control 2012 All 13 8367 921 11 0.1 (1 death) January 16 Biswas, 2014 [40] Haibatpur, West Bengal, Eastern Rural Cross-sectional 2012 33 (5 to 80) 14 780 41 5 0 June 17 and 18 Dey, 2014 [41] Talikoti, Bijapur, Karnata, Southern Semi-rural Cross-sectional 2012 All 20 26,205 101 0.4 0 July–August Harnal, Bijapur, Karnata, Southern Rural Cross-sectional 2012 All 7 960 200 21 0 July–August 19 Kumar, 2014 * [42] Kalamb and Yavatmal, Maharashtra, Western Urban Cross-sectional 2012 - - - - - 4.5 May 19 Kumar, 2014 * [43] Kalamb and Yavatmal, Maharashtra, Western Urban Cross-sectional 2012 - - - - - - May 20 Puri, 2014 [44] Vikas Nagar, Chandigarh, Northern Urban Cross-sectional 2012 All 14 15,000 1875 15 (4 deaths) July 21 Mahanta, 2013 [45] Bagjan, Sivasagar, Assam, North-eastern Rural Cross-sectional 2012 41 (3–70) - 2503 120 4.8 0.83 (1 death) May *: These two studies described the same outbreak. ijerph-19-05738-t0A2_Table A2 Table A2 Sources of outbreaks. Study Risk Factors Assessed Men (%) Women (%) Population Cholera Definition Serogroup Serotype/Biotype Transmission Route/Suspected Exposure Number Examined Number of Infected Individuals Prevalence (95% CI) Comment Dutta, 2021 [26] Water 39 61 Community Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water 34 11 32 More women were affected. Jain, 2021 [27] Water, Environment 46 54 Community Clinical; Culture-confirmed - - Contaminated drinking water 18 4 22 Attack rates were highest in the 11–20 years group Kale, 2020 [28] None - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water 711 109 15 Males and women were equally affected Nayak, 2021 [29] Water, Hygiene - - - Clinical; Culture-confirmed Haitian variant of VC O1 Ogawa biotype El Tor Pond water used to cook foods and clean utensils at a local festival and marriage ceremony 65 27 42 (30 to 54) Children < 5 were not affected. More women were affected Singh, 2020 [30] Water, Hygiene, Knowledge on diarrhea transmission 48 52 - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Drinking untreated municipal water 129 6 5 (2 to 10) - Mukhopadhyay, 2019 [31] Habitation 56 44 Hospital-based surveillance Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor and Inaba Living near water channel and central lake channel. Contamination of drinking water sources due to overflowing of canals and drains during heavy rains 204 63 31 (25 to 38) Age range: 5 months to 99 years. No difference between men and women Goswami, 2019 [32] Habitation location, Water - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Hand pump; drinking water 28 2 7 (2 to 23) Most cases were children (0–10); More males were affected Gopalkrishna, 2019 [33] Water - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Fecal contamination of the river water and leakage in the pipeline 46 6 13 (6 to 26) - Pal, 2019 [34] Water - - - Clinical; Culture-confirmed VC O139 - Heavy rain contaminated muddy water supply 20 15 75 (53 to 89) - Pal, 2017 [35] Water - - - - - Ogawa biotype, ctxB7 variant of Haitian VC Contaminated drinking water source, unhygienic conditions in the house, unsafe disposal of fecal materials, cleaning of excrement-contaminated clothes in nearby water reservoirs, visiting choleric patients 17 11 65 (41 to 83) Prevalence high in children < 1 year Allam, 2015 [38] Water, Hygiene - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water 10 1 - - Bhattacharya, 2015 [37] Water, hygiene - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water 6 4 - - Uthappa, 2015 [36] Water, household size, hygiene, socio-demographics 53 47 - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water source - 138 - Prevalence high in children ≤ 5 year Fredrick, 2015 [39] Water, Hygiene 47 53 - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water 16 9 - - Biswas, 2014 [40] Water, hygiene 69 31 - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water source - 41 - - Dey, 2014 [41] Water, Hygiene - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water 7 5 - All age-groups were affected Kumar, 2014 [43] Water, Environment - - Hospital - VC O1 Ogawa biotype El Tor Contaminated drinking water source - 20 - Leakage in water pipes mixing water with drainage Puri, 2014 [44] Water, Environment, Food, Mass gathering 53 47 Hospital and community Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water source - 8 - - Mahanta, 2013 [45] Demographics, Socioeconomic, Environmental - - - Clinical; Culture-confirmed VC O1 Ogawa biotype El Tor Contaminated drinking water source 13 3 23 - VC = vibrio cholerae; NR = not reported. ijerph-19-05738-t0A3_Table A3 Table A3 Study quality. Study Aim Clearly Stated Setting Provided Study Design or Sampling Method Explained Case Definition of Diarrhea or Cholera Clearly Mentioned Statistical or Analysis Methods Reported Risk Factors for Outbreak (or Causes of Outbreaks) Investigated Case Fatality Ratio Reported Performance of Confirmatory Test (Culture or PCR) Limitations or Potential Confounders Discussed Score Risk of Bias Dutta, 2021 [26] Yes Yes No Yes Unclear Yes Yes Yes Yes 7 Moderate Jain, 2021 [27] Yes Yes No Yes Unclear Yes Yes Yes Yes 7 Moderate Kale, 2020 [28] Yes Yes No No No Yes Yes Yes No 5 Moderate Nayak, 2020 [29] Yes Yes Yes Yes No Yes Yes Yes No 7 Moderate Singh, 2020 [30] Yes Yes Yes Yes No Yes Yes Yes Yes 9 Low Mukhopadhyay, 2019 [31] Yes Yes Yes Yes No Yes Yes Unclear No 6 Moderate Goswami, 2019 [32] Yes Yes Yes Yes No Yes Yes Yes No 7 Moderate Gopalkrishna, 2019 [33] Yes Yes No No No Yes Yes Yes No 5 Moderate Pal, 2019 [34] Yes Yes No No No Yes Yes Yes No 5 Moderate Pal, 2017 [35] Yes Yes Yes Yes No Yes Yes Unclear No 6 Moderate Uthappa, 2015 [36] Yes Yes Yes Yes Unclear Yes Yes Yes Yes 9 Low Bhattacharya, 2015 [37] Yes Yes Yes No No Yes No Yes Unclear 5 Moderate Allam, 2015 [38] Yes Yes No Yes No Yes Yes Yes Yes 6 Moderate Fredrick, 2015 [39] Yes Yes Yes Yes No Yes Yes Yes Yes 9 Low Biswas, 2014 [40] Yes Yes Yes Yes Unclear Yes Yes Yes No 8 Low Dey, 2014 [41] Yes Yes Yes Yes No Yes Yes Yes No 7 Moderate Kumar, 2014 [42] Yes Yes Yes Unclear No Yes No Yes Unclear 5 Moderate Kumar, 2014 [43] Yes Yes Yes No No No Yes Yes Unclear 5 Moderate Puri, 2014 [44] Yes Yes Yes Yes No Yes Yes Yes Yes 8 Low Mahanta, 2013 [45] Yes Yes Yes Yes No Yes Yes Yes No 7 Moderate A score “1” was given for each reported item. Scores were rated as having a low risk of bias (score of 8–9), moderate risk of bias (score of 5–7) or high risk of bias (score 4 or below). PCR: polymerase chain reaction. Figure 1 Cholera outbreaks (n = 565) by state and union territories, India, 2011–2020. Figure 2 Cholera outbreaks (n = 565) by year and state, India, 2011–2020. Figure 3 Cholera outbreaks (n = 565) by year, India, 2011–2020. Figure 4 Cholera outbreaks (n = 565) by year and season, India, 2011–2020. Winter = December to January; Pre-monsoon = March to May; Monsoon = June to September; and Post-monsoon = October to November. Figure 5 Reported cholera cases during outbreaks by state, India, 2011–2020. Figure 6 Rate of reported cholera outbreaks per 100,000 persons, India, 2011–2020. Figure 7 Cholera outbreaks (n = 565) by type of setting (rural vs. urban), India, 2011–2020. DNHDD = Dadra and Nagar Haveli and Daman and Diu. Figure 8 Number of cholera outbreaks (n = 565) by month and transmission routes, India, 2011–2020. Figure 9 Cholera outbreaks (n = 565) in different seasons, India, 2011 to 2020. Winter = December to January; Pre-monsoon = March to May; Monsoon = June to September; and Post-monsoon = October to November. Figure 10 Number of cholera outbreaks (n = 565) by state and transmission routes, India, 2011–2020. Multiple modes of transmission were involved in some outbreaks. Figure 11 Number of cholera outbreaks (n = 565) by transmission routes and year, India, 2011–2020. ijerph-19-05738-t001_Table 1 Table 1 Pooled prevalence of laboratory-confirmed cholera during outbreaks (India, 2011–2020). Study Number of Stool Samples Examined Number of Positive Samples Detection Rate, % (95% CI) Weight (%) Dutta, 2021 [26] 34 11 32 (19 to 50) 7.7 Jain, 2021 [27] 18 4 22 (9 to 47) 6.4 Kale, 2020 [28] 711 109 15 (13 to 18) 8.9 Nayak, 2020 [29] 65 27 42 (30 to 54) 8.4 Singh, 2020 [30] 129 6 5 (2 to 10) 7.4 Mukhopadhyay, 2019 [31] 204 63 31 (25 to 38) 8.8 Goswami, 2019 [32] 28 2 7 (2 to 25) 5.4 Gopalkrishna, 2019 [33] 46 6 13 (6 to 26) 7.3 Pal, 2019 [34] 20 15 75 (53 to 89) 6.8 Pal, 2017 [35] 17 11 65 (40 to 83) 6.8 Allam, 2015 [38] 10 1 10 (1 to 47) 3.8 Bhattacharya, 2015 [37] 6 4 67 (3 to 92) 4.7 Fredrick, 2015 [39] 16 9 56 (32 to 78) 6.9 Dey, 2014 [41] 7 5 71 (33 to 93) 4.8 Mahanta, 2013 [45] 13 3 23 (8 to 52) 5.9 Total (random effects) 1324 276 32 (23 to 44) 100.0 Definition of abbreviation: CI = confidence interval. ijerph-19-05738-t002_Table 2 Table 2 Number of cholera outbreaks during the period 2011–2015 compared with 2016–2020. Transmission Routes Number of Outbreaks during 2011–2015, n (%) Number of Outbreaks during 2016–2020, n (%) Median (Min-Max) Annual Outbreaks Number during 2011–2015 versus 2016–2020 p Value * Unimproved water sources/Non-potable water/Contaminated drinking water 127 (36.6) 114 (52.3) 21 (14–43) vs. 12 (3–75) 0.058 Water pipeline leaks 67 (19.3) 11 (5.0) 8 (6–26) vs. 4 (2–5) 0.028 ** Open defecation 14 (4.0) 1 (0.5) 4 (1–6) vs. 1 (1–1) 0.361 Poor sanitation 6 (1.7) 1 (0.5) 1 (1–4) vs. 1 (1–1) 0.505 Waterborne combined with inadequate sanitation and poor hygiene 2 (0.6) 2 (0.9) 1 (1–1) vs. 1 (1–1) - Foodborne/gathering/close contact 5 (1.4) 1 (0.5) 2 (1–2) vs. 1 (1–1) 0.248 Not reported or unknown 126 (36.3) 88 (40.4) 16 (11–45) vs. 16 (2–37) 1.000 Total 347 (100.0) 218 (100) 66 (40–98) vs. 31 (5–114) 0.058 * p values were calculated using Fisher’s exact test. They are comparing the median annual outbreaks number during 2011–2015 versus 2016–2020. ** p value < 0.05. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Ali M. Gupta S.S. Arora N. Khasnobis P. Venkatesh S. Sur D. Nair G.B. Sack D.A. Ganguly N.K. Identification of burden hotspots and risk factors for cholera in India: An observational study PLoS ONE 2017 12 e0183100 10.1371/journal.pone.0183100 28837645 2. Lipp E.K. Huq A. Colwell R.R. Effects of Global Climate on Infectious Disease: The Cholera Model Clin. Microbiol. Rev. 2002 15 757 770 10.1128/CMR.15.4.757-770.2002 12364378 3. Glass R.I. Svennerholm A.-M. Stoll B.J. Khan M.R. Hossain K.M.B. Hug M.I. Holmgren J. Protection against Cholera in Breast-Fed Children by Antibodies in Breast Milk N. Engl. J. Med. 1983 308 1389 1392 10.1056/NEJM198306093082304 6843632 4. Richterman A. Sainvilien D.R. Eberly L. Ivers L.C. Individual and Household Risk Factors for Symptomatic Cholera Infection: A Systematic Review and Meta-analysis J. Infect. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095414 ijerph-19-05414 Article The Linkage between Ethical Leadership, Well-Being, Work Engagement, and Innovative Work Behavior: The Empirical Evidence from the Higher Education Sector of China Jia Kan 1 Zhu Tianlun 1 Zhang Weiwei 2* https://orcid.org/0000-0002-3199-8059 Rasool Samma Faiz 1* Asghar Ali 3 https://orcid.org/0000-0001-5793-7847 Chin Tachia 1 Tchounwou Paul B. Academic Editor 1 School of Management, Zhejiang University of Technology, Hangzhou 310023, China; jiakan@zjut.edu.cn (K.J.); zhutianlun8888@163.com (T.Z.); tachiachin@zjut.edu.cn (T.C.) 2 School of Cultural Creativity and Management, Communication University of Zhejiang, Hangzhou 310019, China 3 Dr. Hassan Murad School of Management, University of Management and Technology, Lahore 54770, Pakistan; ali.asghar@umt.edu.pk * Correspondence: zww@cuz.edu.cn (W.Z.); samma.shu@hotmail.com (S.F.R.) 29 4 2022 5 2022 19 9 541412 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In this study, we investigate the relationship between ethical leadership (EL), work engagement (WE), well-being, and innovative work behavior (IWB). The significance of these variables has increased in the current era when the influence of technology is exponentially increasing in the education sector. We investigate the role of ethical leadership in determining innovative work behavior. Moreover, we investigate the moderating effect of WB in the relationship between EL and WE. We also examine the mediating impact of WE in the relationship between EL and IWB. We used a questionnaire survey approach to collect data. The target population of this study was the academic personnel, i.e., senior professors, lecturers, and supporting staff associated with the higher education sector located in Zhejiang Province, China. Data were collected in two phases. In the first phase, we sent 300 research questionnaires and received 251 responses. In the second phase, after a three-month interval, we sent 200 questionnaires and received 162 responses. However, over the two phases, we collected a total of 413 questionnaires; 43 were discarded. Therefore, for analysis, we used 370 questionnaires. The data were analyzed using the structural equation modeling through SmartPLS 3.2.2. First, in the direct relationship, results confirm that EL positively influences the IWB. Secondly, WB has a positive and moderating relationship between EL and IWB. Thirdly, we address the relationship between EL and WE. The outcome indicates that there is a positive and significant relationship. Fourth, the results of this study indicate that there is positive and significant relationship between WE and IWB. Finally, the outcomes imply that WE positively mediates between EL and IWB. Ethical leadership and well-being are important for innovative work behavior that supports managers in introducing a supportive workplace environment that promotes good interpersonal relationships with subordinates. Therefore, a good interpersonal relationship between managers and subordinates enhances the work quality. So, ethical leaders provide a supportive work environment to all subordinates regarding their work. ethical leadership well-being innovative work behavior work engagement higher education Organizational Capability and Leadership Improvement Project of the Changchun New Oriental Education Training SchoolSKY-HX-202001933 Organization Leadership Improvement Project of the Chinese Institute of Business AdministrationSKY-HX-20210188 Binjiang-the Road to A Better Life ProjectSKY-HX-20210158 Project of Employment Environment Satisfaction, Dilemma and Demand of Taiwan Compatriots in Mainland ChinaSKY-ZX-20210049 This paper is supported by the Organizational Capability and Leadership Improvement Project of the Changchun New Oriental Education Training School (SKY-HX-202001933), Organization Leadership Improvement Project of the Chinese Institute of Business Administration (SKY-HX-20210188), Binjiang-the Road to A Better Life Project (SKY-HX-20210158) and Project of Employment Environment Satisfaction, Dilemma and Demand of Taiwan Compatriots in Mainland China (SKY-ZX-20210049). ==== Body pmc1. Introduction The contemporary political and economic realities require higher education to contribute to national competitiveness by focusing on the employability of graduates above its traditional role of creating informed citizens and improving the well-being of individuals and society at large [1]. According to the United Nation’s sustainable development goals (SDGs), the higher education sector plays a key role in national development [2,3]. In China, the higher education policy has undergone major shifts to reach its current form, where it is highly internationalized [4]. The government of China has competently updated the systems of the higher education sector through the strategic adoption of neoliberal methods for education market formation in the country. In this regard, employee well-being in higher education institutions plays a significant role in enhancing educational institutions’ performance [5]. Such institutions are the hallmark of the Chinese education regime as the ethics of faculty running them include key traditional elements at their core, including loyalty to education, love of life, and selfless dedication [6]. Ethical leadership, specifically in educational institutions, is considered to be driven by values, including the firm belief in the dignity and rights of others instead of personalities or politics [7]. Ethical leadership enhances the education institution’s performance [8]. Likewise, a study conducted by Schwepker Jr and Dimitriou [9] reports that ethical leadership reduces job stress and improves performance quality in the hospitality industry. Ethical leadership also plays a significant role in keeping teams together in a workplace environment as it moderates the effects on team management and work efficiency [10]. Likewise, ethical leadership also plays a moderating role between psychological ownership of knowledge and knowledge hiding [11]. In their recent study, Marquardt, Casper [12] report that the employees perceive their leaders as less ethical when they emphasize avoiding failure while downplaying the importance of personal learning and development. Therefore, managers must foster an ethical workplace by promoting interpersonal and informational justice [13]. The positive side of leaders increases workplace engagement among the employees associated with the public and services sector organizations [14]. Workplace engagement, as defined by Schaufeli, Salanova [15], is “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption”. The individuals engaged in their work transform the energy into creativity and organizational commitment [16]. Workplace engagement level is high among the employees in their initial days of joining an organization, whereas it stabilizes over time [17]. Workplace engagement has multiple predictors. For example, higher resilience, job satisfaction, and lower morale distress result in increased workplace engagement [18]. The contributors to work engagement include physical, mental, psychological, and cultural engagement with the organization, whereas its consequences include job satisfaction, innovation, and leadership development [19]. Employee well-being refers to mental and emotional health, positive attitude, and job satisfaction in an organizational setting [20]. Although it is not a new concern, it is considered a broader issue in contemporary organizations. Employee well-being generally refers to improving their health in terms of work-related safety [21]. The concept of employees’ well-being is not limited to health and safety, it also incorporates physical activity, job satisfaction, and personal development [22]. According to ILO [23], “the purpose of measuring employee well-being is to complement occupational and safety health measures to make sure workers are safe, healthy, satisfied and engaged at work”. Employee well-being is predicted by authentic leadership, which fosters workplace engagement and innovative work behavior in the employees [24]. Employees exhibit complex behavior to generating, introducing, and applying innovative ideas at their workplace. Innovative workplace behavior gives competitive advantage and sustainability to the respective organization [25]. Previous studies show that innovative workplace behavior is negatively associated with workplace violence [26], workplace ostracism, and workplace incivility [27]. Work engagement and supervisor support significantly predict innovative workplace behavior [28]. Innovative workplace behavior significantly maintains university services’ sustainability and increases industries’ competitiveness [29]. The literature shows that innovative workplace behavior among university faculty members can be cultivated through authentic leadership practices and psychological capital management [30]. The aim of this study is to investigate the relationship between ethical leadership, WE, well-being, and IWB. The significance of these variables has increased in the current era when the influence of technology is exponentially increasing in every field of life. In addition, the COVID-19 pandemic has cast lasting effects on everyday life, especially on work-life [31,32]. The time calls for an exploration of the effects of ethical leadership on work engagement and, eventually, on innovative work behavior [33]. Despite the increased emphasis on ethical leadership, WE, well-being, and IWB, studies focusing on these variables were rarely found. At the same time, the extant literature discusses the importance of ethical leadership, well-being, work engagement, and innovative work behavior [34,35,36]. Most of the researchers have focused on cases where employee well-being and innovative work behavior is exercised in developed countries such as Europe, USA, and Singapore. However, research into the collateral relationship between ethical leadership, well-being, work engagement, and innovative work behavior is still in its infancy and has not provided meaningful results. Only a few studies have examined the direct relationship between ethical leadership and work engagement or between well-being and innovative work behavior [37,38]. However, the relationship between ethical leadership, well-being, work engagement, and innovative work behavior is still unexplored. Especially considering well-being as an intervening construct and work engagement as a moderating variable still needs to be researched. Based on the above-mentioned potential research impetus, we develop a conceptual framework (Figure 1) of this study and address the following research questions (RQ):RQ1. Does ethical leadership influence work engagement and innovative work behavior? RQ2. Does well-being moderate between ethical leadership and work engagement? RQ3. Does work engagement intervene between ethical leadership and innovative work behavior? 2. Theory and Hypotheses Development 2.1. Ethical Leadership and Innovative Work Behavior Several studies explored the phenomena of EL in the context of IWB [39,40,41,42]. Ahmad, Gao [39] point out that EL effectively enhances IWB in workers with less proactive personalities. They also found that psychological safety and interaction between the leaders and followers mediate this relationship. Ullah, Mirza [40] observed that ethical leadership positively influences innovative work behavior, while human capital and social capital mediate their relationship. Haque and Yamoah [42] conducted a comparative study between Canada and Pakistan and suggested that supportive leaders encourage subordinates that affect their creative behavior in the workplace. They found that ethical leadership reduces occupational stress and increases innovative work behavior. They report that the effect of EL on IWB is higher among Pakistani workers as compared to their Canadian counterparts. Ullah, Mirza [43] found that ethical leadership fosters innovative work behavior in employees, while social capital mediates this relationship. Zaman, Wang [44] found that leadership indirectly affects innovative work behavior through psychological capital. So, we proposed the first hypothesis as follows: H1:  Ethical leadership positively impacts innovative work behavior. 2.2. Ethical Leadership and Work Engagement The relationship between ethical leadership and work engagement seems to be getting the attention of researchers in the current era [45,46,47]. Alam, Fozia [48] found that ethical leadership has a positive impact on organizational commitment, with the mediating role of work engagement. Fuller [45] noted that there is a positive relationship between ethical leadership and work engagement. Work engagement has a strong relationship with compassion satisfaction as the teachers exhibit their engagement through care towards their students. Although ethical leadership promotes work engagement, it is not mediated by compassion satisfaction [47]. Ethical leadership exerts positive effects on work engagement and workaholism. However, these relations are not moderated by self-efficacy [46]. The relationship between EL and WE, well-being and firm performance is mediated by ethical culture in Pakistan and Italy. In response to ethical leadership, Pakistani employees showed higher work engagement, while Italian employees exhibited higher levels of well-being [49]. The quality of leadership plays a significant role in the significant connection between EL, well-being, and WE [50]. Based on the above-discussed literature on ethical leadership and work engagement, the present study proposes the following hypothesis for empirical testing: H2:  Ethical leadership positively impacts work engagement. 2.3. Moderating Effect of Well-Being Several authors have studied ethical leadership as an antecedent of the well-being of employees [51]. Ethical leadership is positively associated with trust in management and the psychological well-being of the employees [52]. The relationship between ethical leadership and employee well-being is completely mediated by organizational citizenship anxiety [53]. Employee well-being strongly occurs as a reaction to unethical behavior rather than a consequence of ethical behavior [54]. Teimouri, Hosseini [55] have also found a positive and significant relationship between ethical leadership and employee well-being [56]. Therefore, providing significant evidence between the connection of EL and well-being, the literature also indicates the relationship between well-being and workplace engagement. Work engagement partially mediates the relationship between psychological well-being and employees’ job performance [57]. Zeng, Chen [58] found a positive relationship between well-being and work engagement among secondary school teachers in central China. Xu, Xie [59] also reported a positive relationship between affective well-being and work engagement of the employees. Likewise, Sarwar, Ishaq [49] also argue that EL has a significant relationship with well-being and work engagement. Considering the review of the contemporary relevant literature, the present study develops the following hypothesis for further testing and validation: H3:  Well-being positively moderates between ethical leadership and work engagement. 2.4. Work Engagement and Innovative Work Behavior The meta-analysis of contemporary literature shows that WE has a medium to large correlation effect on IWB [60]. The innovative work behavior of employees is affected by their regulatory focus. Work engagement improves employees’ innovative work behavior by having either a promotion or prevention regulatory focus [61]. Besides being a predicator of innovative work behavior, the work engagement of employees also indicates a learning organization [62]. The WE of the workers brings creativity and novelty in the routine work [63]. Afsar, Al-Ghazali [64] found that WE and interpersonal trust mediate the effect of cultural intelligence on IWB. Further, Montani, Vandenberghe [65] discovered that WE plays a mediating role between workload and innovative work behavior, provided that the workload is moderate. They also found that WE mediated between workload and IWB. It was found that WE and IWB are positively related, provided necessary resources are available for them [66]. Further, the studies of Van Zyl, Van Oort [67] and [68,68] also confer the relationship between WE and IWB. Based on the review of literature, the present study poses the following hypothesis for testing: H4:  Work engagement positively impacts innovative work behavior. 2.5. Mediating Effects of Work Engagement The contemporary literature shows an increasing number of studies addressing the mediating role of WE on IWB with reference to several other variables [38]. The study conducted by Asif, Qing [36] reports a positive relationship between ethical leadership, WE, and IWB. Work engagement mediates the relationship between leadership and job performance [69]. Li, Sajjad [68] argue that work engagement plays a mediating role in the relationship between leadership and innovative work behavior. Figure 1 presents the comprehensive conceptual framework of this study. Thus, we propose the fifth and last hypothesis as follows: H5:  Work engagement positively mediates between ethical leadership and innovative work behavior. 3. Research Methods 3.1. Research Approach We used a quantitative research approach to collect and analyze the data [13]. The data were collected through a questionnaire survey. There are two main reasons to apply the questionnaire survey technique [27]. First, researchers can collect maximum data in a minimum time using this approach. Second, this approach is not expensive as compared to other research approaches [44]. 3.2. Instrument Designing The instrument (questionnaire) was drafted in the English language, but later on, translation was conducted into the Chinese language. Then, the authors conducted an experimental study (pilot study) of the instrument. The respondents of this experimental study were ten doctorate students and ten academic professors. Moreover, the respondents were familiar with the research topic and both languages. These respondents suggested some changes to the instrument. Therefore, the instrument was revised as per the feedback of the respondents. After finalizing the research instrument, they were disseminated among the target population. 3.3. Data Collection and Sampling The target population of this study was academic personnel, i.e., senior professors, lecturers, and supporting staff associated with the higher education sector located in Zhejiang Province, China. The ethics committee reviewed the research. As regards research ethics, the authors informed respondents that their data would remain confidential and be used only for research purposes. The survey was conducted through WJX (https://www.wjx.cn), which is a Chinese-based website that is well-known in China for data collection. WJX is a third-party website that helps to gather data from the given sample. A multilevel approach was adopted to collect the data to avoid common method bias. The questionnaire comprised 19 items related to ethical leadership, well-being, employee, innovative work behavior, and work engagement. Data were collected in two phases. In the first phase, we were sent 300 research questionnaires and received 251 responses. In the second phase, after a three-month interval, we sent 200 questionnaires and received 162 responses. However, over the two phases, we collected a total of 413 questionnaires; 43 were discarded due to missing values. Therefore, for analysis, we used 370 questionnaires. 3.4. Variables and Measures We used ethical leadership as an independent variable and innovative work behavior as a dependent variable. Similarly, we used well-being as a moderating variable and employee engagement as a mediating variable. The items of these variables were adopted from the existing studies [26,27,36,70]. 3.4.1. Ethical Leadership The three items of ethical leadership were taken from Asif, Qing [36]. The sample items of ethical leadership were “My supervisor disciplines employees who violate ethical standards” and “My supervisor sets an example of how to do things the right way in terms of ethics”. According to Joseph F Hair, Ringle, and Sarstedt (2013), the acceptable standard value of Cronbach alpha value must be greater than 0.7. However, the alpha value of ethical leadership was 0.648, which is an acceptable standard. 3.4.2. Innovative Work Behavior The six items of innovative work behavior were adopted and modified from existing studies [26,27]. The innovative work behavior is represented by items such as: “I feel that my I am more efficient than my supervisor/co-worker/subordinate”, “During the past six months, my actual work creativity is increasing day by day”. However, the alpha value of the innovative work behavior was 0.803, which is an acceptable standard. 3.4.3. Well-Being The four items of well-being were adopted and modified from existing studies [70]. The well-being construct was represented by items such as “Do you feel that your boss is empathic and understanding about your work concerns”, “Do people at your work believe in the worth of the organization”. However, the outcome indicates that the alpha value of ethical leadership is 0.714, which is an acceptable standard. 3.4.4. Work Engagement We used seven items to measure work engagement. The scales used in this research were adapted from [70]. The sample items of work engagement were “I am committed to continuous quality improvement in my work”, “My supervisor positively motivates my performance at work”. The results confirmed that the alpha value of work engagement is 0.848, which indicates that all the items were reliable and valid. 3.5. Demographics The respondents’ demographics are given in Table 1. In terms of the gender of the participants, 42% of them were female, and 58% of them were male. The data gathered from private universities made up 44% of the total, and 56% were from public sector universities; 36% of respondents were senior professors, 38% were lecturers, and 26% were supporting staff. Similarly, in terms of the respondents’ education, 39% were doctorate degree holders, and 61% of respondents held postgraduate degrees. 4. Data Analysis 4.1. Reliability and Validity The data were analyzed using the structural equation modeling through SmartPLS 3.2.2. This method is useful in this study for three reasons; first, researchers [1,2] suggested that PLS-SEM is the best technique for exploring the theory and the relation of new variables. The findings of our research found the mediating effect of WE between EL and IWB. Similarly, we also found a moderating effect of well-being between EL and WE, which leads us to find also a mediating and moderating effect of WE between well-being and IWB. Second, CB-based PLS-SEM does not need a large amount of data to run structural question modeling [3]. Therefore, this research collected a moderate number of samples. Third, researchers suggest that PLS-SEM is useful for complex multivariate analysis [4]. This study has assessed the cause-effect, mediation analysis, and mediated moderation analysis for the set conceptual framework. A two-step analysis procedure helped in data analysis [5]. First of all, we assessed the reliability and validity of the constructs. The consistency of the constructs measured the item loading with their relevant factors. The item loadings were arranged from 0.605 to 0.848. Researchers [6] suggested that the minimum item loading should be 0.7. Hence, all indicators have shown their item loadings above the threshold of 0.7. Therefore, items were consistent with their relevant constructs. Cronbach alpha, rho alpha, composite reliability, and average variance extracted measured the consistency and reliability of the constructs. Researchers [7,8] suggested that the value of Cronbach alpha, rho alpha, and composite reliability should be above the threshold of 0.7. The Cronbach alpha values for all constructs were above the threshold of 0.7. The rho alpha and composite reliability values for all constructs were also above 0.7. Therefore, the variable under study was consistent and reliable. At the same time, the average variance extracted values above the threshold of 0.5, according to the researchers’ recommendation [9]. Hence, all constructs were reliable and valid, as given in Table 2. Researchers [10] have introduced a new procedure of Heterotrait-Monotrait ratio of correlations (HTMT) to measure discriminant validity. The HTMT values are used to measure discriminant validity, which is one of the essential parameters to assess model quality. Researchers [11] have suggested that HTMT values should be below 0.9. We observed that HTMT values for all variables were less than 0.9. Hence, all first-order factor analysis has shown a satisfactory level of HTMT ratios, as given in Table 3. 4.2. Multicollinearity Variance inflation factor (VIF) is another measure to assess multicollinearity issues among constructs. VIF values must be below the threshold of 5 [8,12]. All constructs have shown an inner VIF value below the threshold of 3, which shows that there was no multicollinearity issue. PLS-SEM uses Normed Fit Index (NFI) and Standardized Root Mean Square Residual (SRMR) indices to measure the fitness of the SEM model. Researchers [13] suggested that SRMR value must be below the threshold of 0.06, and the NFI value must be above the threshold of 0.8 [14]. The model has shown NFI = 0.911 and SRMR = 0.044, which shows an adequate fitness of the model, as given in Table 4. 4.3. Goodness of Fit The goodness of fit (GoF) shows the effectiveness of the model based on quantitative data [15]. The range of GoF is from 0 to 1 (0.36 = effective, 0.25 = average, below 0.1 is weak). The goodness of fit index shows the plausibility and parsimony of the model. The formula of GoF is “GoF = sqrt ((average AVE) ∗ (average R2))”. The GoF value of 0.51 shows that the model was parsimonious and plausible, as given in Table 5. The f-square value shows the effect of exogenous construct on endogenous constructs [16]. An effect size below 0.02 is considered weak, above 0.15 moderate and above 0.35 as substantial. The moderator (EL*WB) has shown a minimum level of effect size (f2 = 0.024) on WE, while well-being has also shown a substantial effect (f2 = 0.317) on WE. EL has also shown a moderate effect on IWB (f2 = 0.216) and a substantial effect on WE (f2 = 0.524). The WE has also shown a minimum effect level on IWB (f2 = 0.027), as shown in Table 6. Coefficient of determination R-square indicates change in the dependent construct due to the per unit change in the independent construct. It must be above a threshold of 0.1 [5,17]. Innovative Work Behavior has shown 33% and Work Engagement has shown 52% power of prediction in a SEM model, as given in Table 7. 4.4. Hypotheses Testing 4.4.1. Direct and Indirect Effects One researcher [18] suggests that mean values in PLS paths are the same as beta values in regression analysis. Beta measures the per unit change independent variable due to the independent variable. The value of beta was windows with the help of a significant value or probability. Another measure to endorse the beta value was T-test. We tested the hypothesis with the help of beta values and T-test as well as bootstrapping at subsample level 5000. Data showed that gender and program have no effect on the dependent variable of cognitive presence with a p-value greater than 0.05. The findings demonstrate that EL has a positive connection with IWB (M = 0.468, p < 0.001). So, the first hypothesis is accepted. Similarly, EL has a positive connection with WE (M = 0.530, p < 0.001). Hence, it leads us to not reject the hypothesis. The results also show that well-being has a positive and significant effect on WE (M = 0.411, p < 0.001). It leads us to not reject the hypothesis. Work engagement has also shown a positive and significant effect on IWB (M = 0.165, p < 0.001). It leads us to not reject the hypothesis. Work engagement has shown a mediation between EL and IWB (M = 0.087, p < 0.001). It leads us to accept the robust hypothesis as given in Table 8. Figure 2 also shows all possible direct relations among constructs. 4.4.2. Moderation and Mediated Moderation We tested the moderation and meditated moderation, and the findings demonstrate that well-being has a positive connection with WE (M = 0.411, p < 0.001). It leads us to measure the moderating effect of EL*WB on WE, which was found to be positive and significant (M = 0.078, p < 0.001). Hence, the hypothesis was accepted. The nature of the moderation is shown in Figure 3. The mediated moderated effect of WE between EL*WB and IWB was also found to be significant and positive (M = 0.013, p < 0.001). So. the hypothesis was not rejected. However, Figure 3 shows the moderating effect of well-being with the EL and WE. The moderation and mediated moderation detail are also given in Table 9. 5. Discussion In this study, we examine the role of ethical leadership in determining innovative work behavior. Moreover, we investigate the moderating effect of well-being in the relationship of ethical leadership and employee engagement. We also investigate the mediating role of WE in the connection between EL and IWB. First, we found that EL is positively associated with IWB, which supports the first hypothesis. Samma, Zhao [27] conducted a study of workplace violence and innovative work behavior. The results of their study indicate that supportive leaders motivate employees, which enhances the morale of employees and brings creativity to their work behavior. Secondly, we explore the connection of ethical leadership with work engagement, and the findings of our research demonstrate that ethical leadership boosts work engagement. Zhou, Rasool [26] conducted a study among the Chinese workers of SMEs, and the results of their study also show that supportive leaders motivate their followers. So, the followers follow the instruction of the leaders that connects them with their work, goals, mission, and organizational commitment. Third, we test the moderating effect of well-being in the connection between ethical leadership and work engagement. The findings confirmed that well-being positively moderates between ethical leadership and work engagement. The finding of our study is also supported by Iqbal, Qureshi [71]. They conducted a study among a Pakistani organization. Their study shows that ethical leaders work for the employees’ well-being and emotionally engage them with their tasks, converting them into creative workers. Therefore, it has been proven that this research supports our outcomes. Fourth, we suggest that the employees’ work engagement positively affects their creative work behavior. However, our results confirmed that WE has a positive impact on IWE. Some previous studies also support our findings. Rasool conducted a study on the relationship between the workplace environment and sustainable work performance. Their study points out that work engagement develops a bridge from regular work practices to creative work. Finally, in this study, we explore the mediating effect of work engagement on the relationship between ethical leadership and innovative work behavior. Wang, Rasool [72] conducted a study to test the relationship between despotic leadership and the success of the projects, and their results suggest that through work engagement, ethical leaders motivate the workers to feel passionate about their tasks, and loyal to the organization, and put flexible energy to bring the creativity and innovativeness in their work. 6. Conclusions This study has shown that ethical leadership positively impacts the innovative work behavior of the employees associated with higher education in China. Similarly, the outcomes indicate that the well-being of the employees working in China’s higher education positively moderates between ethical leadership and work engagement. Results also note that ethical leadership has a positive impact on work engagement. Similarly, this study concludes that work engagement brings innovative work behavior that affects the individual and overall organizational performance. Finally, this study confirms that work engagement positively mediates between ethical leadership style and IWB. So, this study confirms that a leader who has ethical values and principles works for the well-being of the employees that engage the employees with work. Ultimately, it affects the individual’s creativity. Ethical leadership is important for innovative work behavior that supports managers in introducing a supportive workplace environment that promotes good interpersonal relationships with subordinates. Therefore, a good interpersonal relationship between managers and subordinates enhances the work quality. So, ethical leaders provide a supportive work environment to all subordinates regarding their work. These findings have practical implications for leaders and managers associated with the education sector. Moreover, it will provide relevant and meaningful guidance for the managers and educational institutions. First, the people working in the education sector have to have innovative behavior because they must keep up to date with new knowledge in their relevant discipline. The educational leaders should build a system under which their followers can control their jobs and update their knowledge. Secondly, the teachers and support staff should give feedback to their leaders if they are facing any issues on the job. In this way, ethical leaders will work for the well-being of the employees. Third, the educational institution needs to formally organize the professional development training for the leaders, managers, and support staff; this training will bring creativity to their behavior. Author Contributions K.J. developed the research idea and drafted the manuscript. T.Z. worked on the literature review of the study. W.Z. worked on the research methodology. S.F.R. drafted the manuscript and analyzed the data. A.A. worked on the introduction and literature. T.C. supervised the research project. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The research ethics committee of Zhejiang University of Technology, Communication University of Zhejiang and University of Management and Technology approved this study. Informed Consent Statement Note applicable. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Conceptual Framework. Note: Solid lines show the direct relationships and dotted lines show the moderating and mediation relationships. Figure 2 Path Analysis. Figure 3 Moderation of EL*WB on Work Engagement. ijerph-19-05414-t001_Table 1 Table 1 Sample Distribution. Characteristics F % Gender Female 156 42 Male 214 58 Universities Private 163 44 Public 207 56 Positions Senior Professors 133 36 Lecturers 141 38 Supporting Staff 96 26 Education Doctorate 145 39 Post-Graduate 225 61 ijerph-19-05414-t002_Table 2 Table 2 Reliability and validity. Item Loadings ∝ rho_A CR AVE Ethical Leadership EL1 0.648 0.599 0.66 0.78 0.546 EL2 0.848 EL3 0.706 Innovative Work Behavior IWB1 0.605 0.803 0.826 0.857 0.503 IWB2 0.738 IWB3 0.817 IWB4 0.713 IWB5 0.602 IWB6 0.756 Well-Being WB1 0.716 0.714 0.721 0.821 0.534 WB2 0.764 WB3 0.744 WB4 0.698 Work Engagement WE1 0.773 0.846 0.867 0.883 0.52 WE2 0.825 WE3 0.764 WE4 0.733 WE5 0.611 WE6 0.697 WE7 0.62 ijerph-19-05414-t003_Table 3 Table 3 HTMT Ratios. Ethical Leadership Innovative Work Behavior Well-Being Work Engagement Ethical Leadership Innovative Work Behavior 0.72 Well-Being 0.425 0.257 Work Engagement 0.749 0.472 0.654 ijerph-19-05414-t004_Table 4 Table 4 Good fit model and VIF. Innovative Work Behavior Work Engagement Model Fit Indices Work Engagement 1.497 SRMR = 0.024 NFI = 0.90 Ethical Leadership 1.497 1.116 Well-Being 1.101 ijerph-19-05414-t005_Table 5 Table 5 The goodness of Fit Index. Constructs AVE R-Square Ethical Leadership 0.546 Well-Being 0.534 Innovative Work Behavior 0.53 0.330 Work Engagement 0.52 0.518 0.52 0.424 GoF 0.51 ijerph-19-05414-t006_Table 6 Table 6 Effect size f-square. Innovative Work Behavior Work Engagement EL*WB 0.024 Well-Being 0.317 Ethical Leadership 0.216 0.524 Work Engagement 0.027 ijerph-19-05414-t007_Table 7 Table 7 R-square. R Square R Square Adjusted Innovative Work Behavior 0.332 0.33 Work Engagement 0.52 0.518 ijerph-19-05414-t008_Table 8 Table 8 Direct and Indirect Paths. Hypothesis M SD T Stats p Values Status Ethical Leadership → Innovative Work Behavior 0.468 0.043 10.857 0.000 Not Rejected Ethical Leadership →Work Engagement 0.530 0.025 20.841 0.000 Not Rejected Well-Being → Work Engagement 0.411 0.030 13.675 0.000 Not Rejected Work Engagement→ Innovative Work Behavior 0.165 0.044 3.769 0.000 Not Rejected Ethical Leadership → Work Engagement → Innovative Work Behavior 0.087 0.022 3.900 0.000 Not Rejected ijerph-19-05414-t009_Table 9 Table 9 Moderation and mediated moderation. Hypothesis M SD T Stats p Values Status Well-Being → Work Engagement 0.411 0.030 13.675 0.000 Not Rejected EL*WB → Work Engagement 0.078 0.030 2.602 0.008 EL*WB → Work Engagement → Innovative Work Behavior 0.013 0.005 2.415 0.016 Not Rejected Note: EL = Ethical Leadership; WB = Well-Being. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Mishra S. Braun E. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092238 cancers-14-02238 Review What Zebrafish and Nanotechnology Can Offer for Cancer Treatments in the Age of Personalized Medicine Cascallar María 123 Alijas Sandra 1 https://orcid.org/0000-0003-1622-3068 Pensado-López Alba 34 https://orcid.org/0000-0002-5345-3682 Vázquez-Ríos Abi Judit 125 https://orcid.org/0000-0001-7927-5303 Sánchez Laura 36 https://orcid.org/0000-0001-9479-139X Piñeiro Roberto 27 de la Fuente María 125* Chen Eleanor Y. Academic Editor Ignatius Myron Academic Editor 1 Nano-Oncology and Translational Therapeutics Group, Health Research Institute of Santiago de Compostela (IDIS), SERGAS, 15706 Santiago de Compostela, Spain; maria.cascallarc@gmail.com (M.C.); sandraalijasperez@gmail.com (S.A.); abi.judit.vr@gmail.com (A.J.V.-R.) 2 Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029 Madrid, Spain; roberto.pineiro.cid@sergas.es 3 Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela, Campus de Lugo, 27002 Lugo, Spain; alba.pensado.lopez@rai.usc.es (A.P.-L.); lauraelena.sanchez@usc.es (L.S.) 4 Center for Research in Molecular Medicine & Chronic Diseases (CIMUS), Campus Vida, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain 5 DIVERSA Technologies S.L., 15782 Santiago de Compostela, Spain 6 Preclinical Animal Models Group, Health Research Institute of Santiago de Compostela (IDIS), 15706 Santiago de Compostela, Spain 7 Roche-Chus Joint Unit, Translational Medical Oncology Group, Oncomet, Health Research Institute of Santiago de Compostela, Travesía da Choupana s/n, 15706 Santiago de Compostela, Spain * Correspondence: maria.fuente.freire@sergas.es; Tel.: +34-981-955-704 30 4 2022 5 2022 14 9 223826 3 2022 28 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary Discovering new strategies for cancer treatment is critical, considering that each year millions of deaths are caused by this disease. In this sense, therapies based on nanomedicine are an innovative approach for cancer treatment, not only because they make it possible to perform targeted therapy, but also because they can improve patients’ quality of life. A key step to transfer new treatments from bench to beside is in vivo evaluation of a therapy, where zebrafish as a model organism has a fundamental role. Zebrafish has several benefits that make it ideal for studying the therapeutic capacity of novel nanotechnology-based anticancer therapies. In this review, we evaluate the potential of the nanomedicine and zebrafish synergy to achieve personalized treatments for cancer. Abstract Cancer causes millions of deaths each year and thus urgently requires the development of new therapeutic strategies. Nanotechnology-based anticancer therapies are a promising approach, with several formulations already approved and in clinical use. The evaluation of these therapies requires efficient in vivo models to study their behavior and interaction with cancer cells, and to optimize their properties to ensure maximum efficacy and safety. In this way, zebrafish is an important candidate due to its high homology with the human genoma, its large offspring, and the ease in developing specific cancer models. The role of zebrafish as a model for anticancer therapy studies has been highly evidenced, allowing researchers not only to perform drug screenings but also to evaluate novel therapies such as immunotherapies and nanotherapies. Beyond that, zebrafish can be used as an “avatar” model for performing patient-derived xenografts for personalized medicine. These characteristics place zebrafish in an attractive position as a role model for evaluating novel therapies for cancer treatment, such as nanomedicine. zebrafish nanomedicine cancer personalized medicine drug screening xenograft Instituto de Salud Carlos IIIISCIII and the European Regional Development Fund (FEDER)AC18/00107 AC18/00045 PI21/01262 ERA-NET EURONANOMED III project METASTARGJTC2018-045 ERA-NET EURONANOMED III project PANIPACJTC2018/ 041 Axencia Galega de Innovación (GAIN), Consellería de Economía, Emprego e IndustriaIN607B2021/14 Roche-Chus Joint Unit, Axencia Galega de Innovación (GAIN), Consellería de Economía, Emprego e Industria IN853B 2018/03 Xunta de Galicia Pre-doctoral FellowshipED481A-2018/095 Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia local governmentED431C 2018/28 This research was funded by the Instituto de Salud Carlos IIIISCIII and the European Regional Development Fund (FEDER) (AC18/00107, AC18/00045, PI21/01262); by the ERA-NET EURONANOMED III project METASTARG (grant number JTC2018-045) and the ERA-NET EURONANOMED III project PANIPAC (grant number JTC2018/ 041); and by Axencia Galega de Innovación (GAIN), Consellería de Economía, Emprego e Industria (IN607B2021/14). R.P. was funded by Roche-Chus Joint Unit (IN853B 2018/03) funded by Axencia Galega de Innovación (GAIN), Consellería de Economía, Emprego e Industria. A.P-L. is supported by the Xunta de Galicia Pre-doctoral Fellowship (ED481A-2018/095). L.S. acknowledges the funding given by Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia local government (ref. ED431C 2018/28). ==== Body pmc1. Emerging Cancer Therapeutics Cancer is a major public health problem worldwide and the second-leading cause of death globally. Almost ten million people die from cancer every year, and this number is estimated to reach over 13 million in 2030 [1]. The most common causes of cancer death are lung, liver, and stomach cancers in men, and breast, lung, and colorectal cancers in women [2]. Cancer is a complex genetic disease that is caused by specific changes to genes in one cell or group of cells. These changes include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis [3]. Metastases are the cause of the majority of human cancer deaths [4]. In the particular case of localized-stage colorectal cancer (CRC), the 5-year survival rate is around 90%, declining to 71% and 14% for patients diagnosed with regional and distant metastatic stages, respectively (American Cancer Society, Atlanta, GA, USA, 2017); in the case of pancreatic cancer, it falls from 37% to 3% in the distal metastatic setting [5]; and in non-small-cell lung carcinoma (NSCLC), from 24% to 6% in advanced stages of the disease when metastasis occurs [6]. The process of metastasis is defined by a cascade of complex events in which malignant cells detach from the primary tumor, invade through the basement membrane, and then migrate into the circulation, either via the blood or lymphatic vessels, to finally spread to distant sites to form metastases [7]. Although most early stage tumors can be surgically removed, there is growing evidence that dissemination could indeed happen at a very early stage in the carcinogenesis process [8], a fact that could explain why, in some tumor types such as pancreatic cancer, the 5-year survival even for localized disease is so poor. It is generally accepted that the development of metastatic cancer implies cancer cells from the primary tumor alter several distinct features in order to succeed in this very complex process. Some of these modifications are (i) a change from an epithelial to a more mesenchymal phenotype, (ii) the acquisition of stem-cell properties and phenotypic plasticity, and (iii) a change in their metabolism in a way that promotes survival and metastatic outgrowth [9,10,11,12]. Conventional anticancer treatments mainly target the bulk tumor and often fail to eliminate the highly tumorigenic and chemo-resistant cell subpopulations. NSCLC is a clear example of the results of these prevalent tumor treatments. Curative surgery is the standard of care for early-stage patients with good performance status; however, 35–50% of the resected patients relapse after an apparently successful surgical treatment [13]. For a long time, platinum-based doublets have been the standard first-line treatment option for unresectable advanced NSCLC [14]. Despite the survival improvement achieved with first-line chemotherapy, about 30% of patients do not obtain a tumor response. Moreover, patients who are initially sensitive to treatment acquire resistance and develop tumor progression after a median of about 5 months [15]. The current treatment strategy considers factors such as histology, clinical stage, age, performance status, comorbidities, the patient’s preferences, the molecular study, and an increasingly important focus on the immunological status. During the last years, a growing number of targetable major pathways have been identified, such as EGFR, PI3K/AKT/mTOR, RAS–MAPK, and NTRK/ROS1, leading to a new era of precision medicine [16]. Targeted therapy is the cornerstone of precision medicine, which seeks a molecular understanding of the disease to prevent, diagnose, and treat it. It can also be called personalized medicine, given that every patient receives the treatment that better fits their particular alterations in genes and proteins, providing them with significant responses and lesser toxicities compared with broad-spectrum cytotoxic therapy. The development of targeted therapies has resulted in substantial benefits in terms of survival and quality of life for cancer patients. Over the last twenty years, different drugs have been approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for several tumor types, for example imatinib (gastrointestinal stromal tumors and dermatofibrosarcoma protuberans) [17] and cetuximab (colorectal cancer and head and neck cancer) [18], among others. On the other hand, immunotherapy has become the most revolutionary treatment in solid tumors [19]. The discovery of ligands and receptors regulating T cell activation, called immune checkpoints, has represented a major therapeutic breakthrough in the field. Immune checkpoint inhibitors (ICIs) are a group of antibodies designed to block specific targets present on tumor cells or lymphocyte surfaces (e.g., ipilimumab [20], the first approved anti-CTL4 antibody, and nivolumab [21,22,23,24] first approved anti-PD-1 antibody), consequently boosting the immune system to attack cancer. Immune checkpoints that target the programmed cell death-1 (PD-1), programmed cell death ligand-1 (PD-L1), and cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) have received approval across a wide range of cancer types, including lung cancer, melanoma, and head and neck, among others [16]. The current scenario for cancer research is wide and offers many possibilities for the constant improvement of therapies. In recent years, research into cancer medicine has taken remarkable steps towards more effective, precise, and less invasive cancer treatments. There is a plethora of newly proposed therapeutic options for cancer that are currently under investigation at different levels of maturity of the research stage, with some of them in clinical trials, such as oncolytic viruses [25], immune check-point antagonists [26], therapeutic cancer vaccines [27], natural antioxidants [28], hormone replacement therapy [29], exosome delivery platforms [30], aptamers [31], and thermal ablation and magnetic hyperthermia [32]. These strategies aim to provide the best personalized therapies for cancer patients and highlight the importance of combining multiple disciplines to achieve innovative approaches for the best outcome. Other novel and promising therapeutic strategies that are already a reality in cancer treatment include antibody-drug-conjugates (such as ado-trastuzumab emtansine, approved in 2013 for treating HER2-positive metastatic breast cancer [33]); gene and cell therapy (such as tisagenlecleucel, approved by the FDA in August 2017 for certain pediatric and young adult patients with a form of acute lymphoblastic leukemia whose first-line drugs have failed [34]); and nanomedicine (for example, Doxil®, the first marketed PEGylated liposome loaded with the chemotherapeutic drug doxorubicin [35]). 2. Nanomedicine and Cancer Nanomedicine has been widely explored during the last decades. Different nanosystems composed of a variety of materials have been proposed for the management of several diseases, such as liposomes and other lipid-based and polymer-based nanoparticles, micelles, polyplexes, dendrimers, polymersomes, and drug/protein conjugates [36]. Nanotechnology offers many advantages in drug delivery, including (i) protection of drugs from premature degradation, (ii) increased solubility and stability in biological media, (iii) prevention of premature interactions of drugs with the biological environment, (iv) controlled pharmacokinetics and biodistribution, (v) improved delivery of therapeutics across biological barriers, and (vi) targeting of drugs to the diseased area [37,38]. Due to these properties and their ability to accommodate various types of drugs and biomolecules, with different physicochemical properties and activities, nanocarriers have emerged as attractive candidates for the development of personalized medicine [39,40]. Nanotechnology-based therapeutics are paving the way in the diagnosis, imaging, screening, and treatment of primary and metastatic tumors; however, translating such advances from the bench to the bedside has been severely hampered by challenges encountered in the areas of pharmacology, toxicology, immunology, large-scale manufacturing, and regulatory issues. The latest advances in nanomedicine and cancer have been extensively reviewed in recent works due to the high potential of this nano-based therapy to improve cancer patients’ quality of life [41,42,43]. A clear example is the work of Park et al., who reviewed how drug delivery systems progress over time, including cancer treatments such as Mylotarg® and Doxil® [44]. 3. The Potential of Zebrafish for Preclinical Evaluation of Novel Cancer Therapeutics Zebrafish (Danio rerio) is a vertebrate model species traditionally used for studying developmental biology and vertebrate genetics, and more recently, to model human diseases such as cancer, thus playing a key role in the discovery of new drugs for treating these illnesses [45,46,47]. Zebrafish characteristics define it as a model species between invertebrate models and murine models because it collects the vertebrate traits and allows large experiments [45,46]. One of the features that make zebrafish an appropriate human disease model is its homology with the human genome, around 70%, which increases to 82% in the case of human disease-related genes [48]. Furthermore, there are multiple advantages associated with the use of zebrafish, such as high fecundity and fertilization rate, producing a large offspring [49]. In addition, the external fertilization and optical transparency of embryos and larvae allow direct visualization of the overall development and enable the imaging of cells without the use of invasive techniques [50]. In terms of cancer research, aside from the robustness of zebrafish embryos to be easily manipulated, the adaptive immune system is not active until 2–4 weeks post-fertilization (wpf), and complete immunocompetence is only achieved at 4–6 wpf [51]. This feature, together with the previously mentioned transparency, enables the transplantation of fluorescent cancer cells (xenotransplantation or xenograft) and the visualization and tracking of their growth, biodistribution, metastasis, and neovascularization processes, as well as the evaluation of drug responses [50,52]. The main advantages and disadvantages of zebrafish as a model for human diseases are summarized in Table 1. The set of these characteristics have allowed researchers to develop several genetic and xenotransplantation zebrafish models and thus unravel the cellular, molecular, and physiological basis of different types of cancer, as well as drug response/resistance processes. Some relevant studies are reviewed in the following sections. 3.1. Genetic Models 3.1.1. Forward Genetics Several carcinogens are able to induce human-like tumors in different zebrafish organs (Figure 1) [53]. Thus, studies have been performed, allowing a better understanding of the carcinogenesis process, main target tissues, type of tumor, signaling pathways, and chemoprevention measures. For instance, exposure to N1-nitro-N-nitrosoguanidine (MNNG) in 86 h post-fertilization (hpf) embryos and 3 wpf fry (immersion), 72 hpf embryos (microinjection), and 6 wpf juveniles (diet) showed that embryos and fry are responsive to carcinogenic effects, whereas juveniles are remarkably resistant to neoplasia [54]. Embryos developed mainly hepatic and mesenchymal neoplasms, including chondroma, hemangioma, hemangiosarcoma, leiomyosarcoma, and rhabdomyosarcoma. The blood vessels and testis were the main target organs in fry, developing seminoma, hemangioma, hemangiosarcoma, and various other epithelial and mesenchymal neoplasms. Similarly, it has been shown that exposing zebrafish to Dimethylbenzanthracene (DMBA) at 3 wpf led, principally, to hepatic neoplasia in adults [55], with conservation of human transcriptome profiles, highlighting the potential of zebrafish for modeling human liver cancer [56]. Maid, a protein involved in cell proliferation, is abundantly expressed in the liver hepatocytes’ cytoplasm of zebrafish; its role as a regulator of hepatocarcinogenesis was explored by treating adult zebrafish with Diethylnitrosamine (DEN) for 8 weeks. After treatment, these fish presented distended abdomens, extremely swollen livers, and different types of liver tumors. However, Maid appeared to translocate from the cytoplasm to the hepatocyte nucleus, presumably to participate in growth-inhibitory signaling and display its tumor-suppressor activity [57]. It has been stated that polyploidy in lower vertebrates decreases the probability of inactivation of all alleles of tumor suppressor genes, so the incidence of tumors might be lower [58]. In this regard, the relationship between polyploidy and tumor formation has been investigated through N-nitrosodimethylamine (NDMA)-induced hepatocarcinogenesis [59]. Diploid and triploid 6 wpf zebrafish exposed to this chemical for 8 weeks developed hepatocellular adenomas and trabecular hepatocellular carcinomas after 24 weeks from the beginning of the treatment, although cholangiolar tumors were not detected in triploid fish until 36 weeks, serving as evidence that polyploidy is a protective factor in pathogenesis of this type of tumor, probably indicating a lower probability for putative tumor suppressor genes to be inactivated in polyploid cholangiolar cells. The mutagen ethylnitrosourea (ENU) has been used to generate point mutations, leading to the identification of several mutant zebrafish lines with an increased incidence of spontaneous neoplasia or higher sensitivity to chemical exposure [53]. For instance, Basten et al., in an attempt to study ciliary motility defects in the lrrc50 mutant zebrafish line, unexpectedly found development of seminomas in 2-year-old adults, with a penetrance of >90%. This observation allowed establishment of a correlation between the gen and such testicular germ cell tumors (GCTs) and proposes lrrc50 as a novel tumor suppressor [60]. Similarly, Neumann et al., while screening for cancer susceptibility genes, isolated a zebrafish mutant line with highly penetrant, heritable testicular GCTs in which testicular tumors spontaneously developed. Indeed, DMBA or MNNG exposure resulted in enhanced germ cell tumorigenesis [61]. 3.1.2. Transgenic Zebrafish Lines Several zebrafish cell and tissue-specific reporter lines have been developed over the last years to improve the comprehension and characterization of different cancer traits, such as tumor cell growth, migration, invasion, angiogenesis, drug responses, or interactions with immune cells. Some examples are Tg(mpx:GFP) and Tg(mpeg1:eGFP) [62,63], which fluorescently label neutrophils and macrophages, respectively, or Tg(fli1:eGFP) [64], which labels the vasculature. Furthermore, human or murine oncogene transgenic expression in zebrafish has also helped to understand their role in tumor development; for example, Tg(ptf1a:eGFP-KRASG12V) in pancreatic cancer [65] and Tg(mitfa:HRASG12V; mitfa:GFP) or Tg(mitfa:BRAFV600E); tp53−/− for melanoma [66,67]. The binary transgenic system Gal4/UAS has also been extensively used. Gal4 is a transcriptional activator that, when expressed under the control specific tissue-specific promoters, binds to UAS enhancer sequences in the DNA, recruiting transcription machinery to induce gene expression, so genes under the control of UAS sites are expressed when Gal4 is present [68]. With this methodology, authors have shown, for instance, that crossing Gal4-expressing lines with Tg(UAS:HRASG12V) transgenic line resulted in the development of different types of tumors, such as leukemia, glioma, or chordoma [69,70,71]. As transgenic fish with overexpression of some oncogenes might not survive to adulthood, transgenic inducible lines can also be generated, for instance, TetOn system-based transgenic lines, in which the oncogene expression is induced by doxycycline. Doxycycline-inducible expression of oncogenic KRAS in brain cells under the control of the krt5 and gfap gene promoters using the TetOn system (Tg(TRE:mCherry-KRASG12V; krt5/gfap:rtTa)) led to the development of malignant tumors in the cranial cavity and parenchymal brain tumors, respectively [72]. 3.1.3. Reverse Genetics Morpholinos (MO) are commonly used in zebrafish to achieve transient expression silencing without modifying the genome sequence [73] and thus to determine certain cancer invading mechanisms, such as angiogenesis. For instance, Jacob et al. reported that Plexin-A1 (PlexA1) could be a potential prognostic marker for glioma patients’ survival, as quantitative analysis correlates tumor grade and the level of PlexA1 expression in brain blood vessels [74]. They knocked down PlexA1 in Tg(kdrl:eGFP) zebrafish and observed a significant number of abnormal angiogenic sprouts in intersegmental vessels (ISVs) at 28 hpf, confirming the relevance of PlexA1 in blood vessel development. Royet et al. observed that high expression of Ephrin-B3 in human glioblastoma biopsies promotes tumor growth and angiogenesis by inhibition of EphA4-induced apoptosis [75]. They knocked down Ephrin-B3 in Tg(fli:EGFP) embryos and observed an impaired ISVs formation associated with an increase in apoptosis. Co-silencing of EphA4 resulted in the rescue of the angiogenic defects, suggesting that enhancing EphA4-induced cell death could be envisaged as a relevant strategy to slow glioblastoma (GBM) growth. In order to generate stable mutant models, Targeted Induced Local Lesions in Genomes (TILLING), based on the exposure to ENU and further sequencing [76], has been extensively used. In this sense, mutations in tumor suppressor genes, such as tp53, pten, and apc, have been identified in ENU mutagenesis libraries, and fish were found to develop malignant peripheral nerve sheath tumors (MPNSTs), ocular hemangiosarcomas, and intestinal adenomas, hepatomas, and pancreatic adenomas, respectively [77,78,79,80]. Interestingly, ENU homozygous brca2 mutants were shown to be unable to develop ovaries during sexual differentiation, developing as infertile males that were prone to develop testicular neoplasias during adulthood [81]. By combining the use of vhl zebrafish ENU heterozygous mutants and the exposure to DMBA, Santhakumar et al. established the von Hippel-Lindau protein (pVHL) as a genuine tumor suppressor in zebrafish, due to the increase in the occurrence of hepatic and intestinal tumors in mutants [82]. Although TILLING has proven to be useful to correlate genotypes with phenotypes, the difficulty involved in the screening process, together with the possibility of having further mutations than the one desired, led researchers to introduce other methodologies, such as nuclease-based techniques, which include Zinc Finger Nucleases (ZFNs) and Transcription Activator-Like Effector Nucleases (TALENs). ZFNs were used to generate tet2 mutants, which developed progressive clonal myelodysplasia, culminating in myelodysplastic syndrome, with dysplasia of myeloid progenitor cells and abnormal circulating erythrocytes [83]. As it recapitulates human TET2 loss-of-function phenotypes, this model was proposed as a platform for small-molecule screenings to identify compounds with specific activity against tet2 mutant cells. The function of the neurofibromin 1 (NF1) gene in brain tumorigenesis was explored by Shin et al. through the generation of stable mutant lines for the zebrafish orthologs (nf1a and nf1b) by ENU and ZFNs [84]. nf1a+/−; nf1b−/− mutants in a p53 mutant background presented an increased penetrance of high-grade gliomas MPNSTs as well as hyperactivation of ERK and mTOR pathways, consistent with mouse and human NF1-derived MPNSTs and gliomas. Similarly, bi-allelic cdkn2a/b and rb1 mutants generated by TALENs developed MPNSTs and medulloblastoma-like primitive neuroectodermal tumors, respectively, in a p53 mutant background [85]. Nevertheless, the design of nuclease-based systems is challenging, and there is still a high rate of off-targets. Thus, the introduction of the CRISPR/Cas9 system has allowed the optimization of the genome editing protocols and the improvement of the efficiency and accuracy of zebrafish lines. For instance, p53/nf1-deficient fish were used by Oppel et al. to knock out by CRISPR/cas9 the atrx gene, a known tumor suppressor in gliomas or sarcomas, confirming that its loss facilitates the development of various malignancies, together with the downregulation of telomerase, which causes the alternative lengthening of telomeres [86]. Loss-of-function mutations in SUZ12, a subunit of the Polycomb repressive complex 2, have been identified in a variety of tumors, including MPNSTs. The knockout of suz12a and suz12b in a p53/nf1-deficient model significantly accelerated the onset and the penetrance of MPNSTs, and additional types of tumors were detected, including leukemia with histological characteristics of lymphoid malignancies, soft tissue sarcoma, and pancreatic adenocarcinoma [87]. These are examples of studies in which researchers developed zebrafish models harboring mutations in tumor suppressor genes and novel candidate genes, among others, to investigate their roles and unravel the relationship among mutations and the tumorigenesis and progression of different types of cancer (Figure 2). 3.2. Transplantation Models Transplantation in zebrafish is based on the injection of fluorescently labeled cancer cells into zebrafish embryos. The main transplantation techniques include allotransplantation and xenotransplantation, and both can be orthotopic or heterotopic, depending on whether the cells are injected in an equivalent anatomical site to tumor origin or into a different anatomical site, respectively. Allograft consists of tumor cells being transferred from one individual to another of the same species [88], while xenograft is based on the injection of labeled human, murine, or patient-derived cancer cells into zebrafish, to track their survival, engraftment, growth, behavior, and interaction with the microenvironment [89]. The common sites for heterotopic transplantation are (Figure 3): (a) yolk sac, to track survival and proliferation [90]; (b) duct of Cuvier, to observe migration, invasion, and mesenchymal-epithelial transition (MET) [91]; (c) perivitelline space, to investigate mainly neovascularization [89]; intraperitoneal cavity, when adult individuals are used. The majority of transplantation assays are performed at 2 days post-fertilization (dpf), taking advantage of the transparency of the embryos and the lack of adaptive immunity, although several experiments have been carried out using adults. In order to avoid immune rejection in adults, transplantations require immunosuppression with sublethal γ-irradiation or dexamethasone [92,93] or the use of immunocompromised lines, such as the Rag2, lacking mature T-cells and having a reduced number of B cells, or the compound mutant prkdc−/−, il2rga−/−, lacking T, B, and natural killer (NK) cells [94,95]. In the particular case of allografts, engraftment can also be achieved without the need for immunosuppression by the transplantation from a donor fish to a genetically identical recipient (syngeneic or clonal models) [96], serving as a model for long-term engraftment and self-renewal potential [97,98,99]. An interesting approach combining different strategies allowed Ignatius et al. to confirm the role of tp53 in the development of a wide spectrum of tumors [100]. By using TALENs, they created tp53 mutants in which MPNSTs, angiosarcoma, germ cell tumors, and leukemia spontaneously developed during adulthood, and such tumor cells were transplantable to syngeneic fish, so engraftment of fluorescent-labeled tumors could be dynamically visualized over time. Additionally, White et al. proposed a mutant transparent recipient, known as casper zebrafish (roy−/−; nacre−/−), as a platform to study cancer cell engraftment, proliferation, and distant metastases in vivo [101]. Nevertheless, the prompt recovery from chemical immune ablation, the vulnerability of mutant immunocompromised fish, and the limited number of syngeneic zebrafish lines made embryo xenograft the most cost-effective technique, together with the higher number of individuals used, which increased statistical power and the reduced ethical issues in comparison with adults. The first xenograft assay was performed by injecting melanoma cells in zebrafish blastula, which showed the ability of melanoma cells to survive, proliferate, and specifically migrate to the skin, highlighting the validity of the zebrafish for cancer research [102]. Since then, this technique has been improved and extended for studying several cancer features, including not only survival, proliferation, or migration, but also the ability to invade, form new blood vessels (angiogenesis), metastasize, and respond or resist different treatments. Additionally, researchers have made efforts to mimic human tumor conditions and microenvironments as much as possible, as reviewed by Cabezas-Sáinz et al. [103]. For instance, the use of transgenic zebrafish lines, such as the previously mentioned ones, labeling immune cells or vasculature, has led several researchers to better understand the interaction among tumor cells and macrophages or neutrophils, and their involvement in tumor growth, vascularization, invasion, and metastasis [104,105,106,107]. In this line, Allen et al. recently presented a new model for tumor cell extravasation of both individual and multicellular circulating tumor cells, known as angiopellosis, and their ability to form tumors at distant sites [108]. With the aim of recapitulating, not only the cellular but also the non-cellular environment provided by the specific site and/or organ orthotopic xenografts have been developed, mainly with brain tumor cells. A pioneering study was performed by Lal et al., in which GBM cells behaved differently when injected into the yolk sac or in the brain. While cells in the yolk were unable to proliferate or invade, cells injected orthotopically showed the ability to invade the brain and disperse along the vessels [109]. By combining MOs, orthotopic xenograft, and 4D individual tracking technology, Gamble et al. showed that laminin subunit alpha 5, an important component of blood vessels, increases the attachment of GBM cells to blood vessels, suppressing tumor invasion but promoting tumor formation [110]. Additionally, orthotopic brain xenografts have proven to be unique models to study the ability of different drugs to penetrate the blood–brain barrier [111]. Retinoblastoma has also been studied by orthotopic xenografts. The inhibition of Nodal using short hairpin (shRNA) reduced the ability of retinoblastoma cells to disseminate outside the eye, highlighting the importance of Nodal in promoting growth, proliferation, and invasion [112]. Although the above-mentioned techniques have helped to improve the knowledge of several cancer processes, tumors present high interindividual heterogeneity. In addition, established cancer cell lines often differ significantly from patients’ tumor cells. Thus, to preserve the patients’ tumor biological and genetic profile and improve the accuracy of tumor drug-response studies, zebrafish patient-derived xenografts (zPDXs) have arisen as a potential solution [106]. zPDXs are established from tumor cells or masses isolated from patients during biopsy or excision, which are subsequently hetero- or orthotopically implanted into zebrafish. The pioneers of this technique were Marques et al., who observed cell invasion and metastasis formation after injection of colon, pancreas, and stomach primary tumor samples into the yolk sac [113]. Since then, the survival, proliferation, angiogenesis, or invasion ability of different patient-derived tumor cells have been studied in zebrafish models, from pancreatic, colon, gastric, head and neck, or pituitary cancer, to abdominal liposarcoma or T-cell acute lymphoblastic leukemia [114,115,116,117,118]. Furthermore, in order to improve patients’ treatments, zPDXs have served as a platform to develop drug response/resistance assays and thus move towards personalized medicine. In this sense, several strategies are reviewed below. 4. Zebrafish as a Platform for Drug Screening Zebrafish are used as a screening platform to adjust drug concentrations, to improve combinatorial treatments for a less toxic effect on the patient, or to overcome resistances, as well as a tool to study the mechanism of action of drugs in the organism and to alter the function of a biological pathway without previously knowing the components. Small molecule screening in zebrafish started in 2000 with a work by Peterson et al., who tested the effect of a variety of molecules in the development of vertebrate animals to understand how these molecules can be used to determine the timing of critical developmental events [119]. In the context of cancer, zebrafish xenotransplants have been useful as in vivo preclinical tools for drug testing. This approach has been validated by different works, showing its complementarity with other in vivo models such as the mouse [120]. However, xenotransplantation of human cancer cells into the zebrafish is not without difficulties. For instance, the normal growth of human cells is at 37 °C, and the temperature of development of the zebrafish is 28 °C. To overcome this issue, the field has established 31–34 °C as a consensus temperature for xenograft assays. However, these temperatures could cast some doubts about the efficiency of xenograft models for drug screening and the subsequent translation to the patient. In this sense, Cabezas-Sáinz et al. demonstrated that zebrafish larvae can live until 36 °C, allowing them to test drugs in a cancer model with characteristics close to humans [121]. In addition, Cornet et al. developed the ZeOnco Test, an optimized and standardized (regarding cell labeling, injection site, image acquisition, etc.) xenograft assay, aiming at reducing attrition rate [122,123]. In the same way, xenografted Tg(fli1:EGFP) transgenic models with human lung cancer cell lines were used to compare the effects of different known drugs, promoting these models as a real-time drug screening platform for clinical lung cancer patients [124]. Zebrafish have been used for the development of combined treatment approaches to improve treatment efficacy. An example of this goal was the investigation of Precazzini et al., where the melanoma kita:ras transgenic zebrafish line was used to test the antifungal Clotrimazol in combination with antitumoral drugs, showing a synergistic anti-melanoma effect with limited toxicity [125]. In addition, other authors used the casper transgenic line to evaluate individually and in combination the antitumor activity of chemotherapy drugs used in the clinic [126]. In addition to pharmacological cancer treatments, there are other treatment options, such as radiotherapy, based on the use of ionizing radiation. An example is the work by Costa et al., who combined ionizing radiation and chemotherapy in colorectal cancer tumors xenografted into the nacre (casper and [Tg(fli1:EGFP)]) zebrafish line and observed that the responses achieved in the zebrafish matched the clinical responses of patients [127]. Transgenic Tg(fli1:EGFP) zebrafish with fluorescent vasculature has been used in a wide range of screenings in studies related to the angiogenesis process, such as new synthetic compounds [128,129], analogous molecules for drugs in use [130,131], natural compounds used in traditional medicine [132,133], and natural analogous compounds [134]. The combination between fluorescent vasculature and xenograft transplantation offers a potent cancer research tool to study the action of compounds in vivo, in which to test the potential of natural products in anticancer therapy [135,136], modification of natural compounds [137], and new chemical compound structures that are already utilized in the clinic [138]. Lin et al. combined the fluorescent vasculature of zebrafish with other genetic modifications and cancer cell xenotransplantation to screen and identify new anticancer molecules [139]. Even so, wild-type zebrafish is also used for screening of new molecules obtained from marine organisms [140,141,142] as well as the study of the potential of some fungicides against cancer cells [143]. Furthermore, other specific transgenic zebrafish were used in the study of different tumor drugs. For instance, the vhlhu2117 mutant transgenic zebrafish, which shows an excess of vascularization, was used to evaluate the antiangiogenic effect of the compound largazole [144]. In addition, a transgenic zebrafish for liver cancer overexpressing the oncogene KRAS was used to study the effects of environmental toxicants on tumor development and inflammatory response [145]. In the following sections, and as summarized in Figure 4, different therapeutic approaches for cancer treatment evaluated with the zebrafish model will be discussed. 4.1. Peptides Among the different types of biomolecules, zebrafish have proved their potential for evaluating the activity of novel peptide therapies. Cancer peptide-based therapy might play a role in the treatment of patients, and peptides can be obtained from different sources, such as natural organisms, peptide libraries, and de novo synthesis [146,147]. Some peptides produced by bacteria are also used to treat some types of cancer due to their antitumor effect. A shining example is the microcin E492, which is a peptide produced by the bacteria Klebsiella pneumoniae, which has shown antineoplasic properties in zebrafish embryos xenografted with colorectal cancer cells [148]. Another example, provided by Hsieh et al., studied the effect of TAT-NLS-BLBD-6, a synthesized peptide able to suppress breast cancer growth. This in vivo assay was carried out through a co-microinjection of peptide and labelled breast cells into the zebrafish yolk [149]. Furthermore, the evaluation of anticancer peptides in zebrafish embryos can be carried out by xenotransplantation of treated cells. An example of this assay was performed with NuBCP-9, a growth factor Nur77 derived peptide, which demonstrated apoptotic effect in paclitaxel-resistant lung tumor cells [150]. 4.2. Gene Therapies Zebrafish have also been used in the research of gene therapies based on the introduction of exogenous genomic materials on the organism to study or silence the expression of genes. A significant example is the research performed by Cordeiro et al., who used a specific ssDNA in the fli-EGFP transgenic zebrafish to measure the capacity of this material to reduce the GFP signal; the reduction of the EGFP emission indicates the downregulation of EGFP expression [151]. Transgenic zebrafish line Tg(Kdrl:eGFP)s843 has been used as an in vivo model to study the antiangiogenic effects of miRNA-based therapies. This assay was carried out with xenografts based on miRNA transfected prostate cancer cells, which allowed the evaluation of new vessel formation [152]. In the same vein, Kiener et al. studied the antitumor effect of a miRNA in the same type of cancer by microinjecting transgenic Tg(mpo:GFP)i114 zebrafish with miRNA transfected cells, proving the reduction of the tumor due to the miRNA effect [153]. 4.3. Immunotherapeutics: Monoclonal Antibodies and CAR-T Monoclonal antibodies can target cancer cells by binding to their specific surface antigens [154]. Zebrafish embryos play a meaningful role in the study of the anti-cancer efficacy of monoclonal antibodies, their toxicity, and the comparison between different therapies. Zebrafish were used to study cetuximab, a monoclonal antibody targeting the epidermal growth factor receptor (EGFR), for the treatment of colorectal and head and neck cancer [154]. The response of cetuximab treatment was evaluated using colorectal cancer zebrafish patient-derived xenografts (zPDX), which included the drug in the injected cell suspension, and the results showed that the zebrafish model allows the detection of differential responses to the antibody according to the KRAS mutational status of the tumor [155]. Furthermore, the zebrafish transgenic line Tg(fli1:EGFP) is commonly used to study the antiangiogenic capacity of drugs [156,157,158], such as Bevacizumab, a humanized anti-vascular endothelial growth factor (VEGF) antibody for the treatment of some solid cancers (such as breast and lung cancer). Bevacizumab was studied in this zebrafish transgenic line to define its antiangiogenic effect and antitumor capacity, in contrast to toxicity assay, which was performed in wild-type embryos [154,157,158]. Another monoclonal antibody studied in zebrafish models is ramucirumab, used to treat lung, gastric, and colorectal cancer. Its toxicity was assessed in wild-type zebrafish embryos, and the antiangiogenic and anticancer capacity was tested in the Tg(fli1:EGFP) line, in the same way as Bevacizumab [154,156]. Moreover, chimeric antigen receptor T cell (CAR-T cell) therapy has achieved clinical success in specific tumor types, such as several types of leukemia [159,160]. Recent studies by Pascoal et al., for the first time, evaluated the capacity of CAR-T cells to kill cancer cells in vivo in zebrafish. To carry out this assay, labelled Nalm-6 leukemia cells and CAR-T cells were injected into zebrafish vasculature. The results show that zebrafish embryos are a potential model for in vivo studies of the efficacy of CAR-T cell therapy against cancer [161]. 4.4. Nanomedicines 4.4.1. Toxicity Zebrafish embryos are currently used for nanomedicine toxicity testing due to advantages such as their high fertilization rate, as explained in Section 3. The most common method to perform toxicity assays is the incubation of nanomedicines into zebrafish medium, usually with dechorionated zebrafish. As well as the incubation, microinjection of test drugs into zebrafish circulation ensures that the concentration is absorbed by the embryos [49]. cancers-14-02238-t002_Table 2 Table 2 Zebrafish-based toxicity studies of different nanoparticles for cancer therapies. Nanoparticles Conditions Higher Mortality Rate Morphological Effects Ref. AgNPs 3 hpf embryos 72 h incubation 28.5 °C 100% (3 μg/mL) Yolk sac edema Tail malformation [162] AuNPs 3 hpf embryos 72 h incubation 28.5 °C 100% (300 mg/mL) Yolk sac edema [162] MMDOX 4 dpf embryos 72 h incubation 28 ± 1 °C 100% (100 μg/mL) Uninflated swim bladder Arched body Alteration of the spontaneous swimming activity [163] MSNs-FA 48 hpf embryos 72 h incubation 27 ± 1 °C ~30% (200 μg/mL) Hatching rate [164] Several aspects of zebrafish embryos can be analyzed to determine the toxicity of a specific nanomedicine; some examples are compiled in Table 2. The correct hatching process, malformation appearance, the response of the immune system, and mortality are some of the guidelines to evaluate the toxicity effect of nanoparticles [165,166]. As a result, toxicity tests based on zebrafish have become an indispensable step to assess the effect of several therapies based on nanosystems, from metal-based nanoparticles to lipidic nanosystems. For instance, golden (AuNPs) and silver (AgNPs) nanoparticles for anticancer application were tested to evaluate their toxicity using zebrafish embryos. Mortality rate and morphological anomalies showed differences between nanoparticle types and concentration [162]. However, metal nanoparticles are not the only kind of nanomedicines evaluated by the zebrafish toxicity test. In fact, micelles loaded with doxorubicin hydrochloride (DOX-loaded mixed micelles (MMDOX)), commonly used to treat metastatic breast cancer, were tested by Calienni et al. in zebrafish embryos to rate their toxicity in vivo [163]. Another important example is the research of Wu et al., who used zebrafish embryos to evaluate the biosafety of mesoporous silica nanoparticles coated with folic acid (MSNs-FA) as carriers of therapeutic peptides, evaluating the embryo mortality and hatching [164]. Zebrafish have demonstrated their huge capacity to be a platform for testing different types of nanomaterials, not only for cancer treatment but also for other applications such as antibacterial and heart-associated disease treatment. The review of Jia et al. compiled information about different nanoparticles and their toxicity evaluation using zebrafish [167]. 4.4.2. Biodistribution and Average Life in Circulation In vivo behavior, distribution along the body, and interaction with tumor cells are key qualities to develop new anticancer nanomedicines; therefore, analyzing these aspects is essential to achieve a translation to the clinic of nano-based therapies. Due to this, zebrafish embryos play an important role as a platform to evaluate these properties in vivo. Chang et al. performed an assay to evaluate differences in the distribution of polystyrene nanoparticles and glycol chitosan nanoparticles for cancer treatment along blood circulation. Adult wild-type zebrafish were retro-orbitally injected with nanoparticles to observe their capacity to circulate along the vasculature; this allowed the authors to predict in vivo nanoparticle behavior [168]. In the same way, Gundersen et al. also used wild-type zebrafish to evaluate the biodistribution of chlorpromazine-loaded PEGylated PLGA nanoparticles for leukemia treatment [169]. In another fashion, transgenic Tg(FLK-1: mCherry) zebrafish embryos, in which endothelial cell membranes are fluorescently labeled, were used to evaluate the distribution of nanoparticles throughout the vasculature, showing the interaction of nanoparticles with the blood vessels and the ability to extravasate [170]. Along this transgenic line, other lines with fluorescent endothelial cells have been utilized for these types of assays, such as the Tg(kdrl:GFP)la116tg line, which allowed the observation of the endocytosis of nanoparticles by endothelial cells and their behavior inside them [171]. Another important trait to evaluate is the time that nanoparticles can remain in the organism. This fact depends on the composition of the nanosystem and the response of the body’s immune system, such as macrophage uptake. Leveraging the zebrafish embryo transparency, microinjected fluorescent nanoparticles can be observed over time to evaluate their capacity to stay in the organism. An example that illustrates this usage is the study performed by Wang et al., involving nanosystems that can be used to carry anticancer drugs. In this study, FITC-labelled nanospheres were microinjected and evaluated during 72 h post-injection to study the biodistribution and their elimination progress of by the organism [172]. As the uptake of nanoparticles by macrophages decreases their half-life in circulation, one of the main objectives is testing the capacity of avoiding this nanoparticle uptake. To carry out this type of procedure, transgenic zebrafish reporter lines for fluorescently labelled macrophages, such as Tg(mpeg1:mCherry)UMSF001 and Tg(mpeg1:EGFP), have been used [173,174]. Making use of the Tg(mpeg1:mCherry)UMSF001 line, Evensen et al. studied the differences of anticancer nanoparticles with and without polyethylene glycol, observing a decrease in the uptake of the former by macrophages [173]. Though the evaluation of the behavior of microinjected nanoparticles is key, it is also important to study the effect of nanoparticles that are specifically developed for external treatments. In this field, Jia et al. developed a fluorescence probe, composed of cholesterol, poly(ethylene glycol)2k, and Cy5, for imaging zebrafish cell surfaces and demonstrated their utility for the assessment of nanoparticle toxicity in zebrafish upon observation of epidermal abnormalities related to damage [175]. 4.4.3. Anticancer Drug Delivery in Targeted Medicine Zebrafish has turned into an anticancer nanomedicine platform to evaluate the efficacy of this treatment, since it allows the modeling of several types of cancer and different tumor stages, as explained in Section 3.1. The transgenic Tg(FLK-1:EGFP) zebrafish line, which has green fluorescent endothelial cells, is one of the most common lines used to evaluate the antiangiogenesis capacity of drugs, including nanomedicines. The antiangiogenic effect of curcumin polymeric micelles was evaluated in this transgenic line, resulting in an effective inhibition of embryonic angiogenesis as well as tumor-derived angiogenesis owing to tumor cell xenotransplantation [170]. The microinjection of cancer cells allows assessing the capacity of nanoparticles to interact with xenotransplanted cells as well as their antitumoral efficacy [176,177,178,179]. A recent example is the work of Saraiva et al., who evaluated tumor reduction in xenografted zebrafish embryos treated with nanoemulsions comprising edelfosine, as a triple negative breast cancer treatment [180]. In a similar way, Moret et al. used zebrafish embryos that were the offspring of Casper mutants and fli1a:EGFP transgenic zebrafish to evaluate the efficacy of biodegradable poly(ethylene glycol)-poly(ε-caprolactone) nanoparticles, loaded with docetaxel, for epithelial cancer treatment, by measuring mass tumor reduction and antiangiogenic effect [181]. In addition, metastasis modelling in zebrafish embryos can be performed as a result of spreading across the circulation of xenotransplanted tumor cells. The microinjection or incubation of different types of antitumoral drugs allows the evaluation of their capacity to inhibit these metastatic processes [182,183]. 5. Zebrafish as a Tool in Personalized Medicine Chemotherapy treatments’ efficacy varies among different patients, and the results are not always successful [155]. For this reason, researchers are increasingly focusing on the development of personalized medicine strategies, such as the use of Patient Derived Xenografts (PDXs). PDXs allow the study of a particular tumor and its genome profile as well as its response to a specific treatment, owing to their capacity to maintain tumor heterogeneity. In recent years, zebrafish PDXs (zPDXs) have appeared as a new quick tool to evaluate anticancer treatments [155]. A role model was developed by Wu et al., who injected colon cancer cells from a patient into Tg(fli1:EGFP) transgenic zebrafish; the tumor–microenvironment interaction was observed, and a drug screening was carried, out allowing the selection of the most appropriate treatment for the patient based on the elimination of cancer cells [184]. This Tg(fli1:EGFP) zebrafish model, as well as the casper and nacre models, were used by Fior et al. to inject patient-derived colon cancer cells to screen different therapies. This experiment evidenced that xenografted zebrafish can be used as a fast screening platform to evaluate tumor evolution and relapse [155]. Furthermore, a Casper zebrafish model was used for the research of an effective treatment to inhibit leukemia cell proliferation through microinjection of patient-derived tumor cells [118] and was also used to study a chemotherapy drug combination in order to observe the reduction of tumor gastric mass [126]. In the same line of investigation, Usai et al. injected into wild-type zebrafish three different types of cancer cells from a patient, and different chemotherapy combinations were tested to determine the effective doses necessary to treat each cancer patient [114]. Another example of the use of casper, nacre, and Tg(fli1:EGFP) models is the work of Costa et al., in which a rectal cancer zPDX was generated and then treated with chemotherapy and radiotherapy to distinguish radiosensitive from radioresistant tumors [127]. Furthermore, in other studies, after zebrafish xenotransplantation of patient-derived cancer cells, PCR analysis was performed to analyze the efficiency of several drugs instead of measuring the reduction of tumors, achieving a new method to evaluate anticancer drug screenings [115]. In the field of nanomedicine, zebrafish also play a role as a model for personalized medicine in cancer. The work of Di Franco et al. is a clear example. Pancreatic cancer patient-derived xenografts were used to evaluate different therapies and to determine the best possible treatment for each patient. In this research, albumin nanoparticles loaded with Paclitaxel (nab-Paclitaxel), co-administrated with Gemcitabine, were one of the treatments tested. Overall, this study probes the potential of zebrafish for assessment of therapies based on nanotechnology by following a personalized approach [185]. 6. Clinical Output The zebrafish as a preclinical disease model has proven to be key to inform about human disease mechanisms and therapy. Aside from the generation of powerful cancer models for the identification of therapeutic targets, this disease model plays an instrumental role in the era of precision medicine in oncology, allowing the tailoring of the treatments to the individual characteristics of each patient. Towards this end, recent efforts are being made to make use of the zebrafish as an “avatar” model for the xenotransplantation of cancer cells from individual patients (also known as zebrafish patient-derived xenografts or zPDX) and the subsequent studies of drug efficacy and response (Table 3). However, an important limitation in this regard has been the lack of criteria for the conversion of chemotherapy dosage from human to fish. This issue was recently addressed by Usai et al., who developed a formula to estimate equivalent doses (EDs) to be used on the fish [114]. The authors tested the ED for standard chemotherapy treatment in zebrafish xenotransplanted with cancer cell lines and confirmed their efficacy as determined in clinical studies [114]. Importantly, when tumor fragments derived from patients’ surgical specimens were engrafted in the fish, and these were treated with the ED of chemotherapy drugs, they found a good agreement with observations registered in common clinical practice. In line with this, similar evidence has been observed in other studies, such as the ones published by Fior et al., Costa et al., Rebelo de Almeida et al., and Di Franco et al., who showed a similar response of colorectal cancer and pancreatic cancer patients and zPDX to standard chemotherapy/chemoradiotherapy or targeted therapy for the treatment of these tumors [155,185,186,187]. In this regard, some preliminary indications have also been observed for zPDX derived from gastric cancer samples, although this needs further validation in a larger number of patient samples [184]. In addition, this strategy has been applied for non-solid tumors, such as multiple myeloma and B-cell precursor acute lymphoblastic leukemia, showing that zebrafish xenografts show similar responses to patients [188,189]. These proof-of-concept studies suggest that avatar/zPDX models can reproduce the individual response of each patient to treatment in just a few days, representing an important step forward towards the translation of this model into clinical practice as a predictive tool for the most effective treatment for an individual patient. Indeed, expanding on the initial work by Di Franco et al., a co-clinical trial is currently evaluating if zebrafish is able to predict the therapeutic regimen with the best efficacy for patients with pancreatic, gastrointestinal, or colorectal cancer undergoing chemotherapy in an estimated cohort size of 120 patients [190]. Currently, together with this co-clinical trial, another clinical study is listed on the website of Clinical Trials Gov, in which zebrafish is used as an avatar model [191]. In this trial (NCT01395628), which was already completed, zebrafish was evaluated as a recipient for primary human leukemia samples from 10 patients, to test the anti-proliferative or toxic effects of chemotherapeutics on them. It is worth noting that one of the main limitations of cancer models to predict patient drug responses is their limited complexity and capacity to recapitulate the intratumor heterogeneity, both genetic (clone selection) and cellular (stromal compartment). However, avatar xenotransplantation models generated from surgically resected specimens may preserve the actual complexity of the tumor, overcoming such an important limitation. Despite this, we cannot forget that these models may be limited by other factors, such as the successful engraftment in the fish of the tumor material and therefore the selection of specific tumor clones and the limited number of injected cells [192]. Therefore, despite the promising results of the zebrafish avatar/zPDX models for the prediction of patient treatment response, in order to achieve precision medicine through their use, larger clinical studies are needed to validate this strategy. Aside from the potential use of the zebrafish as a predictive tool of patient drug response, this model system may also represent a valuable tool for the translation of compounds derived from zebrafish screens into the clinic as part of the personalized medicine approach. Two decades ago, zebrafish was mainly used for the development of chemical phenotypic screens, meaning the screening for compounds with therapeutic properties over a specific phenotype or disease. In the last ten years or so, zebrafish models are being used following a “from bench to bedside” approach to identify the best treatment plan for an individual patient. In this sense, the screening of compounds in the zebrafish for the development of selective therapies has experienced a relative advance in recent years, with some compounds already being tested in clinical trials, or close to it, for the treatment of various diseases, including cancer [193]. This experimental approach has been pioneered by the Group of Leonard I Zon at Harvard, who has contributed to the repurposing (or reprofiling) of four compounds, two of them as anti-cancer agents and one for graft-versus-host disease in hematologic malignancies. The first example is ProHema, a derivative of prostaglandin E2 shown to increase the generation of blood stem cells, which was repurposed for its use in blood stem cell transplantation [194]. ProHema has been tested in four phase I or II clinical trials, in three of which it was evaluated for its efficacy in hematologic malignancies (NCT00890500, NCT01627314, and NCT02354417). This compound was later incorporated into a cellular immunotherapy (ProTmune; Fate Therapeutics) used for the prevention of graft-versus-host disease and is currently being evaluated in an ongoing phase II stage clinical trial in hematologic malignancies (NCT02743351). The second compound is the antirheumatic drug Leflunomide, an inhibitor of the dihydroorotate dehydrogenase, which was identified in a zebrafish screening as a drug with the potential to interfere with the growth of melanoma [195]. The drug was included in a phase I clinical trial to test its efficacy in combination with a BRAF inhibitor (NCT01611675), but it was later canceled due to adverse events. The third compound is the all-trans retinoic acid (ATRA), in this occasion identified using a pluripotent zebrafish blastomere culture system, which was shown to suppress the transcription factor c-myb, a driver of adenoid cystic carcinoma [196]. These findings led to the initiation of a phase II clinical trial (n = 18) evaluating the safety and effectiveness of ATRA in treating adenoid cystic carcinoma, completed last year (NCT03999684), in which patient response to ATRA was not observed for the tested dose and schedule [197]. Currently, a second phase II trial (n = 30) is underway, testing the compound in patients with recurrent metastatic adenoid cystic carcinoma of the head and neck (NCT04433169). Another good example of this research approach is Rosuvastatin, used for the treatment of hypercholesterolemia in cardiovascular disorders and repurposed as an antiangiogenic drug after a genetic screen in zebrafish [198]. Rosuvastatin was shown to inhibit the growth of prostate cancer cells and was later included in a phase II clinical trial to evaluate the improvement in the response to its combination with standard chemotherapy in rectal cancer (NCT02569645, still recruiting an estimate of 48 patients). Moreover, zebrafish also allows for the testing of combinatory treatments. A very recently developed immune-deficient adult zebrafish model (prkdc−/−, il2rga−/−) by David Langenau at the Massachusetts General Hospital Research Institute has been used for the identification of a possible treatment for rhabdomyosarcoma [95]. The researchers made use of this model to prove the efficacy of the combination of a poly ADP ribose polymerase (PARP) inhibitor plus temozolomide chemotherapy, both drugs approved for use in the clinic, in eliminating engrafted rhabdomyosarcoma cells, as opposed to single drug treatment, an effect that was confirmed in a mouse xenograft model [95]. This combinatorial treatment is being investigated in a phase I study for Ewings sarcoma or Rhabdomyosarcoma in an estimated cohort of 93 patients (NCT01858168). In addition to the above examples, some other clinically approved compounds, repurposed through zebrafish screening, have not reached the clinical testing stage. Perphenazine (PPZ), a drug approved for psychosis therapy, was found to be effective against T-cell acute lymphoblastic leukemia (T-ALL) in a combined screening for small molecules with toxic effect in MYC-overexpressing thymocytes in zebrafish and T-ALL cells [199]. More recently, a potential use of PPZ for the treatment of endometrial cancer has been suggested based on in vitro and mouse experimental data, expanding the potential use of this compound for solid tumors [200]. A zebrafish genetic model of β-catenin driven hepatocellular carcinoma (HCC) allowed the identification of two antidepressants, amitriptyline and paroxetine, as suppressors of liver growth [201]. Further experiments developed in this study have shown that paroxetine was also able to decrease tumor burden in a mouse HCC model [201]. Another successful reprofiling example was shown by Fernandez del Ama et al., who, using an oncogenic-RAS-driven zebrafish melanoma model, observed that the mTOR inhibitor rapamycin, as well as the compounds disulfiram and tanshinone, synergized with inhibitors of the MEK and PI3K/mTOR signaling pathways to inhibit melanoma development [202]. In summary, we can clearly see the huge role of zebrafish as a cancer model in the development of pre-clinical studies for the identification of compounds with antitumoral properties. However, the identification of anticancer compounds is not enough to make an impact on cancer patient care, as most do not reach the clinical testing, and more direct approaches are needed. In this sense, the field is drifting towards the development and use of zebrafish avatar models for the testing of patient drug sensitivity, since each individual patient may present a different response based on the unique genetic alterations that his/her tumor harbors. Particularly, future work should be aimed at testing the predictive value of zebrafish avatars on a reduced number of therapeutic options (targeted therapies and immunotherapies) but in larger patient cohorts, in order to achieve a truly personalized treatment. The xenograft approach is supported by multiple studies that have validated the development of zebrafish xenografts from patient-derived material [203], and, as discussed before, more studies are coming out showing the true potential of the zebrafish avatars for this purpose, predicting patient responses [115,139,155,156,184,185,186,187,189]. 7. Conclusions The zebrafish is a powerful model for studies of various cancer treatments, including new therapies, such as those based on nanomedicine. Its versatility, which allows it to be used not only in embryonic stages but also as adult individuals, together with the enormous variety of transgenic lines available, are fundamental characteristics of this model. However, more research efforts should be directed toward the development of standardized protocols for tumor cell xenotransplantation and drug effectiveness analysis, as well as toward optimizing the routes of administration in order to translate the results to higher models and more patients. Moreover, since most of the research is performed in larvae, long-term drug exposure and the assessment of response lack translatability to patients, and for that other animal models are needed. Furthermore, the use of zebrafish as an intermediate step between cell culture studies and higher models, such as mice and rats, reduces the number of seconds needed to perform the experiments, thus implementing the 3R (replacement, reduction, and refinement) principle of animal welfare. Author Contributions Conceptualization, M.C. and M.d.l.F.; writing—original draft preparation, M.C., S.A., A.P.-L., A.J.V.-R. and R.P.; writing—review and writing, M.C., A.P.-L., L.S., R.P. and M.d.l.F.; supervision, R.P. and M.d.l.F.; funding acquisition, L.S. and M.d.l.F. All authors have approved this submission. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest M.d.l.F. is the co-founder and CEO of DIVERSA technologies. A.J.V.-R. is the co-founder and COO of DIVERSA technologies. Abbreviations AgNPs Silver nanoparticles ATRA All-trans retinoic acid AuNPs Golden nanoparticles BC Breast cancer BPC-ALL B-cell precursor acute lymphoblastic leukemia CAR-T cell Chimeric antigen receptor T cell CR Complete response CRC Colorectal cancer NSCLC Non-small-cell lung cancer CTLA-4 Cytotoxic T-lymphocyte-associated antigen-4 DEN Diethylnitrosamine DMBA Dimethylbenzanthracene Dpf Days post-fertilization ED Equivalent doses EGFR Epidermal growth factor receptor EMA European Medicines Agency ENU Ethylnitrosourea FDA Food and Drug Administration GBM Glioblastoma GC Gastric cancer GCT Germ cell tumor HCC Hepatocellular carcinoma Hpf Hours post-fertilization ICIs Immune checkpoint inhibitors ISVs Intersegmental vessels MET Mesenchymal-epithelial transition MM Multiple myeloma MMDOX Doxorubicin-loaded mixed micelles MNNG N1-nitro-N-nitrosoguanidine MO Morpholinos MPNSTs Malignant peripheral nerve sheath tumors MSNs-FA Mesoporous silica nanoparticles coated with folic acid NDMA N-nitrosodimethylamine NF1 Neurofibromin 1 NK Natural killer cells PARP Poly ADP ribose polymerase PC Pancreatic cancer PD-1 Programmed cell death-1 PDAC Pancreatic ductal adenocarcinoma PD-L1 Programmed cell death ligand-1 PDX Patient Derived Xenografts PlexA1 Plexin-A1 PPZ Perphenazine PR Partial response pVHL Von Hippel-Lindau protein shRNA Short hairpin RNA TALENs Transcription Activator-Like Effector Nucleases T-ALL T-cell acute lymphoblastic leukemia TILLING Targeted Induced Local Lesions in Genomes VEGF Vascular endothelial growth factor Wpf Weeks post-fertilization ZFNs Zinc Finger Nucleases zPDXs zebrafish patient-derived xenografts Figure 1 Most common carcinogenic substances used for tumor induction in zebrafish. Figure 2 Reverse genetics strategies (in blue) and their respective examples of altered genes and the associated tumor types. Figure 3 Sites for heterotopic transplantation of tumor cells (in red) in zebrafish. Modified from Servier Medical Art (https://smart.servier.com; accessed on 3 March 2022), licensed by a Creative Commons Attribution 3.0 Unported License, and Lizzy Griffiths. Figure 4 Zebrafish as a model for evaluation of different cancer treatments. Modified from Servier Medical Art (https://smart.servier.com; accessed on 3 March 2022), licensed by a Creative Commons Attribution 3.0 Unported License, and Lizzy Griffiths. cancers-14-02238-t001_Table 1 Table 1 Benefits and drawbacks of using zebrafish for modeling human diseases in comparison with other animal models. Advantages Disadvantages Simple anatomy Some mammalian organs are missing External fertilization Optimal temperature at 28 °C, compromising human cell viability Embryo and larvae optical transparency Lack of sexual chromosomes Rapid development and sexual maturation Pooling individuals prevent the observation of interindividual differences High fertility rates Mice genetic homology is higher Large number of individuals and statistical power Low amount of certain tissues for biological assays Robust embryos Genetic duplication High homology in human disease-related genes Protocol variability, limiting the comparison among studies Late activation of the adaptive immune system Need of mammal models for further preclinical studies Cost-effective and easy maintenance Low antibodies availability for molecular assays Easy genetic manipulation Low number of cells for xenograft assays Availability of reporter lines Many existing zebrafish resources and repositories cancers-14-02238-t003_Table 3 Table 3 Studies involving the use of zebrafish PDXs for drug efficacy and response. Tumor Type Patients (n) Aim Outcome Ref. Pancreatic (PC), colorectal (CRC), and gastric cancer (GC) n = 24 (12 PC, 8 CRC, and 4 GC patients) Xenograft establishment (n = 6) Response to chemotherapy options (according to the cancer type) evaluated as partial response (PR) and complete response (CR) Xenografted tumor tissue can engraft and survive in the zebrafish (100%). Response to chemotherapy: ▪ PC: PR to GEM/nab-P (58.33 %), GEM (50%), GEMOX (50%), and FOLFOXIRI (33.33 %). No CR was observed. ▪ CRC: PR to FOLFOX, FOLFIRI and FOLFOXIRI (62.5%), and to 5-FU (37.5%). CR to FOLFIRI (12.5%). ▪ GC: PR to FOLFIRI (100%), FOLFOX, FLOT and ECF (25%). CR to FOLFIRI (25%). [114] Colorectal cancer (CRC) n = 11 Xenograft establishment (n = 5) Sensitivity to standard chemotherapy and targeted therapy Cell engrafted in 5/5 cases (100%), with different success rates based on the percentage of fish showing engraftment (from 47 to 89%). Zebrafish xenograft response to FOLFOX anticipated patient relapse/no relapse within 3 m to 6 m in 4/5 patients (80%). Lack of response to Cetuximab was associated with mutations highly linked to Cetuximab resistance. [155] Gastric cancer (GC) n = 14 Xenograft establishment Assess the efficacy of anti-GC agents: 5-FU, docetaxel, and apatinib (n = 4) Successful transplantation in 9/14 patient samples (64.2 %). Zebrafish xenografts subjected to 5-FU and apatinib showed different degrees of sensitivity. [184] Pancreatic ductal adenocarcinoma (PDAC) n = 15 Xenograft establishment Evaluation of response to chemotherapy Establishment of PDAC xenografts in 15/15 cases (100%). Significant reduction in tumor area observed in 6/15 cases (40%) for at least one chemotherapy scheme (FOLFOXIRI, GEMOX, Gem/nab-P, and GEM. [190] Breast (BC) and colorectal cancer (CRC) n = 6 (3 BC and 3 CRC patients) Response to the anti-VEGF therapy bevacizumab Comparison of patient’s response with matching avatars (n = 2) Zebrafish avatars can reflect both pro- and anti-metastatic effects of bevacizumab. Resistance to bevacizumab of zebrafish avatar correlation with the clinical resistance and disease progression of the matched patients. [187] Multiple myeloma (MM) n = 6 Xenograft establishment (perivitelline space) Evaluate drug response in newly diagnosed (n = 2) and relapsed/refractory patients (n = 4) Efficiency of MM primary cell engraftment of around 80%. Zebrafish xenograft responses to bortezomib and lenalidomide recapitulated patient responses in all 6 cases. [189] B-cell precursor acute lymphoblastic leukemia (BCP-ALL) n = 15 Xenograft establishment (pericardium) Response of BCP-ALL cell lines to venetoclax (n = 7) BCP-ALL were successfully expanded in 9/15 embryos (60%). Xenografts produced varied responses to venetoclax, mirroring in two cases the refractory response to venetoclax of the matching patients. [188] Abbreviations: ECF: 5-Fluorouracil + Cisplatin + Epirubicin; FOLFIRI: 5-Fluorouracil + Lederfolin + Irinotecan; FOLFOX: 5-Fluorouracil + Lederfolin + Oxaliplatin; FOLFOXIRI: 5-Fluorouracil + Folinic acid + Oxaliplatin + Irinotecan; FLOT: 5-Fluorouracil + Lederfolin + Oxaliplatin + Docetaxel; GEM: Gemcitabine; GEMOX: Gemcitabine + Oxaliplatin; GEM/nab-P: Gemcitabine + nab-Paclitaxel; 5-FU: 5-Fluorouracil. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2020 CA Cancer J. Clin. 2020 70 7 30 10.3322/caac.21590 31912902 2. Sung H. Ferlay J. Siegel R.L. Laversanne M. Soerjomataram I. Jemal A. Bray F. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091742 polymers-14-01742 Article Binocular Vision-Based Yarn Orientation Measurement of Biaxial Weft-Knitted Composites Xiang He 123 Jiang Yaming 12 Zhou Yiying 124* https://orcid.org/0000-0001-8383-8068 Malengier Benny 3 Van Langenhove Lieva 3 Wang Peng Academic Editor Xiao Shenglei Academic Editor 1 Ministry of Education Key Laboratory of Advanced Textile Composite Materials, Institute of Composite Materials, Tiangong University, Tianjin 300387, China; he.xiang@ugent.be (H.X.); jiangyaming@tiangong.edu.cn (Y.J.) 2 School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China 3 Department of Materials, Textiles and Chemical Engineering, Ghent University, 9000 Gent, Belgium; benny.malengier@ugent.be (B.M.); lieva.vanlangenhove@ugent.be (L.V.L.) 4 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 999077, China * Correspondence: yi-ying.zhou@connect.polyu.hk 25 4 2022 5 2022 14 9 174215 3 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The mechanical properties of fiber-reinforced composites are highly dependent on the local fiber orientation. In this study, a low-cost yarn orientation reconstruction approach for the composite components’ surface was built, utilizing binocular structured light detection technology to accomplish the effective fiber orientation detection of composite surfaces. It enables the quick acquisition of samples of the revolving body shape without blind spots with an electric turntable. Four collecting operations may completely cover the sample surface, the trajectory recognition coverage rate reached 80%, and the manual verification of the yarn space deviation showed good agreement with the automated technique. The results demonstrated that the developed system based on the proposed method can achieve the automatic recognition of yarn paths of views with different angles, which mostly satisfied quality control criteria in actual manufacturing processes. binocular vision textile composite preform yarn orientation non-destructive testing image processing ==== Body pmc1. Introduction In various industrial fields, fiber-reinforced polymers (FRPs) are more commonly used to develop load-bearing, lightweight products [1,2]. The key benefits are the ability to create complex shapes in a short manufacturing time while maintaining high specific mechanical properties [3]. In addition, it is critical to consider fiber orientation as a major factor in the entire technological process, because the FRP exhibits an anisotropic behavior that is mainly dependent on the fiber orientation. All among the non-crimp fabrics, biaxial weft-knitted (BWK) fabrics, with excellent formability, flexible designability and low manufacturing costs, have been extensively used as reinforcements of composite materials in the automotive and aerospace industries [4,5,6]. However, there will be bending and shearing deformation of yarn during the fabric forming process, and the spacing between yarns will also change and lead to slippage, resulting in changes in fiber orientation and uneven distribution of the local fiber volume fraction [7,8]. The occurrence of the above phenomena will seriously affect the consistency of the final composite with the design goal [9,10]. Therefore, the detection of the fiber orientation after fabric forming is an essential factor to determine the mechanical properties of the composite components. With the development of non-destructive testing (NDT) technology, researchers have carried out a lot of research work on the above problems by using different measurement methods [11,12]. El Said et al. [13] used computed tomography (CT) technology to analyze the local yarn orientation and corner bridging region after a preform forming procedure. However, the cost of CT technology is high, the imaging speed is slow, and it is difficult to detect structural parts with large or complex curvature. Wu et al. [14] characterized the fiber orientation and in-plane and out-of-plane waviness of carbon fiber composites based on eddy current testing technology. This method is applicable to large areas of composite structures and is able to deliver the local fiber orientation in the real state, but it can only be used to detect conductive materials—it is not applicable to insulating materials such as aramid fiber or glass fiber. Nelson et al. [15] showed how image processing methods can be used to create three-dimensional maps of ply orientations and waviness using ultrasonic instantaneous-phase data, but in practical processes, the sample must be soaked in water or sprayed with an ultrasound coupler on the sample surface before testing. Atkinson et al. [16] demonstrated the capabilities and limitations of polarization vision technology as applied to FRP component fiber angle inspections. During the image acquisition process, the sample cannot be moved and the sample shape is relatively flat. This method will result in blind spots when collecting samples with a rotary body or complex curvature, which lacks certain universality. Compared with the above methods, binocular vision detection technology can not only effectively obtain the depth information of the image, but also has advantages such as being extremely fast and cheap, and requiring very little physical space on an inspection/manufacturing line while maintaining competitive precision in comparison to the state of the art [17]. It has been widely used in defect detection, assembly positioning, size evaluation and other aspects in the field of composite material manufacturing [18]. However, we found no report on the application of this technology to yarn orientation detection after preform forming. In the present study, a binocular vision system based on structured light for accurate yarn orientation detection is built. Integrating with an electric turntable, texture information and geometric shape information of the hemispherical shell structure BWK composite material are acquired without blind spots. The efficiency of the proposed method is analyzed systematically. The manually measured results of the yarn space are used to verify the accuracy of the method. 2. Materials and Methods 2.1. Experimental Sample In this paper, aramid BWK fabric was used as the preform; both the warp and weft inserting yarns were made of Kevlar-49 aramid fiber tows, and the warp and weft densities were 4.7 tows/cm, as shown in Figure 1a. Only the weft inserting yarns and knitted loops can be seen from the top view, and the legs of the loop represent the direction of the warp inserting yarns [6]. The hemispherical shell was prepared by the vacuum infusion process (VIP). During the manufacturing process, one lay of fabric was formed on a female mold, which had a diameter of 150 mm. Then, vinyl ester resin R-806 was injected and cured under room temperature. After mechanical trimming, the final part was obtained, as shown in Figure 1b. 2.2. Experimental Setup To realize the precise acquisition of 3D data, a full-view 3D data collection system (as shown in Figure 2) is built. The main equipment used for data acquisition includes two HIKVISION MV-CE013-80UM COMS cameras, two Computar MP1614-MP2 industrial camera lenses, a Tengju X20H structured light projector and a Sanying ERS100 electronic control turntable. The CMOS cameras are black-and-white industrial cameras with a resolution of 1280 × 1024 pixels. The focal length of the lenses is 16 mm. The resolution of the projector is 1280 × 720 pixels. The turntable’s diameter and resolution are 100 mm and 0.00125 deg, respectively. The software platform was written in PCL, OpenCV within the C ++ environment, which realizes the functions of binocular system calibration, image processing and three-dimensional reconstruction of the yarn path. The system is around 500 mm away from the measured object when measuring the hemispherical specimen, and the angle between the two cameras is 60°. The measuring range of this system is approximately 320 × 250 mm, limited by the image resolution of the camera and the distance between the cameras and specimen. 2.3. Outline of Testing The flowchart of the yarn orientation detection is shown in Figure 3. Firstly, we calibrate the binocular camera, and then obtain the location of the rotation axis of the turntable. Afterwards, the sample is placed on the spherical strut mold on the turntable for scanning, and the three-dimensional morphology information and image information of the sample part are acquired, respectively. After each acquisition step, the turntable rotates 90° and repeats the previous acquisition work. The image information collected each time will be filtered and its profile extracted. Next, this is mapped to three-dimensional space. In the acquisition process, the rotating speed of the turntable is 5°/s, and the acquisition time of each camera is 6 s. The total operation time is 72 s. 3. Yarn Path Reconstruction 3.1. Stereo Calibration In this study, an improved Zhang’s calibration method proposed by Song et al. [19] is used to obtain the rotation matrix Rl,, Rr and translation matrix Tl, Tr from the world coordinate system (WCS) to camera coordinate system (CCS). These also include the internal and external parameters of the left and right cameras. In addition, it is also necessary to obtain the pose relationship of the two cameras relative to the same coordinate system through stereo calibration, i.e., rotation matrix R and translation matrix T, so as to calculate the depth information of the point in the WCS [20]. The stereo calibration principle of the left and right cameras is shown in Figure 4. After each acquisition process, the pixel point P in the WCS will be projected on the imaging planes Πl and Πr of the left and right cameras, respectively, and the points Pl and Pr are obtained; then, (1) {Pl=RlP+TlPr=RrP+Tr  Taking the left camera as the reference, if the rotation and translation matrices between the left and right cameras are s are R and T, the relationship between the matching points Pl and Pr is:(2) Pr=RPl+T  Combining Equations (1) and (2) gives:(3) {R=RrRl−1T=Tr−RTl  3.2. Turntable Axis Calibration In order to obtain the relationship before and after the rotation of a point around the axis, it is necessary to calculate the parameters of the turntable axis equation in the WCS and obtain the rotation angle. The calibration method adopted in this paper is as follows. Firstly, a plane circular calibration target is vertically fixed on the turntable, and the rotating platform is controlled to drive the target to rotate. The target is acquired once every 2° of the turntable, and 20 times in total. Then, the motion trajectory of each tag on the target is theoretically a spatial circle centered on the axis of the turntable. The centers formed by the rotation of the tags at different positions should be located at different positions on the axis of the turntable. Finally, the position of the turntable axis in the WCS is obtained by fitting the positions of all circle centers. The solving process is shown in Figure 5. The specific implementation steps are as follows: (1) According to the stereo calibration results, the point set of a column around the rotation axis in the CCS is acquired, as shown in Figure 5b; (2) Calculate the centers of each motion trajectory formed by the rotation of a point around the turntable axis in point set P, and the set of all the obtained centers is O. These centers are located at different positions of the rotation axis, as shown in Figure 5c. (3) The three-dimensional spatial line passing through the center point set O is fitted by the RANSAC method [21], which is the turntable axis, as shown in Figure 5d. 3.3. Acquisition of Three-Dimensional Data Firstly, the coded structured light is projected onto the object surface through the projector, and the image information of the object surface is acquired by the cameras. Then, the three wavelength phase shift profilometry method is used to decode the structured light to obtain the phase information [22]. Combined with the phase constraint and epipolar constraint, the three-dimensional point cloud data are generated. Because a black-and-white camera is used in this study, the point cloud data contain not only the spatial coordinate information of each pixel, but also the gray information with the value range of (0, 255), as shown in Figure 6a. 3.4. Feature Extraction In order to extract the texture feature of yarn orientation, this study firstly uses the mean filtering method to remove the background and small useless features after obtaining the original image, as shown in Figure 6b. Conventionally, edge detection approaches use gradient differential operators such as the Roberts operator, Sobel operator, Prewitt operator and Canny operator [23]. Because of its reliability in analyzing noisy images, the 90° and 0° Sobel operators are utilized in the algorithms, so that the features of yarns in two directions are more prominent [24]. After this, the Gaussian filtering algorithm is used to enhance the visibility of the yarn contour (Figure 6c). Finally, the filtered contour is binarized, and the partially broken contour is connected by closing operation to obtain the complete path of the yarn, as shown in Figure 6d. It can be found from Figure 6d that after binarization, each yarn contour contains too many pixels, resulting in a too wide yarn. Therefore, it is necessary to extract the skeleton of the yarn with a thinning algorithm to simplify the image data; the results are shown in Figure 6e. 3.5. Merging Since the thinned image contains binary data, the findcontour function in OpenCV can be directly used to extract the contour of the yarn. In this paper, contour data with a number of pixels of less than 30 are regarded as noise points and eliminated; moreover, the pixel information of each yarn is saved separately. In Figure 7, the results of the detected two directions are represented by green and yellow lines, respectively, and mapped on the original image. Using the spatial coordinate information of each pixel from Section 3.3, the spatial path of each single yarn can be obtained, i.e., the two-dimensional data are mapped to three-dimensional data. At this time, the data acquired from the second to the fourth acquisition still need to be rotated to their correct location, by rotating around the axis of the turntable to merge in the correct position in the WCS [25]. It is assumed that the equation of the turntable axis obtained in Section 3.1 is Equation (4). The point P (x, y, z) and the rotation angle θ (θ = 90°, 180°, 270°) before rotating are known, and the coordinates of the rotated point P’ can be calculated by the matrix M, namely Equation (5). (4) x−x0a=y−y0b=z−z0c (5) M=[a2H+cosθabH−csinθacH+bsinθ(x0−aK)H+(cy0−bz0)sinθabH+csinθb2H+cosθbcH−asinθ(y0−bK)H+(az0−cx0)sinθacH−bsinθbcH+asinθc2H+cosθ(z0−cK)H+(bx0−ay0)sinθ0001] where H=1−cos(θ), K=ax0+by0+cz0. The relationship between point P and P′ can be expressed by:(6) [x′y′z′1]=M[xyz1] The three-dimensional orientation reconstruction of the whole fabric can be realized by combining the spatial trajectory data of all yarns, as shown in Figure 8, where each yarn path is marked with a random color. It can be seen from the reconstruction results that some of the yarns at the bottom edge of the shell have failed to reconstruct. This is due to the fact that when the hemispherical shell is mechanically cut, the resin at the processing place is subjected to force and microcracks form a white edge, which we have highlighted in the digital photo taken from the final part; see Figure 9. This white edge interferes with image acquisition and ultimately means that the yarn at the bottom edge of the shell is unable to be reconstructed. 4. Results and Verification 4.1. Trajectory Recognition Coverage Rate In order to verify the feasibility of the system, the ratio of the yarn trajectory data coverage area to the pixel area of the original sample image is used as the ‘trajectory recognition coverage rate’ (TRCR) to evaluate the efficiency of yarn path extraction. In the 0° region, for instance, the detailed method works as follows. Firstly, delete the pixels that represent the contour of the sample in the two-dimensional data by finding the most peripheral pixels (Figure 10b,f). Then, the outermost pixels are connected to form a closed region (Figure 10c,g), and the pixel area of yarn paths in the 0° region is calculated. The pixel areas of Figure 10d,h are 559,968 and 573,863 pixels, respectively. Then, the sample’s image after removing the background is binarized, and the pixel area of the black pixel is calculated. For our example, this was 636,110 pixels, as shown in Figure 10j. Finally, the TRCR values of the weft and warp direction are computed, which are 88.03% and 90.21%, respectively (Figure 10k,l). In the same way, the TRCR values of the 90°, 180°, 270° regions are shown in Table 1. In addition, the area of the surface obtained by single scanning in Figure 10a can be obtained as 20,735.68 mm2 through the ‘Compute Area’ function of Geomagic software. One quarter of the hemisphere sample area is 8831.25 mm2; even with 86% TRCR, this system can completely reconstruct the yarn orientation of the sample after four times of acquisition. 4.2. Experimental Evaluation of Accuracy The distance between yarns is an important factor for calculating the fiber volume friction of the composites. Thus, it was used to verify and evaluate the accuracy of the method by comparing the experimental results and scanned results. As shown in Figure 11a, the authentic yarn space was measured by sticking two paper rulers on the sample along the warp and weft direction from the top. For the scanned data, two feature planes along the warp and weft were built, as illustrated in Figure 11b. After this, the intersection points between the two planes and the paths of the warp and weft yarns were the objects for comparison. The results are shown in Figure 12, where the deviations of the yarn distance along the warp and weft directions are maximally 0.48 mm and 0.57 mm, respectively. It can also be seen that, since the manually measured data are the yarn space, its coordinates are located on the ideal sphere (with a radius of 75 mm). However, according to the scanned results, the radius of the sample along the warp direction becomes larger; on the contrary, it becomes smaller along the weft direction. This reflects sample yield distortion after the demolding process. 5. Conclusions The aim of this paper is to provide a measurement method based on binocular vision for the characterization of yarn orientation in the BWK fabric-reinforced composite shell. The relevant conclusions can be stated as follows. (1) A low-cost three-dimensional scanning system based on binocular structured light was built to realize the automatic, rapid and non-blind acquisition of three-dimensional data of the rotating sample. The three wavelength phase shift profilometry was used to reconstruct the three-dimensional morphology of the sample. (2) The reconstruction results show that the TRCR reaches 86%. The assessment of the actual yarn space of the component shows a good correlation between the manual and scanning results. The measurement accuracy and coverage rate of the system have essentially met the quality control requirements of the practical production process. (3) A drawback of this system is that in order to prevent the sample from moving during the rotation of the turntable, the rotation speed of the turntable used in this study is relatively slow. In the future, a firmer sample fixation method can be adopted and the rotation speed of the turntable can be increased, so as to further reduce the time-consuming nature of acquiring complete sample information. (4) The main limitations with the approach outlined in this paper are that the sample shape should not have concavity so as to be fully visible to the camera. A solution to this could be to add another rotating axis of the sample holder. Moreover, this approach is limited to the analysis of the top (visible) layer of a part only. Above all, the experimental results show that this method has remarkable value for equipment based on binocular vision technology to detect the yarn path in composite materials. At the same time, the parameters obtained by this method can be feasibly applied in the simulation practice of the composite forming process to improve the simulation accuracy and provide guidance for the actual manufacturing route. Acknowledgments This work was supported by the Natural Science Foundation of Tianjin (grant number 18JCZDJC10020). The authors also gratefully appreciate the financial support of the China Scholarship Council (grant number CSC202008120134). Author Contributions Funding acquisition, H.X. and Y.J.; Methodology, H.X.; Project administration, L.V.L.; Software, H.X.; Visualization, Y.Z. and B.M.; Writing—original draft, H.X.; Writing—review and editing, Y.Z. and B.M. All authors have read and agreed to the published version of the manuscript. Funding The research were funded by the Natural Science Foundation of Tianjin (grant number 18JCZDJC10020) and the China Scholarship Council (grant number CSC202008120134). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 BWK fabric and composite specimens: (a) BWK fabric; (b) composite sample. Figure 2 The binocular structured light 3D measurement system. Figure 3 Flow chart of detection algorithm for yarn orientation. Figure 4 Principle of stereo calibration for binocular camera. Figure 5 Calibration of turntable axis: (a) process of calibrating the turntable axis; (b) obtained point sets in different colors; (c) circle center fitting; (d) turntable axis fitting. Figure 6 Image processing results: (a) original data; (b) close-up image of mean filter result; (c) convolution; (d) binarization; (e) thinning. Figure 7 Close-up of the yarn edge detection results. Figure 8 Overall reconstruction result. Figure 9 Local defects caused by mechanical cutting. Figure 10 Calculation of the TRCR in reconstructed 0° region: (a) reconstruction result of weft yarn paths; (b) delete the edge contour of the sample from weft reconstruction result; (c) establish reconstruction area boundary in weft reconstruction result; (d) calculate the area of weft direction reconstruction area; (e) reconstruction result of warp yarn paths; (f) delete the edge contour of the sample from warp reconstruction result; (g) establish reconstruction area boundary in warp reconstruction result; (h) calculate the area of warp direction reconstruction area; (i) original image of sample; (j) binary image of sample; (k) TRCR result of warp yarn paths; (l) TRCR result of weft yarn paths. Figure 11 Accuracy evaluation of yarn space: (a) manually measuring yarn space; (b) obtaining the yarn space of the scanned data. Figure 12 Yarn distance comparison results: (a) warp yarn profile location; (b) weft yarn profile location. polymers-14-01742-t001_Table 1 Table 1 The TRCR results of four regions. Title 1 0° 90° 180° 270° Weft 88.03% 86.71% 87.51% 87.78% Warp 90.21% 91.47% 90.39% 91.49% Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Yang Z. Jiao Y. Xie J. Chen L. Jiao W. Li X. Zhu M. Modeling of 3D woven fibre structures by numerical simulation of the weaving process Compos. Sci. Technol. 2021 206 108679 10.1016/j.compscitech.2021.108679 2. Jiao W. Chen L. Xie J. Yang Z. Fang J. Chen L. Effect of weaving structures on the geometry variations and mechanical properties of 3D LTL woven composites Compos. Struct. 2020 252 112756 10.1016/j.compstruct.2020.112756 3. Gao Z. Chen L. A review of multi-scale numerical modeling of three-dimensional woven fabric Compos. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095135 ijerph-19-05135 Brief Report Impact of Providing Peer Support on Medical Students’ Empathy, Self-Efficacy, and Mental Health Stigma https://orcid.org/0000-0001-6059-4857 Abrams Matthew P. 1* Salzman Joshua 1 Espina Rey Andrea 2 Daly Katherine 3 Reid Natasha Academic Editor Neuhaus Maike Academic Editor 1 College of Medicine, University of Central Florida, Orlando, FL 32827, USA; salzmanjo@knights.ucf.edu 2 Focused Inquiry & Research Experience Module Department, College of Medicine, University of Central Florida, Orlando, FL 32827, USA; andrea.espinarey@ucf.edu 3 Department of Clinical Sciences and Student Affairs, College of Medicine, University of Central Florida, Orlando, FL 32827, USA; katherine.daly@ucf.edu * Correspondence: mattpabrams@knights.ucf.edu; Tel.: +1-619-227-9228 23 4 2022 5 2022 19 9 513526 2 2022 15 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Background: Peer-support programs in medical school can buffer feelings of inadequacy, anxiety, social isolation, and burnout, drawing upon the benefits of near-peer-support resources. This study examined the effects of providing support to students in a medical school peer-support program. Methods: Using a pre-post, quasi-experimental study design, the investigators surveyed medical students who were peer supporters in their second through fourth years of medical school with four measures assessing (1) empathy, (2) self-efficacy, (3) mental health stigma, and (4) likelihood to assist peers with mental health problems to examine if serving as a volunteer peer supporter had any effect. Participants included 38 medical students that were actively enrolled peer supporters during the 2020–2021 year at a United States allopathic medical school. Results: Medical students who participated as peer supporters were found to have higher ratings of empathy scores (Z = −1.964, p = 0.050, r = 0.34) and self-efficacy scores (Z = −2.060, p = 0.039, r = 0.35) after participation in the program. No significant changes were noted for mental health stigma or likelihood to assist peers with mental health problems. Discussion: Peer-support programs present a low-cost, sustainable modality to promote wellbeing in medical students. There is a growing body of literature documenting the benefits of peer-support services. This brief, novel study examined the effects of providing peer support on the peer supporters and found higher self-reported ratings of empathy and self-efficacy after participation. These findings underscore peer-support programs as a valuable wellness resource not only for medical students who use the services but for those who provide them as well. peer support medical education empathy mental health stigma ==== Body pmc1. Introduction Medical school is recognized as a difficult and stressful time for students [1,2,3]. Besides the stressors intrinsic to learning a fast-paced, rigorous medical curriculum, many students encounter a culture of competition at their institutions [4]. These factors contribute to imposter syndrome experienced by many students [5]. Further, even for academically adept students, these stressors and social interactions weigh heavily on students [6]. Many medical students report increased symptoms of depression [7], symptoms of anxiety [8], burnout [9], substance misuse [10], and suicidal ideation [11] compared to age-matched peers in the general population. For example, in a recent meta-analysis, the overall prevalence of depressive symptoms among medical students was 27.2%, and the overall prevalence of suicidal ideation was 11.1% [11]. Among medical students who screened positive for depression, only 15.7% sought psychiatric treatment [11]. Consistent with low rates of mental healthcare usage, other studies have found that mental health stigma may make some students reluctant to seek care [12] or disclose their mental health status [13]. These trends continue beyond medical school, as rates of physicians with depression are also higher than their age-matched peers [14]. Barriers to help seeking that have been identified in previous studies include concerns about confidentiality, time, cost, perceived stigma, potential repercussions, and fear of unwanted interventions [15,16]. These high rates of mental health symptoms underscore the need for effective prevention programs. Although medical students indicate social support and shared experiences as important resources to promote positive mental health, medical schools must implement effective wellness strategies within the learning environment to properly support their students [8]. Promoting the wellbeing of medical students and the physician workforce is important for patient care [17]; therefore, medical schools across the nation are attempting to reduce the high burden of mental health symptoms [18]. Much of the research highlighting mental health declines in medical school occurred prior to the COVID-19 pandemic, which has only further exacerbated these concerns by placing greater demands on health professionals and increasing social isolation. A positive trend since the pandemic has been the increase in distance learning [19] and telehealth, making support resources more easily available. Some institutions have implemented wellness interventions such as “resilience days” to fortify learners against burnout [20]. Recently, there has been an increase in student-led initiatives, such as student-led mental health workshops [21]. Peer-support programs—a newly employed wellness intervention in medical education—can be used to buffer feelings of inadequacy, anxiety, social isolation, and burnout, drawing upon the benefits of near-peer-support resources used in other educational settings. For decades, colleges have documented the mental health benefits of peer counseling and peer health education [22]. Peer-support programs are easy to implement and cost-effective—yet, it was not until the early 2000s that medical schools began to create peer -support programs to expand non-clinical wellness services beyond licensed mental health counselors [23]. Benefits of peer support have been demonstrated in a variety of health settings, from anesthesiologists [24] to nursing students [25]. Zhao et al. (2016) found peer caring and resilience improved the subjective wellbeing of both nursing and medical students [25]. Peer-support programs vary in size and structure. Generally, students are selected and trained to serve as ambassadors of wellness to provide an umbrella of non-clinical, non-judgmental, same-day support. At some institutions, peer supporters host events—such as mindfulness, sleep, and stress-reduction interventions [26]—to promote wellness and destigmatize mental health [27]. Peer-support programs can help foster a supportive culture in which students take an active role in their classmates’ wellbeing and combat feelings of competition. Peer-support programs help students facing a range of challenges from sub-clinical mental health struggles (i.e., test anxiety) to clerkship experiences to discrimination to relationship issues [23,28]. For example, peer support was demonstrated to reduce stigma surrounding academic stress and psychological distress [23]. Peer support is critical to encouraging students to seek support and streamlining referrals to counseling services. Students often do not feel as comfortable turning to faculty for mental health concerns [19], and therefore, peer supporters are ideally situated to recognize warning signs in a classmate. Medical students may also worry that showing signs of depressive symptoms or mental health problems may cause them to be deemed unfit by peers and professors. Peer-support programs normalize feelings of uncertainty and sadness and help students realize they are not alone. One of the most common reasons students use peer support is academic stressors or concerns such as failed exams [28]. Further, symptoms of anxiety and depression have a higher prevalence rate in medical students when examinations are imminent, highlighting the importance for research to specify the time of year, alongside academic class, when exploring student mental wellbeing and interventions that are timed strategically near examination periods [29,30]. As such, utilization of peer support has been noted to fluctuate with common medical school milestones [23]. Peer-support services or wellness campaigns around the time of exams can reduce student suicide [31]. Moreover, they are an effective way to encourage students to reach out for help [28]. In one study, 75% of students reported that having peer support available created a supportive atmosphere even if they did not personally plan on using the program [32]. However, there is a gap in the literature regarding the impact that participation in these programs may have on the medical students providing the support. The extended literature on students in other educational settings suggests that students who provide peer support may also benefit [32]. In a peer program of college students who provided assistance to peers with chronic medical conditions, the providers showed high empathy, low stigma, and high confidence after providing support to their peers [33]. Similarly, in a study comparing undergraduate students trained to provide mental health peer support and student workers not trained to provide peer support, peer supporters exhibited lower avoidant coping and more belonging support, providing evidence that participation in these programs may enhance wellbeing for the supporter [34]. Medical student peer supporters have reported personal gratification in making their campuses more welcoming [23]. Peer supporters not only recommend self-care and wellness strategies to others but may end up incorporating those practices into their daily lives [35]. Being a peer supporter ultimately may allow students to develop empathy and increased self-efficacy in addressing difficult mental health topics. Therefore, in this study, the authors examined the impact of providing peer support to their medical student peers on several variables using a pre-post survey model of medical students at a single institution at the beginning and end of the academic year’s peer-support program. 2. Materials and Methods 2.1. Hypotheses The main hypotheses tested were: (1) Providing peer support to medical student peers as part of a standardized peer-support program will be associated with increased self-efficacy in identifying peers’ mental health concerns, increased empathy, and increased likelihood to assist peers with mental health problems; (2) providing peer support to medical student peers will be associated with decreased perceived mental health stigma; and (3) providing peer support will have similar effects on male and female medical students. 2.2. Participants Eligible participants (n = 38) were students in the peer-support program from an allopathic medical school in the southeast United States during the 2020–2021 academic year. Only medical students who were at least 18 years old and active peer supporters in good academic standing met inclusion criteria. All other medical students were excluded from participation in the study. Medical students who identified as second- to fourth-year students were given the opportunity to apply in June of 2020 to serve as a peer supporter for the academic year. There is a detailed application and review process to ensure that the student has appropriate characteristics to serve in this role (see Appendix A). 2.3. Peer-Support Program The University of Central Florida College of Medicine peer-support program consists of second- to fourth-year medical students who volunteer as peer supporters [23]. These students are taught to promote positive mental health and reduce mental health stigma through peer-to-peer interactions. The peer supporters are trained to encourage students to seek professional mental health support when indicated. All peer supporters undergo training from a licensed psychologist on privacy, active listening, QPR suicide prevention, motivational interviewing, and mindfulness/guided relaxation strategies. These individuals provide peer support through one-hour walk-in sessions and bi-annual outreach events. During the COVID-19 pandemic, the walk-in sessions were transitioned from in-person to the online video communications application, “Zoom”. Students receiving peer support at our institution have reported verbally to the program director that participation in peer-support sessions helped them address mental health symptoms. For example, some students who received peer support shared that they were better able to cope with feelings of sadness or stress surrounding exams. Another participant mentioned that after receiving peer support, he had less anxiety about clinical evaluations. Another shared she “felt heard” by the peer supporter in discussing a difficult break-up with her partner. First-year students have called the peer supporters “relatable”. Generally, the program is well-received and considered to create a supportive campus culture. 2.4. Measures In addition to demographics, four measures (see Appendix B) assessing empathy, self-efficacy, mental health stigma, and likelihood to assist peers with mental health problems were adapted from a program where college students assist their peers with chronic conditions [33]. Certain survey items were modified from their original wording to better address the mental health states examined in the present study. 2.5. Demographics Survey Participants were asked to describe their gender, race, and age in years. They were also asked their year in medical school and the number of times they provided peer support during the 2020–2021 academic year. 2.6. Empathy Measure The 7-item empathy scale was adapted from the Interpersonal Reactivity Index (IRI) [36]. This questionnaire quantifies feelings of concern and sympathy for others in unfortunate circumstances. An example item on the questionnaire is: “I often have tender, concerned feelings for people less fortunate than me”. Participants answer each statement using a 5-point Likert scale with response ranges from “does not describe me at all” to “describes me very well”. Higher scores indicate higher levels of empathy. Cronbach’s alpha for this scale demonstrated good internal reliability (α = 0.817). Validity is demonstrated by the use of the IRI with medical students in other studies [37,38]. 2.7. Self-Efficacy Measure The 7-item self-efficacy scale was adapted from the Chronic Conditions Survey [33]. For each item, a scale was used from 0 = “cannot do at all” to 10 = “highly certain can do”. Example items include “confidence I can assist by using motivational skills to help students reflect on their situation”. Cronbach’s alpha for this scale demonstrated good internal reliability (α = 0.895). Support for the validity of this measure in this setting is the prior development and use in a sample of United States college students with chronic health conditions [33]. 2.8. Mental Health Stigma Measure A 6-item measure to assess a participant’s willingness to engage with individuals with psychological conditions was used in this study. It was adapted from the Social Distance Scale used in prior studies (the only adaptation was the shift in wording from “condition” to “psychological condition”) [33,39]. Social distance scales consist of questions about participants’ willingness and comfort to engage with a given type of person. Responses to the six items used in the current study were “Definitely willing”, “Somewhat willing”, “Definitely unwilling”, or “Prefer not to answer”. Social distance scales typically have solid internal consistency and construct validity; for example, positive associations have been noted between believing that people with mental disorders are dangerous and desired social distance [40]. An example statement on the mental health stigma measure is: “How willing would you be to start a collaborative project with someone with a chronic psychological condition?” A lower willingness to engage with individuals with psychological conditions represents higher stigma towards these individuals. Cronbach’s alpha for this scale demonstrated good internal reliability (α = 0.855). 2.9. Likelihood to Assist Peers with Mental Health Problems Measure A 9-item measure to assess participants’ likelihood of assisting peers with mental health problems was adapted from the Chronic Conditions Survey [33]. Each item on this measure referenced one of nine mental health states that medical students may experience, such as “burnout”, “stress”, “loneliness”, and “imposter syndrome”. The primary question posed was: “Imagine that a new student at your medical school with the following problem needs assistance. What is the likelihood that you would volunteer to provide the assistance?” The wording for the measures regarding mental health states was developed by the researchers based on review of prior research and with input from students and a trained psychologist. For each item, students were instructed to “Please use a scale from: 0% = you are positive you would not offer to assist under any circumstances to 100% = you are positive you would offer to assist under any circumstances”. A higher percentage indicated a higher likelihood of assisting peers with mental health problems. Cronbach’s alpha for this scale demonstrated excellent internal reliability (α = 0.923). 2.10. Procedure and Data Analysis This is a within-subjects, pre-post, quasi-experimental study using a demographics survey and four self-report measures. All peer supporters (n = 38) were emailed the survey information twice during the 2020–2021 academic year. The survey was administered using Qualtrics software. The first survey was deployed in September 2020, shortly after the peer supporters were trained. The second survey was deployed in April 2021, after peer supporters had completed most or all of their support sessions. For each survey administration, students were given a two-week period to complete the survey. Responses were de-identified. IBM SPSS Statistics 26 was used to conduct descriptive and inferential statistics along with internal reliability tests. Four scales were analyzed: (1) empathy, (2) self-efficacy, (3) stigma, and (4) likelihood to assist peers with mental health problems. Each scale’s internal consistency was measured using Cronbach’s alpha. Pre-study versus post-study score mean rank comparisons were made using Wilcoxon signed-rank tests. Score percent for each scale were not normally distributed according to Shapiro–Wilk tests for normality; thus, a non-parametric test was chosen. To compare mean rank score differences pre-study and post-study within each gender group (e.g., comparisons within males pre- and post-study), eight additional Wilcoxon signed-rank tests were conducted. To compare all four scales’ pre-study and post-study scores by gender, multiple Welch’s t-tests were conducted given the scores were not normally distributed. An analysis was done comparing post-study survey responders versus non-responders’ baseline characteristics and scores to assess non-response bias. Categorical characteristics were analyzed using chi-square tests, while continuous characteristics and scores were analyzed using Welch’s t-tests. 3. Results Seventy-five percent of peer supporters were in their second or third year of medical school. As most peer supporters were second- or third-year medical students, demographic information is provided for second- and third- year medical students at our institution in Table 1. The survey was distributed to 38 peer supporters, from which 36 completed the first survey, and 17 completed both surveys. The sample population (N = 17) had a median age of 25 (range: 24–27). The sample was composed of 52.9% Asian Americans and 47.1% non-Hispanic White Americans. In terms of gender, students identified as 52.9% men and 47.1% women. On average, the peer supporters who participated in this study offered seven one-hour-long peer support sessions during the 2020–2021 academic year. Four separate Wilcoxon signed-rank tests demonstrated that there was a statistically significant difference with moderate effect sizes between pre-study and post-study empathy scores (Z = −1.964, p = 0.050, r = 0.34) and self-efficacy scores (Z = −2.060, p = 0.039, r = 0.35) but not between pre-study and post-study stigma scores (Z = −0.142, p = 0.887) nor likelihood to assist peers scores (Z = −0.346, p = 0.730). Please see Figure 1 for a box plot of pre-study and post-study scores for each scale. Eight Welch’s t-tests demonstrated a statistically significant mean baseline self-efficacy score difference between males (61.80 ± 19.01 percent) and females (80.59 ± 12.26), t (11.965) = −2.349, p = 0.037. Specifically, being a peer supporter increased males’ post-study self-efficacy and resulted in a self-efficacy score that was more comparable to the post-study self-efficacy scores of females. Further, females started with higher pre-study self-efficacy scores and demonstrated little change in self-efficacy scores between the pre-study and post-study survey period. Please see Figure 2 for a box plot of baseline self-efficacy scores comparing males and females. All other scores were similar between genders. See Table 2 for score comparisons for all four scales. Within males, the mean scores for each scale increased, but only the increase in self-efficacy score (61.80 ±19.01 to 74.71 ± 16.81 percent) was significant (Z = −2.240, p = 0.025; see Table 3). Within females, mean score increased for all scales except for the self-efficacy scale, but none of the changes were statistically significant (Table 3). No statistically significant difference in means or frequencies were observed in baseline participant characteristics or scores between post-study survey responders and non-responders (Supplemental Table S1). Non-responders are defined as peer supporters who had completed the pre-study survey but did not complete the post-study survey; therefore, their “baseline” scores are those collected at the beginning of the study. 4. Discussion There is a growing body of literature documenting the benefits of peer-support services for medical students [27,28]. With rising rates of burnout, anxiety, and depressive symptoms that occur during medical school, it is important to consider preventative wellness strategies that contribute to the protective umbrella of mental health services available to medical students. Peer-support programs present a low-cost, sustainable modality to promote student wellbeing. They do not replace clinical services, but they offer benefits in terms of engaging the medical student community in mental health advocacy and prevention, serving as a pipeline to clinical services, and creating a more supportive learning atmosphere [32]. Thus far, studies have identified the following outcomes associated with peer support for medical students and other health professionals: improving perceptions of support, reducing psychological distress, and increasing resilience [25,32]. The authors of this study sought to examine the impact that involvement in a peer-support program would have on the peer supporters themselves, specifically “how does helping affect the helpers?” This is a novel way of looking at the benefit of a peer-support program among medical students. Our findings indicate that involvement in such programs do indeed offer benefits. Specifically, the first finding of our study was that being a peer supporter increased students’ self-reported empathy and self-efficacy to help others from the start of the program over the course of a year. Role-modeling and social learning theory help explain why being a peer supporter may lead to greater empathy given that the peer supporters engage in training, monthly meetings, and interact during walk-in hours [41,42]. Being around other like-minded peers who value altruism may also contribute to why such a program promotes greater empathy [43]. In terms of self-efficacy, repetitive exposure and practice using peer support skills, such as active listening, likely increases students’ confidence and self-efficacy in applying these interpersonal skills [44,45]. We also found that self-efficacy seemed to increase the most among male peer supporters. Given the exploratory nature of this study, it is unclear why males had much lower baseline self-efficacy scores or showed improvement in self-efficacy. It is plausible that gender-roles may have led male supporters to have had less prior experience providing support to peers compared to their female counterparts [46,47]. Further, compared to females, even after being a peer supporter, males demonstrated lower post-study survey self-efficacy scores compared to females. This may suggest that serving as a peer supporter should complement other strategies to promote self-efficacy among men [48,49]. Women also demonstrated little change in self-efficacy scores over the course of the year, suggesting either a ceiling effect in self-efficacy as measured with this scale or perhaps that serving as a peer supporter is not an effective means to promote self-efficacy among women. Future studies with larger samples and more sensitive tools should further explore differences by gender and other aspects of identity, such as race. Two other variables examined in this study, namely mental health stigma and likelihood to volunteer to help others with mental health problems, did not yield significant changes in any of the analyses. The finding regarding no change in stigma warrants further investigation since this is a previously identified benefit of peer support although many of these studies have been theoretical or perspective pieces. More data are needed to document if peer support affects stigma over time (related to mental health or help-seeking behaviors). Still, one explanation could be that those who choose to participate as a peer supporter have less stigma toward mental health to begin with compared to the average medical student. A similar interpretation can be applied to why an effect was not observed in terms of likelihood to help others with mental health problems. This study has strengths and limitations that should be noted. Strengths include a novel approach to examining the benefits of providing peer support. Limitations include small sample size, lack of true randomization because peer supporters self-select to apply to the program, and lack of a control group. It is possible that medical students experience growth in terms of empathy and self-efficacy by virtue of something other than involvement as a peer supporter, such as practice of medicine skills. Thus, the limited findings should be interpreted cautiously and used as a platform for future research studying the benefits of peer support. Non-response bias can be discarded among those who failed to fill out the post-study survey, as their baseline characteristics and scores did not significantly differ from those who completed both surveys. Despite the benefits, peer-support programs also have some barriers. A one-size-fits-all approach is not effective for all students. In particular, medical schools may not have enough students from marginalized backgrounds to address every contributor to distress, or others may not have adequate infrastructure to match students with supporters. Schools should recognize different forms of support needed for students of different identities, especially marginalized identities or international medical students. Another limitation is that students who would serve as peer supporters are sometimes themselves stressed, tired, and overworked or have compassion fatigue. Structural changes to improve the culture of medicine need to come from the top versus student-led initiatives. In addition, this highlights the importance of thorough application, screening, and training processes for selecting peer supporters to avoid any harm. Finally, medical schools are small communities, and concerns over confidentiality exist [32]. Peer-support programs need to ensure they properly respect and incorporate all aspects of identity for sexual, gender, and racial minorities in medical school. 5. Conclusions Peer-support programs offer some very tangible benefits for the medical student community, and for the peer supporters themselves. This study highlights how involvement as a peer supporter increases empathy and self-efficacy. These are excellent translational skills that medical students can apply to their clinical care with patients on rotations and as they begin residency. These findings add to the growing body of literature on peer-support programs as a valuable mental health and preventative wellness resource in medical education. These findings further underscore the potential benefits not only for medical students who use the services but for those who provide them as well. Acknowledgments The authors thank the peer supporters for their countless efforts to support their classmates and create a culture on campus that destigmatizes mental health. We also would like to thank Bill Barker and Casey Smith for their support of this project. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19095135/s1, Supplemental Table S1: Post-Study Survey Responders vs. Non-responders at Baseline. Click here for additional data file. Author Contributions Conceptualization, M.P.A., J.S. and K.D.; methodology, M.P.A. and K.D.; software, A.E.R.; validation, M.P.A., J.S., K.D. and A.E.R.; formal analysis, A.E.R.; investigation, M.P.A., J.S. and K.D.; resources, M.P.A., J.S., K.D. and A.E.R.; data curation, M.P.A. and J.S.; writing—original draft preparation, M.P.A., J.S. and K.D.; writing—review and editing, M.P.A., J.S. and K.D.; visualization, M.P.A., J.S. and K.D.; supervision, K.D.; project administration, M.P.A. and J.S. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and received a “Not Human Subjects Research” determination (STUDY00003314) by the Institutional Review Board of University of Central Florida. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Appendix A. Peer-Support Application 2021–2022 Please respond yes or no. Are you a returning peer supporter? (If returning, only answer questions 1–4). Are you able to commit to at least one hour-long walk-in (either in person or via Zoom) shift per month, occasional outreach events, and monthly meetings? Are you able to commit to attend a 5 h training on Friday, 23 July from 12–5 pm? Are you currently in good academic standing where participating as a peer supporter will not interfere with or hinder your academic success? Please provide a brief response to the following questions/statements: 5. Please briefly explain why you want to be a peer supporter. 6. What skills or experiences do you have that will enable you to be an effective peer supporter? Or what unique ways do you want to contribute to the program? 7. What are you hoping to gain from joining the Peer-Support Program? 8. Please explain from your own perspective what you believe to be the “key ingredients” of a supportive and healthy medical school environment. 9. What ideas would you like to see implemented as a peer supporter? This could include outreaches, events, or novel ways of supporting your peers. Thank you for your interest. Please submit applications to Dr. Katherine Daly at Katherine.daly@ucf.edu. Applicants will be informed of the final decision by (Date). Appendix B. Survey Measures Appendix B.1. Demographics Questionnaire How would you describe your gender? (Free response box) How old are you? (Free response box) How would you describe your race? (Free response box) Year in medical school? (M1, M2, M3, M4) How many times did you provide peer support in the last year? (Scale from 0 to 100) Appendix B.2. Self-Efficacy Scale We would like to know how skilled/helpful you would be at providing different types of support and assistance to a student who has a mental health concern such as these. Please rate your degree of confidence that you can provide the following types of assistance. Scale indicates a percentage from 0 = “I cannot do this” to 100 = “Absolutely confident I can do this”. Confidence I can assist by listening to the student’s health concerns or worries; Confidence I can assist by offering encouragement; Confidence I can assist by providing comfort; Confidence I can assist by communicating caring and empathy; Confidence I can assist by using motivational skills to help students reflect on their situation; Confidence I can assist by teaching relaxation skills; Confidence I can assist by using mindfulness techniques. Appendix B.3. Mental Health Stigma Scale Please respond to the questions in the following table. Response options: Definitely willing, somewhat willing, definitely unwilling, prefer not to answer. How willing would you be to move next door to someone with a psychological condition? How willing would you be to spend an evening socializing with someone with a psychological condition? How willing would you be to start a collaborative project with someone with a psychological condition? How willing would you be to make friends with a person with a psychological condition? How willing would you be to have a person with a psychological condition marry into the family? How willing would you be to marry or date a person with a psychological condition? Appendix B.4. Empathy Scale The following statements inquire about your thoughts and feelings in a variety of situations. For each item, indicate how well it describes you by choosing the appropriate description. Please answer as honestly as you can. Description options: Does not describe me at all, describes me slightly well, describes me moderately well, describes me very well, describes me extremely well. I would describe myself as a pretty soft-hearted person. I am often quite touched by things that I see happen. When I see someone being treated unfairly, I sometimes don’t feel very much pity for them. Other people’s misfortunes do not usually disturb me a great deal. When I see someone being taken advantage of, I feel kind of protective towards them. Sometimes I don’t feel very sorry for other people when they are having problems. I often have tender, concerned feelings for people less fortunate than me. Appendix B.5. Expected Likelihood to Assist Peer with Mental Health Problems Imagine that a new student at your medical school with the following problem needs assistance. What is the likelihood that you would volunteer to provide the assistance? What is the percentage of likelihood you would volunteer to assist if the student had: Scale indicates a percentage from 0 to 100. 0% = you are positive you would not volunteer to assist. 100% = you are positive you would volunteer to assist. What is the likelihood you would volunteer to assist if the student had burnout; What is the likelihood you would volunteer to assist if the student had stress; What is the likelihood you would volunteer to assist if the student had sadness; What is the likelihood you would volunteer to assist if the student had anxiety; What is the likelihood you would volunteer to assist if the student had loneliness; What is the likelihood you would volunteer to assist if the student had social isolation; What is the likelihood you would volunteer to assist if the student had academic stress; What is the likelihood you would volunteer to assist if the student had imposter syndrome; What is the likelihood you would volunteer to assist if the student had perfectionism. Figure 1 Box Plots of Pre-study and Post-study scores for Each Survey Measure. * Indicates statistically significant mean rank differences (α = 0.05) using Wilcoxon sum-rank tests. Figure 2 Box Plot comparing baseline Self-Efficacy Scores between males and females who completed both surveys. * Indicates statistically significant mean differences (α = 0.05) using Welch’s t-tests. ijerph-19-05135-t001_Table 1 Table 1 Demographic Information of Medical School Students. Second-Year Medical Students (n = 120) Third-Year Medical Students (n = 117) Mean Age 24.0 years 24.2 years Gender Male 49% (59) 60% (70) Female 51% (61) 40 (47) Race/Ethnicity White/Caucasian 48% (58) 54% (64) Asian (including Far East Asia and Pacific Islander) 32% (38) 25% (39) Hispanic /Latino 13% (15) 11% (13) Black/African-American 6% (7) 1% (1) Other 1% (2) 0 (0%) ijerph-19-05135-t001_Table 2 Table 2 Male and Female Pre-study and Post-study Survey Score Comparisons for all Four Scales. ijerph-19-05135-t001_Table 3 Table 3 Pre- vs. Post-Study Survey Score Comparison for All 4 Scales within Males and Females. 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==== Front Nutrients Nutrients nutrients Nutrients 2072-6643 MDPI 10.3390/nu14091953 nutrients-14-01953 Systematic Review Impact of Food-Based Weight Loss Interventions on Gut Microbiome in Individuals with Obesity: A Systematic Review https://orcid.org/0000-0002-6610-5134 Bliesner Aleisha 1 Eccles-Smith Jade 23 Bates Claire 1 Hayes Olivia 1 Ho Jet Yee 1 Martins Catia 456 Truby Helen 1 https://orcid.org/0000-0002-1909-8920 Nitert Marloes Dekker 7* den Hartigh Laura J. Academic Editor 1 School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD 4067, Australia; a.bliesner@uq.edu.au (A.B.); c.bates@uq.net.au (C.B.); o.hayes@uq.net.au (O.H.); jetyee.ho@uq.net.au (J.Y.H.); h.truby@uq.edu.au (H.T.) 2 Department of Obstetric Medicine, The Royal Brisbane and Women’s Hospital, Brisbane, QLD 4029, Australia; jade.ecclessmith@uqconnect.edu.au 3 Mater Research Institute, The University of Queensland, Brisbane, QLD 4101, Australia 4 Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7034 Trondheim, Norway; catia197@uab.edu 5 Centre for Obesity and Innovation (ObeCe), St. Olav University Hospital, 7006 Trondheim, Norway 6 Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL 35294, USA 7 School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4067, Australia * Correspondence: m.dekker@uq.edu.au; Tel.: +61-7-3365-4633 06 5 2022 5 2022 14 9 195322 3 2022 03 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The observation that the gut microbiota is different in healthy weight as compared with the obese state has sparked interest in the possible modulation of the microbiota in response to weight change. This systematic review investigates the effect of food-based weight loss diets on microbiota outcomes (α-diversity, β-diversity, relative bacterial abundance, and faecal short-chain fatty acids, SCFAs) in individuals without medical comorbidities who have successfully lost weight. Nineteen studies were included using the keywords ‘obesity’, ‘weight loss’, ‘microbiota’, and related terms. Across all 28 diet intervention arms, there were minimal changes in α- and β-diversity and faecal SCFA concentrations following weight loss. Changes in relative bacterial abundance at the phylum and genus level were inconsistent across studies. Further research with larger sample sizes, detailed dietary reporting, and consistent microbiota analysis techniques are needed to further our understanding of the effect of diet-induced weight loss on the gut microbiota. diet weight loss obesity microbiota microbiome alpha-diversity beta-diversity short-chain fatty acids This research received no external funding. ==== Body pmc1. Introduction The rate of overweight and obesity is steadily increasing worldwide, affecting over 1.9 billion adults in 2016 according to the World Health Organization [1]. Obesity is associated with a number of chronic diseases including cardiovascular disease, type 2 diabetes, and certain cancers, which place a significant economic burden on the healthcare system [2,3]. Whilst the obese state has traditionally been attributed to energy intake in excess of energy expenditure, more recently, genetics, epigenetics, and the microbiome have been implicated in the aetiology of obesity [4,5]. Lifestyle modification, including dietary changes, remains the recommended first-line intervention for weight loss. The human colon is home to 1014 microorganisms, which interact with multiple systems to influence host health [6]. Microbial composition and short-chain fatty acid (SCFA) production are both influenced by the host diet and the composition of the microbiota has been implicated in the development and maintenance of the obese state. Ley et al. [7] first observed that individuals with obesity had a higher ratio of Firmicutes to Bacteroidetes compared to their lean counterparts. Ridaura et al. [8] demonstrated a causal relationship between the microbiota and obesity by transplanting faecal microbiota from twins discordant for obesity into germ-free mice. Mice receiving faecal transplants from the obese twins gained significantly more weight than those transplanted with the microbiota of the lean twins, and this correlated with lower SCFA production in the obese recipient mice. Numerous studies have aimed to identify the most successful macronutrient composition for weight loss. A recent meta-analysis has shown both low-fat and low-carbohydrate diets to be effective for weight loss, with little difference between the two [9]. Long-term weight maintenance, however, remains difficult, with only 28% of adults maintaining a loss of 10% body weight after 4 years [10]. While similarly effective for short-term weight loss, different macronutrient ratios may affect long-term success, potentially via modulation of the gut microbiota, though the exact mechanism remains unknown. The purpose of this systematic review was to investigate the effect of food-based weight loss diets varying in macronutrient composition on microbiota outcomes (α- and β-diversity, relative bacterial abundance, and SCFA production) in healthy but obese individuals who had lost at least 2 kg of weight attributed to changing their food intake. 2. Materials and Methods 2.1. Search Strategy An electronic literature search was performed on the following databases: PubMed, Scopus, CINAHL, and Embase. Searches were performed on the same day, without filters, including literature from database inception until 26 October 2021. Database searches were performed using the terms ‘overweight’ or ‘obesity’ and ‘weight loss’, and ‘microbiota’ or ‘microbiome’ and related terms (see Table S1). 2.2. Study Selection Studies that met the following criteria were included: (1) subjects must be healthy adults (18–70 years old) with overweight or obesity (BMI > 25 kg/m2), (2) subjects must have achieved ≥2 kg weight loss induced by a hypocaloric diet or a combination of a hypocaloric diet and other lifestyle interventions, and (3) studies that assessed α-diversity, β-diversity, bacterial abundance, or SCFA concentrations. Studies were excluded if they were: (1) published in a language other than English, (2) an abstract only, (3) conducted in animals, children, adolescents, pregnant women, or subjects with chronic illnesses/morbidities, (4) used pre- or probiotics only, faecal microbiota transplant, herbal medicines, pharmacotherapy, a single food only (e.g., avocado), Glucagon-like-peptide 1 (GLP-1) or other gut hormones/peptides, or supplement-based very-low-calorie diets (e.g., Optifast). If a multi-arm trial had a single weight loss intervention arm meeting the above criteria, this specific arm was included and treated as a single-arm trial for analysis. References were imported into an online screening and data-extraction tool (Covidence, v2815) and duplicates were removed following a 2-step process: automatic removal by Covidence, followed by manual removal by two reviewers. The two independent reviewers (A.B. and J.E.-S.) screened articles based on title and abstract against the eligibility criteria. Studies included were assessed based on their full text to produce the final selection of eligible studies. Disagreements were resolved through consensus-based discussions or by a third reviewer’s opinion. 2.3. Data Extraction Two independent reviewers (A.B. and J.E.-S.) extracted data from each full-text including Supplementary Materials using a pre-specified data-extraction template. Information on the first author, publication year, country where the study was conducted, study design, type of intervention, sample size, participant characteristics (sex, age, BMI), duration of intervention, and changes in weight, α- and β-diversity, bacterial abundance, and SCFAs were extracted. Disagreements were resolved through consensus or with a third reviewer. We did not contact study authors for additional information. 2.4. Risk of Bias A risk-of-bias (RoB) assessment was conducted for each study by two independent reviewers (A.B. and J.E.-S.) using the Cochrane RoB tool [11]. The older version of the tool was used as the updated RoB2 is less suited to dietary studies due to difficulties with blinding and a lack of placebo in dietary interventions. RoB was assessed on the basis of sequence generation, allocation concealment, the blinding of participants and personnel for all outcomes, incomplete outcome data for faecal microbiota composition, selective outcome reporting, and other sources of bias. Each criterion was graded as having a high, low, or unclear RoB. Discrepant assessments were resolved by consensus reached through discussion between the two reviewers. 3. Results 3.1. Study Selection A total of 2741 records were retrieved by the database search, out of which 1290 duplicates were removed. A further 1367 records were excluded after title/abstract screening and the full-texts of the remaining 84 articles were assessed for eligibility. Sixty-five articles were excluded following full-text review while nineteen studies were retained for inclusion in this review. A detailed flowchart showing the study selection process is presented in Figure 1. Excluded full-texts with justifications are provided in Table S2. 3.2. General Study Characteristics Characteristics of the included studies are presented in Table 1. A total of 28 dietary interventions of interest were identified across the 19 included studies. Geographically, seven (37%) of the studies were performed in North America, seven (37%) in Europe, three (16%) in the United Kingdom, and two (11%) in East Asia. The publication dates of the included studies ranged from 2006 to 2021, with 16 (84%) studies published within the last 3 years. The mean number of participants enrolled in each dietary intervention arm was 39 (range, 6–97). Two studies (11%) were carried out exclusively in men, fifteen (79%) included both men and women, and two (11%) did not report on the sex of the participants. Mean age of participants ranged from 37 to 64 years old. Participants were either overweight or obese at baseline, with mean BMIs ranging from 26.6 to 36.6 kg/m2. Duration of each dietary intervention arm ranged from 10 days to 12 months. Fourteen studies used 16S rRNA gene amplicon sequencing to characterise the gut microbiota, two studies used shotgun metagenomic sequencing, one study used 16S rRNA-based quantitative FISH, and one study used a combination of qPCR and 16S rRNA amplicon sequencing. 3.3. Dietary Intervention Characteristics Fourteen (74%) studies reported on the macronutrient intake of participants during the intervention (Table 2 and Table 3). Macronutrient distribution ranged from 12% to 34% of energy from protein, 16% to 73% of energy from carbohydrates, and 13% to 50% of energy from fat. Energy intake, reported by 11 studies (58%), ranged from 1195 to 2154 kcal/day. Eleven studies (58%) reported on dietary fibre intake, which ranged from 10 to 33 g per day, and three (16%) reported on the amount of soluble and insoluble fibre consumed. 3.4. Weight Loss The mean weight loss across the 28 interventions was ~6 kg (Table 2 and Table 3). The lowest amount of weight loss achieved was 2.8 kg in two interventions lasting 3 and 8 weeks, respectively [16,22], while the largest amount of weight lost was 15.4 kg in a year-long intervention [7]. 3.5. Changes in α-Diversity Twenty-four interventions (86%) reported on α-diversity changes (Table 2). Eighteen (75%) produced no changes in α-diversity, while three (13%) increased α-diversity. These three interventions included an 8-week high-protein diet [16], a 6-month Mediterranean diet [20], and a 12-week energy-restricted diet [28]. A 16-week low-fat diet increased α-diversity in men but not women [14], while another study found an increase in OTU richness but not Shannon (diversity and richness) index following a 12-week weight reduction program [15]. One study which assigned omnivores to a 16-week vegan diet resulted in a decrease in α-diversity [24], however no changes were found following a similar intervention from the same research group [23]. 3.6. Changes in β-Diversity While not directly related to health outcomes, changes in β-diversity (i.e., the interindividual variation in microbiome composition) indicate whether an intervention had an overall effect on the microbiota. Seventeen interventions (61%) included in this review reported on β-diversity, eleven (65%) of which found no changes post-intervention (Table 2). Two (12%) resulted in a decrease in β-diversity: a 4-week low-carbohydrate, high-fat diet [21] and a 12-week low-carbohydrate diet [25]. β-diversity decreased at 3 months on another low-carbohydrate diet, but returned to baseline levels by 6 months [18]. Three other studies found a change in β-diversity [14,27,28] (one study in men only [14]), but these results were reported graphically and the direction of change could not be determined. No interventions reported increased β-diversity. 3.7. Changes in Relative Bacterial Abundance Changes in relative bacterial abundance were assessed by 23 (82%) interventions. For the purpose of this review, we focused on changes at the taxonomic levels of phylum and genus only. Significant changes are shown in Table 2. There were significant changes in six phyla across eight different interventions. Changes in the relative abundance of Bacteroidetes was inconsistent, increasing after a year-long fat-restricted diet and carbohydrate-restricted diet [7] and decreasing following a 16-week vegan diet [24]. Bacteroidetes also increased at 3 months of a low-fat diet and low-carbohydrate diet but returned to baseline levels by 12 months [18]. Three interventions reported a decrease in Firmicutes [7,15], with one reporting a decrease at 3 months, but not after 12 months [18]. The relative abundance of Proteobacteria decreased following two 16-week vegan diets [23,24] and a 3-month Mediterranean diet [27]. The ratio of Bacteroidetes to Firmicutes was unchanged in four interventions [23,24,25,27] and increased following a 12-week low-carbohydrate diet [25], but not reported in the majority of studies. There were significant changes in 32 genera across 14 different interventions. The majority of genera only changed in one or two interventions, while changes in the other genera were inconsistent between studies. Bifidobacterium increased following two interventions (a normal-protein diet and a high-protein diet [16]) and decreased following two interventions (a high-protein, moderate-carbohydrate, non-ketogenic diet [17] and a low-carbohydrate, high-fat diet [21]). Parabacteroides increased following two interventions [27,28] and decreased in a third [26]. Another study found changes in Bifidobacterium and Parabacteroides abundance at 3 months, but these had returned to baseline levels by 12 months [18]. 3.8. Changes in Faecal SCFAs Seven interventions (25%) measured changes in faecal SCFA concentrations (Table 3). Concentrations were unaffected by all but one intervention in which butyrate concentration, but not total SCFA concentration, decreased [19]. 3.9. Risk of Bias RoB assessment for the 19 included studies is presented in Figure S1. Sequence generation was unclear in seven of the 14 trials that were randomised and only one trial adequately described the method of allocation concealment. RoB due to lack of blinding of participants/personnel and outcome assessors was deemed to be low in all studies, as lack of blinding is unlikely to affect microbiota-related outcomes or measurement of such. RoB due to incomplete outcome data was also rated as low in all studies as missing microbiota-related data are unlikely to be related to the true outcome. No studies had published protocols pre-specifying methods of microbiota analysis; as such, selective outcome reporting was unclear. All studies were deemed free from other sources of bias. 4. Discussion The obese state has been associated with an altered gut microbiota, generating interest in the potential of weight loss to modulate the microbiota. This review found that dietary weight loss interventions had limited effect on bacterial diversity and faecal SCFA concentrations. Changes in bacterial abundance at the phylum and genus level were inconsistent across studies and there was no obvious correlation between macronutrient composition and microbiota outcomes. The minimal effect of food-based weight loss interventions on α-diversity of the gut microbiota is consistent with other literature. A recent systematic review and meta-analysis of food-based, formula-based, and surgical weight loss interventions found a positive dose–response relationship between weight loss and α-diversity [30]. Food-based dietary interventions on their own, however, had an inconsistent effect on α-diversity, with no statistically significant effect when results were pooled [30]. It is likely that the degree of weight loss achieved through food-based weight loss is not large enough to produce the statistically significant change in α-diversity seen within very-low-calorie formula-based diets (VLCD) and with surgical interventions, both of which were excluded from this review. In addition, microbiota metrics were not typically the primary outcome in the studies included in this review. The weight loss interventions reported power calculations based on detecting significance in weight loss rather than microbiota changes. Given the large interindividual variability in the gut microbiota, much larger sample sizes would be needed to detect significant changes following diet-induced weight loss. Low fibre intake in the included studies may also explain the lack of consistent effect on the gut microbiota. In studies that reported fibre intake, this ranged from 10 to 33 g per day. Fibre is the main substrate for bacterial fermentation and observational studies of rural African tribes indicate that high-fibre diets are associated with greater bacterial diversity and SCFA production [31,32]. These tribes consume upwards of 100 g of fibre per day and similarly high levels may be needed to induce microbiota changes in interventional studies, which is unlikely to be feasible without the use of supplements. The diversity of plant foods consumed is also important, with the American Gut Project finding microbial diversity to be associated with the number of unique plant foods consumed each week rather than self-reported categories such as “vegan” or “omnivore” [33]. Low dietary diversity may explain the unchanged or decreased bacterial diversity seen in the two vegan dietary interventions included in this review. Richer and more robust dietary reporting methods, including details on soluble/insoluble fibre intake as well as the type and diversity of plant foods consumed, are needed to better understand the relationship between diet and the microbiota [34]. The finding that dietary weight loss strategies have a limited effect on microbiota-related outcomes is surprising considering previous research showing that 4 days of a completely animal- or plant-based diet rapidly alters gut microbial communities [35]. This suggests that drastic dietary changes are needed to observe an effect. It may also be that the microbiota is resistant to long-term changes [36]. Indeed, a study included in this review observed changes in relative bacterial abundance at 3 months, but these were ameliorated by 6 months despite continued dietary adherence [18]. Long-term studies with frequent microbiota measurements are required to examine the resilience of the microbiota to dietary changes. Differences in baseline microbiota characteristics may also explain inconsistencies across studies, with baseline microbial diversity and gene richness associated with individualized gut microbiota responses [37,38,39]. Gut bacteria produce a wide range of metabolites that have been implicated in health outcomes [40]. SCFAs are among the most commonly measured metabolites in microbiome studies; as such, we limited our review to these metabolites only. Only four studies (21%) meeting inclusion criteria analysed faecal SCFA concentrations, which were mostly unchanged following dietary intervention. Further studies assessing the effect of food-based weight loss interventions on SCFAs, as well as other microbiota-derived metabolites such as trimethylamine N-oxide, secondary bile acids, and tryptophan metabolites, are needed to facilitate a meta-analysis. Differences in study design, population, and methodology limit the conclusions that can be drawn from this review. While we aimed to exclude studies involving participants with comorbidities such as metabolic syndrome, presence of comorbidities was not always described in the included studies. Differences in age, sex, geographic location, and inclusion/exclusion criteria may also represent confounding factors. Several different molecular biology techniques (16S rRNA amplicon sequencing, shotgun metagenomic sequencing, FISH, qPCR) were used to assess the gut microbial composition. Differences in the 16S rRNA gene region amplified and OTU picking protocols and databases may also explain differing results [41]. A wide range of metrics were used to assess α-diversity (e.g., Shannon index, Pielou index, Chao1 index) and β-diversity (e.g., Bray–Curtis dissimilarity, Aitchison distance, weighted and unweighted UniFrac distances), making a meta-analysis not possible. Many studies reported relative changes (e.g., an increase in diversity or decrease in a particular taxa) rather than absolute value changes, further limiting our ability to conduct a meta-analysis. Reporting absolute percentage changes in relative bacterial abundance, as per Kahleova et al. [24], would facilitate quantitative comparison between studies. Due to the lack of species-level sensitivity of 16S rRNA-based techniques, we were only able to compare changes at the genus level. Further research utilising whole-genome sequencing is needed to evaluate the effect of dietary interventions on individual species. Reporting of baseline dietary intake and microbiota composition is also needed to evaluate whether changes are observed only in participants who drastically alter their diet or in those with low bacterial gene richness or lacking certain taxa to begin with. 5. Conclusions There were minimal changes in bacterial diversity and faecal SCFA concentrations following dietary weight loss interventions, with inconsistent changes in relative bacterial abundance at the phylum and genus level. Further studies, adequately powered to detect changes in microbiota-related outcomes, are needed. Greater consistency in the method of microbiota analysis and α- and β-diversity metrics, as well as reporting of absolute changes in these variables, is needed if a meta-analysis is to be conducted. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14091953/s1, Table S1: Search strategy, Table S2: Reasons for excluding studies following full-text assessment, Figure S1: Risk-of-bias assessment. References [13,26,38,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98] are cited in the supplementary materials. Click here for additional data file. Author Contributions A.B. performed the literature search, screening, data extraction, risk-of-bias assessment, and writing—original draft preparation. J.E.-S. contributed to screening, data extraction, risk-of-bias assessment, and writing—review and editing. C.B., O.H. and J.Y.H. were involved in drafting and provided comments on the manuscript. C.M., H.T. and M.D.N. contributed to conceptualization, supervision, and writing—review and editing. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Flow diagram for study selection. nutrients-14-01953-t001_Table 1 Table 1 Characteristics of included studies. Trial, Country n (Female) Age, Years BMI, kg/m2 Intervention Protocol Duration Microbiota Analysis Method Bendsten 2018, Denmark [12] 40 (35) 42 (1) 31.5 (0.4) High-dairy diet: 18% P, 52% C, 30% F, 500 kcal/day deficit, 1500 mg calcium/day 24 weeks 16S rRNA (V3–V4) 40 (34) 45 (2) 30.8 (0.4) Low-dairy diet: 18% P, 52% C, 30% F, 500 kcal/day deficit, 600 mg calcium/day 24 weeks 16S rRNA (V3–V4) Benítez-Páez 2021, Denmark [13] 59 (39) 48.9 (8.6) 32.8 (3.9) Calorie-restricted diet + fibre: 18–20% P, 52–53% C, 32–33% F, 500 kcal/day deficit, 14–22 g/day fibre + 20 g/day prebiotic fibre supplement (10 g inulin + 10 g resistant maltodextrin) 12 weeks Shotgun metagenomics 57 (37) 48.4 (8.3) 34.4 (4.4) Calorie-restricted diet + placebo: 18–20% P, 52–53% C, 32–33% F, 500 kcal/day deficit, 14–22 g/day fibre + placebo supplement (maltodextrin) 12 weeks Shotgun metagenomics Cuevas-Sierra 2021, Spain [14] 82 (54) NR M: 31.9 (3.2) F: 30.9 (3.2) Moderately high-protein diet: 30% P, 40% C, 30% F, 30% energy restriction 16 weeks 16S rRNA (V3–V4) 97 (70) NR M: 32.1 (3.5) F: 31.9 (3.9) Low-fat diet: 18% P, 60% C, 22% F, 30% energy restriction 16 weeks 16S rRNA (V3–V4) Dhakal 2020, USA [15] 58 (44) 45.7 (15.8) 34.6 (7.2) Retail weight reduction program 12 weeks 16S rRNA (V4) Dong 2020, USA [16] 43 (10) 55.9 (10.1) 34.9 (4.5) High-protein diet: 30% P, 40% C, 30% F, 2 weeks ad libitum then 6 weeks 500 kcal/day deficit 8 weeks 16S rRNA (V4) 37 (8) 55.7 (11.4) 34.6 (5.1) Normal-protein diet: 15% P, 55% C, 30% F, 2 weeks ad libitum then 6 weeks 500 kcal/day deficit 8 weeks 16S rRNA (V4) Duncan 2008, Scotland [17] 23 (0) NR >30 High-protein, moderate-carbohydrate, non-ketogenic diet: 30% P, 35% C, 35% F, <8.5 MJ/day, 164 g/d CHO, 12.2 g/day non-starch polysaccharide 4 weeks 16S rRNA-based quantitative FISH Fragiadakis 2020, USA [18] 25 (20) 42.6 (5.8) 32.8 (3.9) Low-carbohydrate diet 12 months 16S rRNA (V4) 24 (19) 39.2 (5.5) 33.7 (3.5) Low-fat diet 12 months 16S rRNA (V4) Gratz 2019, Scotland [19] 18 (0) 49 (12) 36.6 (5.8) Participants followed a 7-day weight maintenance diet followed by three 10-day weight loss diets in a randomized crossover design without washout: 1. Normal-protein diet: 15% P, 55% C, 30% F, energy = 1 × BMR 2. Normal-protein diet enriched with free amino acids and moderate amounts of carbohydrate: 15% P, 15% free amino acids, 40% C, 30% F, energy = 1 × BMR 3. High-protein diet containing moderate amounts of carbohydrate: 30% P, 40% C, 30% F, energy = 1 × BMR 37 days None Gutiérrez-Repiso 2021, Spain [20] 21 (11) 64.0 (4.7) 33.4 (3.3) Mediterranean diet: 20% P, 40–45% C, 35–40% F, 8–10% SFA, 600 kcal/day deficit, 150 min/week walking 6 months 16S rRNA (NR) Jaagura 2021, Estonia [21] 27 (NR) NR 28.9–44.4 Low-carbohydrate, high-fat weight loss diet: 30 ± 10% energy deficit 4 weeks 16S rRNA (V3–V4) Johnstone 2020, UK [22] 24 (16) 20–62 32.8 (4.07) Weight loss diet: 30% P, 40% C, 30% F, 25 g/day fibre, energy intake = RMR 3 weeks qPCR, 16S rRNA (NR) Kahleova 2020, USA [23] 84 (69) 52.9 (11.7) 32.6 (3.7) Low-fat vegan diet: 20–30 g/day fat, high in vegetables, grains, legumes, and fruit, instructed to avoid animal products and added oil, vitamin B12 supplemented (500 μg/day) 16 weeks 16S rRNA (V4) Kahleova 2021, USA [24] 62 (48) 57.4 (NR) 34.0 (NR) Low-fat vegan diet: consisted of fruits, vegetables, grains, and legumes. Animal products and added fats were excluded. Vitamin B12 was supplemented (500 μg/day) 16 weeks 16S rRNA (V4) Ley 2006, USA [7] 6 (4) 53.7 (NR) >30 Fat-restricted diet: 30% F, 10–15 g/day fibre, 1200–1500 kcal/day for women, 1500–1800 kcal/day for men 12 months 16S rRNA (NR) 6 (5) 42.0 (NR) >30 Carbohydrate-restricted diet: 25% C, 10–15 g/d fibre, 1200–1500 kcal/day for women, 1500–1800 kcal/day for men 12 months 16S rRNA (NR) Ma 2021, China [25] 25 (25) NR 26.6 (0.5) Low-carbohydrate diet: 20 g/day carbohydrates in the first week, then 10 g/day extra weekly until reaching 120 g/day at the end of the intervention 12 weeks Shotgun metagenomics 25 (25) NR 26.9 (0.4) Calorie-restricted diet: 1200 kcal/day, 20% P, 55% C, 25% F, 10% SFA, 300 mg/day cholesterol 12 weeks Shotgun metagenomics Nogacka 2021, Spain [26] 9 (4) 49.67 (7.81) >40 Hypocaloric diet: 15% P, 55% C, 30% F, <10% SFA, 20–25 g/day fibre, 20 kcal/kg body weight (~1800–2000 kcal/day) 6–8 months 16S rRNA (NR) Pisanu 2020, Italy [27] 23 (20) 53 (9) 35.2 (4.3) Mediterranean diet: 20% P, 55% C, 25% F, ≥25 g/day fibre, energy = BMR (±10%) 3 months 16S rRNA (V3–V4) Stanislawksi 2021, USA [28] 71 (NR) 40.7 (9.8) 33.1 (4.4) Energy-restricted diet: 34% weekly energy deficit achieved through either daily caloric restriction or intermittent fasting (80% energy deficit on 3 non-consecutive days each week). Moderate intensity physical activity: 300 min per week. 12 weeks 16S rRNA (V3–V4) Zhang 2021, China [29] 26 (22) 36.58 (8.70) 30.44 (3.38) Low-carbohydrate diet: 10–25% C, no energy restriction 12 weeks 16S rRNA (V3–V4) % p: percent of energy from protein, % C: percent of energy from carbohydrates, % F: percent of energy from fat, M: males, F: females, NR: not reported, FISH: fluorescence in situ hybridization. Age and BMI reported as mean (SD). nutrients-14-01953-t002_Table 2 Table 2 Summary of the microbiota changes of included studies. Trial Reported Dietary Intake Weight Loss, kg α-Diversity β-Diversity Relative Bacterial Abundance Bendsten 2018 [12] High-dairy diet: 1649 kcal, 21% P, 47% C, 31% F, 20 g fibre 6.6 (1.3) ↔ Shannon ↔ UniFrac ↔ Low-dairy diet: 1585 kcal, 19% P, 46% C, 32% F, 22 g fibre 7.9 (1.5) ↔ Shannon ↔ UniFrac ↓ Veillonella Benítez-Páez 2021 [13] Calorie-restricted diet + fibre: 1642 kcal, 21% P, 47% C, 31% F, 18 g fibre 6.1 (NR) ↔ Simpson ↔ B–C NR Calorie-restricted diet + placebo: 1730 kcal, 21% P, 46% C, 32% F, 18 g fibre 5.5 (NR) ↔ Simpson ↔ B–C NR Cuevas-Sierra 2021 [14] Moderately high-protein diet: M: 33% P, 50% C, 17% F: 34% P, 49% C, 17% F M: 10.3 (NR) F: 8.9 (NR) M: ↔ Shannon F: ↔ Shannon M: ↔ B–C F: ↔ B–C ↑ Granulicatella ↓ Phascolarctobacterium, Dielma Low-fat diet: M: 25% P, 61% C, 14% FF: 24% P, 63% C, 13% F M: 11.0 (NR) F: 8.6 (NR) M: ↑ Shannon F: ↔ Shannon M: ↑↓ B–C F: ↔ B–C ↔ Dhakal 2020 [15] Retail weight reduction program: 1818 kcal, 24% P, 38% C, 38% F, 18 g fibre 10.2 (NR) ↑ OTU richness ↔ Shannon NR ↑ Tenericutes, Euryarchaeota ↓ Firmicutes, p_Actinobacteria Dong 2020 [16] High-protein diet: NR 3.5 (NR) ↑ Shannon ↔ Aitchison ↑ Akkermansia, Bifidobacterium ↓ Prevotella_9 Normal-protein diet: NR 2.8 (NR) ↔ Shannon ↔ Aitchison ↑ Akkermansia, Bifidobacterium ↓ Prevotella_9 Duncan 2008 [17] High-protein, moderate-carbohydrate, non-ketogenic diet: NR 4.6 (NR) NR NR ↑ Clostridium coccoides-related bacteria (other than Roseburia + Eubacterium rectale) ↓ Total bacterial number, Roseburia + Eubacterium rectale, Bifidobacterium Fragiadakis 2020 [18] Low-carbohydrate diet: 426 kcal/d deficit, 22% P, 32% C, 43% F, 18 g fibre 5.1 (6.7) ↔ Observed ASVs 3 months: ↓ B–C 6 months: ↔ B–C 12 months: ↔ B–C 3 m: ↑ Bacteroidetes, Bacteroides, Parabacteroides, Sutterella, Bilophila, Desulfovibrio, Butyricimonas, Lachnospira, Oscillospira 12 m: ↔ Fragiadakis 2020 [18] Low-fat diet: 484 kcal/d deficit, 21% P, 48% C, 29% F, 20 g fibre 5.6 (5.7) ↔ Observed ASVs 3 months: ↔ B–C 6 months: ↔ B–C 12 months: ↔ B–C 3 m: ↑ Bacteroidetes, Bacteroides, Parabacteroides 3 m: ↓ Actinobacteria, Firmicutes, Bifidobacterium, Dorea, Blautia, Ruminococcus 12 m: ↔ Gutiérrez- Repiso 2021 [20] Mediterranean diet: NR 7.8 (1.9) ↑ Observed ASVs ↑ Shannon ↑ Faith ↑ Pielou ↔ UniFrac ↑ Faecalibacterium Jaagura 2021 [21] Low-carbohydrate, high-fat weight loss diet: 25% P, 23% C, 50% F, 12 g fibre/1000 kcal 7.7 (2.5) ↔ Observed species ↔ Shannon ↓ B–C ↑ Alistipes, Butyricimonas, Odoribacter, Ruminococcus_1 ↓ Bifidobacterium, Collinsella, Dorea Johnstone 2020 [22] Weight loss diet: 1930 kcal, 29% P, 40% C, 30% F, 10% SFA, 25 g fibre, 15 g insoluble fibre, 5 g soluble fibre, 7 g resistant starch 2.8 (NR) ↔ Chao1 ↔ Shannon NR ↔ Kahleova 2020 [23] Low-fat vegan diet: 1294 kcal, 43 g P (13%), 236 g C (73%), 24.3 g F (17%), 33 g fibre, 9 g soluble fibre, 25 g insoluble fibre 6.4 (NR) ↔ AWPD NR ↑ Faecalibacterium ↓ Proteobacteria ↔ Bacteroidetes:Firmicutes, butyrate producing bacteria Kahleova 2021 [24] Low-fat vegan diet: 1315 kcal, 12% P, 69% C, 17% F, 33 g fibre, 9 g soluble fibre, 24 g insoluble fibre 6.0 (NR) ↓ AWPD NR ↑ Eubacterium ↓ Bacteroidetes, Proteobacteria ↔ Bacteroidetes:Firmicutes, butyrate-producing bacteria Ley 2006 [7] Fat-restricted diet: NR 15.4 (NR) ↔ Shannon NR ↑ Bacteroidetes ↓ Firmicutes Carbohydrate-restricted diet: NR 8.0 (NR) ↔ Shannon NR ↑ Bacteroidetes ↓ Firmicutes Ma 2021 [25] Low-carbohydrate diet: 1195 kcal, 26% P, 36% C, 38% F, 10 g fibre 5.3 (NR) ↔ Shannon ↓ B–C ↑ Bacteroidetes:Firmicutes Calorie-restricted diet: 1355 kcal, 18% P, 51% C, 31% F, 11 g fibre 5.1 (NR) ↔ Shannon ↔ B–C ↔ Bacteroidetes:Firmicutes Nogacka 2021 [26] Hypocaloric diet: NR Group 1: <5% BW (n = 5) Group 2: >5% BW (n = 4) Group 2 vs. total at baseline: ↔ Chao1 ↔ Shannon NR Group 2 vs. total at baseline: ↑ Clostridum sensu stricto 1 ↓ Parabacteroides Pisanu 2020 [27] Mediterranean diet: 1341 kcal, 19% P, 50% C, 29% F, 17 g fibre 6.7 (NR) ↔ Shannon ↑↓ B–C ↑ Catenibacterium, Caldilinea, Parabacteroides, Sphingobacterium, Veillonella ↓ Proteobacteria, Megamonas, Roseburia, Ruminococcus, Streptococcus, Sutterella ↔ Bacteroidetes:Firmicutes Stanislawksi 2021 [28] Energy-restricted diet: 1276 kcal, 21% P, 42% C, 35% F 5.8 (3.8) ↑ Observed OTUs ↑ Evenness ↑ Shannon ↑ Faith ↑↓ UniFrac ↑ Parabacteroides, Alistipes, Bacteroides ↓ Subdoligranulum, Collinsella Zhang 2021 [29] Low-carbohydrate diet: 1470 kcal, 34% P, 16% C, 50% F 2.2 (1.2) kg/m2 ↔ Shannon ↔ Simpson ↔ Richness (genus level) ↔ B–C ↔ (phylum level) % p: percent of energy from protein, % C: percent of energy from carbohydrates, % F: percent of energy from fat, M: males, F: females, BW: body weight, NR: not reported, ↑: increase, ↓: decrease, ↔: no change, ↑↓: direction of change not reported, AWPD: abundance-weighted phylogenetic diversity measure, B–C: Bray–Curtis dissimilarity. Weight loss reported as mean (SD). nutrients-14-01953-t003_Table 3 Table 3 Changes in faecal short-chain fatty acid concentrations. Trial Reported Dietary Intake Weight Loss, kg Total SCFAs Butyrate Propionate Acetate Benítez-Páez 2021 [13] Calorie-restricted diet + fibre: 1642 kcal, 21% P, 47% C, 31% F, 18 g fibre 6.1 (NR) NR ↔ ↔ ↔ Calorie-restricted diet + placebo: 1730 kcal, 21% P, 46% C, 32% F, 18 g fibre 5.5 (NR) NR ↔ ↔ ↔ Gratz 2019 [19] Normal-protein weight loss diet: 2154 kcal, 80 g P (15%), 309 g C (57%), 73 g F (31%), 29 g fibre 3.9 (NR) ↔ ↔ ↔ ↔ Normal-protein weight loss diet enriched with free amino acids and moderate amounts of carbohydrate: 2143 kcal, 156 g P (29%), 219 g C (41%), 73 g F (31%), 20 g fibre 4.3 (NR) ↔ ↔ ↔ ↔ High-protein weight loss diet containing moderate amounts of carbohydrate: 2106 kcal, 153 g P (29%), 219 g C (42%), 72 g F (31%), 18 g fibre 4.0 (NR) ↔ ↓ ↔ ↔ Johnstone 2020 [22] Weight loss diet: 1930 kcal, 29% P, 40% C, 30% F, 10% SFA, 25 g fibre, 15 g insoluble fibre, 5 g soluble fibre, 7 g resistant starch 2.8 (NR) NR ↔ (% of total SCFA) ↔ (% of total SCFA) ↔ (% of total SCFA) Nogacka 2021 [26] Hypocaloric diet: NR Group 1: <5% BW (n = 5) Group 2: >5% BW (n = 4) ↔ (Group 2 vs. total at baseline) ↔ (Group 2 vs. total at baseline) ↔ (Group 2 vs. total at baseline) ↔ (Group 2 vs. total at baseline) % p: percent of energy from protein, % C: percent of energy from carbohydrates, % F: percent of energy from fat, NR: not reported, ↓: decrease, ↔: no change. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093333 materials-15-03333 Article Numerical Case Studies about Two-Dimensional CHS Joints with Symmetrical Full-Overlapped Top-Connection Heinemann Patrick * https://orcid.org/0000-0001-7530-7929 Isopescu Dorina-Nicolina * Kalinowska-Wichrowska Katarzyna Academic Editor Department of Civil and Industrial Engineering, Faculty of Civil Engineering and Building Services, “Gheorghe Asachi” Technical University of Iasi, 1, Prof. Dr. Docent Dimitrie Mangeron Blvd., No. 59A, 700050 Iasi, Romania * Correspondence: patrick.heinemann@tuiasi.ro (P.H.); isopescu@tuiasi.ro (D.-N.I.) 06 5 2022 5 2022 15 9 333311 4 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Steel joints made out of circular hollow section profiles are used for many fields of applications, such as wide-span or representative halls for airports. By connecting two inclined pipes to a vertical third pipe, a ramified vertical column is created, where the node is the weakest point in the structure due to the geometrical heterogeneity. The current standards and Design Codes have limitations regarding the geometrical properties of hollow sections joints. However, the kind of steel joint presented in this paper is excluded in the current standards. This paper is about numerical FEA case studies of two-dimensional, circular hollow-section joints to figure out the resistance of atypical steel joints. In the first step, a small-scale model is generated to analyze the influence of the inclination. In the second step, the geometries of the different pipes are extended. The influence of the inclination angle and the stability of the joint are analyzed. It was discovered that the inclination angle between the three pipes has a large influence on the stresses and deflections at the node. By increasing the inclination angle, the maximum applied force can be increased. The extended members change the behavior and the stress distribution. hollow sections FEM analysis welding line construction steel “Gheorghe Asachi” Technical University of IașiThis research received funding from “Gheorghe Asachi” Technical University of Iași. ==== Body pmc1. Introduction Structures made of hollow-section steel profiles are mainly used for two reasons. Firstly, there are aesthetical and architectural aspects. Secondly, there are physical advantages, such as high rotational stiffness and low weight. In widespan structures and large representative halls, such as airports or universities, designing columns are installed to support the roof structure. Some of these columns are built as a combination of two or more single members. A commonly used design is the full-overlapped column with on-top connection. A steel plate is connected to a vertical pipe. Two or more inclined pipes are connected onto this plate. A “Y-shape” is generated, which differs to the Y-shape in current standards. There are uniplanar (two-dimensional) or multiplanar (three-dimensional) versions. The connection between the steel members is either done by a three-dimensional welding line or cast steel. This paper is about the welding line version. The node is defined as a position where the lines of application strike together. Designing the nodes of the column, including the welding line is a complex process. Due to the geometrical and material heterogeneity, the node is the weakest part of the column. Current standards and design codes, such as the Eurocode 3 [1] or CIDECT [2], have limitations regarding the material, load cases, or geometry. Steel joints with inclination angles (angle between the upper pipe and the horizontal level) of less than 30° are offset from the standard. Current Design Codes adopt joints where one or more pipes are connected to the side flank of a third pipe. Joints with a connection at the end of a pipe are not covered in the standards. Designing engineers have to create their own models or execute experimental tests for each geometrical variation to design columns with full-overlapped geometries. The aim of a designing engineer is to choose on one hand the highest resistance variant and on the other hand the most economical variant. By varying the inclination angle, the resistance of the joint is changed and the structure can be optimized economically. The overall column length is determined by the height of the hall. The ratio of the length between the members is chosen by the designing engineer. The case studies in this paper analyze the effect of varying the pipe length to archive an economical design of the structure. The novelty of this study is about a commonly used special steel joint, which is undefined in current standards. This article helps the designing engineer to choose the most economical structure under the aspects of chord length ratio and inclination angle. It is hard to find comparable studies in the literature, which shows the relevance of the analysis. Mainly, the analyzed steel joints have a standard defined geometry. Parallels in the process of analyzation, similar steel joints, or numerical analyzations in the field of the full-overlapped Y-joint are presented. Variations of joints including similar geometrical characteristics were analyzed by Heinemann et al. in a multiplanar [3] or uniplanar way [4,5,6]. It was found that there are large differences in the resistance of the nodes. The highest stresses arose at the welding line. Azari-Dodaran et al. [7] extended the observation and explained that there are significant differences in the resistance between uniplanar and multiplanar steel joints for different load scenarios under extraordinary conditions. He performed investigations of steel joints under high temperatures. Jankovic [8] conducted studies about brace to chord connections concerning a Y-joint. A calculation method was shown where the influence of a second pipe on a first pipe was calculated. A substitute force affected the pipe. The focus was on the designing of the pipe. The inclined force was transferred into a local stress at the side of the first pipe. Mia et al. [9] performed a numerical analysis about special steel joints, defined as XT-joints. There was no overlapping node, but a single pipe was affected by more than two other pipes. The inclination angle was given as 90°. There was no variation of the inclination angle. The cross-sections and mesh dimensions are comparable to the model in this paper. A focus was given to the pipe thickness. The load case was a combination of compressional, tensional, and bending forces. It was found that the stress and deflection distribution at the connection of the pipes is not linear. No fracture arose, and the analysis was done in the elastic state. Rezadoost et al. [10] conducted studies about X-joints, where the joint area is reinforced. The reinforcement adopted the shape of the joint and was made of welded tubes. Especially the effect and behavior of the reinforced part is comparable to the analyzed joint in this paper. The X-joint has an inclination angle of 90°. A fillet weld was set to the model. The mesh is a combination of shell and solid elements. A focus is on the variation of geometrical parameters, e.g., the diameter ratio of the pipes. As it is done for the full-overlapped study in this paper, there are pipes with smaller and larger cross-sections. It was found that there is a large influence of the diameter ratio on the resistance—in this case, on the fatigue resistance. Advanced numerical analyses are shown to underline the importance of finite element studies regarding civil engineering structures. Kolanu et al. [11] did finite element studies on welded T-panels under compression. It was figured out that the computational time increases exponentially if the mesh size decreases. A difference of 4% between the numerical and experimental deformations occurred. Horajski et al. [12] carried out advanced FEM studies on welded thin-walled structures. A numerical algorithm was found to adopt the welding process. Vakili-Tahami et al. [13] performed FEA studies on welded T-joints with steel panels. Several plate thicknesses were analyzed. It was found that the differences between the numerical and experimental results depend on the plate thicknesses and welding direction. This paper is about numerical case studies of uniplanar joints made of circular hollow section profiles (CHS) with full-overlapped top connection. The aim is to figure out the influence of the inclination angle on the stability of the column. With the results, the designing engineer will have guidance in their decision to choose an economic inclination angle without executing a laboratory test. The paper is split into two parts. Firstly, there is a case study of a small-scale model. The pipe length was shortened to neglect the influences of stability failure. The focus is on the failure of the welding line instead of the pipe member. The second part is about geometrically expanded models with different inclination angles. On one hand, the influence of the chord length on the maximum affected force on the joint is focused; on the other hand, the influence of the inclination angles on the maximum affected force is analyzed. Espinosa [14] showed that the influence of the chord length becomes negligible for a length-to-diameter ratio of larger than 3. This conclusion is analyzed for the special joint, defined as a full-overlapped Y-joint in Section 3.2 and Section 3.3. The ratio is given with 12 to 23, depending on the chord length. 2. FEA—Model and Analysis The vertical pipe is defined as a chord and the two connected members are defined as braces or branches. The chord had an overall length of 350 mm and the braces were set to 175 mm, independent from the inclination angle. The vertical chord had a cross-section of 127.0 × 3.6 mm (DN 100), as defined in the EN 10219-2 [15]. At the chord’s top, there was a flat, 5-mm steel plate as an adapter between the braces and the chord. The two braces had a smaller diameter, including a cross-section of 51.0 × 2.6 mm (DN 40). This steel profile is defined in the standard EN 10219-2 [15]. In steel construction, different kinds of welding lines exist. Zamzami [16] explained the different parameters and influences of the welding line types; these are butt-, fillet-, or cruciform weldings. Saini et al. [17] explained that in numerical studies, the welding line does not to be modeled if there is no high bending. However, for this study, the welding line was set as a 3-mm fillet weld, which followed the shape of the braces and the top plate of the chord. Due to the complex shape, other welding line types are not realizable. In the view of assembling on the construction site, the members are typically arranged including a gap of 1–2 mm. By implementing the welding line, the pipes are connected via the welding line. So, there is no direct contact between the pipes and the force can only be transferred through the welding line. This presumes a precise preparation of the welding line, without deflects or imperfections. Comparable results between the numerical and experimental model were archived by robotic welding processing. By idealizing this to the numerical model, the contact between the braces and brace-to-chord was set as frictionless. The connection between the brace and weld or top plate to weld was set as fixed. The force was only transferred through the welding line. However, this is a numerical adoption of a realistic welding process. A connection between the pipes and the steel plate can only arise for lager deformations in the plastic state. The in-plane models have the same inclination angles of 30°, 45°, and 60° for both braces. Figure 1 visualizes the general model, including the design of the welding line. Beside this, the load case is shown. The axial compression force is visualized in this figure as an example of one inclination angle. The braces’ ends are simply supported. So, the displacement is blocked, while rotation is allowed. The displacement at the end of the chord, which is affected by the force, is blocked for the horizontal components. The vertical direction is unblocked. Typically, loads of a roof are constant and static loads, such as dead or snow loads; these loads are transferred as compression loads to the columns. The axial compression force is applied to the cross-section area of the chord in the dimension (N/mm2) to obtain a uniformly compression. The matched force in the results (Section 3) is the total force in the dimension (kN). The analysis is split into three parts. First, there is a case study of a small-scale model. In Section 3.2 and Section 3.3, there are case studies including expanded geometries. Regarding the small-scale model, a compression force affects the chord’s end in an axial direction. The compression load is iterated to the limit state (yield stress) of the material by increasing the force. The aim is to observe the influence of the inclination angle on the stresses and deflection in the welding line area. Cracks are the result of various load scenarios. They arise by exceeding the ultimate strength or durableness. If there are dynamic loads, fatigue cracks will appear. These multiaxial stresses in the area of the cracks are complex to calculate [18,19,20,21]. Even due to the geometrical heterogeneity of the node, a multiaxial state of stress arises [22]. The material was chosen as steel type S 235. The main properties of the material are as follows: yield stress is 250 MPa, Poisson’s ratio is 0.3, density is 78.5 kN/m3. The elastic model was analyzed. Heinemann et al. [23] and Younise et al. [24] analyzed the influence of materials regarding standard-defined nodes. Carbon, wood, aluminum, and different steel alloys were the subjects of the simulation. The differences in the results for the various steel alloys are small. An exception was found for the Aluminum material. The software ANSYS [25] was used for the numerical simulation. The mesh was made of 10-mm tetrahedral solid elements with a quadratic element shape function. At the area of the welding line, there was a refinement of 1.3-mm elements. The refinement of the mesh ensures precise results at decisive positions, while the computational time is minimized. A focus was given to the welding line, including its three-dimensional shape. To obtain the best-fitting numerical adoption, solid elements instead of shell elements were chosen. Hobbacher [26] suggested an element size of “0.4 x t” for solid elements, regarding the design of a welding line. Heinemann et al. [5] conducted prestudies to figure out the influence of different mesh types on the stress distribution and deflection in case of Y-joints. It was found that the chosen mesh is precise and saves computational time. In Figure 3, the 5-mm top plate and the triangle shape of the fillet weld are shown. A mesh metric analyzation was performed on the models to evaluate the quality of the mesh. As an example, the evaluation of the 45° small-scale model is presented. The skewness tended to zero, and there was a value of 0.25 on average. The maximum value was smaller than 1. Problems regarding the aspect ratio occur for values larger than 1000. The maximum value for the aspect ratio in this case study was given as 104. The average value was 1.92. The Jacobian value was calculated by 5.4. The average value was 1.01. The common limit value was given as 30. The overall mesh quality was evaluated in the range of 0 to 1. A value of 0.819 was given for the quality on average. For the verification of the stress results, the following study was consulted. Heinemann et al. [27] carried out experimental and numerical studies about full-overlapped CHS steel joints without intermediate plates. The geometrical properties are similar to the small-scale model in Section 3.1. The numerical analyzation process is comparable and the mesh choice superimposable. The experimental tests were done for the verification of the numerical model by comparing the von-Mises stresses. Several strain gauges were fixed at the specimen. As the geometry and the load scenario were symmetrical, two strain gauges were set in-between both branches nearby the welding line. So, the position was at the top area of the vertical welding line. Strain gauge 2 was located on brace 1 and strain gauge 3 was located on brace 2. Due to the symmetry, both sensors recorded nearly the same strains. The strains are compared with the numerical and experimental analysis at the same position. As an example, concerning one position, Figure 2 shows the differences between the numerical and experimental results as an example for one strain gauge position (Pos. 2 and 3). The range of the inclination angles is enlarged. It was found that the numerical analysis is in good agreement with the experimental results. Especially, in the case of the 25° model, the numerical and experimental results are nearly superimposable. The range of differences is small. This validation study is seen as a pattern for the numerical analysis in this paper. 3. Results 3.1. FEM Analysis of the Small-Scale Joint The first part of Section 3 is about small-scale CHS joints with full-overlapped top connection. The brace-to-chord ratio is fixed and given as 1:2. In Figure 3, the geometry and the mesh are visualized including the triangular shape of the welding line, which transfers the force. The welding line is connected to the 5-mm top plate. Next to the geometry, the von-Mises stress distribution is visualized on the surface of the model. The maximum stresses arise at the welding line (yellow or red parts) or at the contact area between the brace and the welding line. The deflections result of the 30°, 45°, and 60° models have a similar distribution. Figure 4 shows the deformation of the CHS steel joint. The overall deformation is small by setting a true-to-scale representation. For the analyzation of the failure mode, an optical magnification factor of 280 is implemented to the graphic. The maximum deflections arise at the chord’s end. Due to the applied force, the largest axial compression deformation occurs at this point. The minimal deflections are at the welding line between both branches and on the top of the steel plate. Figure 5 shows the results of the maximum compression force, which is applied to the model to reach the elastic limit state of the joint. The graph has an increasing trend. The differences in the maximum compression force are small for 30° and 45° with values of 10.3 kN and 11.25 kN. By increasing the inclination angle to 60°, an axial compression force of 18.25 kN can be induced. However, by the implementation of a larger angle, a lower load is absorbed. There is a correlation between a higher applied compression force and the resistance of the joint. If the inclination angle is steep, the horizontal steel plate tends to deform largely, which results in higher stresses at the welding line. In Figure 6, the deflection of the symmetrical CHS system is presented. The shape of the graph increases nearly linearly. The 30° model has the smallest deflections, while the 60° angle model has the highest. By increasing the inclination angle, the maximum applied force is increased by 85% and the deflection is increased by 27%. 3.2. FEM Analysis of the Expanded Geometry (Variable Geometry with Fixed Braces Length) 3.2.1. Expanded 30° Model To analyze the failure mode according to the stability, the length of the chord is expanded, while the braces’ lengths are fixed. Examples for failure by stability are the arising of buckling or bending effects at the pipes before reaching the ultimate limit state of the welding line. Generally, the length of the chord varies in the range of 1.5 m to 3.0 m for three different inclination angles. As an example of the general model, Figure 7 shows the 45° case. Figure 7a–h show the models including the variable chord length. The delta steps are 0.20 m with an exception for the first case, which is equal to 0.30 m (Figure 7b). To see the difference regarding the chord length, an equal scale is used in Figure 7. The vertical repositioning of the braces would change the constructive detail. By doing this, a K-joint is created, which is already defined in the Design Code Eurocode [1]. The load case is changed to the model in Section 3.1. Figure 8 shows the results for the maximum compression force. There is a decreasing trend, except in the 2.2 m and 3.0 m models. A similar shape is shown in Figure 9 regarding the results of the maximum deflections. The 1.5-m chord system has the highest deflection. Overall, the range of the deflections between the models is small. Generally, there is a maximum range of reduction regarding the compression force of −14% and −12% for the deflections in case of the 30° model. The boundary conditions for the chord’s end and braces’ ends are set as simply supported. The braces are affected by axial compression forces, which are equal to both braces. Djokovic [28] explained the advantage of including bending moments in the load scenario. In future studies, bending load cases will be added. Case studies of expanded brace-lengths models are presented in Section 3.3. The maximum stresses arise at the welding line between both branches and at the outer edges of the welding. The results are presented in the elastic limit state, when the yield stress of the material is reached. There is no failure by stability of the pipes before the limit state of the welded node is reached. The resistance of the welding line is decisive. 3.2.2. Expanded 45° Model This paragraph is about an expanded CHS joint in the version of the 45° inclination angle. The process of analyzation, load scenario, meshing, and profile dimensions is analogue to the 30° model in Section 3.2.1. Figure 10 shows the distribution of the von-Mises stresses in case of the 45° model. The maximum stresses arise at the side flank of the welding line, which is visualized in the yellow and red area. No failure by stability occurs due to the combination of the symmetrical geometry and load. The results for the maximum compression force and deflection are shown in Figure 11 and Figure 12. The graph of the compression force decreases nearly linearly. In contrast to this behavior, the results of the maximum deflections quadratically decrease. The system that includes the 1.5-m chord model has the highest deflections and stresses. The maximum compression force is reduced by a factor of 25% and 30% regarding the deflections, in case of the 45° inclination angle system. 3.2.3. Expanded 60° Model The third variation of an expanded CHS joint model with a fixed chord length includes the inclination angle of 60°. The analyzation process, including the geometrical and material parameters, is equal to the case studies in Section 3.1 and Section 3.2. The maximum stresses arise at the outer flank of the welding line, at the brace-to-chord and brace-to-brace connections (front side in Figure 13). Due to the axial compression without any imperfections, there is no failure by stability regarding the chord member. The results for the maximum compression force and the maximum deflection are printed in Figure 14 and Figure 15. Both graphs have a nearly linear and decreasing shape. The maximum range for the compression force is between 6.5 kN and 12.0 kN. However, by shortening the chord’s length from 3.0 m to 1.5 m, the applied compression force can be increased by a factor of 1.8 in the case of the 60° model. The delta of the deflection results is small. Generally, there is a reduction of 80% for the maximum compression force and 45% for the deflections in the case of the 60° inclination angle model. 3.3. FEM Analysis of the Expanded Geometry (Variable Geometry) To estimate the influences of the expanded geometries, a second case study regarding expanded geometry is presented in this Section. The ratio of the expanded pipes’ lengths is changed, while the overall column length is fixed, as shown in Figure 16. Figure 16a–e show the model depending on the different length ratio. The chord length is set to 0.5 m to 2.5 m. The influence of variable chord and brace lengths on the maximum applied force in the elastic state is analyzed. The chord length varies in the range of 0.5 m to 2.5 m, staggered in 0.5 m intervals. The overall vertical column length is fixed to 3.0 m. Consequently, the length of the braces varies in the range of 0.5 m to 2.5 m equally to both members in vertical projection. This analysis is a theoretical study to figure out the behavior of the node in different geometrical situations compared with realistic geometries (Section 3.2). The ratio of the length between the chord and the brace is partly unrealistic for structures on site. The inclination angles are set to 30°, 45°, and 60°. Figure 16 shows the general geometry for the different length ratios in case of the 60° model. The analyzation process, mesh, and load case are equal to the models in Section 3.1 and Section 3.2. Figure 17 visualizes the von-Mises stress distribution as an example for the 30° model including a chord length of 1.5 m. The maximum stresses arise at the welding line, between both branches. The forming of the stress peaks depends on the inclination angle. Figure 18 shows the results for the maximum compression force for each brace, depending on the inclination angle and chord length. For all three model types, there is a quadratically increasing trend. The gap between results for the 30° and 45° model is smaller than for the 60° graph. The orientation of the trend is in contrast to the trend orientation of the results in Section 3.2. However, there is the same conclusion that if the member’s length increases, the resistance of the joint will decrease. Figure 19 shows the results for the maximum deflections depending on the inclination angle and chord length. Mainly, there is a decreasing trend. The results of the 30° and 45° models are nearly superimposable, with an exception for the 0.5-m chord-length model. The 60° model has the largest deflections in comparison with the two other inclination angle models. 4. Discussion The results presented in this paper show the relevance of the studies regarding symmetrical full-overlapped steel joints with on top connections. The Eurocodes [1] and Design Codes [2] disregard these geometries. The model presented in this paper is not comparable to the common systems in the standard. In the case of Y-joints or K-joints, there is an influence of the inclination angle on the maximum force. However, due to the intermediate plate and the different geometry, it is not possible to compare the maximum affected forces between both systems. In contrast with the conclusion of Saini [11], the welding line must be included to the numerical system, even if there are only axial forces. The highest stresses arise at the area around the welding line. Azari-Dodaran et al. [7] compared uniplanar and multiplanar steel joints. This paper presents uniplanar joints. By taking into account the results of the multiplanar joint of Heinemann et al. [3] and Azari-Dodaran et al. [7], the uniplanar models have a difference in resistance of −12% to +54%. The results regarding the influence of chord length on limit force are in contrast with the results of Espinosa et al. [8]. A large influence on the limit force occurred for a large ratio of the chord length. There was a maximum reduction of resistance of 80% in case of the expanded chord and fixed brace length. If the length of one member type is enlarged, the maximum affected force to the joint will decrease. If the overall length of the column is fixed, there will be a larger reduction of the maximum affected force compared with systems with unfixed geometries. Different failure modes of stability (buckling) arise for a small length ratio. This is characterized by a large pipe length and results in higher stresses or smaller applicable force to the joint. The cross-section diameters of the braces are smaller than the diameter of the chord. However, smaller cross-sections will have a smaller resistance to buckling effects, if there is a larger length. The influence of the inclination angle on the limit forces was investigated by comparing the results of this paper to the results of circular hollow-section joints with asymmetrical shape of Heinemann et al. [4]. The symmetrical joints have a difference in resistance of −13% to +25%, depending on the inclination angle. Jankovic [8] presented a method to design hollow-section joints by calculating the interaction forces between the pipes. The connection forces are affected to the second pipe. The method is not applicable for the on-top-connection joint, which is presented in this paper. The welding line was neglected in Jankovic’s analyzation; however, the welding line represents the contact between the pipes. The stress distribution at the welding line is complex in the full-overlapped model. There is no possibility to calculate this distribution easily. The second aspect is about the overlapped geometry. A combination force of two pipes affects a third pipe regarding the model in this paper. This is not adoptable by Jankovic’s method. Mia et al. [9] conducted numerical case studies about XT-joints. No different inclination angles were tested. The stress distribution between the brace and the chord is comparable to the stress distribution in this paper. Besides this, the deformation behavior is comparable to the full-overlapped steel joints for the compression load case. No comparison regarding the force application of an overlapped joint can be done. Rezadoost et al. [10] carried out studies about X-joints. It was found that the diameter ratio of the chord and brace has got a large influence on the resistance in the compression load case. A similar conclusion is drawn for the full-overlapped analysis by adopting the geometrical properties. By varying the inclination angle, the contact area between the braces and the top plate is enlarged. This variation has got an influence on the stress distribution at the top plate. 5. Conclusions This paper presents numerical studies regarding full-overlapped columns made of circular hollow-section profiles with symmetric inclination angles. Small-scale models and different expanded models were analyzed. The following four conclusions are drawn: Firstly, the inclination angle has a large influence on the maximal force that can be applied to the structure. Besides this, the inclination angle has a large influence on the dimension of the maximal deflections. By increasing the inclination angle, the maximum force is increased by 85%, combined with an increase of 27% regarding the deflection. If the designing engineer chooses a full-overlapped column with on-top connection, the structure will be economically optimized by implementing an obtuse angle between the brace and horizontal level. This result is valid for structures with intermediate plates and unequal diameters for the brace and chord. Secondly, if there is a large chord length combined with a fixed brace length, the maximum load and global deformations will decrease. Depending on the inclination angle, the following influences occur by varying the chord length: 30° model—max. force −14%, deflections −12%; 45° model—max. force −25%, deflections −30%; 60° model—max. force −80%, deflections −45%. By comparing the results among the inclination angle models, the maximum resistance was observed to vary between 81% and 257%. The deflections rose to between 242% and 294% in maximum. In case of choosing a full-overlapped CHS column with on-top connection and fixed brace length, the most economical structure is archived by taking a short chord. In this case, the 1.5-m chord length modes enable the highest applicable force. Thirdly, if one pipe is enlarged, the resistance will decrease due to different failure modes. If the overall length is fixed and the pipe length becomes larger, the maximum affected force to the joint will be smaller than for the fixed-brace-length models. This reduction of the applied force occurs because of the smaller cross-section of the brace compared to the chord. The bending effect is larger in the case of a smaller cross-section diameter. This bending effect in case of large members with a small cross-section is explained in the Eurocode [1]. However, to archive the highest applicable force to a joint with full-overlapped top connection, the length of the pipe will be chosen as short and the ratio of the brace-to-chord length will tend to 1. This designing conclusion is valid for the three inclination angles, 30°, 45°, and 60°. Fourthly, regarding the enlarged models combined with a fixed overall length, the steep 60° model generated the highest compression force compared with the 30° and 45° systems. The 45° model is affected by the smallest force for this specific case. The areas including the highest stresses in the welding line are equal for both extracted systems. To choose an economical model, the designing engineer should pick an obtuse angle for this special kind of column. The stress distributions are not comparable to current analyses in the literature (Section 4). Standard defined joints, such as the K- or Y- joints [8,9,10], are different in geometry. Due to the side connection, a different distribution of stresses arises in comparison to joints with intermediate plates and top connection. In comparison with multiplanar models [3,7], differences of up to 54% occur. The geometry including an intermediate plate reduces the resistance compared with joints with full-overlap (overlap of three members) [27]. In this case, the force is transferred directly into the chord and is not distributed in the intermediate plate. Acknowledgments The authors of this paper thank the Department of Civil and Industrial Engineering of the TU Iasi for the scientific support. Author Contributions Conceptualization, P.H. and D.-N.I.; methodology, P.H.; software, P.H.; validation, P.H.; formal analysis, P.H.; investigation, P.H.; data curation, P.H.; writing—original draft preparation, P.H.; writing—review and editing, D.-N.I.; visualization, P.H.; supervision, D.-N.I.; project administration, D.-N.I. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 System and Load case CHS. Figure 2 Validation Experimental–Numerical results [27]. Figure 3 Von-Mises stress detailed over cross-section. Figure 4 Deflection symmetrical 45° model—Section Cut. Figure 5 Maximum Compression Force CHS Symmetrical. Figure 6 Deflection CHS Symmetrical. Figure 7 Variable Chord lengths, 1.5–3.0 m. Figure 8 Results—Maximum Compression Force. Figure 9 Results—Maximum Deflection. Figure 10 Stresses—symmetrical 45° model. Figure 11 Results—Maximum Compression Force. Figure 12 Results—Maximum Deflection. Figure 13 Stresses 60° Model. Figure 14 Results—Maximum Compression Force. Figure 15 Results—Maximum Deflection. Figure 16 Variable Geometry with Chord Length 0.5–2.5 m. Figure 17 Von-Mises Stress Distribution—1.5-m chord length. Figure 18 Results—Maximum Compression Force. Figure 19 Results—Maximum Deflection. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. DIN EN 1993-1-8: 2010-12 Eurocode 3: Bemessung und Konstruktion von Stahlbauten—Bemessung von Anschlüssen Deutsches Institut für Normung Berlin, Germany 2010 2. Wardenier J. Kurobane Y. Packer J.A. van der Vegte G.J. Zhao X.L. Design guide for circular hollow section (CHS) connection under predominantly static loading CIDECT Series ‘Construction with Hollow Sections’ Serial No. 1 Verlag TÜV Rheinland Cologne, Germany 1991 3. Heinemann P. Isopescu D.-N. Maxineasa S.G. Numerical Case Study about Three-Dimensional CHS Joints with Overlapped Top Connection Proceedings of the CNCM17: XVII National Conference of Metal Constructions Bucharest, Romania 28–29 October 2021 submitted for publication 4. Heinemann P. Isopescu D.-N. Maxineasa S.G. FEM Analysis for the Behavior of Two-Dimensional CHS Joints with Asymmetrical Full-Overlapped Top-Connection Mater. Proc. CMSS21 2021 in press 5. Heinemann P. Isopescu D.-N. Maxineasa S.G. Case studies on finite element modelling of welded joints Bull. Polytech. Inst. Jassy Constr. Archit. Sect. 2021 67 79 94 6. Heinemann P. Isopescu D.-N. Maxineasa S.G. Numerical Case Studies about Two-Dimensional SHS Joints with Symmetrical and Asymmetrical Top-Connection Proceedings of the International Scientific Conference CIBv—Civil Engineering and Building Services Brașov, Romania 4–5 November 2021 in press 7. Azari-Dodaran N. Ahmadi H. Static behavior of offshore two-planar tubular KT-joints under axial loading at fire-induced elevated temperatures J. Ocean. Eng. Sci. 2019 4 352 372 10.1016/j.joes.2019.05.009 8. Jankovic M. Lucic D. Code for the design and optimization of K gap joints composed of circular hollow sections Proceedings of the 8th International Conference “Civil Engineering—Science and Practice” GNP Kolašin, Montenegro 8–12 March 2022 9. Mia J. Islam A. Kabir A. Islam M. Numerical Analysis of tubular XT Joint of jacket type offshore structures under static loading BMJ 2020 6 299 318 2519-5972 10. Rezadoost P. Nassiraei H. SCFs in FRP-strengthened steel tubular X-joints under compression: Parametric study and formulation Proceedings of the 12th International Congress on Civil Engineering Mashhad, Iran 12–14 July 2021 11. Kolanu N.R. Raju G. Ramji M. A unified numerical approach for the simulation of intra and inter laminar damage evolution in stiffened CFRP panels under compression Compos. Part B 2020 190 107931 10.1016/j.compositesb.2020.107931 12. Horajski P. Bohdal L. Kukielka L. Patyk R. Kaldunski P. Legutko S. 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On the application of a critical plane approach to the life assessment of welded joints Procedia Eng. 2018 213 448 458 10.1016/j.proeng.2018.02.044 23. Heinemann P. Isopescu D.-N. Maxineasa S.G. The influence of materials on the behavior of joints with multiple bar connections IOP Conf. Ser. Mater. Sci. Eng. 2021 1138 12023 24. Younise B. Rakin M. Gubeljak N. Međo B. Sedmak A. Numerical prediction of ductile fracture resistance of welded joint zones Procedia Struct. Integr. 2016 2 753 760 10.1016/j.prostr.2016.06.097 25. ANSYS Inc. Products 2019 R3 © 2006–2019, Software Application Available online: https://www.ansys.com/ (accessed on 26 April 2022) 26. Hobbacher A. Recommendations for Fatigue Design of Welded Joints and Components XI- II-2151r4-07/XV-1254r4-07, IIW International Institute of Welding Paris, France 2008 27. Heinemann P. Isopescu D.-N. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095048 ijms-23-05048 Article Genome-Wide Identification and Characterization of the CC-NBS-LRR Gene Family in Cucumber (Cucumis sativus L.) Zhang Wanlu 123 Yuan Qi 123 Wu Yiduo 1 Zhang Jing 1 Nie Jingtao 123* Septiningsih Endang Academic Editor 1 College of Horticulture Science, Zhejiang AF University, Hangzhou 311300, China; zhangwanlu27@163.com (W.Z.); yq20gxm@163.com (Q.Y.); wuyiduo0523@163.com (Y.W.); jingzhang0411@163.com (J.Z.) 2 Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China 3 Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang AF University, Hangzhou 311300, China * Correspondence: njt@zafu.edu.cn 02 5 2022 5 2022 23 9 504809 4 2022 29 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The NBS-LRR (NLR) gene family plays a pivotal role in regulating disease defense response in plants. Cucumber is one of the most important vegetable crops in the world, and various plant diseases, including powdery mildew (PM), cause severe losses in both cucumber productivity and quality annually. To characterize and understand the role of the CC-NBS-LRR(CNL) family of genes in disease defense response in cucumber plants, we performed bioinformatical analysis to characterize these genes systematically. We identified 33 members of the CNL gene family in cucumber plants, and they are distributed on each chromosome with chromosome 4 harboring the largest cluster of five different genes. The corresponding CNL family member varies in the number of amino acids and exons, molecular weight, theoretical isoelectric point (pI) and subcellular localization. Cis-acting element analysis of the CNL genes reveals the presence of multiple phytohormone, abiotic and biotic responsive elements in their promoters, suggesting that these genes might be responsive to plant hormones and stress. Phylogenetic and synteny analysis indicated that the CNL proteins are conserved evolutionarily in different plant species, and they can be divided into four subfamilies based on their conserved domains. MEME analysis and multiple sequence alignment showed that conserved motifs exist in the sequence of CNLs. Further DNA sequence analysis suggests that CsCNL genes might be subject to the regulation of different miRNAs upon PM infection. By mining available RNA-seq data followed by real-time quantitative PCR (qRT-PCR) analysis, we characterized expression patterns of the CNL genes, and found that those genes exhibit a temporospatial expression pattern, and their expression is also responsive to PM infection, ethylene, salicylic acid, and methyl jasmonate treatment in cucumber plants. Finally, the CNL genes targeted by miRNAs were predicted in cucumber plants. Our results in this study provided some basic information for further study of the functions of the CNL gene family in cucumber plants. cucumber CC-NBS-LRR bioinformatic gene expression miRNA National Natural Science Foundation of China31701915 Zhejiang Province Public Welfare Technology Application Research ProjectLGN19C150007 Start-Up Found from Zhejiang Agriculture & Forestry University2017FR006 This research was funded by National Natural Science Foundation of China, grant number No. 31701915, Zhejiang Province Public Welfare Technology Application Research Project, grant number No. LGN19C150007, and a Start-Up Found from Zhejiang Agriculture & Forestry University, grant number 2017FR006. ==== Body pmc1. Introduction Cucumber (C. sativus L.) is one of the most important vegetable crops in the world. During cucumber production, the cucumber is challenged with various serious plant diseases at different stages of development. For example, PM and downy mildew (DM) are the most common and serious diseases in cucumber plants, which lead to a significant reduction in the yield and quality of cucumbers. Therefore, one of the most desirable strategies to control these diseases is to breed resistant cultivars by cloning the resistance genes in cucumber plants. The NLR genes are the largest disease-resistant gene family in different crops by functioning to block pathogen invasion [1]. However, the function of these genes in cucumber plants has not been well documented. Therefore, it is of interest and importance to understand the function of the NLR gene family in disease response in cucumber plants. Plants are constantly confronted by various pathogens and external adverse environment cues at different developmental stages. Therefore, plants have developed two layers of defensive mechanisms to resist the invasion of pathogens during this long-term evolutionary process [2]. PAMP-triggered immunity (PTI) is the first defensive mechanism which occurs on the plant cell membrane. It is an immunity reaction triggered by the recognition of pathogen-associated molecular patterns (PAMPs) by a variety of pattern recognition receptors (PRRs) on the plant cell membrane. However, pathogens secrete effectors in response to PTI immunity, leading some pathogens to disrupt the first line of defense of the cell membrane and enter the cell. As a stronger defense system in the cell, effector-triggered immunity (ETI) is immediately activated to prevent further invasion by pathogens. As receptors, the NLR genes directly or indirectly recognize the pathogen effectors [3,4]. At the same time, special responses are activated, including hypersensitivity responses (HR), cell death, and the accumulation of hydrogen peroxide. The disease resistance gene (R gene) is a dominant resistant gene in plants and it can specifically detect pathogens to trigger resistance to disease [5]. The R gene is characterized by race specificity and higher efficiency. The NLR gene family is one of the largest R gene families in plants and contains nucleotide-binding sites (NBS) and leucine-rich repeat (LRR) domains [6]. They play an important role in protecting plants from various pathogens. Based on the N-terminal domain, this family can be divided into three categories: (coiled coil) CC-NBS-LRR (CNL), (toll/interleukin-1 receptor) TIR-NBS-LRR (TNL) and (resistance to powdery mildew 8, RPW8) RPW8-NBS-LRR (RNL) [7]. The sequences of this family of genes have a very important conserved domain—the NB-ARC domain (nucleotide-binding adaptor, shared by NOD-LRR proteins, APAF-1, R proteins, and the CED4 domain, also was named the NBS domain), which plays an important role in plant disease resistance and signal transduction [8,9,10,11]. The NBS domain consists of eight conserved motifs—P-LOOP (phosphate-binding loop), GLPL (Gly-Leu-Pro-Leu, also called kinase 3), RNBS-A (resistance nucleotide binding site-A), RNBS-B (resistance nucleotide binding site-B), RNBS-C (resistance nucleotide binding site-C), RNBS-D (resistance nucleotide binding site-D), kinase 2 and MHDV (Met-His-Asp-Val), and the conservation of these eight motifs is inconsistent across different plant species [12,13,14,15,16]. The C-terminal LRR structure is composed of various leucine or proline and aspartic acid amino acids that can specifically recognize proteins and participate in protein–protein interactions [17,18]. The LRR domains of NLR proteins often serve as detectors of pathogen invasion, either by directly interacting with pathogen-released effectors or by monitoring the status of effector-targeted host proteins such as RIN4 (RPM1-interacting protein 4) [19]. Upon recognition, the conformation of the NBS domain changes from an ADP-bound condensed state to an ATP-bound state exposed to the N-terminal domain, triggering a downstream hypersensitivity response that ultimately leads to apoptosis and spread of infected cells, and NBS proliferation inhibits pathogens [20]. In the NLR family, the CNL type is a class of important clades of plant disease resistant genes. Researches showed that CNL family genes can cause cell death in plants. For example, cell death was triggered in tobacco when a CNL protein (AT1G12290) was transiently expressed. However, in Arabidopsis, it was found that the Botrytis Susceptible1 Interactor (BOI) could regulate cell death by negatively regulating the level of AT1G12290 proteins [21,22]. The similar event of hybrid necrosis also occurs in cotton and wheat [23,24]. On the other hand, the CNL genes are also involved in the process of resisting invasion by different diseases in plants. For example, the protein encoded by the CNL genes was found to be resistant to Verticillium wilt in cotton and Arabidopsis [25,26]. In melon, CNL genes play a role in resistance to viral infection and aphid infection [27]. The role of the disease-resistant effects of CNL genes also exists in rice, wheat, and tomato [28,29,30,31,32]. In cucumber plants, two NLR genes, CsRSF1 and CsRSF2, were reported recently to positively regulate resistance to Sphaerotheca fuliginea [33]. Therefore, the CNL genes also play a role in disease resistance in cucumber plants. However, to what extent these genes contribute disease resistance and how they function in this process largely remain elusive. In this study, 33 CNL genes were analyzed in cucumber plants through the bioinformatic method. The distribution, gene cluster, and characteristics of the CNL family members were analyzed. The Cis-elements of the promoters and conserved motifs of encoded proteins of these CNLs were predicted. Phylogenetic and synteny analysis of the CNL family members in different plant species indicated that this class of genes is evolutionarily conserved. Heatmap analysis using RNA-seq data indicated that the expression of CNL genes was induced by various biotic and abiotic stresses. Gene expression analysis further implied that these genes exhibit a temporospatial expression pattern, and their expression is also responsive to PM infection, ethylene, salicylic acid, and methyl jasmonate treatments in cucumber plants. Moreover, the CNL genes targeted by miRNAs were predicted. The results provide an understanding for the further study of the function of CNL family of genes in cucumber plants. 2. Results 2.1. Identification and Characterization of the CNL Family of Genes in Cucumber Plants In total, 33 CNL genes were identified in cucumber plants using the genome of the Chinese Long cucumber, using Arabidopsis CNL genes as the query sequences [34]. Their protein sequences were verified using Pfam and NCBI databases for protein functional domains. We found that the physical chromosomal locations of the 33 CNL genes in cucumber plants are uneven (Figure 1). The majority of CNL genes in cucumber plants were located on chromosome 2 with 10 CNL genes, followed by chromosome 2 and 3 with six CNL genes. Five, two, and three CNL genes were located on chromosomes 5, 6, and 7, respectively. However, only one CNL gene was located on chromosome 1. Among the 33 CNL genes in cucumber plants, 16 genes existed in the form of five gene clusters, approximately accounting for 48.48% of the total CNL genes. These five gene clusters were located at the beginning or end of chromosomes 2, 3, 4, 5, and 7. However, no gene cluster existed on chromosomes 1 and 6 (Table S1). The largest gene cluster, consisting of five genes, was located on chromosome 4. Thus, it is likely that the probability of gene duplication on chromosome 4 was higher than that of other chromosomes in cucumber plants. Additionally, the basic characteristics of the CNL genes and the encoded proteins were predicted (Table 1), including CDS length, the number of amino acids and exons, molecular weights, theoretical isoelectric point (pI), and subcellular localization. In all CsCNL genes, the CDS length was between 879 and 5403 bp and the number of exons ranged from one to seven. The length of the CNL proteins ranged from 293 to 1801 amino acids, and the isoelectric point was in the range from 5.57 to 9.15. In addition, the molecular weights varied significantly, ranging from 32.98491 kDa (Csa5G165310) to 207.11112 kDa (Csa2G014830). The result of subcellular localization showed that 19 (57.58%), 13 (39.39%) and 1 (3.03%) of these CNL proteins were located in the nucleus and cytoplasm, nucleus, or cytoplasm, respectively. 2.2. Cis-Acting Element Analysis of the Promoters of the CNL Genes in Cucumber Plants The promoters of the 33 genes in the cucumber CNL family were analyzed for cis-acting elements. The results showed that more than 20 different cis-elements were found (Figure S1 and Table S2). The elements contained five kinds of plant-hormone-related elements, such as gibberellin (P-box), salicylic acid (TCA-element), abscisic acid (ABRE), methyl jasmonate (TGACG-motif, CGTCA-motif), and auxin (TGA-element). It indicated that the cucumber CNL family of genes may be participating in the regulation of phytohormone response. The promoter of each sequence contained one to five kinds of plant-hormone-related elements. There were 23, 15, 24, 27, and 17 genes that contained abscisic-acid-, auxin-, gibberellin-, methyl-jasmonate-, and salicylic-acid-responsive elements, respectively. It showed that one gene may have more than two of the same hormone-related elements, with the most numerous being the element response to methyl jasmonate (Figure S1). On the other hand, other important cis-elements also existed in these promoters, responsive to biotic and abiotic stresses, such as low temperature stress (CCGAAA), responsive elements involved in light- and drought-induced MYB binding sites (MBS); endosperm-specific negative expression (AACA_motif), meristem expression (CAT-box), mesophyll cell differentiation (HD-Zip 1), and seed-specific regulation (RY-element). 2.3. Analysis of the Phylogenetic Relationship and the Conserved Motifs of CNL Proteins in Cucumber Plants To analyze the evolutionary relationships of the CNL gene family members, phylogenetic tree was constructed using the neighbor-joining method. The CNL proteins of cucumber, cabbage, and Arabidopsis were divided into 4 clades (Figure 2). There were five, and nine CNLs in CNL-A and CNL-B clades, respectively. The remaining 19 cucumber CNLs were all clustered in the CNL-C clade, while the CNL-D clade did not contain any cucumber CNLs. The amino acid sequences of the 33 cucumber CNL proteins were extracted for motif conservation analysis, and 20 motifs were screened out (Figure 3A, Table S3). We found that most of the amino acid sequences were arranged by order of “motif13-motif9-motif7-motif16-motif14-motif20-motif11-motif2-motif1-motif10-motif3-motif5-motif8-motif6-motif19-motif4-motif17-motif15-motif18-motif12”. P-LOOP (motif7), RNBS-A (motif16), kinase-2 (motif14), RNBS-B (motif20), RNBS-C (motif11), GLPL (motif2), RNBS-D (motif1), and MHDV (motif5) were conserved regions of NBS in cucumber plants and other species (Figure 3B, Figure S2, Table S3). However, some motifs were lost during the evolution of the CNLs. Only six sequences of CNL protein contained all the 20 motifs, and the rest of them lacked several motifs, for example, the encoded proteins of Csa5G647580 and Csa5G165310 only contained four conserved motifs. In addition, motif6, motif9, motif12, and motif15 were obviously either increased or deleted and shifted. Combined with a phylogenetic tree analysis, it was found that the similarity of sequence was closer, the more related motifs were contained (Figure 3A). At the C-terminus of the sequence, the motifs of the LRR family are mainly arranged in a tandem crossover manner. There were seven motifs belonging to the LRR family in the sequences of CNL proteins (motif4, motif5, motif8, motif12, motif15, motif17, and motif19). The results indicated that there are multiple LRR family motifs in one CNL protein. Moreover, a cucumber CNL protein containing all 20 motifs was aligned with homologous protein sequences from other species. We found that the conserved motifs in the NBS domain existed in different species (Figure 3B, Figure S3). The results showed that the CNL proteins were conserved in dicotyledonous and monocotyledonous plants. 2.4. Synteny Analysis of the CNL Genes of Cucumber To reveal the origin and evolution of the CNL gene family members, we carried out a comparative syntenic analysis on CsCNL genes with another four plants, including melon, watermelon, tomato, and soybean (Figure 4; Table S4). The results showed that a total of 14 CsCNL genes had syntenic relationship with those in melon, and 17 corresponding orthologs were identified in melon. Meanwhile, 11 CsCNL genes showed a syntenic relationship with those in watermelon, and 11 corresponding orthologs were identified in watermelon. Among these orthologous pairs, nine CsCNL genes (Csa2G012670, Csa2G014830, Csa2G075440, Csa2G433370, Csa3G172400, Csa3G814390, Csa4G015840, Csa4G638480, and Csa5G165310) had their corresponding orthologs both in melon and watermelon, suggesting that these genes might play an important role in the evolution of the CNL genes. Moreover, three and eight pairs of orthologous CNLs were identified between cucumber and tomato, and cucumber and soybean, respectively (Figure 4; Table S4). The results showed that Csa6G490170 was related to at least two pairs of homologs in tomato and soybean. The above results showed that the CNL genes showed stronger homology among cucumber, melon, and watermelon, than that among cucumber and two other species, which corresponded to the fact that they belonged to Cucurbitaceae crops. 2.5. Expression Analysis of the CNL Family Genes in Cucumber Plants Using the published RNA-seq data on the CuGenDB website, the expression level of the 33 CNL genes in different cucumber organs or tissues (root, stem, leaf, male flower, female flower, ovary, and tendril) was summarized (Figure 5, Table S5). The result showed that Csa7G420890 was highly expressed in roots, leaves, and female flowers. However, some genes, such as Csa2G012670 and Csa2G014830, were only highly expressed in the roots. The expression levels of some genes in all seven tissues were very low, such as Csa5G266890, Csa5G647580, and Csa6G375730. According to the analysis of the cis-acting elements of the promoter, the expression level of the CNL family of genes might be affected by biotic and abiotic stresses. To verify whether CNL genes are involved in responding to stress, the expression level of the CNL genes induced by various stresses in previous studies was summarized using heatmaps (Figure 6). The results showed that the expression level of some genes had no obvious difference under stress by DM and PM, such as Csa2G433370, Csa5G266890, Csa5G647580, Csa5G647550, and Csa6G375730. Some CNL genes were up-regulated after infection with DM in cucumber plants, such as Csa5G647590, and Csa3G172400 (Figure 6A, Table S6). We found that the expression level of all genes in resistant materials (SSL508-28) was higher than that in susceptible materials (D8) at the same time point after PM inoculation (Figure 6B, Table S7). For example, the expression level of the Csa4G016430 gene in the SSL508-28 inbred line was higher than that in the D8 inbred line at 0 and 24 hpi (hours post-inoculation). In addition, CNL genes were also involved in abiotic stress responses. The expression of cucumber CNL genes is up-regulated under salt treatments, especially the expression of Csa2G012670, which is significantly increased (Figure 6C, Table S8). Most CNL genes are generally up-regulated when induced by low temperatures, while the expression levels of minority genes were unchanged or down-regulated (Figure 6D, Table S9). Taken together, these results suggested that CNL genes might be involved in responding to biotic and abiotic stresses. 2.6. Analysis of the Expression of CNL Genes by qRT-PCR Based on the above results, six typical CNL genes were selected to verify their tissue-specific expression patterns by qRT-PCR (Figure 7). The results showed that these genes were all highly expressed in roots, which was consistent with the above results. Csa3G684170 had relatively high expression in other tissues, except for the stem, cotyledons, and hypocotyl. Compared with the gene expression level in cotyledons, Csa4G016460 had higher expression levels in other tissues. The expression level of Csa4G638480 was lower in leaves and fruits, but higher in other tissues. The expression level of Csa7G420890 was relatively higher in roots, stems, hypocotyls, and female flowers, and the lowest in fruits. Csa2G014830 and Csa2G435460 had the highest expression in roots and low expression in other tissues. The results indicated that CNL genes might play different roles in the growth and development of cucumber. On the other hand, the induced expression patterns of these genes responding to PM was performed using a pair of near-isogenic lines, S1003 (resistant inbred line) and NIL(Pm5.1) (susceptible inbred line) (Figure 8) [36]. The expression of Csa4G016460, Csa3G172400, and Csa4G638480 in S1003 were significantly lower than that in NIL(Pm5.1) at 0 hpi. Except for the above genes, the expression of the other three genes (Csa2G014830, Csa2G435460 and Csa3G684170) had no difference at 0 hpi between S1003 and NIL(Pm5.1). The expression of Csa4G016460 was continuously decreased after inoculation in two lines. The expression level of Csa4G016460 in S1003 was significantly higher than that in NIL(Pm5.1) at 12, and 24 hpi, but the expression of Csa4G016460 in NIL(Pm5.1) at 48 hpi was higher. Csa2G014830, Csa2G435460, Csa3G684170, Csa3G172400, and Csa4G638480 had similar expression patterns post-inoculation. Their expression level reached the highest at 12 hpi and then decreased in S1003 and NIL(Pm5.1). It also found that the expression of Csa2G014830 was significantly lower at 12 hpi, and 24 hpi in NIL(Pm5.1) than that in S1003, and there was no difference at 48 hpi between the two lines. Except for 0 and 24 hpi, there was a significant difference in the expression level of Csa2G435460 between S1003 and NIL(Pm5.1). The expression of Csa2G435460 in S1003 had a significantly higher level than that in NIL(Pm5.1) at 12 hpi, but had an opposite expression pattern at 24 hpi. Csa3G684170 had a similar expression pattern in two materials while the expression was significantly higher in NIL(Pm5.1) than S1003 at 24 hpi. There were no significant difference in the two lines at 48 hpi. The expression patterns of Csa3G172400 and Csa4G638480 were similar between S1003 and NIL(Pm5.1). The expression levels of Csa3G172400 and Csa4G638480 were significantly lower at 0 hpi at S1003. In addition, the expression levels of Csa3G172400 and Csa4G638480 were significantly lower at 12 hpi at NIL(Pm5.1), while they were significantly higher at 24 hpi and 48 hpi at NIL(Pm5.1). Five CNL genes reached highest expression level at 12 hpi, which showed that they might be corelated with the PM resistance conferred by Csmlo1. These results showed that the CNL genes might be involved in PM resistance. 2.7. Expression Patterns of the CNL Genes Responding to Hormones in Cucumber Plants The cis-elements of the predicted promoters of the CNL genes showed that the promoters of most CNLs contained 1–5 cis-elements responding to phytohormones. To further analyze the effect of phytohormones on the expression of CNL genes, the cucumber was treated with different exogenous hormones, which related to stress, and the expression patterns of seven CNL genes were selected to be monitored within a short period of time after hormone application. The results showed that the expression level of seven CNL genes was changed, ranging from 0 h to 24 h after ethylene treatment (Figure 9). Specifically, it was found that expression of Csa3G684170, Csa2G014830, and Csa3G172400 was firstly decreased and then increased, and the expression reached the highest level at 12 h. The expression patterns of Csa2G420890 and Csa4G016460 were similar to the previous three genes, but the expression reached the highest level at 24 h after ethylene treatment. The gene expression levels of Csa4G638480 and Csa2G435460 were increased firstly and then decreased, and the expression reached the highest level at 12 h. It was speculated that, within 24 h of exogenous ethylene application, the CNL genes showed various expression patterns responding to ethylene, which might balance the relationship between development and the response to the environment. Salicylic acid (SA) had been reported to respond to various disease resistance mechanisms in different plants [34,37,38]. Therefore, salicylic acid treatment was carried out on cucumber seedlings to further study the effect of salicylic acid on cucumber CNL gene expression by qRT-PCR (Figure 10). The expression pattern of the seven CNL genes showed a similar tendency responding to salicylic acid. The expression level of these genes increased continuously and then decreased back to normal. The difference in the pattern was that expression level of Csa3G684170, Csa2G014830, Csa3G172400, and Csa2G420890 reached the highest at 12 h, while the expression level of the other three genes reached the highest at 6 h after salicylic acid treatment. These results indicated that the expression of the CNL genes was induced by salicylic acid treatment in the early stages of treatment, especially at 6 h and 12 h. Finally, aiming to analyze the effect of methyl jasmonate on the expression of CNL genes, the expression pattern of the CNL genes was monitored at 0–24 h after methyl jasmonate treatment, by qRT-PCR (Figure 11). We found that the expression levels of all genes kept relatively low at 0 h, 6 h, and 12 h after treatment, and the expression levels were largely increased at 24 h after treatment. The above results indicated that the expression of CNL genes was induced by different exogenous hormones. 2.8. Prediction Analysis of the Binding Site of CNL Genes Targeted by miRNA in Cucumber Plants In order to determine whether the cucumber CNL genes were regulated by microRNA through a targeted binding site, the published miRNAs sequences were used to predict the binding site of CNL genes targeted by miRNA on the psRNATarget website [39,40]. The miRNA sequencing and prediction analysis has been performed in a previous study. The mature miRNA sequence was used for analysis of the binding site of the CNL genes. It turned out that 30 genes might be regulated by 33 kinds of microRNA (Table S10 and Figure 12). Among them, 17 CNL genes might be targeted by miRNA482, accounting for 17% of the CNL genes in cucumber plants. This is followed by the number of CNL genes targeted by miRNA2118, miRNA396, and miRNA156, accounting for 9%, 9%, and 8% of all CNL genes, respectively. Six, five, and five CNL genes were targeted by miRNA157, miRNA172, and miRNA395, respectively. In addition, it was shown that most of the interactions between the targeted CNLs and various miRNAs were at the transcriptional level, and only a few of them were at the translational level. We also found that several miRNAs could target one CNL gene, such as Csa4G638480, the homologous gene of AtADS1 in Arabidopsis, targeted by miRNA396, miRNA164, and miRNA390 (Table S10). Meanwhile, multiple CNL genes might be targeted by one miRNA, for example, the miRNA482 could target 17 CNL genes (Table S10). 3. Discussion In this study, a total of 33 CNL genes were obtained from cucumber plants by the bioinformatics method (Figure 1 and Table 1), accounting for 0.135% of the whole gene numbers in cucumber plants. There were 455, 111, and 89 CNL genes in rice, tomato, and potato plants, respectively [41,42,43]. In addition, they accounted for 0.994%, 0.310%, and 0.153% of each gene numbers, which was larger than the proportion of CNL genes in cucumber plants. There were 69 CNL genes found in maize, with 0.089% proportion of the whole genes, which was lower than the proportion of CNL genes in cucumber plants [44]. In cucumber plants, 48.48% of the CNL genes were presented on the chromosome in the form of gene clusters (Table S1). This evolution phenomenon might have a positive effect on defending from various pathogens in nature. Evolutionary events, such as unequal crossover, insertion/deletion, and gene conversion, occurring in the NLR genes provided the possibility for increasing the rate of mutation and the formation of denser gene clusters on chromosomes, which has been found in lettuce and radish plants [45,46,47]. Based on the prediction of gene structure and protein physicochemical properties, we found that CsCNL genes had similar characteristics to the CNL genes in Chinese cabbage, cabbage and kiwifruit [1,13,48]. We speculated that the function of the CNL proteins may be affected by their characteristics. Cis-elements play an important role in various life processes, such as plant growth and development, stress response, hormones response, and signal transduction. The promoters of CNL genes were analyzed for cis-acting elements (Figure S1, Table S2), and we found that many important cis-elements existed in the promoters, such as various plant-hormone-related elements, light-related elements. Among them, 16 gene members contained an element named “W-box”, which is the DNA binding site of the WRKY protein induced by salicylic acid [38]. Research suggested that it could positively regulate RPP8, a DM resistance gene, in Arabidopsis [49]. The results suggested that the CNL genes might be implicated in the defense against certain fungal diseases, and the CNL genes might also play a role in defense against various diseases in cucumber plants. To verify the roles of these hormonal elements in disease resistance, we monitored the expression patterns of the CNL genes under different hormone treatments by qRT-PCR (Figure 9, Figure 10 and Figure 11). We speculated that the expression level responding to methyl jasmonate at 24 h was particularly high due to the multiple elements related to methyl jasmonate response. These results indicated that CNL genes were induced by different hormones (ethylene, salicylic acid, and methyl jasmonate). Motif conservation analysis was performed on the CNL gene family proteins, and 20 motifs were obtained (Figure 3, Figure S2). It was found that eight conserved motifs of the NBS domain existed in most of the proteins. In addition, motif6, motif 9, motif 12, and motif 15 were obviously either increased, or deleted and shifted, which might be related to the different genetic variation generated by natural selection in the face of the various adversities in cucumber plants (Figure 3A). However, some conserved motifs of individual CNL genes were lost during the evolution process, for example, Csa5G165310 lost 16 motifs in the sequence. Studies have reported that the loss rate of NLR genes in cucumber plants was higher than that of other plants. Because the copy number of cucumber NBS-LRR genes was affected by gene loss, this resulted in further diversity of the resistance conferred by them [50]. Synteny analysis showed that a higher synteny was maintained among cucurbit crops (Figure 4, Table S4). This result demonstrated the existence of more orthologous pairs between cucumber plants and other cucurbit crops, due to the closer relatedness of evolution. The results of tissue-specific expression analysis showed that the expression level of CNL genes was relatively high in the root, and the expression level in other tissues was varied (Figure 7). Some genes had different expression levels in different tissues. For example, Csa2G014830 has a high expression level in the roots, while its expression level is relatively low in tendrils and other tissues (Figure 4, Figure 7). Moreover, the expression levels of different genes in the same tissue were different, such as Csa4G016460 and Csa7G420890. Although the expression of some CNL genes in the leaves was relatively low, they were induced to higher levels when they were faced infection by PM and hormone treatment. This indicated the precise regulation of the CNL genes to balance the development and stress responses. Moreover, the results by qRT-PCR were slightly different from the previous studies, which might be caused by using a different cucumber inbred line. Heatmap analysis using published data found that the expression of cucumber CNL genes was also increased under abiotic stresses, such as salt stress and low temperature treatment (Figure 6). The results showed that these genes might not only confer resistance to pathogens, but also play a role in abiotic stresses. Inoculation experiments of PM pathogens were performed using one pair of NIL in cucumber plants, and gene expression at different time points after inoculation was detected by qRT-PCR (Figure 8). The expression was very low at 0 hpi, but the gene expression level of the CNL genes (such as Csa3G172400) increased rapidly after inoculation and reached the peak at 12 hpi. Previous studies have confirmed the existence of a recessive disease resistance gene in cucumber plants, Csmlo1, which could confer durable resistance to PM in cucumber plants [51]. The expression of the CsMLO1 gene reached the highest level at 12 hpi of PM, which indicated that the CsMLO1 gene played a key role in responding the attack of PM at this period post-inoculation of PM on cucumber plants. According to the induced expression results of the above CNL genes, we found that five genes have a similar expression pattern to CsMLO1. These results indicated that the functions of these genes might correlate with the Csmlo1 resistance pathway in the process of resisting the invasion of PM. The expression of these CNL genes was strictly controlled at a low level when there were no pathogens invading [51]. This was to prevent damage to plants caused by constitutive activation and spontaneous reaction, such as hypersensitivity reactions and reactive oxygen species (ROS) production [52]. On the other hand, the CNL family of genes were known as a class of dominant disease-resistance R genes, and they play a role in the plant’s defense mechanism (PTI and ETI) [4,53]. The above results indicated that the CNL genes might be involved in disease resistance pathways controlled by dominant genes, and might also be coupled with the Csmlo1 pathway that plays a role in PM resistance, or the other diseases resistance pathways. However, these assumptions still need further experiments to be carried out for confirmation. Therefore, the study of CNL genes is of great significance for cultivating durable and stable disease-resistant cucumber varieties. The miRNAs are a class of small non-coding RNAs about 20-24nt in size, they mainly function to regulate gene expression at the post-transcription level in both plants and animals [54]. They play important roles in various life stages of plants, for example, the conserved miR156–miR172 were reported to regulate plant vegetative phase changes [55]. The miRNAs have been shown to be responsive to various biotic and abiotic stresses [56,57,58,59,60], including pathogen infection. Furthermore, miR396 negatively regulates rice blast resistance by inhibiting multiple OsGRFs genes [61], and miR482 is an ancient and conserved miRNA family of 22nt in length, and it plays an important role in the development and the process of resistance to diseases and stresses [62]. Studies have reported that miRNA482 could participate in the defense against pathogens by regulating their targeted genes, such as miR482 targets of the mRNA of CNL genes in cotton [39]. This study indicated that only 12% of NLR genes were targeted by the members of the miR482 family, and miRNA482 was inhibited when its targeted NLRs were induced to defend against pathogen invasion [39]. In the presence of pathogens, miR482 was silenced and the expression of the targeted genes NLR were increased, resulting in plant resistance. Specifically, miRNA482 cleaves the P-LOOP motif of the target NLR genes and produces a large amount of phasiRNA, which can enhance the silencing effect of miR482 on NLR genes. Thus, miR482 and its target genes NLR, as well as the generated phasiRNA, are involved in the process of plant immunity [37]. Moreover, the known miRNAs sequences were used to predict the possible target sites on cucumber CNL genes. It was found that one CNL gene could be targeted by different miRNAs, and the same miRNA could also target multiple CNL genes. Among them, miRNAs (such as miRNA156 and miRNA172) might regulate CNL genes and play an important role in growth and development. In addition, miRNA156 has been proven to function in the development of the age pathway and it is involved in responding to biotic stresses [55,63]. Importantly, miRNA482 was predicted to target 17 CNL genes in cucumber plants (Table S10). Previous studies have shown that miRNA482 could negatively regulate the CNL gene and play an important role in plant disease resistance [64]. These results indicated that miRNA482 and CNL genes might also be correlated in disease resistance in cucumber plants, and the CNL genes might be regulated by miRNA to balance the development and defense against pathogens. Therefore, the present study provided a direction for further research on the mechanism of action between miRNAs and CNL genes in cucumber plants. According to the previous studies, miRNA482 could cleave the target genes, for example, the CNL genes, and generate a large number of phasiRNAs, which were then involved in the response to pathogenic invasion in the plant’s ETI immune system. Therefore, we speculated that these CNL genes might be regulated by miRNAs, and they could cooperate with each other to play a role in disease resistance in cucumber plants. 4. Materials and Methods 4.1. Identification of Cucumber CNL Family Genes Firstly, blast homologous sequences of CNL in cucumber plants were searched on the CuGenDB website (http://cucurbitgenomics.org/, accessed on 14 March 2022) by using the Arabidopsis CNL family of genes as the query sequences [65]. Secondly, the NB-ARC domain was identified by PF00931 on the Pfam website (http://pfam.xfam.org/, accessed on 22 February 2022), then homologous protein sequences from Arabidopsis were scanned for “hmmsearch”. Then, the candidates and conserved domains of CNLs were confirmed by a NCBI CD search (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi, accessed on 8 March 2022). In addition, the coiled-coils structure was confirmed on the Paircoil2 website (http://cb.csail.mit.edu/cb/paircoil2/paircoil2.html, accessed on 25 February 2022), and the P-value parameter was set as 0.025 [66]. 4.2. Analysis of Gene Characteristics, Genomic Distribution, and Cis-Acting Elements in Promoters Information concerning the CNL genes, including exon numbers and CDS, were retrieved from the CuGenDB website and proved using FGENESH (http://linux1.softberry.com/berry.phtml?topic=fgenesh&group=programs&subgroup=gfind, accessed on 26 April 2022). The theoretical pI and molecular weight of the identified CNLs were calculated using the ProtParam website (https://web.expasy.org/protparam/, accessed on 22 April 2022). Moreover, we predicted the subcellular localization of CNL proteins by using the CELLO website (http://cello.life.nctu.edu.tw/, accessed on 8 April 2022). The cucumber genome annotation file was downloaded from the Ensembl Plants website (http://plants.ensembl.org/index.html, accessed on 16 April 2022) for analysis. According to the gene position, TBtools was used to map the physical location of the CNL genes. A gene cluster was regarded as the distance between two adjacent NLR genes being <200 kb, with ≤8 non-NBS-LRR genes between the two NLR genes [38]. Furthermore, the promoter sequence (2 kb upstream of the gene initiation codon) of CNL genes was submitted to the Plant CARE website for cis-element analysis (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/, accessed on 23 April 2022), and then visualized using TBtools. 4.3. Analysis of the Conserved Motif and the Synteny of the CNL Using the MEME Suite website (https://meme-suite.org/meme/, accessed on 25 March 2022), the amino acid sequences of 33 CNL genes were added for motif prediction. The results were downloaded in a MEME.xml format, and then be input into TBtools software for visualization. The whole genome sequence files and gene annotation files of soybean, tomato, and maize from the Ensembl Plant website were downloaded, and the whole genome sequence files and gene annotation files of cucumber, melon, pumpkin, and watermelon from the CuGenDB website were downloaded. The collinear relationship between different species was plotted with the help of TBtools software. 4.4. Transcriptome Analysis of the Genes Expression of the CNLs The cucumber transcriptome data (PRJNA80169) was found via the cucumber genome website, and the expression levels of 33 CNL family genes in seven tissues, including roots, stems, leaves, male flowers, female flowers, carpels, and tendrils, were analyzed. The expression levels of these genes were plotted into a heatmap using TBtools software. Similarly, heatmaps of CNL gene family expression were drawn for the cucumber transcriptome data of PM treatment (PRJNA321023) [67], DM treatment (PRJNA388584), salt stress treatment (PRJNA437579), and low temperature stress treatment (PRJNA438923). 4.5. Tissue-Expression Analysis Cucumber 9930 materials and qRT-PCR analysis were used for tissue-specific expression experiments. The roots, stems, leaves, cotyledons, hypocotyls, male flowers, female flowers, and fruits of cucumber 9930 were sampled. The experiment had three biological replicates and each organ from 15 cucumber plants were harvested and used as a sample in each replicate. 4.6. Cucumber Materials and the Treatment of PM and Hormones Cucumber seedlings were planted in a constant temperature incubator (16 h day and eight hours night; temperature 25 °C). Before the treatment, the seedlings were cultivated in an incubator free of pathogens. Cucumber 9930 materials were used for hormone-responding experiments, and S1003 and NIL(Pm5.1) materials were used for the inoculation experiments with PM [36,68]. At the two-leaf stage, different hormones, salicylic acid (2 mmol/L), methyl jasmonate (100 μmol/L), ethylene (200 ppmol/L), and abscisic acid (100 μmol/L) were sprayed on the cucumber 9930 leaves. Each treatment had three biological replicates and the leaves of 15 plants were harvested as samples at different time points after treatments (0 h, 6 h, 12 h, and 24 h) in each replicate. At the two-leaf stage, plants were inoculated with the PM pathogen by spraying a spore suspension (1 × 105 spores/mL) evenly onto the leaves. Samples were taken at 0 h, 12 h, 24 h, and 48 h after inoculation for expression analysis. The experiment had three biological replicates and the leaves of 10 plants were harvested and used as a sample at different time points in each replicate. 4.7. Total RNA Extraction and qRT-PCR Analysis Total RNA from different cucumber tissues was extracted by the Triozol method, the DNA was removed, and then was reversely transcribed into cDNA using the PrimeScript first Strand cDNA Synthesis Kit (Takara, Japan). The primers for qRT-PCR were designed in Primer 5 and the CsActin was used as an internal control gene (Table S11). The qRT-PCR was carried out using the SYBR Premix Ex Taq II Kit (Takara, Japan), PCR was performed on a StepOne PlusTM (ABI, America) real-time PCR machine. The qRT-PCR parameters were set as 95 °C for 10 s, 60 °C for 15 s, and 72 °C for 25 s for 45 cycles, and melting curve analysis was performed with the default settings on the instrument. The 2−ΔΔCt calculation method was used for quantitative expression analysis. Data were analyzed using normalization algorithms. 4.8. Prediction of Binding Sites of the CNL Genes Targeted by miRNA Downloaded miRNA sequences were from psRNATarget (https://www.zhaolab.org/psRNATarget/, accessed on 9 April 2022) [69]. The cDNA library was chosen, with the library entitled “Cucumis sativus (cucumber), cds, cucumber genome sequencing project, version 2”, and the rest of the parameters were set as default. The predicted binding sites of CNL genes were screened out in the Excel sheet. 5. Conclusions In this study, a total of 33 CNL genes were identified in cucumber plants. The distribution of CNL genes on the chromosome was uneven, some genes existed on the chromosomes in the form of gene clusters. The corresponding CNL proteins were varied in the number of amino acids and exons, molecular weight, theoretical isoelectric point (pI) and subcellular localization. Analysis of cis-elements revealed that the promoters of the CNL genes included many important elements in the growth and development of plants, such as hormone-response elements, drought-response elements, cold-response elements and damage-inducing elements. Analysis of the phylogenetic tree and conversed motifs suggested that the CNL proteins are evolutionarily conserved. Syntenic analysis indicated that more orthologous pairs existed in the CNL gene family among the Cucurbitaceae crops (cucumber, melon, and watermelon), compared to tomato and soybean plants. Heatmap analysis and tissue-specific expression analysis of the CsCNL genes demonstrated their diverse spatiotemporal expression patterns. Moreover, heatmap analysis also indicated that the expression of CNL genes were induced by various biotic and abiotic stresses. The qRT-PCR analysis showed that those genes also responded to PM infection, and treatment with ethylene, salicylic acid, and methyl jasmonate in cucumber plants. Finally, we predicted that many CNL genes were targeted by miRNAs, especially the 17 CNL genes targeted by miRNA482. Our results provided the basis for further study of the function of CNL genes in cucumber plants. Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 31701915), Zhejiang Province Public Welfare Technology Application Research Project (No. LGN19C150007), and a Start-Up Fund from Zhejiang Agriculture & Forestry University (2017FR006). Special thanks to Professor Gang Wu for his help in editing this article. Supplementary Materials The following supporting information can be downloaded at: www.mdpi.com/article/10.3390/ijms1697963/s1. Click here for additional data file. Author Contributions W.Z. and J.N. planned and designed the study and wrote the manuscript. W.Z. and Q.Y. performed the experiments. W.Z. implemented the software. Y.W. and J.Z. collected the sequences of genes in this work. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement This study did not involve humans or animals. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement Not applicable. Conflicts of Interest The authors have declared that no competing interests exist. Figure 1 The distribution of the CNL genes located on the chromosomes in cucumber plants. The genetic distance of seven chromosomes were represented by the scale in megabases (Mb) on the left. The CNL genes are displayed using nomenclature for genome version 2 of Chinese Long cucumber. Blue lines represent the location of the gene on each chromosome. Figure 2 Phylogenetic relationships of CNL proteins among cucumber, cabbage and Arabidopsis. The unrooted phylogenetic tree was constructed by MEGA7.0 by neighbor-joining method with 1000 bootstrap replicates. The CNLs are divided into four major subfamilies. Different subfamily was indicated with different colors. The black triangles represented cabbage, black squares represented Arabidopsis, and black circles represented cucumber. Figure 3 Analysis of the conserved motifs of CNL proteins. (A) MEME analysis of the CNL proteins in cucumber plants. (B) Alignment of the conserved NBS domain of CNL proteins between cucumber and other species. Black color: the homolog level was 100%; Pink color: the homolog level was greater or equal to 75%; Blue color: the homolog level was greater or equal to 50%. Maize, XP_008666515.1; bitter melon, XP_022145177.1; melon, XP_008453965.1; tomato, XP_004245923.1; soybean, XP_006599131.1; pumpkin, CmaCh14G014850.1; Arabidopsis, At1G10920; watermelon, Cla97C07G142810.1; and rice, XP_015651137.1. Figure 4 Synteny of the CNL genes among cucumber and other species. Gray lines in the background represent collinear blocks in cucumber plants and other genomes. The collinear gene pairs with CNL genes between different species were highlighted by the blue lines. Red inverted triangle indicated the locations of the CNL genes. Cucumber (C. sativus L.), melon (Cucumis melo L.), watermelon (Citrullus lanatus L.), tomato (Solanum lycopersicum L.), and soybean (Glycine max L.). Figure 5 Tissue-specific expression of the CNL genes in cucumber plants. The transcriptional levels of CsCNL genes in seven tissues of cucumber 9930 were investigated based on public transcriptome data (PRJNA80169) [35]. The genome-wide expression of CsCNL genes were shown on a heatmap using a log2RPKM value, and −6.00 to 8.00 was artificially set with the color scale limits according to the normalized value. The color scale showed increasing expression levels from blue to red. Figure 6 The expression patterns of the CNL genes under biotic and abiotic stresses in cucumber plants. (A) The expression of cucumber CNL genes in response to DM, post-inoculation at 0 h, 6 h, and 24 h. (B) The expression of cucumber CNL genes in response to PM, post-inoculation at 0 h, and 48 h. D8_CT, D8 line control; D8_PM, D8 line, 48 h post-inoculation with PM; SSL508-28_CT, SSL508-28 line as control; SSL508-28_PM, SSL508-28 line, 48 h post-inoculation with PM. (C) and (D) The expression of cucumber CNL genes in response to salt stress and chilling stresses. Hpi, hours post-inoculation. The genome-wide expression of CsCNL genes were shown on a heatmap using log2RPKM value, and −8.00 to 6.00, −9.00 to 9.00, −10.00 to 6.00 were artificially set with the color scale limits according to the normalized value. The color scale was shown increasing expression levels from blue to red. Figure 7 Tissue-specific expression analysis of the CNL genes in cucumber plants by qRT-PCR. R: root; S: stem; C: cotyledon; H: hypocotyl; L: leaf; M: male flower; Fe: female flower; and Fr: fruit. The vertical axis is relative to the expression level and x-axis represents different tissues. Values are mean ± SE (n = 3). Figure 8 The expression patterns of the CNL genes responding to PM in cucumber plants. The expression levels of the CNL genes were detected in S1003 and NIL(Pm5.1) for 0 h, 12 h, 24 h, and 48 h after inoculation. S1003, resistance cucumber inbred line; NIL, and susceptible cucumber inbred line NIL(Pm5.1). The expression level of transcript at 0 h was set to a value of ‘1′. Values are mean ± SE (n = 3) (* and ** indicate significant differences between S1003 and NIL(Pm5.1) at p = 0.01 and 0.05, respectively). Figure 9 Expression analysis of the CNL genes responding to ethylene treatment in cucumber plants. The vertical axis is relative expression level and 0, 6, 12, 24 h on the x-axis indicate the treatment time. The expression level of transcript at 0 h was set to a value of ‘1′. Values are mean ± SE (n = 3). Figure 10 Expression analysis of the CNL genes responding to salicylic acid treatment in cucumber plants. The vertical axis is relative expression level and 0, 6, 12, 24 h on the x-axis indicate the treatment time. The expression level of transcript at 0 h was set to a value of ‘1′. Values are mean ± SE (n = 3). Figure 11 Expression analysis of the CNL genes responding to methyl jasmonate in cucumber plants. The vertical axis is relative expression level and 0, 6, 12, 24 h on the x-axis indicate the treatment time. The expression level of transcript at 0 h was set to a value of ‘1′. Values are mean ± SE (n = 3). Figure 12 The predicted binding site of the CNL genes targeted by miRNAs in cucumber plants. Two dots indicated paired successfully between bases, and one dot indicated that there is also a pairing between U and G in the secondary structure. Blank space indicated that two bases failed to be paired. ijms-23-05048-t001_Table 1 Table 1 Characteristics of the CNL family of genes and the corresponding proteins in cucumber plants. Gene ID Length of CDS (bp) Number of Amino Acids Number of Exons Molecular Weight (kDa) Theoretical pI Subcellular Localization Csa1G201260 Csa2G008000 2409 2469 803 823 7 5 90.94932 95.32828 5.72 6.80 N/Cyto Cyto/N Csa2G012670 2439 813 5 92.46041 7.93 N Csa2G014830 5403 1801 7 207.11112 5.57 N Csa2G074130 2745 915 2 105.95585 8.29 N Csa2G075440 4056 1352 4 152.92906 6.35 N/Cyto Csa2G096930 2913 971 1 110.91379 6.20 N/Cyto Csa2G403680 3966 1322 4 151.81125 6.54 N Csa2G433340 2898 966 2 111.92962 8.38 N/Cyto Csa2G433370 3579 1193 4 136.25234 6.98 N Csa2G435460 2721 907 2 104.05611 7.23 N Csa3G172400 3444 1148 1 131.34413 7.38 N/Cyto Csa3G684170 3267 1089 1 125.61899 6.10 Cyto/N Csa3G814390 2409 803 1 93.33195 9.15 N Csa3G814400 2466 822 1 94.42609 7.10 N Csa3G815400 2676 892 1 103.11633 6.43 N Csa3G822360 3159 1053 2 120.24954 6.27 N Csa4G015840 3261 1087 1 124.35665 6.84 Cyto/N Csa4G015850 3240 1080 1 123.31024 7.36 N/Cyto Csa4G016360 2874 958 2 109.57171 7.09 N Csa4G016430 2520 840 3 96.12670 6.14 Cyto/N Csa4G016460 2841 947 2 108.23233 7.33 N Csa4G638480 2463 821 5 94.24655 6.58 N Csa5G165310 879 293 1 32.98491 6.80 Cyto Csa5G266890 3141 1047 1 119.3791 6.04 Cyto/N Csa5G647550 3213 1071 6 121.78672 6.70 N Csa5G647580 1143 381 3 44.46930 8.70 N/Cyto Csa5G647590 1884 628 7 71.53999 5.97 N Csa6G375730 2751 917 2 106.78455 8.21 N Csa6G490170 2454 818 5 93.59007 6.05 N Csa7G239020 3621 1207 2 138.19007 7.59 N/Cyto Csa7G420890 1665 555 2 64.71685 7.61 N Csa7G425940 3060 1020 5 116.40068 6.86 N N represents nucleus; Cyto represents cytoplasm. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095051 ijms-23-05051 Article Deoxyelephantopin Induces Apoptosis and Enhances Chemosensitivity of Colon Cancer via miR-205/Bcl2 Axis https://orcid.org/0000-0002-3456-6083 Ji Haoyan 123 Zhang Kui 123 Pan Guangzhao 123 Li Changhong 123 Li Chongyang 123 https://orcid.org/0000-0002-7403-8808 Hu Xin 123 Yang Liqun 123* https://orcid.org/0000-0003-1178-1570 Cui Hongjuan 123 Taguchi Y-h. Academic Editor Wang Hsiuying Academic Editor 1 State Key Laboratory of Silkworm Genome Biology, Medical Research Institute, Southwest University, Chongqing 400715, China; 18303478069@163.com (H.J.); zhangk87@163.com (K.Z.); m18983708534_2@163.com (G.P.); lichanghong960223@163.com (C.L.); chongyang1520@gmail.com (C.L.); huxinusing@163.com (X.H.); hcui@swu.edu.cn (H.C.) 2 College of Sericulture, Textile and Biomass Sciences, Southwest University, Chongqing 400716, China 3 Cancer Center, Medical Research Institute, Southwest University, Chongqing 400716, China * Correspondence: cysylq@swu.edu.cn; Tel.: +86-023-68251731; Fax: +86-023-68251128 02 5 2022 5 2022 23 9 505128 3 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Colon cancer (CC) is one of the major causes of cancer death in humans. Despite recent advances in the management of CC, the prognosis is still poor and a new strategy for effective therapy is imperative. Deoxyelephantopin (DET), extracted from an important medicinal plant, Elephantopus scaber L., has been reported to exhibit excellent anti-inflammatory and -cancer activities, while the detailed anti-cancer mechanism remains unclear. Herein, we found that DET showed a significant CC inhibiting effect in vitro and in vivo without obvious organ toxicity. Mechanistically, DET inhibited CC cells and tumor growth by inducing G2/M phase arrest and subsequent apoptosis. DET-mediated cell cycle arrest was caused by severe DNA damage, and DET decreased the Bcl2 expression level in a dose-dependent manner to promote CC cell apoptosis, whereas restoring Bcl2 expression reduced apoptosis to a certain extent. Moreover, we identified a microRNA complementary to the 3′-UTR of Bcl2, miR-205, that responded to the DET treatment. An inhibitor of miR-205 could recover Bcl2 expression and promoted the survival of CC cells upon DET treatment. To further examine the potential value of the drug, we evaluated the combinative effects of DET and 5-Fluorouracil (5FU) through Jin’s formula and revealed that DET acted synergistically with 5FU, resulting in enhancing the chemotherapeutic sensitivity of CC to 5FU. Our results consolidate DET as a potent drug for the treatment of CC when it is used alone or combined with 5FU, and elucidate the importance of the miR-205-Bcl2 axis in DET treatment. colon cancer deoxyelephantopin Bcl2 apoptosis chemosensitivity the Natural Science Foundation of Chongqingcstc2019jcyj-zdxmX0033 the Fundamental Research Funds for the Central UniversitiesXYDS201912 We are grateful for the support of the Natural Science Foundation of Chongqing (cstc2019jcyj-zdxmX0033), the Fundamental Research Funds for the Central Universities (XYDS201912). ==== Body pmc1. Introduction Colon cancer (CC) is considered to be the third most prevalent cause of death from cancer after lung and breast cancers [1,2]. Although there has been a significant improvement in the treatment of colon cancer, the prognosis remains dismal. CC, as with many other solid tumors, progresses in stages and involves a variety of oncogenes and tumor suppressor genes [3]. Therefore, further investigation is required to understand the molecular mechanism of CC occurrence and develop novel effective therapeutic strategies. Drug therapy is one of the main methods of tumor treatment. Many monomers of traditional medicine can inhibit migration and invasion and promote the apoptosis of tumor cells, which inhibits the occurrence and development of cancer. To date, increasing traditional medicine monomers have been used in clinical practice and the effect is remarkable. Therefore, drug therapy has good prospects for the treatment of cancer. However, current clinical treatments still have limitations, such as the serious side effects of chemotherapy drugs and tolerance to long-term use [4,5,6]. DET is a plant sesquiterpene lactone derived from Compositae Elephantopus scaber L. The first known report of DET in 1975 documented significant inhibitory activity on rat ascitic fluid [7]. Many subsequent reports highlighted the inhibitory effect of DET on a variety of cancer cells. For example, DET targets multimolecular signal transduction pathways to inhibit the growth and promote the apoptosis of cervical cancer SiHa cells [8]. DET also induces apoptosis of HCC cells through oxidative stress, inhibition of NF- kappa B, and mitochondrial dysfunction [9]. DET can inhibit the activity of triple-negative breast cancer cells through extracellular activity and protein function mediated by ROS [10]. Research has also witnessed an inhibitory effect of DET on the lung metastasis of mammary adenocarcinoma TS/A cells in mice [11]. DET is a sesquiterpene lactone, and the primary mechanism of almost all sesquiterpene lactones is the induction of oxidative stress for various biological and pharmacological activities [12,13,14,15,16,17,18,19,20]. Research shows that deoxyelephantopin induces ROS-mediated autophagy in human colorectal cancer in vitro and in vivo [21]. Herein, we report that DET presented an ideal colon cancer cell inhibition effect in vitro and in vivo without obvious organ toxicity. DET treatment inhibited the proliferation, colony formation, and cell cycle of colon cancer cells and repressed the progression of the tumor in the immune-deficient mouse model. Mechanistically, DET reduced the level of anti-apoptosis protein Bcl2, and the overexpression of Bcl2 inhibited apoptosis induced by DET. Further, we identified a microRNA complementary to the 3′-UTR of Bcl2, miR-205, which responded to the DET treatment and was upregulated. Finally, our results elucidate the importance of the miR-205-Bcl2 axis in DET treatment, and obtaining DET enhances the chemosensitivity of colon cancer to 5-Fluorouracil. 2. Results 2.1. DET Inhibits Colon Cancer without Obvious Organ Toxicity The chemical structural formula of DET is shown in Figure 1A. First, from the result of the 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay shown in Figure 1B, it is apparent that there was a dose-dependent increase in the inhibitory effect of DET on the four types of colon cancer cells. Moreover, it was also evident that the inhibitory rate of DET on colon cancer cells had a time effect—namely, the inhibitory rate of cancer cells increased with the extension of time. Among the four cancer lines, the most prominent inhibitory rate changes were observed in HCT116 and sw620 cells. We treated HCT116 and sw620 with different concentrations of DET (2, 5, and 10 µM, and dimethyl sulfoxide (DMSO) was used as a control) for 48 h. Microscopic observation revealed a significant reduction in the number of alive cells of HCT116 and sw620 with the gradual increase in DET concentration (Figure 1C). Next, the results of the EdU assay, as shown in Figure 1D,E, suggested that the signals of cell proliferation in the DET group were lower than those in the control group, indicating the ability of DET to inhibit the DNA replication of HCT116 and sw620 cells. Furthermore, the results of soft agar validated that the tumorigenic ability decreased apparently in a DET dose-dependent manner compared with the control group, which shows that DET can inhibit the self-renewal capability of colon cancer cells in vivo (Figure 1F). Meanwhile, we subcutaneously injected HCT116 cells into immunodeficient mice. A week later, the mice were randomly assigned to two groups: the control group and the DET treatment group. After two weeks, the xenograft tumors were removed and weighed. The results indicated that tumors in the DET group were significantly smaller as compared to those in the control group. The tumors’ weight and volume were also significantly lower than those in the control group (Figure 1G,H). Further, the immunohistochemistry (IHC) staining study showed that the Ki-67-positive signals that reflect the cell proliferation ability of the DET group were lower than those of the control group (Figure 1I). Together, these results confirmed that DET could inhibit the tumorigenesis of colon cancer cells in vitro and in vivo. Most importantly, as is evident in Figure 1J, mice organs did not manifest significant pathological alterations, indicating that DET had minimal side effects and little toxicity to mice’s visceral organs. 2.2. DET Arrests Colon Cancer Cells at the G2/M Phase by Inducing DNA Damage Since cell proliferation is tightly related to cell cycle progression, we explored the effect of DET on the cell cycle. As detailed in Figure 2A,B, cell cycles of HCT116 and sw620 cells treated with DET were arrested at the G2/M phase compared with the control group. Meanwhile, we detected the relevant genes regulating the cell cycle by qRT-PCR assay and identified the DET-induced significant downregulation of the mRNA level of CDK1 and CyclinB1, which regulate the G2/M phase (Figure 2C). Thereafter, detection of the cell cycle proteins by Western blot demonstrated the downregulation of protein levels of CDK1 and CyclinB1 in a dose-dependent and time-dependent manner (Figure 2D,E). Further, DNA damage marks of DET-treated HCT116 and sw620 cells were evaluated due to the interference of the DNA damage response with cell cycle progression [22,23,24,25,26,27]. We observed that HCT116 and sw620 cells treated with DET demonstrated obvious tailing by the comet assay, which indicated DET-induced DNA damage in HCT116 and sw620 cells (Figure 2F,G). The Western blot assay also confirmed that the expression level of γ H2A, a DNA-damage-specific marker, was upregulated in a DET dose-dependent manner (Figure 2H,I). 2.3. DET Induces Apoptosis in Colon Cancer Cells Apoptosis is a key factor for inhibiting cell proliferation. The TUNAL assay elucidated significantly higher apoptosis levels of HCT116 and sw620 cells treated with DET as compared to the control group (Figure 3A). Meanwhile, the transmission electron microscope (Figure 3B) showed the apparent apoptotic bodies in colon cancer cells treated with DET. Next, the supernatant and adherent HCT116 and sw620 cells treated with DET were collected after 48 h. The cells were stained with an AnnexinV-APC kit. As shown in Figure 3C,D, DET-treated HCT116 and sw620 cells claimed a certain degree of early and late apoptosis, but there was no obvious apoptosis in the control group. At the same time, we found that the apoptosis level of HCT116 and sw620 cells treated with combined DET and Z-VAD-FMK (an inhibitor of apoptosis) was lower than that of the DET group alone. The results of Western blot documented a dose-dependent and time-dependent increase in the expression levels of marker proteins of apoptosis, C-caspase3 and C-PARP, upon DET treatment (Figure 3E,F). 2.4. DET Induces Apoptosis by Inhibiting Bcl2 in Colon Cancer Cells Treating HCT116 and sw620 cells with a concentration gradient of DET, the mRNA protein levels of the anti-apoptotic protein Bcl2 were found to be reduced significantly and stably (Figure 4A,B). Therefore, we presumed Bcl2 to be a pivotal factor in DET-mediated apoptosis. Next, we employed lentiviruses expressing Bcl2 to stably infect HCT116 and sw620 cells. The Western blot assay confirmed Bcl2 overexpression after viral infection, and DMSO and empty vector treatments were used as the controls (Figure 4C). The MTT assay showed that the cell inhibitory rate of the Bcl2 overexpression group was higher than that in the control group, while it was lower than the DET group in HCT116 and sw620 cells (Figure 4D). Further, we verified that DET and HA14–1 (Bcl2-specific inhibitor) exhibited synergistic curative effects on colon cancer cells by Jin’s formula (q value ≥ 1.15) (Figure S1A). Likewise, the results of EdU, as illustrated in Figure 4E, revealed that the EdU-positive signals of the Bcl2 overexpression group were greater than in the DET group alone in HCT116 and sw620 cells. This indicates that Bcl2 overexpression might partially restore the DNA replication inhibited by DET. The soft agar assay also substantiated a similar outcome wherein the clone spots in the Bcl2-overexpressed HCT116 and sw620 cells treated with DET were smaller and fewer than those in the control group, but larger and more numerous in comparison to the colon cancer cells treated with DET (Figure 4F). Furthermore, the results of apoptosis detected by flow cytometry also verified that the overexpression of Bcl2 could partly prevent the apoptosis of HCT116 and sw620 cells induced by DET (Figure 4G). Consistent with the previous conclusions, Western blot also revealed that, compared with the DET group, the expression of C-caspase3 and C-PARP in the Bcl2-overexpressed HCT116 and sw620 cells was significantly lower (Figure 4H). Moreover, the combined treatment results demonstrated that the combined DET and HA14–1 group had higher protein expression levels than the group with or without HA14–1 in HCT116 and sw620 cells (Figure S1B). The in vivo experimental results from Figure S1C,D also indicate that the tumor weight and volume of mice in the DET group and the combined treatment group were lower compared with the control group, while the combined treatment group was lower than the DET group and there were significant differences between the two groups. These findings were further substantiated by the results of IHC staining, which revealed diminished Ki-67 proliferation signals in the combined group compared to those in the DET group. However, there was no significant difference between the control group and the HA14–1 group (Figure S1E,F). This implies that the combination of DET and HA14–1 can enhance the apoptosis of colon cancer induced by DET. These results indicate that the DET-mediated inhibition of Bcl2 expression induced the apoptosis of colon cells. 2.5. miR-205 Is Directly Targeted by Bcl2 and Induces the Apoptosis of Colon Cancer Cells Bioinformatics analysis in three databases revealed 32 genes to be potential targets of miR-205 (Figure 5A). A putative binding site for miR-205 was identified in the 3′-UTR of Bcl2 (Figure 5B). We constructed reporter plasmid vectors containing the wild type or mutant seed sequence in the 3′-UTR fragment of Bcl2 and found that the luciferase activity of the wild-type reporter was significantly reduced by miR-205 transfection in 293FT cells, while it remained unchanged in the mutant reporters, suggesting that miR-205 acts directly on downstream target Bcl2 (Figure 5C). From the real-time quantitative fluorescence PCR, shown in Figure 5D, it is quite prominent that the expression of miR-205 was downregulated after inhibitor treatment. Furthermore, compared with inhibitor treatment alone, the expression of miR-205 in the cells treated with inhibitor and DET was higher. This indicated that DET might induce miR-205 expression. Moreover, the results of the MTT assay, as detailed in Figure 5E, confirmed that the downregulation of miR-205 could partly rescue the survival rate of colon cancer cells. Further, we collected the floating and adherent cells of the experimental group and the control group by flow cytometry and found that the apoptosis in the combined miR-205 inhibitor and DET group was significantly lower as compared to that in the DET group alone (Figure 5F,G). Western blot detection (Figure 5H) also confirmed that the downregulation of miR-205 promotes the expression of Bcl2 and prevents the apoptosis of HCT116 and sw620 cells induced by DET. Thus, DET could induce the apoptosis of colon cancer cells via the miR-205-Bcl2 axis. 2.6. DET Enhances Chemosensitivity to 5-Fluorouracil (5FU) in Colon Cancer Cells It is well known that 5-Fluorouracil (5FU) is a pyrimidine analog that interferes with thymidylate synthesis and has clear activity against colon tumors in clinical practice. However, the drug resistance of 5FU reduces its therapeutic efficacy [28,29]. The synergy between 5FU and DET was noted over a broad range of time. The synergistic effect of DET and 5FU was noted in a wide range of time, indicated by q values of ≥1.15, adopting Jin’s formula. It was observed that treatment with 5FU and DET synergistically enhanced DET-mediated cell growth suppression (Figure 6A). Meanwhile, apoptosis detected by flow cytometry also showed that the apoptotic level in the combined group was greater than that in the 5FU group or DET group alone in HCT116 and LS174T cells (Figure 6B,C). The results of Western blot further substantiated that the combined 5FU and DET treatment group had higher C-caspase3 and C-PARP protein expression than those treated with DET or 5FU alone in HCT116 and LS174T cells (Figure 6D). To extend these in vitro findings to in vivo tumor growth, the combination of DET and 5FU in immunodeficient mice injected with HCT116 cells was investigated. A month later, tumors obtained from mice treated with the combination of DET and 5FU were found to be significantly smaller than tumors from mice treated with DET or 5FU alone (Figure 6E,F). Additionally, the combination, with a stronger effect of promoting apoptosis, had a more significant impact than DET alone on tumor growth (Figure 6G,H). These findings suggest that DET could enhance the chemosensitivity to 5FU of colon cancer cells and provide a theoretical basis for the more extensive clinical treatment of colon cancer. 3. Discussion The ethanol extracts of E. scaber significantly inhibited the growth of cancer cells and induced apoptosis [30]. One of the main bioactive compounds isolated from E. scaber is deoxyelephantopin, which inhibits the development of human cancer cells [31,32]. The monomer has been found to impede the growth of breast cancer [10], lung cancer [33], lymphoid cancer [34], and nasopharyngeal carcinoma [32], and induce apoptosis of osteosarcoma cells [35] and hepatoma cells [9]. This study further consolidates the prominent anti-cancer activity of DET both in vivo and in vitro. At the same time, we also found that DET blocked the cell cycle of colon cancer in the G2/M phase, which may be caused by DET-induced DNA damage. Several studies have identified miR-205 as a radiosensitizing miRNA that directly or indirectly inhibits DNA damage repair [36,37,38]. These prompted us to consider whether the DET-induced upregulation of miR-205 in this study promotes colon cancer cell apoptosis through some functional targets of miR-205 in response to DET-induced DNA damage, which remains to be further explored. We found that DET mediated the steady and dramatic inhibition of Bcl2 expression at the mRNA level and protein level in the process of elucidating the apoptosis of colon cancer cells induced by DET. During tumorigenesis and cancer progression, cancer cells rely on the dysregulation of the BCL2 protein family and tend to survive [39,40]. For example, somatic copy number amplification of both the MCL1 and BCLxL genes in human cancers is an instance of such imbalance [41]. We ascertained that Bcl2 overexpression could support the survival of colon cancer cells upon DET treatment. In line with these findings, our data reveal that the combination of Bcl2 inhibitor HA14–1 and DET was more effective than DET alone in inhibiting the growth of colon cancer cells both in vitro and in vivo. These data explain the mechanisms of the DET-induced apoptosis of colon cancer and reveal the vital role of Bcl2 in this process. Many studies have substantiated that microRNA is involved in the regulation of biological growth and development and plays a primary role in the occurrence and development of tumors [42,43,44]. Thus, we predicted that DET could downregulate Bcl2 expression at the transcriptional level. Our work suggested that miR-205, which directly interacts with Bcl2, is upregulated in colon cancer cells upon DET treatment. Additionally, the inhibitor of miR-205 could recover Bcl2 expression and reduce DET-induced apoptosis in human colon cancer cells. Research on the structure–activity relationship of plant sesquiterpene lactones has shown that the presence of an alkylating center (α-methylene-γ-lactone, α-methylene-δ-lactone, conjugated cyclopentenone, or conjugated side chain ester) is essential for their anti-cancer and immunomodulatory activity [45,46,47]. We found that the compound structure of DET contains multiple Michael acceptor structures through medicinal chemical structure analysis, which may be important pharmacodynamic functional groups. However, whether it exerts its effect through the covalent binding of Bcl2 or other protein cysteines needs further investigation. The discovery of 5FU is a blessing for many cancer patients, but the resistance of the tumor limits the therapeutic effect [28,40,48]. The most exciting finding of our current study was that the combination of DET and 5FU had a better tumor-inhibitory effect on colon cancer. Collectively, DET can significantly enhance the chemosensitivity of the chemotherapeutic medicine 5FU to colon cancer cells and has an ideal tumor-inhibitory effect. 4. Materials and Methods 4.1. Preparation of DET DET was purchased from Chengdu Herbpurify Co., Ltd., Chengdu, China. The chemical purity of DET was over 98% as judged by NMR spectrometry. 4.2. Lentiviral Constructs and Infection The full-length coding sequence of overexpressed Bcl2 was ligated into the pCDH-CMV-MCS-EF1-puro vector by Wuhan GeneCreate Biological Engineering Co., Ltd. After this, lentivirus constructs, including pCDH-CMV-MCS-EF1-puro-Bcl2, empty vector, and packaging plasmid (pLP1,pLP2,PLP/VSVG), were transfected into 293FT cells by ViaFect transfection reagent (Promega, Madison, WI, USA) [49]. The virus supernatant was collected after two days. Thereafter, HCT116 and sw620 cells were infected with the virus-containing supernatant. After infection, the cells were cultured in the presence of 2 µg/mL puromycin (Life Technologies; Thermo Fisher Scientific, Inc, Waltham, MA, USA) twice. Finally, the drug-resistant cells were pooled. 4.3. RNA Sequencing and qRT-PCR HCT116 and sw620 cells (American Type Culture Collection, ATCC).were treated with DMSO or DET and incubated in a 5% CO2 incubator at 37 °C. After two days, the cells were harvested, and the total RNA in HCT116 and sw620 cells was extracted with Trizol reagent, following the manufacturer’s protocol [50]. After this, the extracted RNA was used for reverse transcription and qRT-PCR according to the operation and instructions of the reverse transcription and quantitative kit (TakaraBio Inc., Kusatsu, Japan). 4.4. Cell Proliferation Analysis The cell growth curve was analyzed by MTT. To determine the viability of the cells, 800 cells were cultured in a 96-well plate for 1, 3, 5, and 7 days and the cells were estimated by the MTT method at the fixed time point [51]. All the experiments were repeated three times independently. 4.5. Detection of Phosphatidylserine Externalization by Annexin V and PI Staining HCT116 and sw620 cells were seeded in 100 mm2 culture dishes and incubated in the 5% CO2 incubator at 37 °C for 48 h. The cells were then treated with 5 mmol/L DET, while the negative control was treated with vehicle DMSO. After 48 h of treatment, both adherent and suspension cells were harvested and washed once with PBS and once with 1× Annexin V binding buffer. After this, cells were resuspended in 1× Annexin V binding buffer. Subsequently, the treated cells were stained with Annexin V-FITC and PI (50 µg/mL). The cells were then vortexed and incubated in the dark at room temperature for 20~30 min. The cells were then analyzed by flow cytometry using quadrant statistics for apoptotic cell populations. 4.6. Western Blot Assay The treated cells were collected and certain amounts of protease inhibitor (Roche), phosphatase inhibitor (Sigma Aldrich, St. Louis, MO, USA), and RIPA lysate (Beyotime, China) were added based on the amount of the cells. The cells were then cracked in ice for 1 h and centrifuged. The total content of protein was elucidated by using the Bradford assay, and 50 mg of protein was subjected to 12% SDS-PAGE to separate the protein. After electrophoresis, the proteins were transferred onto a PVDF membrane (Millipore, Kenilworth, NJ, USA), followed by blocking using skim milk/BSA for 2 h, and then incubated with primary antibodies at 4 °C overnight. Subsequently, the membrane was incubated with the corresponding secondary antibody for 2 h at room temperature. For detection, the membrane was incubated with the help of an enhanced chemiluminescence (ECL) detection kit, and detection analysis system (Clinx Science, Shanghai, China) were used to visualize and capture proteins. [52]. 4.7. EdU Staining The colorectal cancer cells in 24-well plates were treated with DET. When there was a significant difference in the cell phenomenon between the control group and the experimental group, 10 µL of 10 mmol/L EdU (5-Ethynyl-2′-deoxyuridine, from Invitrogen, Carlsbad, CA, USA) was added to each well. After this, the plates were kept in an incubator for 30 min and then fixed with 4% PFA for 15 min. After punching holes with 0.5% TritonX-100 at room temperature for 20 min, 0.5 mL of Click-iT reaction cocktail was added to each well and incubated away from light for 30 min at room temperature. Next, the nuclei were stained with DAPI. EdU-positive cells in random fields were counted. 4.8. Soft Agar The ability of colony formation was conducted on HCT116 and sw620 cells by the soft agar assay. First, 1.5 mL 2 × DMEM medium containing 0.6% agarose was added to six-well plates [53]. Next, 1000 cells in the logarithmic phase mixed with a medium containing 0.3% agar and different concentrations of DET were added to the bottom glue [54]. After three weeks of culture at 37 °C in CO2 incubators, colonies were captured by microscopy and stained with MTT, and scanned with a scanner. Each sample in this experiment was assessed in triplicate. 4.9. Luciferase Reporter Assay For the luciferase reporter assay, 293FT cells were co-transfected with 200 nM miR-205 mimic or NC (RiboBio Co., Ltd., Guangzhou, China) and 650 ng of pGL3-Basic-Bcl2-3′-UTR-WT, pGL3-Basic-Bcl2-3′-UTR-MUT. Cells were collected 48 h after transfection and analyzed with the Dual-Luciferase Reporter Assay System (Yeasen Biotechnology Co., Ltd., Shanghai, China). Moreover, 293FT cells were co-transfected with the pGL3-Basic vector and NC was used as a control. 4.10. Animal Studies All animal experiments were performed under the Guidelines of the Institute for Laboratory Animal Research, Southwest University (Chongqing, China). Twelve adult immunodeficient mice were injected with HCT116 cells at the left and right armpits on day 1. Tumors became visible after a week. Next, drugs or saline were injected intraperitoneally once every two days for half a month. DET (30 mg/kg) and free HA14–1 (400 nmol) were dissolved in DMSO before injection. Tumor volume (V) was measured with calipers before each injection and calculated by the formula V = (LXW2)/2, where L is the length and W is the width of the tumor [55]. 4.11. Animal Ethics Twelve adult immunodeficient mice (Hunan Slike Jingda Laboratory Animal Co., Ltd., Changsha, China, animal qualification number: SCXK-2019-0004), body weight (25 ± 20) g, were used. The animal experiments in this study were carried out in the Institute for Laboratory Animal Research, Southwest University, and approved by the Ethics Committee of Experimental Animals of Southwest University (approval number IACUC-20190320-02). 4.12. Statistics Analysis GraphPad was employed for statistical analysis. All experiments were confirmed using at least three independent experiments. All the results in this study are represented as the mean ± standard deviation (SD). A significant difference was observed in Student’s unpaired t-test. p < 0.05 was considered to indicate a statistically significant result. 5. Conclusions This study confirms the anti-human colon cancer activity of DET in vivo and in vitro and elucidates that DET can induce apoptosis of colon cancer by the miR-205-Bcl2 axis, as well as enhance the chemosensitivity of 5FU to colon cancer, which has a bright prospect for the treatment of colon cancer patients. 6. Patent Not applied. Acknowledgments We are very grateful to Ruochen Liu, Zhen Dong, and Zhao Erhu for the technical support and helpful comments. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23095051/s1. Click here for additional data file. Author Contributions G.P., C.L. (Chongyang Li), and H.J. carried out the molecular/cell biology experiments. C.L. (Changhong Li) and H.J. conducted the animal experiment. K.Z. revised the article. X.H. performed the statistical analyses. H.J. designed the study and wrote the manuscript. L.Y. and H.C. supervised the study and revised the manuscript. H.C. was responsible for funding acquisition. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The animal study protocol was approved by Institutional Animal Care and Use Committee of Southwest University (IACUC-20190320-02 and 20 March 2019).” for studies involving animals. Data Availability Statement The data presented in this study are available on request from the corresponding author. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abbreviations DET Deoxyelephantopin 5FU 5-Fluorouracil Bcl2 B-cell lymphoma-2 CC Colon Cancer IHC Immunohistochemistry MTT 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide DMSO Dimethyl sulfoxide Figure 1 Deoxyelephantopin (DET) inhibits colon cancer without obvious organ toxicity. (A) The chemical structure of DET. (B) Graphic representation of results from MTT assays to determine cell inhibition rate of HCT116, sw620, sw480, and HCT15 cells. (C) Morphology of HCT116 and sw620 cells after treatment with DMSO or different concentrations of DET for 48 h. Scale bar = 20 µm. (D) Fluorescence images of EdU incorporation in HCT116 and sw620 cells treated with DET or DMSO for 48 h. Cells were stained to detect EdU (green) and DAPI (blue) to highlight nuclei, and images were superimposed. Scale bars = 20 µm. (E) The percentage of EdU+ cells (EdU+/DAPI+ × 100%) was evaluated in 4 random fields per sample. (F) The self-renewal capability of 5 µM DET-treated HCT116 and sw620 cells. Scale bar = 100 µm. (G) Measurement of the weight of the xenograft tumors. (H) The volume changes of xenograft tumors treated with DET. (I) The Ki-67 signal of mice was detected by immunohistochemistry. Scale bar = 20 µm. (J) H&E staining of the heart, liver, lung, spleen, and kidney in mice treated with DET or DMSO. Scale bar = 100 µm. All data are represented as the mean ±SD. A two-tailed unpaired Student’s t-test was carried out. * p < 0.05, ** p < 0.01, *** p < 0.001, versus control. Figure 2 DET arrests colon cancer cells at the G2/M phase by inducing DNA damage. (A,B) The cell cycle of HCT116 and sw620 cells was analyzed by flow cytometry after treatment with DMSO or 5 µM of DET for 48 h. (C) Quantitative real-time PCR assay was conducted to identify the relevant genes regulating cell cycle after treatment of HCT116 and sw620 cells with DET. (D) Western blot assay was performed to evaluate the cell-cycle-related protein levels in the indicated concentrations (0, 2, 5, 10 µM) and indicated times (0, 24, 48, 72 h); GAPDH served as the control. (E) Protein levels were calculated based on the grayscale value of protein bands and normalized with the grayscale value of GAPDH bands. (F,G) Cell tailing exhibited by comet assay after DMSO or 5 µM of DET treatment for 48 h. Scale bar = 20 µm. (H,I) The expression level of γ H2A, a DNA-damage-specific marker, estimated by Western blot. All data are represented as mean ± SD. A two-tailed unpaired Student’s t-test was carried out. ns: not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, versus control. Figure 3 DET induces apoptosis in colon cancer cells. (A) The cell apoptosis level of DET-treated HCT116 and sw620 cells as detected by TUNAL assay. (B) Scanning electron microscope images of sw620 cells treated with DET (5 µM) or DMSO for 48 h. Arrows highlight the apoptosis bodies. Scale bar = 5 µm. (C,D) The apoptosis was determined by Annexin V-FITC/PI staining and flow cytometry. (E) Western blot assay was performed to assess apoptosis-related protein levels in the indicated concentrations (0, 2, 5, 10 µM) and indicated times (0, 24, 48, 72 h). (F) Protein levels are estimated based on the grayscale value of protein bands and normalized with the grayscale value of Tubulin bands. All data are shown as the mean ± SD. A two-tailed unpaired Student’s t-test was executed. * p < 0.05, ** p < 0.01, *** p < 0.001, versus control. Figure 4 DET induces apoptosis by inhibiting Bcl2 in colon cancer cells. (A) qRT-PCR assay to detect the expression of Bcl2. (B) The expression of Bcl2 in HCT116 and sw620 cells treated with different concentrations of DET for 48 h. (C) The expression of Bcl2 in 5 µM DET-treated HCT116 and sw620 cells with Bcl2-overexpressed or empty vector. (D) Graphic representation of results from MTT assays to determine cell inhibition rate of Bcl2-overexpressed HCT116 and sw620 cells treated with different concentrations of DET for 72 h. (E) EdU-positive cells in Bcl2-overexpressed HCT116 and sw620 cells after treatment with 5 µM DET. Scale bar = 20 µm. Quantification of EdU-positive HCT116 and sw620 cells also shown in the panel. (F) Soft agar assay was performed to assess colony formation ability of Bcl2-overexpressed HCT116 and sw620 cells. Scale bar = 100 µm. Colony numbers in the panel were quantified. (G) Bcl2-overexpressed HCT116 and sw620 cells were treated with DMSO and 5 µM DET for 48 h and apoptosis was determined by Annexin V-FITC/PI staining and flow cytometry. (H) The expression of apoptosis proteins was detected in Bcl2-overexpressed HCT116 and sw620 cells after treatment with DET for 48 h. All data are expressed as the mean ± SD. A two-tailed unpaired Student’s t-test was carried out. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, vs. control. Figure 5 miR-205 is directly targeted by Bcl2 and induces the apoptosis of colon cancer cells. (A) Venn diagram showing the potential miR-205 targets predicted by databases. (B). The putative binding site of miR-205 and Bcl2 is shown. A schematic of the construction of wild-type or mutant pGL3-Bcl2 3′-UTR vectors is indicated. (C) The 293FT cells were co-transfected with miR-205 and wild-type or mutant Bcl2 3′-UTR fused to the Renilla luciferase vector. The relative firefly luciferase activities were determined. (D) The expression of miR-205 after DET treatment or DET and the miR-205 inhibitor treatment for 48 h. (E) Cell proliferation of HCT116 and sw620 cells was promoted by miR-205 inhibitor. (F,G) Flow cytometry assay showing that miR-205 inhibitor significantly reversed the apoptosis of cell metastasis in HCT116 and sw620 cells. (H) Western blot assays were used to detect the expression of proteins after treatment with the miR-205 inhibitor. All data were used as mean ± S.D., n = 3, significant difference was examined by Student’s t-test. * p < 0.05, ** p < 0.01, *** p < 0.001, versus control. Note: Inhibitor in this figure refers to the inhibitor of miR-205. Figure 6 DET enhances chemosensitivity to 5-Fluorouracil (5FU). (A) A panel of colon cell lines was treated with 5 µM DET, with or without 5FU (5 µM), for 1, 3, 5, 7 d. (B) HCT116 and LS174T cells were treated with 5 µM DET, with or without 5FU (5 µM), and the apoptosis was detected with flow cytometry. (C) Quantification results of apoptosis rate of HCT116 and LS174T cells. (D) HCT116 and LS174T cells were treated with DET, with or without 5FU, and then the expression levels of apoptosis proteins were explored by Western blot assay. (E) The volume changes of xenograft tumors treated with DET, with or without 5FU. (F) Tumor weight of the xenograft tumors in each group. (G,H) The nuclear tumor staining intensity of Ki-67 and C-PARP was evaluated using immunohistochemistry. (I) Positive expression rates of the Ki-67 and C-PARP in (G,H). Scale bar = 20 µm. All data were expressed as mean ± S.D., n = 3, significant difference was verified by Student’s t-test. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, versus control. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Torre L.A. Bray F. Siegel R.L. Ferlay J. Lortet-Tieulent J. Jemal A. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094798 ijms-23-04798 Article RNA-Seq Analysis Identifies Transcription Factors Involved in Anthocyanin Biosynthesis of ‘Red Zaosu’ Pear Peel and Functional Study of PpPIF8 Ma Zhenyu 123† https://orcid.org/0000-0001-7781-7674 Wei Chuangqi 23† Cheng Yudou 23 https://orcid.org/0000-0001-9279-0393 Shang Zhonglin 1 Guo Xiulin 23 Guan Junfeng 23* Liu Changning Academic Editor Wang Ke Academic Editor Xu Jin Academic Editor 1 College of Life Science, Hebei Normal University, Shijiazhuang 050024, China; mazhenyuqqtt@163.com (Z.M.); shangzhonglin@hebtu.edu.cn (Z.S.) 2 Institute of Biotechnology and Food Science, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China; weichuangqi@163.com (C.W.); chengyudouyn@163.com (Y.C.); myhf2002@163.com (X.G.) 3 Plant Genetic Engineering Center of Hebei Province, Shijiazhuang 050051, China * Correspondence: junfeng-guan@263.net † These authors contributed equally to this work. 27 4 2022 5 2022 23 9 479802 4 2022 24 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Red-skinned pears are favored by people for their attractive appearance and abundance of anthocyanins. However, the molecular basis of anthocyanin biosynthesis in red pears remains elusive. Here, a comprehensive transcriptome analysis was conducted to explore the potential regulatory mechanism of anthocyanin biosynthesis in ‘Red Zaosu’ pear (Pyrus pyrifolia × Pyrus communis). Gene co-expression analysis and transcription factor mining identified 263 transcription factors, which accounted for 6.59% of the total number of transcription factors in the pear genome in two gene modules that are highly correlated with anthocyanin biosynthesis. Clustering, gene network modeling with STRING-DB, and local motif enrichment analysis (CentriMo) analysis suggested that PpPIF8 may play a role in anthocyanin biosynthesis. Furthermore, eight PIFs were identified in the pear genome, of which only PpPIF8 was rapidly induced by light. Functional studies showed that PpPIF8 localizes in the nucleus and is preferentially expressed in the tissue of higher levels of anthocyanin. The overexpression of PpPIF8 in pear peel and pear calli promotes anthocyanin biosynthesis and upregulates the expression of anthocyanin biosynthesis genes. Yeast-one hybrid and transgenic analyses indicated that PpPIF8 binds to the PpCHS promoter to induce PpCHS expression. The positive effect of PpPIF8 on anthocyanin biosynthesis is different from previously identified negative regulators of PyPIF5 and MdPIF7 in pear and apple. Taken together, our data not only provide a comprehensive view of transcription events during the coloration of pear peel, but also resolved the regulatory role of PpPIF8 in the anthocyanin biosynthesis pathway. Pyrus anthocyanin RNA-Seq transcription factors WGCNA PpPIF8 Natural Science Foundation of Hebei ProvinceC2020301024 Agriculture Science and Technology Innovation Project of HAAFSThis research was funded by the Natural Science Foundation of Hebei Province (Project No.C2020301024) and the Agriculture Science and Technology Innovation Project of HAAFS (2019-2-1). ==== Body pmc1. Introduction Pear (Pyrus) is an important fruit and planted throughout the world. Red-skinned pears generally have a higher commodity value and are more favored by consumers. As a type of water-soluble flavonoid compounds, anthocyanin is responsible for the red coloration in pear peel [1]. Dietary anthocyanin has a preventive effect on cancer, cardiovascular diseases, and other chronic diseases due to its strong antioxidant capacity [2,3]. Anthocyanins are synthesized by a specific branch of the phenylpropanoid biosynthesis pathway. The precursor of anthocyanin is phenylalanine, which is catalyzed by a series of enzymes, including PAL (phenylalanine ammonia lyase), C4H (cinnamate 4-hydroxylase), 4CL (4-coumarate: CoA ligase), CHS (chalcone synthase), CHI (chalcone isomerase), F3H (flavanone 3-hydroxylase), FLS (flavonol synthase), DFR (dihydroflavonol 4-reductase), F3′H (flavanone 3′-hydroxylase), F3′5′H (flavanone 3′,5′-hydroxylase), ANS (anthocyanidin synthase), and UFGT (UDP-glucose flavonoid 3-O-glucosyltransferase) [1,2]. Although these anthocyanin biosynthesis genes are conserved, there are significant differences in the regulatory mechanisms of anthocyanin biosynthesis among plant species [1,4]. Light is considered as the key factor governing anthocyanin biosynthesis in plants [4]. The basic leucine zipper (bZIP) protein ELONGATED HYPOCOTYL 5 acts as a central regulator for the induction of anthocyanin biosynthesis [1,5]. The overexpression of HY5 promotes anthocyanin accumulation in many plant species, such as Arabidopsis, strawberry, and apple [6,7]. The experiments suggested that loss-of-function of HY5 could reduce the anthocyanin level in Arabidopsis [8], strawberry [9], and tomato [10]. As a transcription factor, HY5 accumulates in the light and directly binds to the promoters of early and late anthocyanin biosynthesis genes, such as CHS, CHI, F3H, F3′H, DFR, and ANS [8,9,10]. Furthermore, HY5 directly targets multiple downstream transcription factors that control anthocyanin biosynthesis; of these, MYB10 has been widely studied. In apple and pear, HY5 positively regulates MYB10 expression by binding the G-box motif of the MYB10 promoter to promote anthocyanin biosynthesis [6,11]. Since HY5 lacks a typical transactivation domain, its transcriptional activity is primarily regulated by the interaction with B-box family proteins (BBX) [12,13]. As a BBX member, a 14-nucleotide deletion mutation in the coding region of the PpBBX24 gene is associated with the red skin of ‘Red Zaosu’ pear [14]. In addition to the light signal components, anthocyanin biosynthesis is also regulated by the MBW complex, which is composed of three protein families (MYB, bHLH, and WDR) in vivo [1,15]. In recent years, multiple MYB transcription factors have been characterized as regulators of anthocyanin biosynthesis by binding to the promoters of late anthocyanin biosynthesis genes and specifically regulating their expression [1,16,17]. In Arabidopsis, the overexpression of AtMYB75 (PAP1) promoted anthocyanin biosynthesis, while plants downregulating AtMYB75 and its homologs PAP2, MYB113, and MYB114 showed obvious anthocyanin deficiencies [18]. As a homologous gene of AtMYB75, MYB10 has been reported to be associated with the coloration of a variety of fruits, such as pear [19], sweet cherry [20], apple [21], and plum [22]. In apple, the expression of MdMYB10 is highly correlated with anthocyanin levels during fruit development and the overexpression of MdMYB10 elevates anthocyanin biosynthesis [21]. Similar to the study in apple, the expression of pear PpMYB10 is induced by light and ectopic overexpression of PpMYB10 in Arabidopsis enhances anthocyanin accumulation in immature seeds [19]. In addition to MYB10, other MYBs, such as PpMYB12 and PpMYB114, were also reported as positive regulators of anthocyanin biosynthesis [23,24]. Phytochrome-interacting factor (PIFs) is a subset of basic helix–loop–helix (bHLH) transcription factors that are involved in the light response of plants [25,26]. There are at least eight PIFs (PIF1 to PIF8) in the Arabidopsis genome, and most members of the PIFs family possess the conserved bHLH domain allowing them to form dimers and bind to DNA, a conserved APB motif required for the interaction with Pfr form of phyB [27,28]. Classically, members of the PIFs family act as negative regulators during photomorphogenesis. Upon light exposure, PIFs are degraded by physical interaction with the biologically active Pfr conformer, resulting in the initiation of photomorphogenic development programs, such as the inhibition of hypocotyls, the opening of apical hooks, and chloroplast development [26]. Previous studies showed that different PIF members vary in their abilities to regulate anthocyanin biosynthesis. In Arabidopsis, PIF3 positively regulates anthocyanin accumulation in an HY5-denpendent manner under far-red light [29], whereas PIF4 and PIF5 inhibit anthocyanin biosynthesis under red-light conditions [30]. For Rosaceae plants, PyPIF5 and MdPIF7 were reported as negative regulators in regulating anthocyanin biosynthesis in pear and apple [31,32]. However, whether there are other PIFs involved in regulating anthocyanin biosynthesis is rarely understood. In this study, comprehensive transcriptome analysis was conducted to explore the potential regulatory mechanism of anthocyanin biosynthesis in ‘Red Zaosu’ pear. Gene co-expression analysis, transcription factor mining, and local motif enrichment analysis (CentriMo) analysis identified that light-induced PpPIF8 may play a role in anthocyanin biosynthesis. Functional studies showed that PpPIF8 localizes to the nucleus and is preferentially expressed in the tissue of higher levels of anthocyanin. The overexpression of PpPIF8 in pear peel and pear calli promotes anthocyanin biosynthesis and upregulates the expression of anthocyanin biosynthesis genes. Yeast-one hybrid and transgenic analyses indicated that PpPIF8 binds to the PpCHS promoter to induce PpCHS expression. Taken together, our data not only provide a comprehensive view of transcription events during the coloration of pear peel, but also resolve the regulatory role of PpPIF8 in the anthocyanin biosynthesis pathway. 2. Results 2.1. Anthocyanin Accumulation in the Peel of ‘Red Zaosu’ Pear after Debagging To investigate the response of pear fruit to light, we removed the bags at 120 days after full bloom (DAFB). The initial color of the pear peel was yellow–white. On the 5th day after debagging, the peel of D group fruits showed a slightly pink color, especially around the lenticel pit area. During the 7 to 15th day, the peel of D group fruits gradually developed an obvious red streak, while the color of B group fruits was still yellow–white (Figure 1A). Consistent with visual observation, the anthocyanin content of D group fruits increased continuously, reaching a peak at the 15th day after debagging, whereas the anthocyanin content of B control fruits remained lower during the entire period (Figure 1B). These observations suggest that light promotes the anthocyanin accumulation of ‘Red Zaosu’ pear. 2.2. Overview of RNA Sequencing To dissect the regulation mechanism of anthocyanin induction by light, total RNA was extracted from B group and D group samples at 0 days (initial sample), 1, 3, 5, and 7 days after debagging. Three biological repeats were set for each timepoint. A total of 27 samples were sequenced by the Illumina platform, and 184.76 Gb of clean data were obtained, with a Q30 percentage of ≥92.97%. The clean reads were mapped to the reference genome sequence of Pyrus bretschneideri ‘DangshanSuli’ V1.1 [33]. The mapped rates were between 74.90% and 76.76% (Supplemental Table S2). We conducted PCA and sample correlation analysis based on the FPKM values of all the expressed genes to evaluate the repeatability and heterogeneity of sequencing samples. As expected, the three biological replicates of each sample were clustered together, indicating good reproducibility within biological replicates. Furthermore, the samples were divided into two groups on the principal component PC1 according to bagging and debagging treatment, suggesting that light after debagging was the main factor leading to the gene expression difference between D group and B group samples. From the perspective of PC2 axis, the samples were separated by the time after debagging, which indicated that the time of debagging plays a secondary role in the heterogeneity between samples (Figure 2). Consistently, the correlation analysis of the samples exhibited very tight clustering among the three biological replicates. All B group samples exhibited a closer correlation with the initial sample (0d), while all D group samples were strikingly different (Supplemental Figure S1). Taken together, these results suggest that the repeatability of sequenced samples is valid, and light after debagging, rather than the time of debagging, is the predominant contributor to gene expression differences between D group and B group samples. 2.3. Differential Gene Expression Analysis To investigate the transcription differences between D and B group samples, differentially expressed genes (DEGs) were calculated at each timepoint. We used the following threshold, FDR < 0.01 and fold change ≥ 2, as the criterion. A total of unique 4658 DEGs were identified across all timepoints between D and B group samples. Overall, the numbers of upregulated genes were much higher than downregulated genes in D group samples at four different timepoints. The largest and lowest numbers of DEGs were identified at 7d and 5d, respectively. The numbers of DEGs varied from 1827 to 2591 for upregulated genes and from 290 to 514 for downregulated genes (Figure 3A and Figure S2). There were 1052 common DEGs across all timepoints, accounting for one-quarter of the total unique DEGs. The number of time-specific DEGs was 691, 363, 310, and 712 at 1, 3, 5, and 7 days after debagging (Figure 3B). These results indicate that anthocyanin accumulation after debagging is largely due to the activation of gene expression. To evaluate RNA-Seq reliability, several anthocyanin biosynthesis genes were identified and validated by RT-qPCR. The transcript abundances of these genes were induced rapidly at 1 day after debagging but peaking at different timepoints in RNA-Seq data. However, in the B group sample, the expression of these anthocyanin biosynthesis genes remained low across all the timepoints. Among these genes, PpCHS (gene24418) exhibited the highest expression level after debagging (Figure 4A). Consistent with RNA-Seq data, the expression of these anthocyanin biosynthesis genes also showed a similar induction pattern by RT-qPCR in D group samples. In addition, we found that PpCHS, PpFSL (gene33758), and PpUFGT (gene12084) are more sensitive to light after debagging than other anthocyanin biosynthesis genes, based on their relative fold change (Figure 4B). 2.4. Weighted Gene Co-Expression Network Analysis (WGCNA) 2.4.1. Module Construct and Module–Trait Correlation To investigate the gene regulatory network during the coloration of pear peel, 4658 unique DEGs were subjected to weighted gene co-expression network analysis (WGCNA). A total of seven modules were identified (Figure 5A,B). Of these modules, the “Turquoise” module contained the largest gene numbers (2434 genes), followed by “Blue” module (980 genes) (Figure 5B, Supplemental Table S3). An analysis of the module–trait relationships revealed that the “Blue” module was highly correlated with pear anthocyanin content (r = 0.90, p = 2 × 10−10) and PpBBX24 expression pattern (r = 0.87, p = 5 × 10−9). The “Turquoise” module was positively correlated with transcript abundance of PpHY5 (r = 0.77, p = 3 × 10−6), PpMYB12 (r = 0.99, p = 2 × 10−22), and PpBBX24 (r = 0.91, p = 5 × 10−11) (Figure 5C). The eigengene expression pattern showed that the genes in the “Blue” module were gradually induced after debagging, while the genes in the “Turquoise” module were rapidly induced in the D group samples. For both “Blue” and “Turquoise” modules, the genes in the B group samples remained unchanged (Figure 5D,E). Because genes in both “Blue” and “Turquoise” modules are positively correlated with anthocyanin accumulation and highly induced after debagging, we then combined the “Blue” and “Turquoise” gene list for further analysis afterward. Next, we analyzed the enrichment pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis [34] on the gene list in both “Blue” and “Turquoise” modules to explore enriched biological pathways. The photosynthesis-related terms (“Photosynthesis pathway”, “Porphyrin and chlorophyll metabolism”) and the flavonoid biosynthesis-related terms (“Phenylpropanoid biosynthesis and Flavonoid biosynthesis”) were significantly enriched. In addition, the plant hormone signal transduction pathway was also substantially enriched (Figure 5F). 2.4.2. Analysis of Transcription Factors Transcription factors (TFs) play a vital role in regulating anthocyanin biosynthesis [1]. To extensively explore TFs involved in this process, the protein sequences of genes in in “Blue” and “Turquoise” modules were extracted and subjected to Plant Transcription factor & Protein Kinase Identifier and Classifier (iTAK) tools (http://itak.feilab.net/cgi-bin/itak/index.cgi, accessed on 30 June 2020) to predict TFs. A total of 263 TFs were identified and classified by transcription factor family (Table 1). The number of MYB family members was 40, followed by 35 TFs from the AP2-ERF family, 24 TFs from the WRKY family, and 18 TFs from the bHLH family. Additionally, TFs from other families, such as NAC, HB, bZIP, and B-box, were also found in the modules (Table 1). To find the key TFs among these 263 TFs, we selected the top 55 TFs based on their KME value (KME ≥ 0.9). Among these 55 TFs, the well-known PpMYB10 and PpMYB12 were clustered together [22,23]. Strikingly, the PpPIF8 (also known as UNE10 in Arabidopsis) is clustered with PpMYB10 and PpMYB12 as well (Figure 6A), which suggests that PpPIF8 may also participate in regulating anthocyanin biosynthesis. As expected, PpHY5 was also strongly induced by debagging. In addition, B-box family members PpCOL13 and PpBBX32 were included in the modules (Figure 6A). We next selected six TFs, including PpHY5, PpMYB10, PpMYB12, PpCOL13, PpBBX32, and PpGT2, to validate RNA-Seq results. These TFs were rapidly induced at 1d and remained relatively stable until 7d after debagging. However, the expression of these TFs did not change much in the B group samples (Figure 6B). To further explore the association of these top 55 TFs, the protein sequences of these TFs were extracted and subjected to STRING analysis. Functional enrichment analysis of protein domains of these 55 TFs showed that the WRKY DNA-binding domain, Myb-like DNA-binding domain, and Helix–loop–helix DNA-binding domain were enriched (Figure 6C). Visualization of the STRING result showed that three clusters were identified in the network, and the first cluster (turquoise nodes) consisted of 15 members, including PpMYB10, PpMYB12, and PpHY5. The second cluster (pink nodes) contained four TFs, including PpPIF8, which connects to PpHY5 in the first cluster. The third cluster only has four genes (Figure 6C). These data indicate that the known PpMYB10 and PpMYB12 cluster with PpHY5 forms a hub network to regulate anthocyanin biosynthesis. PpPIF8, with previously unknown function in the regulation anthocyanin biosynthesis, may also participate in this process. 2.4.3. Identifying Transcription Factors Regulating Gene Expression in “Blue” and “Turquoise” Modules To discover the primary transcription factors that regulate gene expression in “Blue” and “Turquoise” modules, we extracted 2000 bp of genomic sequence located upstream of starting codon ATG of the 3158 genes in these two modules (Supplemental Table S3) and subjected them to CentriMo analysis. A total of 87 TF binding sites were found, belonging to multiple transcription factor families such as the WRKY, MYB, bZIP, and bHLH protein families (Supplemental Table S4). Surprisingly, five phytochrome-interacting factors (PIFs) were identified: PIF3, PIF4, PIF5, PIF7, and PIF8/UNE10. The potential binding sites were located within 400 bp upstream of the starting codon ATG (Figure 7 and Figure S3). This result suggests PIFs may be involved in regulating gene expression in “Blue” and “Turquoise” modules. 2.5. Functional Study of PpPIF8 in the Regulation of Anthocyanin Biosynthesis In our WGCNA analysis, both transcription factor mining and CentriMo analysis [35] indicated that PpPIF8 may play a role in regulating anthocyanin biosynthesis (Figure 6 and Figure 7). Therefore, we focused on the PpPIF8 for functional study. 2.5.1. Genome-Wide Identification of PIFs in Pear Because the PIFs family members have not yet been systematically identified in pear, we first performed genome-wide identification of PIFs in pear. A total of eight members were identified as PIFs in the pear genome by the homology BLAST and conserved domain search. These PpPIFs were named according to their closest Arabidopsis homologs, and each PpPIF contained a conserved bHLH_AtPIF_like domain (Figure 8A and Table 2). We then calculated the physical and chemical properties of these PpPIFs. As shown in Table 2, PpPIF7a is the smallest member (398 amino acids), and PpPIF3a is the largest (716 amino acids). The molecular weight of the PpPIFs varies from 44 kDa (PpPIF7a) to 76.56 kDa (PpPIF3a) and their isoelectric points are between 6.09 (PpPIF3) and 9.39 (PpPIF1). The instability index of PpPIFs was larger than 53, suggesting PpPIFs may not be stable in vivo (Table 2). To address the evolutionary relationship of PIFs in pear, apple, and Arabidopsis, a phylogenetic tree was constructed using full-length protein sequences. As shown in Figure 8B, the eight PpPIFs can be divided into four clades, PIF1, PIF3, PIF5, and PIF8. PpPIF1 is located in the PIF1 clade, while PpPIF3 and PpPIF3a are in the PIF3 clade, PpPIF5 and PpPIF5a are in the PIF5 clade, and PpPIF8, PpPIF7a, and PpPIF7b are in the PIF8 clade. Within each clade, PpPIFs cluster closer with apple PIFs than Arabidopsis PIFs. In addition, PIF2 and PIF6 members are only present in the Arabidopsis and apple genome, but not found in the pear genome (Figure 8B). We then compared the expression pattern of PpPIFs in both B and D group samples. As shown in Figure 8C, PpPIF1, PpPIF3, and PpPIF3a exhibited relatively high expression levels compared with other PpPIFs. However, their expression levels were not observed to be regulated by light after debagging. The PpPIF5 and PpPIF7b expression levels were relatively low, and they were also not regulated obviously by light. Strikingly, PpPIF8 was rapidly and strongly induced by light, while PpPIF7a was only slightly induced by light at 7 d after debagging (Figure 8C). These data suggest PpPIF8 may play a unique role in regulating anthocyanin biosynthesis in pears. 2.5.2. Verification of PpPIF8 Expression To confirm the expression pattern of PpPIF8, RT-qPCR was performed. As shown in Figure 9A, the overall expression pattern of PpPIF8 is similar in both RT-qPCR results and RNA-Seq data. The transcript of PpPIF8 was rapidly and strongly induced after debagging, but remained at a low expression level in B group samples (Figure 9A). 2.5.3. Tissue-Specific Expression Analysis of PpPIF8 To gain insight into the relationship between the tissue-specific expression pattern of PpPIF8 and anthocyanin level, we extracted total RNA and anthocyanin from nine different tissues (Flower-F, Sepal-S, Young leaves-YL, Mature leaves-ML, Young fruitlet-YF, Expansion stage peel-EP, Expansion stage flesh-EF, Mature stage peel-MP, and Mature stage flesh-MF). As shown in Figure 9B, PpPIF8 is highly expressed in young leaves (YL), which also accumulate the highest level of anthocyanin. Compared with the flesh tissue of ‘Red Zaosu’, the peel tissues of the fruit exhibit higher levels of PpPIF8 expression and anthocyanin accumulation (Figure 9B). Furthermore, the Pearson correlation analysis showed that the expression of PpPIF8 is positively correlated with anthocyanin level (r = 0.7462, p = 0.0209), which implies that PpPIF8 may positively regulate anthocyanin biosynthesis. 2.5.4. Transcriptional Activity and Subcellular Localization Analysis of PpPIF8 To determine whether PpPIF8 has transcriptional activity, we performed transactivation activity assays in yeast. The full-length CDS of PpPIF8 was fused in-frame with the GAL4 DNA-binding domain in the pGBKT7 vector and the construct was transformed into the Y187 yeast strain. The yeasts carrying BD-PpPIF8 or empty pGBKT7 vector could not grow on the selective medium (SD/−Trp−His), while the positive control BD-AtBZS1 grew well, indicating that PpPIF8 has no transcriptional activation activity (Figure 9C). The correct subcellular localization of a protein is important for its function. We cloned the full-length CDS of PpPIF8 fused with a C-terminal GFP tag under 35S promoter. The agrobacterium harboring the p35S-PpPIF8-GFP vector was co-infiltrated with nucleus marker (NSL-mCherry) strain into tobacco leaves. As Figure 9D shows, the green fluorescence of PpPIF8-GFP was detected exclusively in the nucleus and overlapped well with the red nucleus marker, whereas in the control tobacco cells transformed with an empty vector, green fluorescence was present in the nucleus and cytoplasm (Figure 9D). This result suggests that PpPIF8 is specifically localized in the nucleus. 2.5.5. Overexpression of PpPIF8 Promotes Anthocyanin Accumulation in Pear To confirm the role of PpPIF8 in anthocyanin biosynthesis in pear, we transiently overexpressed PpPIF8 in the peel of ‘Red Zaosu’ pear. After 5 days of light treatment, we found that the overexpression of PpPIF8 induced anthocyanin biosynthesis surrounding the injection site, while no red color was observed in the injection area of the empty vector (Figure 10A). Furthermore, we induced pear calli from the flesh cells of the young fruitlet of red-skinned Pyrus communis ‘Xiuzhenxiang’ and overexpressed the PpPIF8 (PpPIF8_OE) in pear calli. The coloration phenotype was observed after confirming the transgene by immunoblotting (Figure 10C). As expected, the color of PpPIF8_OE calli turned red after 5 days of light treatment, while the color of non-transgenic control (CK) calli stayed pale yellow (Figure 10B). Consistent with the phenotype observation, the anthocyanin content of PpPIF8_OE calli was 10-fold higher than that of the CK calli (Figure 10D). Together, these data suggest that PpPIF8 positively regulates anthocyanin biosynthesis. 2.5.6. RNA-Seq Analysis of PpPIF8_OE Transgenic Pear Calli To further explore the mechanism of PpPIF8 in regulating anthocyanin biosynthesis, the light-grown PpPIF8_OE and the non-transgenic control pear calli were used for deep RNA-Seq analysis. Compared with the control sample, 923 genes were significantly upregulated and 707 genes were downregulated in PpPIF8_OE pear calli (Figure 11A). The anthocyanin biosynthesis genes PpPAL (gene3545), PpC4H (gene22826), Pp4CL (gene20027), PpCHS (gene24418), PpCHI (gene40086), PpF3H (gene13883), PpF3′H (gene3191), PpFLS (gene33758), PpANS (gene13320), and PpUFGT (gene12084) were all upregulated in PpPIF8_OE pear calli, of which PpCHS had the smallest FDR value (Figure 11A). Consistent with RNA-Seq data, the RT-qPCR results also showed a similar expression trend (Figure 11B). Besides anthocyanin biosynthesis genes, both RNA-Seq and RT-qPCR results show that the transcription factors PpMYB10 and PpMYB12, but not PpHY5, were also upregulated, whereas PpCOL13 and PpGT2 were slightly downregulated in PpPIF8_OE pear calli (Figure 11C). These results suggest that PpPIF8 may function upstream of PpMYB10 and PpMYB12. Furthermore, the KEGG enrichment analysis was conducted for DEGs. For the PpPIF8 upregulated genes, the terms significantly enriched were: Flavonoid biosynthesis, Secondary metabolites biosynthesis, Plant hormone signal transduction, Photosynthesis proteins. However, for the PpPIF8 downregulated genes, the terms significantly enriched were: MAPK pathway, Transcription factor, Signal transduction, Plant–pathogen interaction (Figure 11D). Together, these data suggest that PpPIF8 promotes anthocyanin biosynthesis mainly by activating gene expression, such as PpCHS, PpMYB10, and PpMYB12. CHS is a key enzyme in the anthocyanin biosynthesis pathway. However, in the RNA-Seq result of PpPIF8_OE pear calli, PpCHS showed the smallest FDR value among other anthocyanin biosynthesis genes (Figure 11A). Thus, we speculated whether PpPIF8 could directly regulate PpCHS. To test this hypothesis, we cloned the promoter sequence of PpCHS (proPpCHS, 1600 bp upstream of the starting codon ATG) from ‘Red Zaosu’ and predicted the potential cis-acting regulatory elements using the PlantCARE program. Multiple light-responsive elements were detected, such as G-box, Box 4, TCT motif, TCCC motif, Sp1 motif, and chs-CMA1a motif (Figure 11E). It is reported that in Arabidopsis, PIF3 binds to the G-box of CHS. Therefore, we speculated that G-box may be also important for PpPIF8 binding to PpCHS promoter of which G-box is considered as PIF binding motif in previous literature [29]. We then performed a yeast-one hybrid assay to test if PpPIF8 can directly bind to the PpCHS promoter. As Figure 11F shows, the yeast cells co-transformed with AD-PpHY5 and proPpCHS-pHIS2.1 vectors were set as positive control and could grow on both SD/−Leu−Trp and SD/−Leu−Trp−His plus 5 mM 3-AT mediums. As with the positive control, yeast cells co-transformed with AD-PpPIF8 and proPpCHS-pHIS2.1 vectors could also grow on both selective mediums. However, the negative control yeast cells co-transformed with empty AD and proPpCHS-pHIS2.1 vectors could only grow on SD/−Leu−Trp medium (Figure 11F). This result demonstrates that PpPIF8 can directly bind to the PpCHS promoter. 3. Discussion 3.1. Light Is a Key Factor for Anthocyanin Biosynthesis in Pear Peel Bagging is an effective approach to improve fruit quality and appearance. However, bagging seriously affects the coloration of fruit peel due to blocking light penetration. Therefore, light is considered as a key environmental factor that regulates anthocyanin biosynthesis [36]. Generally, anthocyanin content and the expression of anthocyanin biosynthesis genes is lower in bagged fruits compared with debagged fruits. In this study, we found that the peel of debagged ‘Red Zaosu’ pear turns red gradually under light, while the peel of bagged fruits stays pale white (Figure 1), indicating that light is indispensable for “Red Zaosu” coloration. These data are consistent with previous observations of pear and apple [36,37]. 3.2. Comprehensive RNA-Sequencing and Gene Co-Expression Network Analysis Identify Potential TFs Involved in Anthocyanin Biosynthesis RNA-Sequencing (RNA-Seq) is a powerful tool for dissecting the transcription events during anthocyanin biosynthesis [36,38]. Here, we performed time-series RNA-Seq using ‘Red Zaosu’ pear, and 4658 unique DEGs were identified after debagging (Figure 3), suggesting extensive transcriptional reprogramming occurs after exposure to light. Among these DEGs, multiple anthocyanin biosynthesis genes were induced by light after debagging (Figure 4), which is consistent with previous research [36]. WGCNA is a powerful tool to construct gene co-expression network and calculate module-trait relationship [39]. In our study, seven modules were identified and the “Blue” and “Turquoise” modules were highly correlated with anthocyanin biosynthesis (Figure 5A–C). KEGG enrichment analysis revealed that genes in these two modules are related to light-regulated pathways, such as chlorophyll metabolism, photosynthesis, and flavonoid biosynthesis (Figure 5F), suggesting light after debagging is the main cause of these physiological changes. Transcription factors play an important role in regulating anthocyanin biosynthesis, and many TFs have been identified during coloration of pear peel [1,36]. In our study, 263 TFs were identified in the “Blue” and “Turquoise” modules (Table 1). Consistent with previous studies, some key TFs, such as PpHY5, PpMYB10, and PpMYB12, were also included (Figure 6A). Moreover, there are also many TFs have not yet been characterized (Figure 6A); these TFs can direct subsequent studies. Further cluster study found that PpPIF8 clustering was highly correlated with PpMYB10 and PpMYB12 (Figure 6A). STRING analysis also indicated PpPIF8 is associated with PpHY5 (Figure 6C). CentriMo analysis showed that PIFs are enriched in the promoters of genes in the “Blue” and “Turquoise” modules (Figure 7 and Figure S3). These results suggest that PpPIF8 may play a role in regulating anthocyanin biosynthesis. 3.3. Identification of PIFs in Pear Genome PIFs act downstream of phytochrome to regulate a range of photomorphogenic development processes, such as hypocotyl elongation, cotyledon opening, and anthocyanin biosynthesis [26]. We first identified PIFs in the pear genome. Like Arabidopsis and apple, there are eight PpPIFs in pear genome, and each of them contains a conserved bHLH-AtPIF-like domain (Figure 8A). Surprisingly, in the comparison of expression patterns, only PpPIF8 was strongly and quickly induced by light after debagging (Figure 8C and Figure 9A), which is similar to AtPIF8 in Arabidopsis and VvPIF7 in grape skin [40,41]. These data suggest that PpPIF8 transcripts may play a unique role among these PpPIFs in regulating anthocyanin biosynthesis in pear. 3.4. Tissue-Specific Expression, Subcellular Localization, and Transcriptional Activity of PpPIF8 Tissue-specific expression analysis showed that PpPIF8 is preferentially expressed in the tissue with higher anthocyanin content, with a correlation coefficient of 0.7462. Strikingly, as for the fruits, the expression level of PpPIF8 in pear peel is over 50-fold greater than pear flesh (Figure 9B). Similarly, VvPIF7 is also highly expressed in the grape peel tissue, but not flesh, at the pre-verasion and post-verasion stages [41]. Collectively, these data suggest that tissue-specific expression of PpPIF8 may participate in regulating anthocyanin biosynthesis in the peel of pear fruit. As transcription factors, nuclear localization and transcription activity are essential for their function. A co-localization assay showed that PpPIF8 was expressed specifically in the nucleus (Figure 9D). This is consistent with MdPIF8 in apple [42]. In addition, transactivation activity assays in yeast cells showed that PpPIF8 does not have self-activation activity, suggesting that other transcriptional regulators may be required for its transcription activity in vivo (Figure 9C). In support of this speculation, MdPIF7 also lacks self-activation activity, but interacts with MdBBX23, which directly regulates MdHY5 expression [32]. Therefore, the identification of the PpPIF8-interacting partners in the future may help elucidate how PpPIF8 regulates gene expression in vivo. 3.5. PpPIF8 Positively Regulates Anthocyanin Biosynthesis To investigate the function of PpPIF8 in the anthocyanin biosynthesis pathway, we overexpressed PpPIF8 by transient infiltration in pear peel and stable transformation of pear calli. Both results showed that PpPIF8 promotes anthocyanin biosynthesis in light (Figure 10). This is consistent with the role of Arabidopsis PIF3, but in contrast to PIF4 and PIF5 [29,30]. In Arabidopsis, the overexpression of PIF3 induces anthocyanin accumulation, while pif3 mutants showed decreased anthocyanin levels in far-red light [29]. Unlike PIF3, Arabidopsis PIF4 and PIF5 negatively regulate anthocyanin accumulation under red light [30]. During our research, two PIFs were published as negative regulators in regulating anthocyanin biosynthesis in apple and pear [31,32]. In apple, the overexpression of MdPIF7 decreased anthocyanin accumulation in transgenic apple calli and MdPIF7 antisense suppressing apple calli increased anthocyanin [32]. In red Chinese sand pear ‘Yunhongyihao’, PyPIF5 is downregulated by light after debagging and negatively regulates anthocyanin accumulation through the PyPIF5–PymiR156a–PySPL9–PyMYB114/MYB10 cascade [31]. However, in our data, the expression of both PpPIF5 and PpPIF5a is not obviously different between bagged and debagged samples (Figure 8C). We speculate that this discrepancy may arise for at least two reasons. The first is that different pear varieties were used. The strip-colored ‘Red Zaosu’ pear, which is a specific PpBBX24 mutation pear [14], is used in our study, whereas “Yunhongyihao” is an evenly colored pear. The second possible reason is due to the difference in temperature and light intensity and quality between south and north China. In summary, as for anthocyanin biosynthesis, different PIFs function as either positive or negative regulators under different light spectra. 3.6. Molecular Mechanisms of PpPIF8 in Regulation of Anthocyanin Biosynthesis To investigate how PpPIF8 promotes anthocyanin biosynthesis, DEGs were identified by RNA-Seq in PpPIF8_OE transgenic pear calli and the control calli. In accord with the function of PpPIF8 in promoting anthocyanin biosynthesis, multiple anthocyanin biosynthesis genes, PpMYB10, and PpMYB12 are upregulated in PpPIF8_OE transgenic pear calli (Figure 11A). KEGG enrichment analysis showed that some light-regulated terms are enriched in PpPIF8-upregulated genes, such as ‘Flavonoid biosynthesis’ and ‘Photosynthesis protein’ (Figure 11D). These data suggest PpPIF8 has a positive role in light-regulated processes. Similar to the PIF3 in Arabidopsis [29], PpPIF8 can also directly binds the promoter sequence of PpCHS (Figure 11F). Therefore, PpPIF8 promotes anthocyanin biosynthesis probably through direct activation of the expression of anthocyanin biosynthesis genes. Previous research showed that the red color of ‘Red Zaosu’ was highly associated with PpBBX24 mutation, and that this mutation was possibly specific to ‘Red Zaosu’ [14]. Furthermore, in Arabidopsis, the AtBBX24 interacts with AtHY5 and negatively regulates its transcriptional activity [43], and the promotion effect of AtPIF3 on anthocyanin biosynthesis is also dependent on AtHY5 [29]. Therefore, we speculated that PpBBX24 mutation in ‘Red Zaosu’ might derepress PpHY5 activity, and the promotion effect of PpPIF8 on anthocyanin biosynthesis might depend on this elevated PpHY5 activity in the ‘Red Zaosu’ background. In summary, our data not only provide a comprehensive view of transcription events during the coloration of pear peel, but also reveal a simple molecular mechanism of PpPIF8 in the regulation of anthocyanin biosynthesis in pear. Upon exposure to light, PpPIF8 is upregulated and then directly binds the promoter sequence of PpCHS to induce its expression. In addition, PpPIF8 may regulate the expression of PpMYB10 and PpMYB12 in a currently unknown way. Further efforts should be paid to identify PpPIF8’s partners in vivo and clarify how PpMYB10 and PpMYB12 are regulated by PpPIF8. 4. Materials and Methods 4.1. Plant Materials and Treatments The fruits of ‘Red Zaosu’ (Pyrus pyrifolia × Pyrus communis) were harvested from Zhao County, Shijiazhuang City, Hebei Province. In total, 300 fruits were covered with lightproof double-layered paper bags at 40 days after full blossom (DAFB). Half of the fruits were debagged at 120 DAFB (D group), and the remaining bagged fruits were used as the control (B group). One hundred and fifty fruits of both D and B groups were randomly divided into three biological replicates and sampled at 1, 3, 5, 7, and 15 days after debagging. The fruits just before debagging were regarded as the 0-day sample. At each sampling time point, the fruits were photographed, and fruit peels were scraped, immediately frozen in liquid nitrogen, and stored at −80 °C until use. 4.2. Induction and Transformation of Pear Calli The pear calli were induced from the flesh cells of the young fruitlet of red-skinned Pyrus communis ‘Xiuzhenxiang’, as reported previously [44] and subcultured on Murashige and Skoog (MS) solid medium (Coolaber, Beijing, China) supplemented with sucrose, 2,4-dichlorophenoxyacetic acid, and 6-benzylaminopurine at 22 °C under dark conditions. The coding sequence of PpPIF8 without the stop codon was amplified from cDNA of debagged fruit sample, cloned into pENTR/SD/D-TOPO vector (named PpPIF8-pTOPO afterward) (Invitrogen, Waltham, MA, USA), and then recombined into pGWB5, which contains a C-terminal GFP tag destination vector by LR CLONASE Enzyme Mix (Invitrogen, Waltham, MA, USA). The resulting vector (PpPIF8_OE) was confirmed by sequencing and then introduced into Agrobacteria tumefaciens strain EHA105 (Weidi Biotechnology, Shanghai, China) using the freeze–thaw method. For the transformation of pear calli, agrobacterium carrying the PpPIF8_OE vector was cultured until an OD600 of 0.6 in LB liquid medium supplied with 50 µg/mL kanamycin and 50 µg/mL rifampicin. The EHA105 cells were collected by centrifugation at 5000× g for 15 min and then resuspended in transformation buffer (MS liquid medium, 10 mmol/L MES, 200 μmol/L Acetosyringone, pH 5.6) to a final OD600 of 0.5. The pear calli were incubated with A. tumefaciens carrying PpPIF8_OE vector for 15 min. After co-culture on MS solid medium for 2 days, the calli were then screened on MS solid medium containing 30 mg/L hygromycin under continuous dark conditions at 22 °C. For the light treatment, the freshly cultured PpPIF8_OE and non-transgenic pear calli were exposed to light (light intensity: 100 mmol m−2 s−1; photoperiod: 16 h light/8 h dark) for 5 days and then used for analysis. The overexpression of PpPIF8 in transgenic pear calli was confirmed by immunoblotting. In brief, total protein was extracted with 2 × SDS buffer (100 mM Tris–HCl, pH = 6.8; 20% glycerol; 4% SDS; 2% β-mercaptoethanol; 0.01% bromophenol blue). The samples were boiled at 95 °C for 15 min and centrifuged at 13,000× g for 10 min. The denatured samples were separated by 10% SDS-PAGE and transferred onto a PVDF membrane (Millipore, Burlington, MA, USA). The membrane was blocked with 5% non-fat milk followed by antibody incubation. Chemiluminescence signals were visualized using SuperSignal West Dura Extended Duration Substrate (Thermo Scientific, Waltham, MA, USA) and X-ray film. The GFP monoclonal antibody (HT801-01) and HRP-conjugated Goat anti-mouse secondary antibody (HS201-01) were purchased from TransGene (Beijing, China). 4.3. Anthocyanin Extraction and Measurement The anthocyanin content was measured according to a previous report [45]. Briefly, the fruit peel, calli, and different tissues of pear were ground into fine powder in liquid nitrogen. For each sample, 0.2 g tissue powder was extracted in the dark with 5 mL methanol–HCl (99:1, v/v) at 4 °C overnight. The extract was then centrifuged at 13,000× g for 15 min, and the absorbances of the resulting supernatant was measured by a UV–visible spectrophotometer UV2600 (Shimadzu, Kyoto, Japan) at wavelengths of 530 and 657 nm, respectively. The anthocyanin content was calculated as follows: anthocyanin content = (A530 − 0.25 × A657) × v/w, where v = volume of the extract (mL) and w = weight of tissue powder (g). 4.4. RNA Extraction and RT-qPCR Total RNA was extracted using RNAprep Pure Plant Plus Kit (for polysaccharides and polyphenolic-rich samples) (TIANGEN, Beijing, China) according to the manufacturer’s instructions. RNA purity and concentration were determined using a NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA, USA). RNA integrity was evaluated using agarose gel electrophoresis. Reverse transcription was performed using PrimeScriptTM RT Reagent Kit with gDNA Eraser (Takara, Kusatsu, Japan) and real-time fluorescence quantitative PCR (RT-qPCR) was conducted using TB Green Premix Ex TaqTM II (Tli RNaseH Plus) kit (Takara, Kusatsu, Japan) on a 7500 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). PpACTIN7 was used as the internal reference. Three biological repeats were performed for each sample and the relative expression levels of genes were calculated by the 2−ΔΔCt method [46]. Primers used for RT-qPCR are shown in Supplemental Table S1. 4.5. RNA-Seq Analysis and WGCNA Total RNA was extracted from both D and B group samples and each time point set with three biological repeats. RNA quality was evaluated by agarose gel electrophoresis and Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). The libraries were generated using the NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) and then sequenced with the Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA) by Biomarker Technologies Corporation (Beijing, China). The clean reads were mapped to the reference genome of Pyrus bretschneideri ‘DangshanSuli’ V1.1 by HISAT2 software [33,47]. The gene expression levels were calculated based on the number of fragments per kilobase of transcript per million reads mapped (FPKM). Differential expression analysis was performed using the R package DESeq2 [48]. PCA and sample correlation were analyzed in R software (version 3.6) and visualized using ggplot2 package in R and TBtools, respectively [49]. WGCNA was performed using the WGCNA package in R [39]. KEGG enrichment analysis was completed by TBtools and plotted using ggplot2 package in R. For the STRING analysis, the protein sequences were uploaded on the server (https://cn.string-db.org, accessed on 23 August 2021). Pyrus × bretschneideri was set for the organism option and the default setting for other parameters. The network was visualized using Cytoscope software (version 3.9.0) [50]. For local motif enrichment analysis (CentriMo), promoter sequences (2000 bp upstream of the starting codon ATG) were extracted by TBtools and then analyzed using the online tool CentriMo (version 5.2.0. https://CentriMo-suite.org/CentriMo/tools/centrimo, accessed on 26 November 2021). The JASPAR CORE (2018) plants motif database was selected to identify enriched known motifs in these promoter sequences [35]. 4.6. Identification of the PpPIF Genes and Phylogenetic Tree Construction The protein sequences of eight Arabidopsis PIFs were retrieved from The Arabidopsis Information Resource (TAIR 10) (https://www.arabidopsis.org/, accessed on 10 December 2021) and used as query sequences to search homologs Pyrus bretschneideri ‘DangshanSuli’ V1.1 genome database by BLAST using TBtools software [33,49]. The obtained sequences were further searched in the Conserved Domains Database (https://www.ncbi.nlm.nih.gov/CDD, accessed on 12 December 2021) to confirm whether the candidate PpPIFs contain the typical bHLH_AtPIF_like domain (cd11445). The molecular weights, isoelectric points, and instability index of PpPIFs were calculated using ExPASy ProParam tool (https://web.expasy.org/protparam/, accessed on 15 December 2021). For constructing the phylogenetic tree, the PIFs protein sequences from Arabidopsis, apple, and pear were aligned with Multiple Protein Sequence Alignment (MUSCLE) using the default settings in MEGA version 11 software [51], and a phylogenetic tree was constructed by applying the maximum likelihood method with 1000 bootstrap repeats using MEGA version 11 software [51]. 4.7. Subcellular Localization Analysis The full-length CDS of PpPIF8 without stop codon was cloned into pCambia1300-GFP vector under 35S promoter using pEASY-Basic Seamless Cloning and Assembly Kit (TransGene, Beijing, China). The empty vector was used as a control. The resulting p35S-PpPIF8-GFP vector was transformed into Agrobacterium tumefaciens (GV3101) and then co-infiltrated with nuclear marker (NSL-mCherry) strain into Nicotiana benthamiana leaves. After 48 h of infiltration, the fluorescence signal was detected using a laser confocal microscopy (Leica TCS SP8, Wetzlar, Germany). 4.8. Transient Overexpression Assay in ‘Red Zaosu’ Fruit Transient overexpression assay in ‘Red Zaosu’ fruit was conducted as previously described, with minor modifications [52]. The bagged ‘Red Zaosu’ pear fruit, which was harvested at 132 DAFB, was used for transient overexpression assay. The A. tumefaciens strains carrying p35S-PpPIF8-GFP and empty vectors separately were cultured in LB medium overnight. The cells are harvested by centrifugation and resuspended in infiltration buffer (10 mM MgCl2, 10 mM MES, and 150 mM Acetosyringone) to a final OD600 of 0.8. The resuspended cell was injected into ‘Red Zaosu’ pear fruit. The fruits were then incubated in the dark for two days and transferred to continuous white light condition for observation. 4.9. Transactivation Activity Assay Transactivation activity assay was performed as previously described with minor modifications [52]. The BD-PpPIF8 vector was constructed by recombining MluI-digested PpPIF8-pTOPO vector into a gateway-compatible pGBKT7 (BD) vector using LR CLONASE Enzyme Mix (Invitrogen, Waltham, MA, USA). The BD-PpPIF8, together with the negative controls (empty BD) and positive control (BD-AtBZS1), were individually transformed into yeast strain Y187 using the Super Yeast Transformation Kit ΙΙ (Coolaber, Beijing, China). The yeast transformants were sequentially screened on SD/−Trp and SD/−Trp/−His plates. The transactivation activity was determined by yeast growth on SD/−Trp/−His plates. 4.10. Yeast One-Hybrid Assay The promoter sequence of PpCHS (gene24418) was cloned from ‘Red Zaosu’ pear using Universal GenomeWalker 2.0 kit (Clontech, Mountain View, CA, USA) and ligated into pHIS2.1 vector by pEASY-Basic Seamless Cloning and Assembly Kit (TransGene, Beijing, China). The resulting plasmids proPpCHS-pHIS and AD-PpPIF8 were co-transformed into Y187 strain using the Super Yeast Transformation Kit ΙΙ (Coolaber, Beijing, China). The transformants were successively screened on SD/−Leu−Trp plates and tested on SD/−Leu−Trp−His plates plus 5 mM 3-amino-1,2,4-triazole(3AT) at 30 °C for 3 days. Co-transformants containing pGADT7 and proPpCHS-pHIS or AD-PpHY5 and proPpCHS-pHIS were used as negative and positive control, respectively. 4.11. Statistical Analysis Statistical analysis was performed with GraphPad Prism software (version 9). Significant differences (* p < 0.05, ** p < 0.01, and *** p < 0.001) were determined with Student’s t-test. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23094798/s1. Click here for additional data file. Author Contributions J.G., C.W. and Z.S. designed the project; Z.M., C.W. and Y.C. performed the experiments; Z.M. and C.W. analyzed the data; Z.M., C.W., X.G. and J.G. wrote the article. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The raw RNA-Seq data of this study have been deposited in the Genome Sequence Archive in China National Center for Bioinformation (https://www.cncb.ac.cn/, accessed on 1 April 2022) under the project number: PRJCA009255. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Debagging promotes anthocyanin accumulation in the peel of ‘Red Zaosu’ pear. (A) Color changes in ‘Red Zaosu’ pear. ‘0’ was the initial sample before debagging. ‘B’ represents bagging samples, while ‘D’ indicates debagging samples. (B) Changes in the anthocyanin content after debagging. Error bars denote the standard deviations. Asterisks above the bars indicate significant differences (*** p < 0.001, * p < 0.05) obtained by two-tailed Student’s t-test within each timepoint. Figure 2 Principal component analysis (PCA) plots showing repeatability and heterogeneity of sequencing samples. Figure 3 Differential gene expression analysis. (A) Counts of DEGs. (B) Upset plot showing the intersection of DEGs across all timepoints. Figure 4 Expression of anthocyanin biosynthesis genes. (A) Heatmap illustrating the expression of anthocyanin biosynthesis genes. The numbers within the grids are original FPKM values, whereas row-scaled FPKM values are used for plotting. (B) Validation of gene expression by RT-qPCR. Error bars denote the standard deviations. Asterisks above the bars indicate significant differences (*** p < 0.001, * p < 0.05) obtained by two-tailed Student’s t-test within each timepoint. Figure 5 WGCNA and KEGG enrichment analysis. (A) Module construction of WGCNA. (B) Genes counts in each module. (C) Heatmap showing module–trait relationships. The numbers in each grid are Pearson correlation coefficient and significant p value in brackets. (D) Eigengene expression in “Blue” module. (E) Eigengene expression in “Turquoise” module. (F) Scatter plot showing top 20 enriched KEGG pathways in “Blue” and “Turquoise” modules. Figure 6 Analysis of transcription factors. (A) Cluster analysis of 55 transcription factors expression in “Blue” and “Turquoise” modules. (B) Validation of transcription factor expression by RT-qPCR. Error bars denote the standard deviation. Asterisks above the bars indicate significant differences (*** p < 0.001) obtained by two-tailed Student’s t-test within each timepoint. (C) STRING-DB analysis of the transcription factors. Figure 7 The distribution preference of the PIF8 binding site in the promoter region of genes in the “Blue” and “Turquoise” modules. Figure 8 Genome-wide identification of PpPIFs. (A) PpPIFs contains a conserved bHLH_AtPIF_like domain. (B) Phylogenetic tree analysis of PIFs from pear, apple, and Arabidopsis thaliana. (C) The expression pattern of PpPIF1, PpPIF3, PpPIF5, and PpPIF8. ‘B’ represents bagged samples, while ‘D’ indicates debagged samples. Figure 9 Expression, transcription activation, and subcellular localization of PpPIF8. (A) Verification of PpPIF8 expression in RT-qPCR results and RNA-Seq data. (B) Expression patterns of PpPIF8 in different tissues. The abbreviation in the graph are as follows: Flower-F, Sepal-S, Young leaf-YL, Mature leaf-ML, Young fruitlet-YF, Expansion stage peel-EP, Expansion stage flesh-EF, Mature stage peel-MP, and Mature stage flesh-MF. (C) Transcriptional activation of PpPIF8 using a yeast assay. The GAL4 DNA-binding domain (B,D) alone was used as the negative control. The BD-AtBZS1 was used as the positive control. SD/−Trp, synthetic dextrose medium lacking Trp; SD/−Trp-His, synthetic dextrose medium lacking both Trp and His. (D) Subcellular localization of PpPIF8 in Nicotiana benthamiana leaf cells. Scale bar: 75 μm. Figure 10 Overexpression of PpPIF8 promotes anthocyanin accumulation. (A) Transiently overexpression of PpPIF8 in the peel of ‘Red Zaosu’ pear; empty vector was used as the negative control. (B) Overexpression of PpPIF8 promotes anthocyanin accumulation in pear calli. (C) Conformation of PpPIF8 overexpression by immunoblotting; β-actin was used as an internal reference. (D) Determination of anthocyanin contents in (B). Error bars denote the standard deviations. Asterisks above the bars indicate significant differences (*** p < 0.001) obtained by two-tailed Student’s t-test. Figure 11 RNA-Seq analysis of PpPIF8_OE transgenic pear calli. (A) A volcano plot illustrating DEGs between PpPIF8_OE and control pear calli. Genes significantly upregulated and downregulated in PpPIF8_OE pear calli are shown in red and blue, respectively. (B) Validation of anthocyanin biosynthesis genes expression by RT-qPCR. Error bars denote the standard deviation. Asterisks above the bars indicate significant differences (*** p < 0.001) obtained by two-tailed Student’s t-test. (C) Validation of transcription factor expression by RT-qPCR. Error bars denote the standard deviation. Asterisks above the bars indicate significant differences (*** p < 0.001) obtained by two-tailed Student’s t-test. (D) KEGG pathway analysis of upregulated and downregulated genes in PpPIF8_OE pear calli. (E) Promoter analysis of PpCHS. (F) Yeast one-hybrid analysis of the interaction between PpPIF8 and the PpCHS promoter. SD/−Leu−Trp, synthetic dextrose medium lacking Leu and Trp; SD/−Leu−Trp−His+3AT, synthetic dextrose medium lacking Leu, Trp, and His plus 5 mM 3AT. ijms-23-04798-t001_Table 1 Table 1 Transcription factors in “Blue” and “Turquoise” modules. Type of TF No. Description AP2-ERF 35 Ethylene responsive transcription factor AUX/IAA 6 AUX/IAA transcriptional regulator bHLH 18 Basic helix–loop–helix protein bZIP 7 Basic-leucine zipper protein B-box 8 B-box type zinc finger family protein C2H2 12 C2H2-type zinc finger family protein C3H 3 zinc finger (CCCH-type) family protein GNAT 7 Acyl-CoA N-acyltransferases protein GRAS 5 GRAS family transcription factor HB 14 Homeobox-leucine zipper protein LOB 5 LOB domain-containing protein MYB 40 Myb domain protein NAC 15 NAC domain-containing protein SWI/SNF 3 SWIB/MDM2 domain superfamily TAZ 3 BTB and TAZ domain protein Tify 3 Jasmonate-zim-domain protein Trihelix 7 Trihelix transcription factor WRKY 24 WRKY family transcription factor Other TFs 48 Total TFs 263 ijms-23-04798-t002_Table 2 Table 2 Characteristics of phytochrome-interacting factors (PIFs) genes in pear. Gene Name Gene ID Amino Acid Length (aa) MW (KD) pI Instability Index Best Hits PpPIF1 gene2064 (LOC103961475) 508 55.21 9.39 68.07 AtPIF1 PpPIF3 gene16013 (LOC103955840) 713 76.40 6.09 53.64 AtPIF3 PpPIF3a gene15524 (LOC103955304) 716 76.56 6.12 55.57 AtPIF3 PpPIF5 gene8334 (LOC103947396) 550 60.37 6.47 54.50 AtPIF5 PpPIF5a gene2521 (LOC103965596) 541 59.29 6.32 57.78 AtPIF5 PpPIF8 gene35851 (LOC103935970) 442 47.55 7.14 54.29 AtPIF8 PpPIF7a gene31629 (LOC103931367) 398 44.00 9.20 71.73 AtPIF8 PpPIF7b gene3216 (LOC103931008) 406 44.85 9.02 64.49 AtPIF8 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Liu H. Liu Z. Wu Y. Zheng L. Zhang G. Regulatory mechanisms of anthocyanin biosynthesis in apple and pear Int. J. Mol. Sci. 2021 22 8441 10.3390/ijms22168441 34445149 2. Zhang Y. Butelli E. Martin C. Engineering anthocyanin biosynthesis in plants Curr. Opin. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093171 materials-15-03171 Article Preliminary Feasibility Investigation on Reutilization of Recycled Crushed Clay Bricks from Construction and Demolition Waste for Cement-Stabilized Macadam Wu Dongxing 1 Chu Wenchao 2 Wang Longlin 34* https://orcid.org/0000-0003-0442-7477 Wang Wensheng 5* Wang Haoyun 5 Shangguan Xuanhao 5 Cui Xiang 6 Palmero Paola Academic Editor 1 First Detachment, Guangxi Transportation Comprehensive Administrative Law Enforcement Bureau, Nanning 530007, China; wudx_gx@163.com 2 China State Construction Railway Investment & Engineering Group Co., Ltd, Beijing 100053, China; luogbjlu@163.com 3 Bridge Engineering Research Institute, Guangxi Transportation Science and Technology Group Co., Ltd., Nanning 530007, China 4 School of Civil Engineering, Southeast University, Nanjing 211189, China 5 College of Transportation, Jilin University, Changchun 130025, China; wanghy1717@mails.jlu.edu.cn (H.W.); sgxh1719@mails.jlu.edu.cn (X.S.) 6 Lunan Technician College, Linyi 276000, China; wanghjlu@163.com * Correspondence: wll955@163.com (L.W.); wangws@jlu.edu.cn (W.W.); Tel.: +86-0431-8509-5446 (W.W.) 27 4 2022 5 2022 15 9 317114 3 2022 25 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Utilizing recycled crushed clay brick (RCB) from C&D waste in road engineering construction as the substitute for natural aggregates has attracted a lot of attention, which would be a promising step forward towards sustainable development and green construction. The objective of this study is to assess the feasibility of cement-stabilized macadam (CSM), incorporating various RCB fine aggregate substitution ratios. For this purpose, the physical and chemical properties of RCB fine aggregate was tested, and RCB exhibited a porous surface micro-morphology, high water absorption and pozzolanic activity. Subsequently, a comprehensive experimental investigation of modified CSM with RCB has been carried out based on laboratory tests concerning the mechanical and shrinkage properties. Results showed that higher RCB fine aggregate substitution ratio resulted in lower unconfined compressive strength, and the negative influence of RCB on unconfined compressive strength would decrease gradually, varying curing time; however, the higher the RCB substitution ratio was, the larger the indirect tensile strength at 90 d curing time of the late curing period was. CSM containing RCB had an overall increasing accumulative water loss rate, accumulative strain of dry shrinkage and average coefficient of dry shrinkage, except that 20% RCB resulted in an excellent dry shrinkage property. Moreover, RCB with pozzolanic activity reacted very slowly mainly at later ages, enhancing the interfacial transition zone. cement-stabilized macadam recycled clay brick mechanical performances durability shrinkage characteristics leaching toxicity Scientific and Technological Project of the Science and Technology Department of Jilin Province20210508028RQ Scientific Research Project of Department of Education of Jilin ProvinceJJKH20221019KJ China Postdoctoral Science Foundation2021T140262 Innovation and Entrepreneurship Training Fund of Jilin UniversityS202110183260 This research was funded by the Scientific and Technological Project of the Science and Technology Department of Jilin Province (grant number: 20210508028RQ), Scientific Research Project of Department of Education of Jilin Province (grant number: JJKH20221019KJ), the China Postdoctoral Science Foundation (grant number: 2021T140262). This research was also supported by Innovation and Entrepreneurship Training Fund of Jilin University (grant number: S202110183260). ==== Body pmc1. Introduction The generation of construction and demolition (C&D) waste, such as clay brick, concrete, etc., is being accelerated by construction of new and old urban areas and renovation of the urban–rural fringe [1,2]. It was reported that America produces 145 million tons of C&D waste annually [1], India produces about 14.5 million tons [3], while as much 1.8 billion tons was generated in 2017 in China [4]. As the leading country of world municipal solid waste generation (accounting for about 30% of the global total), an increasing volume of C&D waste accounts for one third of the total municipal solid waste in China; however, the ratio of recycling and reprocessing C&D waste is only around 5% [5]. Furthermore, according to the statistics, C&D waste was estimated to constitute to approximately 25~45% of the total solid waste in landfill [6]. Bricks, as a widely used construction material, are inevitably damaged during the reconstruction process, such as through demolition and construction activities. Over the past 50 years, approximately 20~30 billion clay bricks have been produced in China, which have been or will be turned into a huge amount of C&D waste [7]. Currently, a relatively complete set of reutilization technologies for clay bricks has been formed, in which the clay bricks from C&D waste are separated, screened, crushed and stripped of impurities to manufacture the recycled crushed clay brick (RCB) [8,9]. However, based on the consideration of environmental protection and resource conservation including carbon emissions reduction, the mining of natural aggregates has been banned in many areas of China. There is an imbalance between the supply and demand of aggregates as well as an increasingly prominent contradiction between natural aggregate consumption and C&D waste disposal. Some studies have investigated RCB reutilization as a partial cement substitute due to its pozzolanic activity or coarse and fine aggregate replacements [10]. It has been observed that it is feasible to reuse RCB as a partial substitute in cementitious construction materials. Although it may slightly reduce the mechanical and durability properties, the experimental results indicated that adding RCB could still meet application requirements [11]. Furthermore, recycled aggregates have been used as coarse or fine aggregates for pavement bases or subbases in about 38 states of America [12]. Semi-rigid base has been widely used for road engineering due to its higher strength, better loading distribution and excellent wholeness over a period of decades. Its main materials generally include cement-stabilized macadam (CSM), lime–fly-ash-stabilized macadam, cement–fly-ash-stabilized macadam and lime-stabilized macadam, etc. [13], and the semi-rigid base is becoming the predominant type of base and subbase for highways and urban roads, especially in China [14,15]. CSM, as one of the main semi-rigid base materials, is a kind of composite construction material composed of proper aggregate gradation, 3~8% cement of aggregate by weight as well as optimum content of water [16]. The potential of CSM incorporating RCB is worth studying in order to improve the application of CSM and supply an opportunity to use RCB from C&D waste, which is widely accepted as a cost-effective reutilization means [12,17]. Previous studies have attempted to explore the application of RCB as aggregate for cement-based material. The comparative study of Arulrajah et al. reported that about 25% RCB could be satisfactorily added to recycled aggregates in the application of pavement subbase, and the RCB content has marginal effects on the mechanical properties and relatively obvious influences on dry density as well as moisture content of CSM [18]. Data from several studies suggested that the substitution ratio of recycled aggregates in pavement materials should not exceed up to 80% [19,20]. Remarkably, a great deal of studies have investigated the effect of RCB as coarse or fine aggregate on cement-based materials. It has been conclusively proven that RCB replaces coarse aggregate only partially, while the replacement rate of RCB for fine aggregate can be as high as 100%. Generally, there will be a reduction in the mechanical properties of cement-based materials by replacing coarse aggregate with RCB. The lower inherent strength of RCB may be considered as one of the major factors for the strength reduction of cement-based materials due to the main skeleton component of coarse aggregate [21]. Nevertheless, several studies have reported that the replacement rate of coarse aggregate by RCB could be up to 50% in cement-based material, which still meets the application requirements and the RCB replacement rate within 20% would have no prominent negative influence on cement-based material [1,22]. Unlike coarse aggregate replacement, the possibilities of applying RCB fully instead of fine aggregate in cement-based materials have been investigated, and the clear evidence from experimental observations supported the full utilization of RCB replacement and the benefits of RCB fine aggregate for the machinal and shrinkage properties [23,24]. It has been demonstrated that a lower drying shrinkage was measured for cement-based material with 20% RCB replacing fine aggregate, which may be due to the restraining effect of RCB and the internal curing effect [23]. In addition, the findings of these research confirmed that the incorporation of RCB with pozzolanic activity into cement-based material can inhibit alkali silica reactions [25]. Consequently, the active pozzolanic materials in RCB aggregates would react with the hydration product (calcium hydroxide, Ca(OH)2), generating the hydrated calcium silicate (C-S-H), which can lead to an expansion in cement-based material [24]. The benefits of RCB aggregate, therefore, may be driven by pozzolanic action provided by fine RCB aggregate. The purpose of this paper is to explore the feasibility and mechanism analysis of cement stabilized macadam with RCB as a fine aggregate. In this paper, RCB fine aggregate, crushed by jaw crusher, was selected and its physical and chemical properties analyzed by energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) technologies. Unconfined compressive strength, indirect tensile strength and dry shrinkage of modified CSM incorporating various RCB fine aggregate substitution ratios were tested to evaluate the mechanical and shrinkage properties. Finally, the pozzolanic activity was also tested for RCB using chemical detection methods for a further understanding of the strength mechanism of modified CSM. 2. Raw Materials and Methods 2.1. Raw Materials The cement adopted is rapid-hardening ordinary silicate cement (P.O 42.5R), and its setting time and strength of P.O 42.5R were tested, which could satisfy regulations of standards (GB175-2007, JTG/T F20-2015). In this study, 5% of cement dosage in mixtures is used in the base course of pavement. The type of natural aggregates used for CSM in this study is natural crushed basalt aggregate (BA). The RCB was collected from the construction waste in a shantytown demolition site near Changchun, China. First, construction waste collected from C&D were treated to clean up sundries such as waste wire, wooden frame, glass, and so on. Second, recycled waste clay bricks were crushed into particles using an impact jaw crusher, and the maximum size was controlled, set to 31.5 mm. Finally, those RCB were cleaned and sieved. In the case of large-scale implementation, the classification and screening equipment for C&D waste is suggested to be adopted for the material selection. Their apparent specific gravity, water absorption, needle particles, crushing values, liquid limit and plastic limit were tested. Technical indicators are summarized in Table 1, which comply with Chinese standards (JTG/T F20-2015). It can be found from EDS spectrum analysis in Figure 1a that RCB contains a lot of oxygen, silicon, aluminum, calcium, carbon, iron, magnesium and other elements. There are many oxide forms in RCB due to the high oxygen element content, which would provide some potential activity. The porous surface micro-morphology and high oxides are responsible for larger water absorption and crushed value [4,26]. Figure 1b shows the mineral compositions of RCB aggregate by the XRD pattern. There are several significant peaks representing inorganic crystalline phase of quartz (SiO2), a small amount of weak peaks representing the crystalline phase of feldspar (K2Al2Si6O6), and hematite (α-Fe2O3) in the XRD pattern, in which the quartz (SiO2) is the major crystalline phase. Based on its physical along with chemical properties, RCB aggregate has potential of pozzolanic reaction and cementitious activity [24,27]. 2.2. Gradation Design and Samples Preparation 2.2.1. Aggregate Gradation The difference of aggregate gradation is the main reason for the diversity of mechanical and shrinkage properties of CSM. Referring to Chinese standard (JTG/T F20-2015), the aggregate gradation range and CSM gradation curve are illustrated in Figure 2, based on our previous research results and engineering experience [28]. Considering that RCB with large water absorption, low density as well as soft particles, the BA (≤4.75 mm) was substituted by RCB at six ratios of 0%, 20%, 40%, 50%, 60%, 80%, named CSM-BA, CSM-RCB20, CSM-RCB40, CSM-RCB50, CSM-RCB60 and CSM-RCB80, respectively. 2.2.2. Mixture Design and Samples Preparation Referring to the Class C heavy compaction test method T0804-1994 in Chinese standard (JTG E51-2009), optimum water content (OWC) and maximum dry density (MDD) for CSM containing various RCB substitutions could be determined. According to the aggregate gradation structure specified in Figure 2 and 5% cement dosage of CSM, the Proctor standard compaction tests under six grades of water content with an interval of 0.5% were carried out. The thoroughly blended aggregates and cement were poured into the cylindrical compactor in three layers at 98 times of hammers per layer. According to the obtained law for dry density along with water content from compaction test, OWC and MDD of CSM samples have been obtained using quadratic polynomial fit method, summarized in Figure 3. With RCB substitution ratio in CSM, the MDD of CSM decreased gradually, while the OWC showed an increasing trend. This result may be explained by the fact that compared to BA, RCB is provided with larger water absorption as well as lower density (as listed in Table 1). According to the above-obtained MDD and OWC of CSM samples with different substitution ratios of RCB, the CSM samples were prepared with 98% compactness by the static compaction in Chinese standard (JTG E51-2009). Referring to T0843-2009 in JTG E51-2009, a series of cylindrical samples (ϕ150 mm × 150 mm) were made to measure unconfined compressive strength and indirect tensile strength. Following T0844-2009 in Chinese standard (JTG E51-2009), cuboid beam samples (100 mm × 100 mm × 400 mm) were molded for testing dry shrinkage. After 2 min steady pressure for cylindrical samples or 5 min steady pressure for cuboid beam samples, the applied pressure was unloaded, and the test mold was removed. The compacted samples were demolded after 4~6 h for cylindrical samples or 24 h for cuboid beam samples and were stored carefully in sealed plastic bags and cured at a typical controlled condition (20 ± 2) °C as well as RH > 95%, referring to T0845-2009 in the Chinese standard (JTG E51-2009). 2.3. Experimental Methods The CSM samples were firstly prepared with different substitution ratios of RCB, and then the CSM samples could be used for unconfined compressive strength test, indirect tensile strength test and dry-shrinkage test. The flowchart with illustrations of the experimental study is shown for describing the research phases in Figure 4. 2.3.1. Unconfined Compressive Strength Unconfined compressive strength tests were carried out for scraped cylindrical CSM samples (after curing for 7 d, 28 d, 90 d, 180 d) through 2000 KN hydraulic pressure universal testing machine in accordance with T0805-1994 in Chinese standard (JTG E51-2009), as demonstrated in Figure 4. The cylindrical sample and spherical support were placed at the center of the vertical load and loaded at the rate of 1 mm/min until damage, meanwhile recording its maximum load (PC). Then unconfined compressive strength (RC) can be obtained by Equation (1). (1) RC=PCA=PCπD2, where A is cross-sectional area (mm2), D is diameter for cylindrical samples (mm). 2.3.2. Indirect Tensile Strength In line with T0806-1994 in JTG E51-2009, indirect tensile strength tests were performed for cured cylindrical CSM samples (90 d) using a hydraulic pressure universal testing machine, as shown in Figure 4. The cylindrical sample was placed horizontally on the fixture to ensure that two battens were placed at both cross-section ends of the test sample and perpendicular to the lifting platform of the hydraulic pressure universal testing machine, then stable loading of 1 mm/min until failure. Indirect tensile strength (RT) could be calculated by recording maximum load (PT) in terms of Equation (2):(2) RT=2PTπDh(sin2α−aD), where h is sample height after immersion (mm), D is sample diameter (mm), α is central angle corresponding to half width of batten (°), a is batten width (mm). 2.3.3. Dry Shrinkage On the basis of T0854-2009 in Chinese standard (JTG E51-2009), dry shrinkage test in Figure 4 was conducted for cuboid beam CSM samples with different substitution ratios of RCB after curing for 7 d, and the dry shrinkage coefficient could be calculated to evaluate the volume shrinkage degree of samples after water loss. Firstly, both ends of the cuboid beam CSM sample were ground flat and bonded with plexiglass sheets. Then, the cuboid beam CSM sample was located on several lubricant coated glass rods in a shrinkage instrument. Next, two dial indicators should be fastened at shrinkage device ends to contact the two ends of the cuboid beam CSM sample. The shrinkage device and samples were placed in a dry shrinkage chamber, with controlled constant conditions (20 ± 1 °C, RH 60% ± 5%). After the dry shrinkage test started, the changes in micrometer and mass data were be recorded for the tested cuboid beam samples once a day during the 1st week, and once every 2 days after that. After the dry shrinkage observation, the cuboid beam samples were dried in an oven to measure their dry mass (mp). Dry shrinkage coefficient (αdi) and water loss rate (ωi) could be represented using the below equations:(3) ωi=(mi−mi+1)/mp, (4) δi=(∑j=14Xi,j−∑j=14Xi+1,j)/2, (5) εi=δi/l0, (6) αdi=εi/ωi, (7) αd=∑εi/∑ωi, where mi and mi+1 are the i-th and (i + 1)-th weighted sample mass (g), δi is the i-th dry shrinkage (mm), Xi,j and Xi,j+1 are the j-th and (j + 1)-th dial indicator readings for the i-th observation (mm), εi is the i-th dry shrinkage strain of samples (%), l0 is the length of standard samples (mm), αdi is the i-th dry shrinkage coefficient (%) and αd is the total dry shrinkage coefficient (%). 3. Results and Discussion 3.1. Unconfined Compressive Strength Testing 3.1.1. Analysis of Influence of RCB Substitution Ratios Figure 5 illustrates unconfined compressive strength results for CSM containing various RCB substitution ratios at various curing time. In general, unconfined compressive strength (RC) decreases with RCB substitution rate increasing, and the RC value increases along with curing time. As shown in Figure 5, with the increase of RCB substitution ratio, the unconfined compressive strength of all CSM groups at the same curing time generally shows a downward trend, which is consistent with the conclusions in the previous studies [12,17]. Replacing BA with RCB will form a weak area inside CSM-RCB samples, resulting in a lower unconfined compressive strength of modified CSM mixtures incorporating RCB. At the same time, the unconfined compressive strength of CSM samples is related not only to the mechanical strength of aggregates, but also to the mechanical performance of interfacial transition zone. In the unconfined compression process, the failure of CSM samples generally starts from the interfacial transition zone, but the aggregate itself is rarely crushed. The RCB fine aggregates used in this study was produced by the crushing of construction waste, and the RCB angularity is not obvious compared with natural BA aggregate. Therefore, the bite force in the interfacial transition zone formed with mortar would become weaker, which is unfavorable to the unconfined compressive strength of modified CSM incorporating RCB. With the increase of RCB substitution ratio, this kind of weak interfacial transition zone will also increase inside the CSM mixtures, so the unconfined compressive strength of modified CSM incorporating RCB decreases with the increase of RCB substitution ratio. 3.1.2. Analysis of Influence of Curing Time As illustrated in Figure 6, it is worth noting that the growth law of unconfined compressive strength of CSM-RCB samples is similar to that of ordinary CSM-BA sample without RCB, and CSM samples with different RCB substitution ratios have basically the same variation trends of unconfined compressive strength. The unconfined compressive strength of all CSM groups increased sharply during the first 7 d curing period, and then its growth rate gradually slowed down from 28 d to 180 d. The growth rates of unconfined compressive strength of CSM samples with different RCB substitution ratios are also different. This may be because the hydration product by the reaction of cement and water could have wrapped the un-hydrated components, resulting in a slowdown tendency of unconfined compressive strength enhancement. In summary, most of the measurements of the early unconfined compressive strength of the CSM samples were completed after 7 d curing, and the 28 d unconfined compressive strength of CSM samples can reach about 70% of the 180 d unconfined compressive strength. These results are consistent with those of Yan’s findings [16], indicating that RCB has an obvious effect on the early unconfined compressive strength of CSM, while the negative influence of RCB on the unconfined compressive strength of CSM would decrease gradually, varying curing time. This is because that the pozzolanic reaction in CSM usually occurs at later ages after the hydration reaction of cement, and the pozzolanic reaction rate is slower than hydration reaction rate. 3.1.3. Regression Analysis of Unconfined Compressive Strength Considering curing time and RCB substitution ratio, the unconfined compressive strength of CSM samples can be analyzed by the regression method, and the fitting equation is given by strength = 2.8861 + 1.2409 × ln(x) − 0.0159 × y, (R2 = 0.9801), where y is RCB substitution ratio. The fitting coefficients also indicate that the RCB substitution ratio has a negative effect on unconfined compressive strength, but the curing time has a positive effect. Figure 7 compares the experimental and predictive values of unconfined compressive strength results, which could be adopted for unconfined compressive strength prediction. 3.2. Indirect Tensile Strength Testing Figure 8 presents the variations of indirect tensile strength of CSM samples with different RCB substitution ratios at curing time of 90 d. As illustrated in Figure 8, as RCB substitution ratio increases, the indirect tensile strength overall presents an upward trend, which is consistent with the analysis results in the previous studies [16,29,30]. Obviously, RCB aggregate can significantly improve the indirect tensile strength of CSM samples with RCB substitution ratios from 0% to 50%; after that, the indirect tensile strength growth trend of the CSM samples becomes slow and then tends to stabilize. Compared to the CSM sample without RCB, the indirect tensile strength of CSM samples with RCB substitution ratio from 20% to 80% at curing time of 90 d increased by 17.3%, 35.3%, 54.7%, 60.4% and 62.6%, respectively. In addition, it is clearly shown that the growth speed of the indirect tensile strength for the CSM samples gradually slowed down. This result may be explained by the fact that the pozzolanic reaction usually occurs after cement hydration, and its reaction rate is relatively slow, mostly occurring during middle and late curing periods. Consequently, at 90 d curing time of the late curing period, the indirect tensile strength of modified CSM incorporating RCB would be further enhanced. 3.3. Dry Shrinkage Testing 3.3.1. Analysis of Water Loss From Figure 9, the accumulative water-loss rate curve of the CSM sample incorporating 20% RCB is slightly lower compared to the ordinary CSM-BA sample without RCB. Simultaneously, the accumulative water-loss rate of CSM at the same curing time decreased first and then increased as the RCB substitution ratio increased, and the turning point is the RCB substitution ratio of 20%. This is due to the porous surface of the RCB and microcracks accompanied with the crushing production process of RCB. Furthermore, the RCB aggregate displayed a higher water absorption, so substituting the natural BA aggregate with the RCB fine aggregate would increase the OWC of the CSM samples incorporating RCB, as presented in Figure 4. It is generally considered that the water diffusivity is positively correlated with the water content (Bakhshi et al., 2012), and the larger the water content in the early curing stage of CSM samples, the more water will evaporate in the later curing stage of CSM samples, resulting in the greater dry shrinkage and water loss rate. The accumulative water loss rate of CSM-RCB80 sample at curing time of 29 d is about twice that of the ordinary CSM-BA sample without RCB. Moreover, it is clearly seen that accumulative water loss rate curves for all CSM samples are in gradually slowing upward trends with curing time increasing. The water loss rates change fast during the early curing period and gradually slow down during the later curing period. 3.3.2. Analysis of Accumulative Strain of Dry Shrinkage The variations of accumulative strain of dry shrinkage changing with time are illustrated in Figure 10. As seen in Figure 10, except that the accumulative strain results of the dry shrinkage of CSM samples incorporated with 0% and 20% RCB are similar, the accumulative cumulative strain of the dry shrinkage of CSM samples at the same curing time generally increases with the increase of RCB substitution ratio, which is similar to the variations of accumulative water-loss rate. Concurrently, it should be noted that similar strain variations of dry shrinkage can always be observed for most CSM, including in this study [31], the accumulative strain of dry shrinkage increases along with curing time, and develops rapidly before a curing time of 10 d; however, the strain of dry shrinkage also displays an increasing trend at a gradually decreasing variation rate with time. Interestingly, there are similar variation trends in the accumulative water loss rate along with accumulative strain of dry shrinkage for CSM incorporating 20% RCB compared to ordinary CSM-BA without RCB, that is, the variation curves of CSM-RCB20 tend to move downward. These findings may be taken to indicate that adding an appropriate amount of RCB as a BA substitute could restrain dry shrinkage of CSM partly. Along with the increase in RCB substitution ratio range from 20% to 80%, however, there is an increasing trend of accumulative water-loss rate as well as an accumulative strain of dry shrinkage for CSM samples, since the evaporation of capillary water is significant, owing to its slow pozzolanic reaction rate. Hence, the RCB substitution ratio should be strictly controlled in practical engineering, and the water lost would attempt to compensate for the reduction of cracks caused by dry shrinkage during the semi-rigid base course conditioning period. 3.3.3. Relationship Analysis between Dry Shrinkage Strain and Water-Loss Rate Figure 11 provides the relationships between accumulative strain of dry shrinkage and accumulative water-loss rate among CSM samples containing different RCB substitution ratios. It can be seen from the data in Figure 11 that, with successive increases in accumulative water-loss rate, the accumulative strain of dry shrinkage of CSM samples shows a gradual rise, and the slopes of the curves incline to a rapid development. In addition, with the accumulative strain of dry shrinkage being equal, the RCB substitution ratio could increase the accumulative water-loss rate for samples, these curve slopes between accumulative strain of dry shrinkage and accumulative water-loss rate decrease with the increase of the RCB substitution ratio. These relationships may partly be explained by the lower impact of free water in macrovoids inside CSM on dry shrinkage, but the obvious impact of the dehydration of hydration products of cement and the evaporation of capillary water on dry shrinkage [32,33]. As the accumulative water-loss rate continues to grow, the evaporation of capillary water and dehydration of hydration products, which have great influence on dry shrinkage, can thereby give rise to the slope increasing. 3.3.4. Analysis of Coefficient of Dry Shrinkage Generally, the coefficient of dry shrinkage is used to reflect the sensitivity of base course materials to water, that is, the sensitivity coefficient. The more sensitive to water the base course material is, the greater its coefficient of dry shrinkage, indicating a worse crack resistance. Figure 12 provides the coefficient of dry shrinkage and average coefficient of dry shrinkage, respectively. As can be seen, the dry shrinkage coefficient of total CSM samples increases gradually as curing age increases, and the dry shrinkage curve is likely to remain steady after a longer curing time. By contrast, because of the larger water absorption of RCB, increasing RCB substitution ratio in aggregates can result in a larger coefficient of dry shrinkage. The average coefficient of dry shrinkage of CSM sample incorporating RCB of 80% is 43.5% higher than that of ordinary CSM-BA sample without RCB. Moreover, the coefficient of dry shrinkage first decreases and then increases, and its minimum value appears at the RCB substitution ratio of 20%, and the average coefficient of dry shrinkage of CSM-RCB20 is less than that of CSM-BA. This result may be explained by the fact that the water loss of macrovoids inside CSM samples has no obvious influence on dry shrinkage. As discussed above, considering the porous surface of RCB and its higher water absorption, the porosity of CSM samples would be elevated, and the water content is generally required for CSM material compaction. Meanwhile, as an important part of the CSM base construction, curing directly affects the final strength of base course and the formation of potential cracks, which should be paid great attention to. During the curing period of base course, it is necessary to frequently spray water to prevent the rapid loss of water on the surface from causing dry shrinkage cracks, and ensure that sufficient water is given to meet the needs of cement hydration. If possible, a geomembrane or geotextile can be used to cover to reduce the volatilization of water and reduce the number of spraying water. In actual pavement works, according to Chinese standard (JTG E51-2009), the semi-rigid base material is usually required to be cured for 7 d after construction to reach a certain strength. 3.4. Strength Mechanism Analysis For a deep understanding of the strength mechanism of modified CSM incorporating RCB intuitively, chemical detection methods have been used to test the pozzolanic activity. The EDTA titration method has been extensively applied as an effective means to check the cement content of CSM. The Ca2+ from the hydration product could be considered to be the same for all CSM samples due to the cement dosage in mixture, while the Ca2+ provided by the RCB aggregates has a positive correlation with the RCB substitution ratio. In the meantime, the pozzolanic materials in RCB aggregates continuously react with the hydration product (calcium hydroxide) to generate hydrated calcium silicate (C-S-H), which is insoluble in water, reducing the concentration of Ca2+. The ethylenediaminetetraacetic acid disodium (EDTA-2Na) is a chelating agent with six coordination atoms, which can combine with metal ions such as Ca2+ and Mg2+ to produce the complexes with cyclic structure due to the chelation [34]. From Figure 13, it should be noted that modified CSM containing pozzolanic components reacts slowly, divalent cation consumption as Ca2+ mainly occurs at later ages. These findings could prove the pozzolanic material of RCB aggregates, and the pozzolanic reaction rate is slower than hydration reaction rate. The continuous pozzolanic reaction will form the crystal structure, cementing into a whole and enhancing the interfacial transition zone, which has a positive effect explained from chemical reaction on the strength of the modified CSM incorporating RCB. 4. Conclusions The reutilization of RCB in CSM is a promising solution for C&D waste disposal. The present work evaluates the feasibility of CSM incorporating various RCB fine aggregate substitution ratios ranging from 0% to 80%. The experimental results and discussions lead to the following conclusions: (1) The negative influence of RCB on the unconfined compressive strength of CSM would decrease gradually, varying the curing time due to a slower pozzolanic reaction rate. In contrast, the higher the RCB substitution ratio, the larger the indirect tensile strength at 90 d curing time of the late curing period. (2) Substituting RCB for natural aggregate resulted in an overall increasing accumulative water-loss rate, accumulative strain of dry shrinkage and average coefficient of dry shrinkage, except for the CSM with 20% RCB (i.e., CSM-RCB20), which obtained an excellent dry shrinkage property, and the average coefficient of dry shrinkage reached the minimum value. (3) CSM incorporating RCB with pozzolanic activity reacts very slowly to form the crystal structure, cementing into a whole and enhancing the interfacial transition zone, which has a positive effect on the strength of modified CSM mainly at later ages. The present study demonstrated the primary feasibility of incorporating recycled crushed clay brick from C&D waste as fine aggregate into cement-stabilized macadam of pavement base, which would be a sustainable means for achieving environmental protection and resource conservation, and even reducing the carbon footprint of raw materials mining. However, more research is needed, including in-depth comprehensive performances, economic assessment and sustainability evaluation for deeper investigation into the practical applications of CSM with RCB, which is a tough work. Moreover, the possibility of the entrance of rainwater via pavement cracks is impossible to avoid, and the layer position and performance of CSM incorporating RCB should be also considered in the future studies. Author Contributions Conceptualization, D.W. and W.W.; methodology, L.W., W.W. and H.W.; validation, D.W., L.W. and X.S.; formal analysis, D.W., W.C., L.W., W.W. and X.C.; investigation, W.C., W.W., H.W. X.S. and X.C.; writ-ing—original draft preparation, W.C., W.W. and H.W.; writing—review and editing, D.W., L.W., X.S. and X.C.; project administration, W.W.; funding acquisition, W.W. and X.S. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The testing and analysis data used to support the findings of this study are included within the article. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Properties of RCB aggregate: (a) EDS spectrum; (b) XRD pattern. Figure 2 Aggregate gradation curve of CSM. Figure 3 MDD and OWC results of CSM samples. Figure 4 The flowchart of this study. Figure 5 The unconfined compressive strength of CSM with different RCB substitution ratios. Figure 6 The unconfined compressive strength of CSM with different curing times. Figure 7 Comparison of unconfined compressive strength between experimental and predictive values. Figure 8 The indirect tensile strength results of CSM. Figure 9 The accumulative water loss rate of CSM changing with time. Figure 10 The accumulative strain of dry shrinkage changing with time. Figure 11 The accumulative strain of dry shrinkage changing with accumulative water loss rate. Figure 12 The coefficient of dry shrinkage: (a) coefficient of dry shrinkage with time; (b) average coefficient of dry shrinkage. Figure 13 The EDTA consumption with curing for CSM samples. materials-15-03171-t001_Table 1 Table 1 The technical indicators of BA and RCB aggregates. Aggregates Apparent Specific Density Water Absorption (%) Flakiness Content (%) Crushed Stone Value (%) Liquid Limit (%) Plasticity Index Coarse BA 2.735 1.24 9.57 21.63 / / Fine BA 2.689 1.73 / / 18.06 4.34 Coarse RCB 2.331 17.36 9.97 41.53 / / Fine RCB 2.116 17.60 / / 37.91 8.50 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Wong C.L. Mo K.H. Yap S.P. Alengaram U.J. Ling T.C. Potential use of brick waste as alternate concrete-making materials: A review J. Clean. Prod. 2018 195 226 239 10.1016/j.jclepro.2018.05.193 2. Arulrajah A. Piratheepan J. Disfani M.M. Bo M.W. Geotechnical and geoenvironmental properties of recycled construction and demolition materials in pavement subbase applications J. Mater. Civ. 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==== Front Cells Cells cells Cells 2073-4409 MDPI 10.3390/cells11091395 cells-11-01395 Article Cytotoxicity and Wound Closure Evaluation in Skin Cell Lines after Treatment with Common Antiseptics for Clinical Use Ortega-Llamas Laura 123† https://orcid.org/0000-0002-4984-1017 Quiñones-Vico María I. 1234† García-Valdivia Marta 123 https://orcid.org/0000-0001-6852-0836 Fernández-González Ana 123* https://orcid.org/0000-0003-1311-8035 Ubago-Rodríguez Ana 1234 https://orcid.org/0000-0002-3261-6745 Sanabria-de la Torre Raquel 123 https://orcid.org/0000-0002-4186-1435 Arias-Santiago Salvador 12345 Lin Pei-Hui Academic Editor 1 Cell Production and Tissue Engineering Unit, Virgen de las Nieves University Hospital, 18014 Granada, Spain; laura.ortega.llamas@gmail.com (L.O.-L.); maribelqv@ugr.es (M.I.Q.-V.); martagv9495@gmail.com (M.G.-V.); aur@ugr.es (A.U.-R.); raquelsanabriadlt@gmail.com (R.S.-d.l.T.); salvadorarias@ugr.es (S.A.-S.) 2 Biosanitary Institute of Granada (ibs.GRANADA), 18014 Granada, Spain 3 Andalusian Network of Design and Translation of Advanced Therapies, 41092 Seville, Spain 4 Dermatology Department, School of Medicine, University of Granada, 18014 Granada, Spain 5 Dermatology Department, Virgen de las Nieves University Hospital, 18014 Granada, Spain * Correspondence: ana.fernandez.gonzalez@juntadeandalucia.es † These authors contributed equally to this work. 20 4 2022 5 2022 11 9 139512 3 2022 18 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In recent years, new therapies, such as skin cell lines injections, have emerged to promote re-epithelialization of damaged areas such as chronic ulcers or to treat patients with severe burns. Antiseptics are commonly used during wound clinical management to avoid serious infections, but they may delay the healing process due to their apparent cytotoxicity to skin cells. The cytotoxicity of ethanol, chlorhexidine digluconate, sodium hypochlorite, povidone iodine and polyhexanide was evaluated in this in vitro study on human fibroblasts and keratinocytes. Treatments were applied to each cell type culture every 48 h for 14 days. To determine the cytotoxic of antiseptics, cell viability (Live/Dead®) and cell proliferation (AlamarBlue™) assays were performed on cell monolayers. Cell migration capacity was evaluated with a wound closure assay. Results showed how chlorhexidine digluconate and ethanol significantly reduced the viability of keratinocytes and inhibited cell migration. Povidone iodine followed by chlorhexidine digluconate significantly reduced fibroblast cell viability. Povidone iodine also inhibited cell migration. Sodium hypochlorite was the least detrimental to both cell types. If epithelial integrity is affected, the wound healing process may be altered, so the information gathered in this study may be useful in selecting the least aggressive antiseptic after treatment with new emerging therapies. antiseptics cell migration cytotoxicity fibroblasts keratinocytes wound healing wound regeneration ==== Body pmc1. Introduction The skin is the first physical protective barrier in the human body against water, microorganisms, mechanical trauma, chemical substances and damage caused by ultraviolet light. It is composed of three layers: the epidermis, dermis and hypodermis [1,2]. In the epidermis, there are four types of cells that form a stratified epithelium. Keratinocytes are the most frequent, and, other less abundant cell types are found among them: melanocytes, Langerhans cells and Merkel cells. The dermis is the layer beneath the epidermis. It consists mainly of connective tissue, fibroblasts and collagen fibers. The hypodermis is the deepest layer, located below the dermis and mainly composed of adipocytes [1,2]. During the regeneration of damaged skin, processes such as cell proliferation, cell migration, secretion of growth factors and specific cytokines, among others, are present. An alteration in any of these events can delay the wound healing process and lead to a chronic wound, which may be the focus of significant bacterial colonization [3]. Burns or hard-to-heal ulcers promote bacterial colonization and biofilm formation. These infections can cause sepsis with fatal consequences for the patient. Therefore, antimicrobial treatments with antibiotics or antiseptics are often required for successful wound healing [4,5]. The correct proliferation of fibroblasts and keratinocytes during the skin regeneration process is essential. Moreover, these cells are mainly involved in the inflammatory response, one of the most crucial phases for the epithelial regeneration process [3]. In order to prevent and treat localized skin infections in clinic, the use of antiseptics has been encouraged due to the emergence of bacterial resistance to antibiotics. Antiseptics are clinically applied topically on the skin to prevent infection due to their ability to reduce microorganisms [6]. Applying antiseptics at low concentrations is recommended since, according to previous studies, high concentrations can damage the treated tissue and affect the cell viability and migration capacity of fibroblasts and keratinocytes, which can delay the healing process [6,7]. Despite their clinical use for wound healing, antiseptics and antibiotics can affect the viability of skin cells, but there are few studies analyzing the impact of antiseptics on fibroblasts and keratinocytes cultured in vitro [8,9]. The main objective of this study was to evaluate the cytotoxicity of common antiseptics for clinical use on fibroblasts and keratinocytes involved in epithelial regeneration in concentrations ranging from the concentration used in clinical practice to a 1% dilution of the stock solution. In addition, it provides information about the effect of these antiseptics on two essential aspects of the wound healing process: cell migration and proliferation. This research provides useful evidence for selecting an adequate antiseptic treatment during wound management where cell suspension or BASS are used as advanced therapy. 2. Materials and Methods 2.1. Cell Isolation and Culture Human fibroblasts (HFs) were isolated from skin samples (9 cm2) from plastic, dermatological or urological surgery with the prior consent of the patients in compliance with the requirements for human cell and tissue donation (Royal Decree-Law 9/2014, of 4 July) [9]. The dermis and epidermis were separated by mechanical processing. The dermis was incubated for 18–24 h in a 2 mg/mL solution of type I collagenase (Gibco, Thermo Fisher Scientific, Carlsbad, CA, USA). After the incubation time, the dermis was neutralized with a specific medium for dermal fibroblasts (DFM). Cell suspensions were centrifuged at 1000 rpm for 10 min. Türk (Sigma Aldrich, St. Louis, MO, USA) and Trypan Blue (Sigma Aldrich, St. Louis, MO, USA) solutions were used for cell counting and determining viability after initial processing. Fibroblasts were seeded at a density of 100,000–140,000 cells/cm2 at initial processing and at 5000–7000 cells/cm2 after passing. The immortalized human keratinocyte cell line HaCaT was used as a model to study of keratinocyte cytotoxicity [10]. HaCaT cells were seeded at a density of 10,000 cells/cm2. 2.2. Cell Viability Assays with Antiseptics HFs and HaCaT cells were seeded in 24-well plates (Thermo Fisher Scientific, Carlsbad, CA, USA) at a density of 10,000 cells/cm2. Cell culture monolayers in 24-well plates were treated with five different antiseptics which were applied for three minutes every 48 h for 14 days. For this purpose, the medium was removed before each treatment. Subsequently, 500 µL of the antiseptic solution was applied to each well. After three minutes, the wells were washed with Dulbecco’s phosphate-buffered saline solution (DPBS, Sigma Aldrich, St. Louis, MO, USA) and finally the medium was added to the treated cell wells and incubated at 37 °C, 5% CO2 until the next treatments. The antiseptics used were: 70% ethanol (Betamadrileño SL, Madrid, Spain), 2% chlorhexidine digluconate (HiBiSCRUB®, Molnlycke Health Care AB, Madrid, Spain), 0.02% sodium hypochlorite (Microdacyn, Sonoma Pharmaceuticals, CA, USA), povidone iodine 100 mg/mL (LAINCO, SA, Barcelona, Spain) and 0.1% polyhexanide (Prontosan, B Braun Medical, Barcelona, Spain), all approved for clinical use. A range of concentrations of the stock solution of each antiseptic was used: 1%, 5%, 10%, 50% and 100% (concentration used in the clinic). All solutions were diluted in DPBS. A preliminary study was used to determine the concentration which provided considerable cell viability and showed the most significant difference between different treatments. Finally, the 1% concentration of the stock antiseptic solution (0.7% ethanol, 0.02% chlorhexidine digluconate, 0.0002% sodium hypochlorite, 1 mg/mL povidone iodine and 0.001% polyhexanide) was tested three times and statistical analyses were performed with this concentration. Cell viability and proliferation were determined at days 3, 7, 10 and 14 using two different protocols: Live/Dead® Cell Viability Assay (Thermo Fisher Scientific, Carlsbad, CA, USA) and AlamarBlue assay (Invitrogen™ alamarBlue™ HS Cell Viability Reagent, Thermo Fisher Scientific, Waltham, MA, USA). 2.2.1. Live/Dead® Cell Viability Assay The Live/Dead® cell viability assay is a colorimetric assay which consists of preparing a staining solution that combines two fluorescent reagents, calcein AM (green fluorescence, Ex/Em 494/517 nm) and ethidium homodimer-1 (red fluorescence, Ex/Em 517/617 nm). It allows live cells to be differentiated from dead cells by staining them green and red, respectively. The Live/Dead® staining solution was applied on days 3, 7, 10 and 14 of treatment. Then, it was incubated in the dark for 30 min at room temperature. After the incubation time, the solution was removed, the plates were washed with DPBS, and fluorescence was measured at 405 nm using a Leica DM2000 microscope (Leica, Wetzlar, Germany). The images obtained were analyzed using ImageJ v1.47 software (National Institutes of Health, Bethesda, MD, USA) to determine the percentage of cell viability. 2.2.2. AlamarBlue Cell Proliferation Assay AlamarBlue™ HS Cell Viability Reagent is a ready-to-use resazurin-based solution which allows proliferation to be quantified from the reducing capacity of living cells [11]. On days 3, 7, 10 and 14 of treatment, alamarBlue reagent was added to each well (10% vol/vol), incubated in the dark at 37 °C, 5% CO2 for four hours and fluorescence was measured at 560/590 nm using a 96-well plate [11]. Cell proliferation was measured by the degree of reduction in the reagent and the concentration of live cells (cells/cm2) after each treatment, by establishing a standard curve. Measurements at 1% concentration of stock solutions of the antiseptics were tested three times. 2.3. Wound Closure Assay A wound closure assay, also known as a Scratch Test, was performed to determine the impact of antiseptics on cell migration and wound closure. HFs and HaCaT cells were seeded in 12-well plates (Thermo Fisher Scientific, Carlsbad, CA, USA) at a density of 10,000 cells/cm2. When the cells were confluent and adherent to the plate, the DFM was removed and each well was scratched with a sterile 10 µL pipette tip to simulate wound formation [12]. Cells were then treated for three minutes with the different antiseptics mentioned above, at a concentration of 1% of the clinically used stock solution. Control cells were left untreated. Cell debris and traces of the antiseptic solution were removed by washing with DPBS [12]. Then, the cells were incubated and images were taken of the scraped area of each well until the control was completely closed. The images were analyzed with ImageJ software. Equations (1) and (2) were used to calculate the percentage of wound closure and cell migration rate (µm/h), respectively [13]. The percentage of wound closure at hour 0 was considered 0% and the percentage of reduction of the scratched area was calculated at 6, 12 and 24 h for HFs and at 12, 24, 36 and 48 h for HaCaT cells. Wound closure was monitored until the control was completely closed in each cell line [12,13]. (1) Wound closure (%)=(At=0−AtAt=0)×100  (2) Cell migration rate (μmh)=(Wi−Wft) where “At=0” is the area of the initial wound just after scratching, “At” is the wound area after “n” hours of initial scratching, “Wi” is the average of the initial wound width in µm, “Wf” is the average of the final wound width in µm and “t” is the time of the assay (in hours) until the control wound closed [12]. 2.4. Statistical Analysis Statistical analysis of the data was carried out using the program GraphPad Prism (GraphPad Prism 8.0 Software, Inc., La Jolla, CA, USA). The data obtained are expressed as the mean ± the standard error of the mean (SEM). For the analysis, a factorial analysis of variance (ANOVA) was applied to determine the effect of each factor present. Once the ANOVA test was applied, a post hoc analysis was performed with the Tukey’s test for all factors to determine the degree of significance when comparing the factor classes. p ≤ 0.05 was considered statistically significant. 3. Results 3.1. High Concentrations of Antiseptics Cause a Total Reduction of Cell Viability in Skin Cell Lines All treatments caused high toxicity in both HaCaT cells and HFs when the concentrations used clinically were applied (70% ethanol, 2% chlorhexidine digluconate, 0.02% sodium hypochlorite, povidone iodine 100 mg/mL and 0.1% polyhexanide). At day 3, treatments with these concentrations caused 100% cell death compared to the untreated control. When the concentration of the stock solutions for the antiseptics tested was reduced to 50%, 10% or 5%, the impact on cell viability remained highly noticeable in all treatments. A significant reduction in cell viability was observed when comparing each treatment with the untreated control. However, after treatment with sodium hypochlorite, a smaller reduction in cell viability was observed. Sodium hypochlorite was found to be the least toxic antiseptic on skin cell lines at the concentrations tested (Figures S1–S10). 3.2. Common Antiseptics for Clinical Use Diluted to 1% Affect Cell Viability in Skin Cell Lines 3.2.1. Chlorhexidine Digluconate and Ethanol Affected the Viability of HaCaT Cells More Than the Other Treatments The concentration range tested was reduced to 1% of the stock solutions. At this concentration, treatments with chlorhexidine digluconate and ethanol in HaCaT cells significantly reduced the percentage of living cells compared to the other treatments, turning out to be the most toxic antiseptics for this cell line. However, polyhexanide and sodium hypochlorite reflected similar cell viability to the untreated control (Figure 1 and Table S1). Significant differences were observed between the different antiseptics and days of treatment (Figure 2). At day 3, 7 and 10, significant differences were observed in cell viability after treatment with chlorhexidine digluconate compared to ethanol, sodium hypochlorite, polyhexanide and the control (Figure 2a–c). At day 14, a significant reduction in cell viability was observed after treatment with chlorhexidine digluconate and ethanol compared to sodium hypochlorite, polyhexanide, povidone iodine and the control (Figure 2d). 3.2.2. Povidone Iodine and Chlorhexidine Digluconate Reduced HFs Cell Viability Compared to the Other Treatments In HFs at 1% concentration of antiseptics in the stock solution, chlorhexidine digluconate, ethanol and povidone iodine significantly reduced the percentage of live cells compared to the other treatments. Povidone iodine and chlorhexidine digluconate were the antiseptics that caused the greatest impact. However, treatment with sodium hypochlorite reflected a cell viability similar to the untreated control (Figure 3 and Table S2). At day 3, cell viability was significantly reduced after treatment with chlorhexidine digluconate and povidone iodine, compared to ethanol, sodium hypochlorite, polyhexanide and the control (Figure 4a). At day 7, a significant decrease in cell viability was seen after treatment with povidone iodine compared to the other treatments (Figure 4b). At days 7, 10 and 14, a significant reduction in cell viability was observed after treatment with chlorhexidine digluconate compared to ethanol, sodium hypochlorite, polyhexanide and the control (Figure 4b,d). In addition, after treatment with povidone iodine, a significant reduction in cell viability was observed at days 10 and 14 compared to ethanol, sodium hypochlorite, polyhexanide and the control (Figure 4c,d). 3.3. Common Antiseptics for Clinical Use Reduce Cell Growth and Proliferation in Skin Cell Lines 3.3.1. Chlorhexidine Digluconate and Povidone Iodine Significantly Affect HaCaT Cells Growth Compared to the Other Treatments In HaCaT cells, no significant differences in the number of live cells/cm2 were observed compared to the control and between the different treatments at day 3. On day 7, a significant reduction in cell density was observed after treatment with chlorhexidine digluconate compared to ethanol, sodium hypochlorite, polyhexanide and the control, and after treatment with povidone iodine compared to the control. On day 10, a significant decrease in cell density was observed after chlorhexidine digluconate and povidone iodine treatments with respect to ethanol, sodium hypochlorite, polyhexanide and the control. At day 14, treatment with ethanol produced a significant reduction in cell density compared to the control. Likewise, on day 14, a significant reduction in cell density was observed after treatment with chlorhexidine digluconate compared to treatments with sodium hypochlorite, polyhexanide and the control. Finally, on day 14, treatment with povidone iodine caused a significant reduction in cell density compared to treatment with polyhexanide and the control (Figure 5a,b). 3.3.2. Chlorhexidine Digluconate and Povidone Iodine and Ethanol Had Greater Impact on HF Proliferation Compared to the Other Treatments In HFs, significant differences were observed in the number of live cells/cm2 according to the treatment applied and compared to the control. At day 3, cell density was significantly reduced after chlorhexidine digluconate and povidone iodine treatments compared to ethanol, sodium hypochlorite, polyhexanide and the control. At day 7, treatment with ethanol significantly reduced cell density compared to sodium hypochlorite and the control. Furthermore, at day 7, treatment with chlorhexidine digluconate and povidone iodine resulted in significantly lower cell density than cells treated with sodium hypochlorite and the control. Finally, at day 7, treatment with polyhexanide significantly reduced cell density compared to the control and sodium hypochlorite. On days 10 and 14, after treatment with ethanol, chlorhexidine digluconate, povidone iodine and polyhexanide, a great impact on the number of live cells/cm2 was observed, a significant reduction in cell density was found compared to sodium hypochlorite and the control. Finally, at day 14, a significant reduction in cell density was observed after treatment with sodium hypochlorite compared to the control (Figure 5c,d). 3.4. Common Antiseptics for Clinical Use Affect Cell Migration of Skin Cell Lines 3.4.1. Chlorhexidine Digluconate Inhibits Cell Migration Capacity in HaCaT Cells Compared to Other Antiseptic Treatments Cell migration in HaCaT cells treated with chlorhexidine digluconate was inhibited and wound closure did not occur compared to the control which closed at 48 h. With the other treatments, wound closure could be observed at 48 h and there was no significant difference between these treatments and the control (Figure 6). The percentage of wound closure was calculated 0, 12, 24, 36 and 48 h after applying each treatment. Between 0 and 12 h there were no significant differences in the percentage of wound closure between any treatment and the control. At 24 h, a significant inhibition of cell migration was observed after treatment with chlorhexidine digluconate compared to ethanol, sodium hypochlorite, povidone iodine, polyhexanide and the control. At 36 h, significant inhibition of wound closure was also observed after treatment with chlorhexidine digluconate compared to treatments with sodium hypochlorite, povidone iodine and the control. Finally, at 48 h, significant inhibition in wound closure was observed for cells treated with chlorhexidine digluconate compared to ethanol, sodium hypochlorite, povidone iodine, polyhexanide and the control (Figure 7a and Table S3). In addition, a reduction in the cell migration rate was observed after treatment with chlorhexidine digluconate, however, no significant differences were observed between the different treatments and the control (Figure 7b and Table S4). 3.4.2. Povidone Iodine Inhibits Cell Migration Capacity in HFs Compared to Other Antiseptics Cell migration was inhibited in HFs treated with povidone iodine and wound closure did not occur compared to the control which closed at 24 h. Regarding the other treatments, wound closure could be observed at 24 h and there was no significant difference between these treatments and the control (Figure 8). The percentage of wound closure after scraping was calculated 0, 6, 12 and 24 h after the application of each treatment. At 6 h, a significant inhibition of cell migration was observed in fibroblasts treated with povidone iodine compared to the control. At 12 and 24 h, the inhibition of wound closure after treatment with povidone iodine was significant compared to treatments with ethanol, chlorhexidine digluconate, sodium hypochlorite, polyhexanide and the control (Figure 9a and Table S5). Therefore, 24 h after treatment, all wounds made by the scratch test had closed similarly to the control, except for fibroblasts treated with povidone iodine, which had a great impact on the migration capacity of this cell type preventing wound closure. In addition, a significant reduction in the cell migration ratio was observed after treatment with povidone iodine compared to the other treatments and the control. The cell migration speed was also significantly reduced after treatment with ethanol compared to treatments with chlorhexidine digluconate and polyhexanide (Figure 9b and Table S6). 4. Discussion Antiseptic treatments are crucial for preventing infections during epithelial regeneration in the wound healing process [14]. Current therapies in major burn patients usually include epidermal substitutions with cultured epithelial autografts and allografts, which have shown good results, but the main disadvantage is the time required to obtain them due to the long culture time [15]. New strategies are emerging as alternatives. Among these new therapies are devices based on suspensions of epithelial cells (fibroblasts and keratinocytes among others) obtained by biopsies and without the need for cell culture to promote the re-epithelialization process of the damaged area [15,16]. Therefore, due to the emergence of new therapies it is interesting to evaluate the impact of the most commonly used antiseptics in the clinic on skin cell lines, such as fibroblasts and keratinocytes. Some studies make evident the inherent cytotoxicity of antiseptics in vitro, while in vivo or clinical results seem to be controversial. According to our results, povidone iodine, chlorhexidine digluconate and ethanol are the most cytotoxic antiseptics in skin cell lines. Chlorhexidine digluconate and ethanol reduced cell viability to 0% after 14 days of treatment in HaCaT cells. On the other hand, the viability of HFs was also reduced to 0% from day 7 after treatment with povidone iodine and from day 10 after treatment with chlorhexidine digluconate. In contrast, treatments with sodium hypochlorite did not significantly affect cell viability which remained similar to the untreated control in both cell types. In addition, in the wound closure assay, the cytotoxicity of chlorhexidine digluconate and povidone iodine could be observed, since after treatments with these antiseptics, the cell migration capacity of HaCaT cells and HFs, respectively, was inhibited, preventing wound closure compared to other treatments and the control which closed at 48 h in HaCaT cells and at 24 h in the case of HFs. Povidone iodine is a common clinical antiseptic, effective against a wide variety of microorganisms [14,17]. It is used to treat ulcers, open wounds, burns and even on healthy skin in preparation for surgery. However, despite its germicidal power, previous studies have shown that high concentrations of povidone iodine are toxic to proliferating cells and can slow down the healing process [14]. Moreover, according to previous in vitro studies, even low concentrations can have cytotoxic effects and cause an attenuation of cell proliferation in fibroblasts, as well as in osteoblasts and myoblasts [18]. For example, Hajská et al. evaluated the potential toxic effect of 16 antiseptic treatments on murine and human dermal fibroblasts [19]. Interestingly, 24 h-treatment with povidone iodine at a concentration of 100 mg/mL was categorized as a semi-toxic agent. In this line and in agreement with the results of our study, Thomas et al. showed that povidone iodine reduces both migration and proliferation of fibroblasts in a dose-dependent fashion [20]. Furthermore, Hirsch et al. evaluated the cytotoxic effect of five min-application of different antiseptic treatments and concluded that povidone-iodine-based antiseptics showed the lowest bactericidal potential and the strongest cytotoxicity against human skin cells [21]. Our preliminary test results showed that concentrations above 1% caused total elimination of cell growth. Even low concentrations of povidone iodine produced a significant inhibition of cell viability and migration capacity in HFs that could be observed in both viability and wound closure assays. Therefore, the cytotoxic effect of povidone iodine on fibroblasts, a critical cell type for wound healing, was confirmed. There is some controversy in the literature about the possible cytotoxic mechanisms which may be causing this inhibition of HF viability and migration capacity. For instance, Chou et al. [22] treated primary human corneal fibroblasts and a human corneal epithelial cell line with 0.1–5% of povidone-iodine for one minute and found that the mitochondrial dehydrogenase and intracellular esterase activities as well as cell membrane integrity were abolished by the treatment. They stated that the changes in membrane characteristics caused cells to become resistant to sodium dodecyl sulfate (SDS) lysis and cells did not respond to external stimuli, thus concluding that povidone iodine at 0.1% or higher causes immediate cell death through fixation rather than apoptosis or necrosis. This cytotoxic mechanism is also reported by Lee et al. [23] who support this theory that cellular fixation is the primary mechanism of cell death from povidone iodine, rather than apoptosis or necrosis. Moreover, they showed how the cytotoxic effect of this treatment is maximal at 30 min exposure to 1% povidone-iodine. However, Sato et al. [24] reported that povidone-iodine treatment induced apoptosis and necrosis in HeLa cells and rat oral mucosa after one or two days of exposure. In this line, Nomura et al. [25] studied whether apoptosis or necrosis might cause cell death after povidone iodine treatment as they found that povidone induced a significant accumulation of cells in the G0/G1 phase of the cell cycle at their IC50 concentration. Nevertheless, after analyzing the induction of apoptotic signals, they found that povidone-iodine did not induce apoptosis. Therefore, there are different hypotheses about the responsible mechanisms underlying povidone cell death. Further research including molecular techniques is necessary to confirm or refute these hypotheses. Chlorhexidine digluconate is another of the most commonly used antiseptics for the preventing infections, preparing the surgical site, burns wounds, etc. [9,26]. According to the literature, this antiseptic is applied to the skin for three minutes and it has been shown to have inherent cytotoxicity [8,27,28]. This fact has been observed in our study. Monolayer cultures of HFs and HaCaT cells treated with chlorhexidine digluconate at the concentration used in the clinic (2%) caused 100% cell death. Even when the concentration was reduced to 0.02%, cell viability was significantly reduced in both cell types. Furthermore, the a three-minute application of chlorhexidine digluconate inhibited cell migration capacity in HaCaT cells. Therefore, the inherent cytotoxicity of these antiseptics can be confirmed. One possible mechanism underlying this cytotoxicity reported in the literature is oxidative stress. In this way, Zhang et al. [29] reported that chlorhexidine induced reactive oxygen species (ROS) production and an upregulated expression of Nrf2, pNrf2, and HO-1 in HaCaT cells. These proteins are involved in maintaining of the intracellular oxidation-reduction homeostasis. Other authors supported the theory that chlorhexidine alters the cell cycle. Coelho et al. [30] evaluated the cell cycle of chlorhexidine-exposed human gingival fibroblasts and revealed a decrease in the number of cells in G0/G1 and an increase in the number of cells in S phase in a concentration dependent manner. This is supported by Verma et al., who found that after 1% chlorhexidine treatment, the majority of this type of cells were found to be in G0/G1 phase with very few cells in the S phase and G2/M phase of the cell cycle [31]. Finally, another reported mechanism of cell damage is a reduction in metabolic activity [30]. In our study, the mechanisms responsible for the cell viability and migration reduction were not evaluated. Therefore, further research is necessary to provide additional information in this regard. Sodium hypochlorite is an effective antiseptic against bacteria, fungi and viruses, indicated for the care of wounds such as ulcers or burns [32]. Previous studies have shown that the viability of HFs can be reduced by up to 30% after applying sodium hypochlorite at high concentrations or undiluted [32]. In our preliminary study, different concentrations were tested, from undiluted antiseptic, which did affect cell viability, to 1% of the stock solution. Analysis of the 1% dilution of the sodium hypochlorite stock solution showed that this antiseptic does not affect cell viability in skin cell lines compared to the other treatments. In this study, a three-minute application was established according to clinical instructions. However, application time and antiseptic concentration are variable parameters in the literature. For instance, Ortega-Peña et al. demonstrated that sodium hypochlorite at 0.057% showed a recovery trend of the fibroblast population after 24 hours of treatment [33]. Accordingly, sodium hypochlorite could be selected as the least aggressive for this cell type compared to the other antiseptics tested. The new therapies cited above for wound healing mention strategies based on cell suspensions, so studying the cytotoxic effect directly on skin cell lines may provide useful information about the clinical use of these antiseptics after treatments with these new therapies. In addition to advanced therapies based on skin cell lines, other cell sources can be used to stimulate wound healing and regeneration. The application of human perinatal cells is a promising strategy for wound treatment since they have anti-inflammatory, immunomodulatory, anti-cancer, anti-fibrotic, anti-apoptotic, and anti-oxidant effects [34]. These cells, which include human mesenchymal stem cells (hMSCs) derived from the umbilical cord (hUC-MSCs), placenta (hPMSCs) and amniotic membrane (hAMSCs) among others, have shown promising results in preclinical and clinical studies on cutaneous wound healing [34,35,36]. Therefore, these advanced therapies can benefit from the insight of this research since studying the impact of common clinical antiseptics not only in skin cell lines but also on other sources of cells such as MSCs, would provide useful information about wound care protocols after administering these therapies. However, a limitation of this in vitro study is that it evaluates the cytotoxic effect of antiseptics on cells cultured in monolayer. This system is not representative of a well-perfused human wound. In fact, human tissue has a higher tolerance for external influences, including antiseptics, than cultured human cells [28]. Surgical wounds are a well-vascularized environment, comprised of multiple cell types and soft-tissue layers, and may have a higher tolerance for antiseptic solutions than in vitro tissue cultures [37]. Therefore, in vitro culture of skin cell lines may not accurately represent the impact on a biological system, where this cytotoxicity would be less pronounced. In this sense, research on how antiseptic treatments affect wound healing in vivo showed a different impact compared to that observed in vitro. For example, Wang et al. found that povidone iodine treatment enhanced wound healing through increased expression of transforming growth factor beta, neovascularization and re-epithelialization in a rodent model of acute skin wounds [38] and this treatment also increased the healing rate of human chronic leg ulcers [39]. To conclude, further research including in vivo studies or combinations of cytotoxicity assays with germicidal activity assays at low concentrations of antiseptics, is still required to determine the concentration and application time that do not affect cellular integrity and remain effective against microorganisms, in order to better characterize the clinical significance of the results obtained in vitro. Acknowledgments We gratefully acknowledge financial support from the Ministry of Health and Families of the Andalusian Regional Government (PIGE-0242-2019) and from the Carlos III Health Institute (PI17/02083). The work of María I. Quiñones Vico was supported by a predoctoral fellowship (BOE22/10/2019) from the Ministry of Science, Innovation and Universities of Spain. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells11091395/s1, Figure S1: LIVE/DEAD® images of HaCaT cells after ethanol treatment and control at days 3, 7, 10 and 14; Figure S2: LIVE/DEAD® images of HaCaT cells after chlorhexidine digluconate treatment and control at days 3, 7, 10 and 14; Figure S3: LIVE/DEAD® images of HaCaT cells after sodium hypochlorite treatment and control at days 3, 7, 10 and 14; Figure S4: LIVE/DEAD® images of HaCaT cells after polyhexanide treatment and control at days 3, 7, 10 and 14; Figure S5: LIVE/DEAD® images of HaCaT cells after povidone iodine treatment and control at days 3, 7, 10 and 14; Figure S6: LIVE/DEAD® images of HFs after ethanol treatment and control at days 3, 7, 10 and 14; Figure S7: LIVE/DEAD® images of FHs after chlorhexidine digluconate treatments and control at days 3, 7, 10 and 14; Figure S8: LIVE/DEAD® images of HFs after sodium hypochlorite treatments and control at days 3, 7, 10 and 14; Figure S9: LIVE/DEAD® images of HFs after sodium hypochlorite treatments and control at days 3, 7, 10 and 14; Figure S10: LIVE/DEAD® images of HFs after povidone iodine treatments and control at days 3, 7, 10 and 14; Table S1: Mean cell viability percentage ± SEM for each treatment and control in HaCaT cells at days: 3, 7, 10 and 14; n = 3; Table S2: Mean cell viability percentage ± SEM for each treatment and control in HFs at days: 3, 7, 10 and 14; n = 3; Table S3: Mean wound closure percentage ± SEM for each treatment and control in HaCaT cells at hours; 12, 24, 36 and 48; n = 3; Table S4: Average cell migration rate (µm/h) ± SEM after each treatment and control in HaCaT cells. n = 3; Table S5: Mean wound closure percentage ± SEM for each treatment and control in HF at hours; 6, 12 and 24; n = 3; Table S6: Average cell migration rate (µm/h) ± SEM after each treatment and control in HFs n = 3. Click here for additional data file. Author Contributions Conceptualization, M.I.Q.-V., A.F.-G. and S.A.-S.; methodology, L.O.-L., M.G.-V. and M.I.Q.-V.; software, M.I.Q.-V. and A.U.-R.; validation, M.I.Q.-V., A.F.-G. and A.U.-R.; formal analysis, M.I.Q.-V. and A.U.-R.; investigation, L.O.-L., M.G.-V., M.I.Q.-V. and R.S.-d.l.T.; resources, L.O.-L. and R.S.-d.l.T.; data curation, L.O.-L. and M.I.Q.-V.; writing—original draft preparation, L.O.-L.; writing—review and editing, M.I.Q.-V., A.F.-G. and A.U.-R.; visualization, L.O.-L.; supervision, M.I.Q.-V. and S.A.-S.; project administration, A.F.-G. and S.A.-S.; funding acquisition, S.A.-S. All authors have read and agreed to the published version of the manuscript. Funding The work of María I. Quiñones Vico is supported by a predoctoral fellowship (BOE 22/10/2019) from the Ministry of Science, Innovation and Universities of Spain. This study is part of her doctoral research in the Biomedicine’s program of University of Granada. This research has received competitive funding in the call for grants for the financing of Research, Development and Innovation in Biomedicine and Health Sciences in Andalusia, for the year 2019 (PIGE-0242-2019) and from the Carlos III Health Institute (PI17/02083). Institutional Review Board Statement The study was approved by the Provincial Ethics Committee of Granada (Spain). Informed Consent Statement Informed consent was obtained from all surgery donors in compliance with the requirements for donation of human cells and tissues (Royal Decree-Law 9/2014, of 4 July). Data Availability Statement Data is contained within the article. Conflicts of Interest The authors declare no conflict of interest. Abbreviations BASS Bioengineered Autologous Skin Substitute CHLO Chlorhexidine DFM Dermal Fibroblast Medium DPBS Dulbecco’s phosphate-buffered saline HFs Human Fibroblasts hMSCs Human Mesenchymal Stem Cells hAMSCs Human Amniotic Membrane derived MSCs hPMSCs Human Placenta derived MSCs hUC-MSCs Human Umbilical Cord derived MSCs ROS Reactive Oxygen Species SDS Sodium Dodecyl Sulfate SEM Standard Error of the Mean Figure 1 LIVE/DEAD® images of HaCaT cells after each treatment at 1% of stock solution and the control at days 3, 7, 10 and 14. (a–d) HaCaT cells after ethanol (0.7%) treatment at days 3, 7, 10 and 14, respectively. (e–h) HaCaT cells after chlorhexidine digluconate (0.02%) treatment at day 3, 7, 10 and 14, respectively. (i–l) HaCaT cells after sodium hypochlorite (0.0002%) treatment at days 3, 7, 10 and 14, respectively. (m–p) HaCaT cells after povidone iodine (1 mg/mL) treatment at days 3, 7, 10 and 14, respectively. (q–t) HaCaT cells after polyhexanide (0.001%) treatment on days 3, 7, 10 and 14, respectively. (u–x) Control (without treatment) at days 3, 7, 10 and 14, respectively. Dead cells are represented in red and live cells in green. n = 3. Magnification 10×. Figure 2 Statistical analysis of cell viability in HaCaT cells after antiseptic treatment on days 3, 7, 10 and 14. Bar graph of cell viability percentage in HaCaT cells at (a) day 3, (b) day 7, (c) day 10, and (d) day 14 of treatment. * p ≤ 0.05; ** p ≤ 0.01; **** p ≤ 0.0001. n = 3. p values ≤ 0.05 were considered statistically significant. Figure 3 LIVE/DEAD® images of HFs after each treatment with 1% antiseptic stock solution and the control at days 3, 7, 10 and 14. (a–d) HFs after ethanol (0.7%) treatment at days 3, 7, 10 and 14, respectively. (e–h) HFs after chlorhexidine digluconate (0.02%) treatment at days 3, 7, 10 and 14, respectively. (i–l) HFs after sodium hypochlorite (0.0002%) treatment at days 3, 7, 10 and 14, respectively. (m–p) HFs after povidone iodine (1 mg/mL) treatment at days 3, 7, 10 and 14, respectively. (q–t) HFs after polyhexanide (0.001%) treatment at days 3, 7, 10 and 14, respectively. (u–x). Control at days 3, 7, 10 and 14, respectively. Dead cells are represented in red and live cells in green. n =3. Magnification 10×. Figure 4 Statistical analysis of cell viability in HFs after antiseptic treatment on days 3, 7, 10 and 14. Bar graph of cell viability percentage in HFs at (a) day 3, (b) day 7, (c) day 10, and (d) day 14 of treatment. ** p ≤ 0.01; **** p ≤ 0.0001. n = 3. p values ≤ 0.05 were considered statistically significant. Figure 5 Graphical representation of cell density in HaCaT cells (a,b) and HFs (c,d) after each treatment and the control. (a) Line graph of cell density in HaCaT cells after each treatment and the control at days 3, 7, 10 and 14. (b) Bar graph with statistical analysis of cell density in HaCaT cells after antiseptic treatment and the control at days 3, 7, 10 and 14. (c) Line graph of cell density in HFs after each treatment and the control at days 3, 7, 10 and 14. (d) Bar graph with statistical analysis of cell density in HFs after antiseptic treatment and the control at days 3, 7, 10 and 14. * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001. n = 3. p values ≤ 0.05 were considered statistically significant. Figure 6 Wound closure assay on HaCaT cells (a–e) HaCaT cells after ethanol (0.7%) treatment at 0, 12, 24, 36 and 48 h after scratching, respectively. (f–j) HaCaT cells after chlorhexidine digluconate (0.02%) treatment at 0, 12, 24, 36 and 48 h after scratching, respectively. (k–o) HaCaT cells after sodium hypochlorite (0.0002%) treatment at 0, 12, 24, 36 and 48 h after scratching, respectively. (p–t) HaCaT cells after povidone iodine (1 mg/mL) treatment at 0, 12, 24, 36 and 48 h after scratching, respectively. (u–y) HaCaT cells after polyhexanide (0.001%) treatment at 0, 12, 24, 36 and 48 h after scratching, respectively. (z,aa–ad) Control at 0, 12, 24, 36 and 48 h after scratching, respectively. n = 3. Figure 7 (a) Percentage of wound closure ± SEM for each treatment and the control in HaCaT cells at: 0, 12, 24, 36 and 48 h. (b) Bar graph of average cell migration rate (µm/h) after each treatment and the control in HaCaT cells. ns: no significant differences, * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; n = 3. p values ≤ 0.05 were considered statistically significant. Figure 8 Wound closure assay on HFs. (a–d) HFs after ethanol (0.7 %) treatment at 0, 6, 12, and 24 h after scratching, respectively. (e–h) HFs after chlorhexidine digluconate (0.02%) treatment at 0, 6, 12, and 24 h after scratching, respectively. (i–l) HFs after sodium hypochlorite (0.0002%) treatment at 0, 6, 12, and 24 h after scratching, respectively. (m–p) HFs after povidone iodine (1 mg/mL) treatment at 0, 6, 12, and 24 h after scratching, respectively. (q–t) HFs after polyhexanide (0.001%) treatment at 0, 6, 12, and 24 h after scratching, respectively. (u–x) Control at 0, 6, 12, and 24 h after scratching, respectively. n = 3. Figure 9 (a) Percentage of wound closure ± SEM for each treatment and control in HFs at: 0, 12, 24 and 36 h. (b) Bar graph of average cell migration rate (µm/h) after each treatment and control in HFs. ns: no significant differences, * p ≤ 0.05; ** p ≤ 0.01; **** p ≤ 0.0001; n = 3. p values ≤ 0.05 were considered statistically significant. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Jia T. Qiao W. Yao Q. Wu W. Kaku K. 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23094730 ijms-23-04730 Article The Role of Telocytes and Telocyte-Derived Exosomes in the Development of Thoracic Aortic Aneurysm https://orcid.org/0000-0003-3373-8194 Aschacher Thomas 12* Aschacher Olivia 3 Schmidt Katy 4 https://orcid.org/0000-0002-7200-4145 Enzmann Florian K. 5 Eichmair Eva 2 Winkler Bernhard 1 Arnold Zsuzsanna 1 Nagel Felix 6 https://orcid.org/0000-0002-4641-7202 Podesser Bruno K. 6 Mitterbauer Andreas 7 https://orcid.org/0000-0001-8331-6497 Messner Barbara 2 Grabenwöger Martin 1 https://orcid.org/0000-0001-7257-110X Laufer Günther 2 Ehrlich Marek P. 2 https://orcid.org/0000-0001-8529-1166 Bergmann Michael 7 Rhee Won Jong Academic Editor 1 Department of Cardiovascular Surgery, Clinic Floridsdorf and Karl Landsteiner Institute for Cardio-Vascular Research, 1210 Vienna, Austria; bernhard.winkler@gesundheitsverbund.at (B.W.); zsuzsanna.arnold@gesundheitsverbund.at (Z.A.); martin.grabenwoeger@gesundheitsverbund.at (M.G.) 2 Department of Cardiac Surgery, Medical University of Vienna, 1090 Vienna, Austria; eva.eichmair@meduniwien.ac.at (E.E.); barbara.messner@meduniwien.ac.at (B.M.); guenther.laufer@meduniwien.ac.at (G.L.); marek.ehrlich@meduniwien.ac.at (M.P.E.) 3 Department of Plastic, Reconstructive and Aesthetic Surgery, Medical University of Vienna, 1090 Vienna, Austria; olivia.aschacher@meduniwien.ac.at 4 Center for Anatomy and Cell Biology, Medical University of Vienna, 1090 Vienna, Austria; katy.schmidt@meduniwien.ac.at 5 Department of Vascular Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria; fkenzmann@gmail.com 6 Department of Biomedical Research, Medical University of Vienna, 1090 Vienna, Austria; felix.nagel@cardio.lbg.ac.at (F.N.); bruno.podesser@meduniwien.ac.at (B.K.P.) 7 Department of General Surgery, Medical University of Vienna, 1090 Vienna, Austria; andreas.mitterbauer@meduniwien.ac.at (A.M.); michael.bergmann@meduniwien.ac.at (M.B.) * Correspondence: thomas.aschacher@gesundheitsverbund.at; Tel.: +43-1-277-00-74316 25 4 2022 5 2022 23 9 473004 4 2022 23 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). A hallmark of thoracic aortic aneurysms (TAA) is the degenerative remodeling of aortic wall, which leads to progressive aortic dilatation and resulting in an increased risk for aortic dissection or rupture. Telocytes (TCs), a distinct type of interstitial cells described in many tissues and organs, were recently observed in the aortic wall, and studies showed the potential regulation of smooth muscle cell (SMC) homeostasis by TC-released shed vesicles. The purpose of the present work was to study the functions of TCs in medial degeneration of TAA. During aneurysmal formation an increase of aortic TCs was identified in human surgical specimens of TAA-patients, compared to healthy thoracic aortic (HTA)-tissue. We found the presence of epithelial progenitor cells in the adventitial layer, which showed increased infiltration in TAA samples. For functional analysis, HTA- and TAA-telocytes were isolated, characterized, and compared by their protein levels, mRNA- and miRNA-expression profiles. We detected TC and TC-released exosomes near SMCs. TAA-TC-exosomes showed a significant increase of the SMC-related dedifferentiation markers KLF-4-, VEGF-A-, and PDGF-A-protein levels, as well as miRNA-expression levels of miR-146a, miR-221 and miR-222. SMCs treated with TAA-TC-exosomes developed a dedifferentiation-phenotype. In conclusion, the study shows for the first time that TCs are involved in development of TAA and could play a crucial role in SMC phenotype switching by release of extracellular vesicles. telocytes aorta thoracic ascending aortic aneurysms exosomes cellular senescence miRNA SMC-phenotype switching ==== Body pmc1. Introduction Thoracic ascending aortic aneurysms (TAA) are in most cases asymptomatic, but can present an increased risk of aortic dissection and can consequently lead to death [1]. Anatomy and function of the ascending aorta are complex and dependent on a normal extracellular matrix (ECM). Aortic ECM remodeling can lead to an increase of collagen fibers and loss of vascular smooth muscle cell (vSMC) contractility. The remodeling processes can be induced by chronic oxidative stress [2]. This repetitive cellular stress leads to cellular senescence, which includes the secretion of pro-inflammatory cytokines, growth factors, and extracellular matrix degradation proteins [3]. However, most vSMCs in a healthy aortic wall exhibit a contractile phenotype that maintains vascular tone. During the formation of TAA vSMCs can dedifferentiate into a synthetic phenotype, which is characterized by a decrease in contractile protein expression, degradation of ECM, and increased production of matrix metalloproteinases (MMPs) [4]. This process of vSMC dedifferentiation is called SMC phenotype switching [5]. These characteristic changes of TAA tissue may result in decreased arterial structural stability, thereby increasing the chance for development of aortic aneurysm and leading to a potentially lethal dissection [6,7]. Telocytes (TCs) are a recently defined interstitial cell type [8], and can be found in most organs [8,9]. TCs were detected in a wide range of tissues including the heart and cardiac valves [10,11,12], small blood vessels [13], and in other major organ systems and tissues [14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Most recently, we described the presence of TCs in the human ascending aortic tissue [28]. Moreover, detailed analysis clarified cell marker specificity for CD34, ckit, PDGF-a/b and a-SMA in cell cultures of isolated aortic TCs [28]. With negative staining of CD90 and CD31, they clearly differed from pericytes, and other cells found in aortic tissue [2,28,29]. TCs play different roles from mechanical support to immune surveillance depending on their specific locations within different tissues [19,20]. The morphology of TCs is characterized by spindle- or stellar-shaped small cell bodies, and a variable number of prolongations, called telopodes (Tps). Telopodes, in turn, include thick sections, the podoms, which contain mitochondria, endoplasmic reticulum, and other organelles and the podomers, which are the thin extended sections. Together they form an interstitial network around the vasculature with homo- and heterocellular junctions to release shed vesicles and exosomes, which might have the ability to control the blood vessels. However, the relation of TCs to blood vessels and vSMCs, as well as participation to intercellular signaling, tissue renewal, and regeneration was described previously [22,23,24]. Most widely described expression markers for TCs include CD34, Vimentin, ckit and platelet-derived-growth-factor receptor-® (PDGFR-®) [25]. A physiological role for vascular TCs is assumed by their expression of Krüppel-like factor-4 (KLF-4), vascular endothelial growth factor (VEGF), and angiogenic miRNAs [26,28]. TCs express MMP-9 and play an essential role in ECM degradation during angiogenesis [27]. The human aorta is noted to have a highly heterogeneous microenvironment with many CD34 expressing cell types [30], the most important of which are pericytes and endothelial progenitor cells (EPCs). Whereby, pericytes and supra-vasa are double positive for CD34 and CD90 (fibroblast marker), EPCs are positive for CD34 and CD31 [31]. The presence of EPCs in the adventitia is thought to be associated with their potential for neovascularization and capability of smooth muscle lineage progression [29]. In this context, EPCs which are additionally CD133 positive (synonym AC133, Prominin) were described as ‘primitive circulating stem cells’ [32,33]. CD34+/CD133+ EPCs express high levels of VEGF [34,35], and lose CD133 expression during an early stage of differentiation [36]. Since SMC phenotype switching occurs early in the development of aortic disorders, and the mechanisms by which this occurs are not completely understood, our overall goal was to investigate the regulatory role of TCs on SMC phenotype and aneurysm disorders. We hypothesized that the number of TCs, and their functional role in releasing vesicles plays a crucial role in SMC phenotype regulation during aneurysm formation. Here, we evaluated and characterized TCs and EPCs in patients with either aortic root dilatation or sporadic TAA. 2. Results 2.1. Increased Number of TCs in Aortic Aneurysm Disease To investigate the role of TCs in aortic aneurysms, we investigated the number of TCs using immunohistochemistry. Immunofluorescence is an ideal method to measure the occurrence of TCs by detection of telocyte-specific markers in aortic tissue sections, as described previously [28], and TAA samples were obtained from patients undergoing heart transplantation or elective aneurysm surgery. Patient baseline characteristics are presented in Table 1. It should be noted that TAA patients differ with respect to cardiovascular risk factors from patients undergoing heart transplantation. A significance was also observed in chronic renal failure, ejection fraction, and a higher proportion of patients taking aspirin and ®-blocker, whereby the significance is due to obtained HTA specimens from heart transplant recipients and their chronic heart insufficiency. Table 2 and Table 3 show patient’s characteristics with focus on aortic diameter and intimal thickness measured by immunohistochemistry. We identified significantly more CD34+/ckit+ TCs in tissue from TAA patients (n = 29) compared to HTA samples (n = 23) (Figure 1A). In detail, we observed an increase of TCs in adventitial, medial, and intimal layers of the diseased aorta (p < 0.001) (Figure 1A,B). The highest percentage increase in TC number was detected in the intimal layer of TAA samples (Figure 1A). Moreover, correlation studies show a significance in the number of TCs correlated to the aortic diameter, the higher the aortic diameter of aneurysmal aorta, the higher the presence of TCs (adventitia, R = 0.346 p < 0.01; media, R = 0.454 p < 0.01; intima, R = 0.2819 p < 0.05) (Figure 1C,D). Double staining of well-known TC markers (CD34, ckit, vimentin, and PDGFR-®), as well as the lack of endothelial marker CD31 confirmed TC specificity (Supplemental Figures S1 and S2). Notable, the thickness of the tunica intima showed a significant decrease after aneurysmal formation compared to healthy aorta (p < 0.001) (Figure 1E). In the non-aneurysmatic aorta of the HTA samples TC were predominantly found in the tunica adventitia as compared to tunica media and tunica intima (p < 0.001 and p < 0.05, respectively). This corresponds to our previous observations, where aortic TCs were mainly located in the adventitial layer and their perivascular network [28,32]. In summary, during aneurysmal formation and advanced stage of thoracic aortic disease, the number of TCs increased significantly with noticeable distribution of TCs to the intimal layer. Most recently, CD34+/CD133+ EPCs and CD34−/ckit+ hemopoietic stem cells (HSCs) have been described to form a discrete progenitor cell niche for the development of thoracic aortic disease [29]. Immunostaining showed distribution of CD34+/CD133+ positive cells in aneurysmal tissue (Figure 1F). Further, we observed ckit positive, and CD34 negative cells HSCs in HTA samples, which were significantly decreased after aneurysmal formation in TAA cells (Figure 1G,H). 2.2. Comparison, Characterization, and Analysis of Released Exosomes of HTA- and TAA-TCs To further investigate various protein expression markers in HTA and TAA samples, TCs were isolated from HTA and TAA samples as previously described [8,9,28] (Figure 2A). Cells were sorted based on CD34 and ckit protein expression and purification of CD90 negative cells to distinguish TCs from dedifferentiated vSMCs or fibroblasts. After reaching ~80% of confluence at cell culture, we performed mRNA and protein analysis. The TC phenotype was characterized by mRNA expression of ckit, vimentin, PDGFR-α/-®, KLF-4, and CD29/integrin β–1 in TCs (Figure 2B). TCs isolated from TAA specimens showed a significant increase of vimentin (p < 0.01), PDGFR-α (p < 0.05), and KLF-4 (p < 0.01) compared to TCs isolated from HTA specimens. Moreover, Western blot analysis was conducted in two individual HTA-TC cell cultures and two individual TAA-TC cell cultures, to analyze protein levels of TC expression markers as well as vSMC-dedifferentiation markers (Figure 2C). This revealed higher protein levels of ‘contractile’ SMC-phenotype marker SM-calponin, α-SMA in HTA-TCs, whereby ‘synthetic’-marker vimentin and KLF-4 were decreased in HTA-TCs compared to TAA-TCs. Using TEM, we observed a close interaction of TCs via their telopodes with endothelial cells (ECs), fibroblasts (FBs), and vSMCs. Interestingly, we found exosome-containing multivesicular carriers (MC) primarily located in TC podomeres (Figure 2D(a)), suggesting a paracrine activity of TCs. Higher magnifications showed possible communication between TC and vSMC with synthetic phenotype morphology (Figure 2F(a–c)). We observed an invagination of telopodes by vSMC and formed caveolae close to the cell convergence of TC and vSMC. We next isolated exosomes from HTA and TAA cell cultures and analyzed their phenotype using immunoblotting (Figure 2G–I). The quality, concentration (particle per ml) and particle diameter (nm) of exosomes was confirmed by qNano analysis (Figure 2G). Exosomes were characterized by KLF-4 and VEGF-A protein. These proteins were increased in TAA cell cultures versus HTA cultures (Figure 2H and Supplement Figure S1). Exosomal surface marker CD34, CD63, HSP90, and TSG101 revealed exosome purity (Figure 2H,I). MicroRNA (miR) expression profiles were performed to analyze miRs involved in phenotype-switching of vSMCs (Figure 2F). TCs isolated from TAA showed a significant increase of miR-146a, miR-221, and miR-222, which were previously described for dedifferentiated vSMC, p < 0.001, p < 0.01, and p < 0.01, respectively (Figure 2J). Whereas miR levels found in contractile vSMCs were decreased, miR-143 and miR-145, p < 0.05 and p < 0.05, respectively. In TAA-exosomes, reduced levels of miR-21 (p < 0.05) and miR-145 (p < 0.01), and increased levels of miR-146a (p < 0.01), miR-221 (p < 0.05), and miR-222 (p < 0.001) confirmed miR levels which are mainly found in dedifferentiated vSMCs. 2.3. Exosomes Isolated from TCs Influence vSMC Phenotype Characteristics We then investigated whether exosomes, isolated from HTA- versus TAA-cultured TCs, had a regulatory potential to markers involved in SMC dedifferentiation. When vSMC were cultured in the presence of exosomes isolated from TAA-TCs they tended to become less spindle-shaped, and to develop the more irregular morphology associated with synthetic vSMCs, compared to exosomes isolated from HTA-TCs or exosome isolation procedure from cultured vSMCs as control (Figure 3A). vSMCs treated with TAA-exosomes showed a trend of decreased mRNA expression of smooth muscle-cell myosin-heavy-chain 11 (SMMHC), -SMA, and SM-calponin compared to control groups (Figure 3C). TAA-exosome treatment of vSMCs significantly increased mRNA expression of SMC-dedifferentiation markers collagen-1 (p < 0.01), vimentin (p < 0.01), and KLF-4 (p < 0.01) (Figure 3B). Cell-metabolism assays (MTT) and cell proliferation assays demonstrated an increase of vSMC cell proliferation in vSMCs treated with TAA-exosomes (p < 0.01), compared to control group (Figure 3C,D). MTT assays were conducted 1 and 4 days after treatment start (Figure 3C). Moreover, these effects were very similar to those observed in samples which dedifferentiation process of vSMCs were induced by recombinant PDGF-BB protein in a concentration of 20 ng treatment [36]. Cell proliferation assay was used to exclude off-target effects by several dilutions of undiluted (0), 2-times, 4-times, and 6-times exosome concentrations compared to control supernatant of vSMCs (Figure 3D). Similar to RT-qPCR results, ELISA measurement of collagen-I confirmed an increase of protein concentrations after TAA-exosome or PDGF-BB treatment, p < 0.01 and p < 0.001, respectively (Figure 3E). Additionally, wound healing assay was performed on vSMCs treated with isolated TC-exosomes or TC-conditioned medium (TCM) (Figure 3F,G). Migration distance of vSMCs was increased after 24 h of treatment with TAA-exosomes (p < 0.01), whereby, a significant increase was observed after treatment with both ‘whole’ cell culture supernatant of sorted HTA- and TAA-TCs, compared to specific controls (p < 0.01) (Figure 3F). The assumption that exosomes may initiate or regulate SMC-dedifferentiation would merit gain-of-function and loss-of-function analysis of miR. 3. Discussion The adventitia of the ascending thoracic aorta represents a specialized perivascular niche. The occurrence of TCs in aortic human tissue has been demonstrated previously [28]. For the first part, we classify that TCs are playing a crucial role in enhancement of aneurysm formation depending on the expression profile of aortic TCs and their released exosomes. For the first time, we classify TCs by their antigenic profile, function, and location associated with aneurysm formation and show a significant increase of TCs in the diseased aorta, which correlated to advanced pathogenicity. Based on the expression profile of TCs and the high occurrence of well-known factors during aneurysmal formation (e.g., KLF-4 and VEGF-A) [1], we characterize their potential for smooth muscle lineage progression. This finding is supported by a cell culture experimental subset showing that the treatment of vSMC with TC-exosomes leads to dedifferential-phenotype changes. This release of TC-related factors is involved in vSMC phenotype switching, which could play a crucial role in the development of instable aortic tissue. Popescu et al. discussed a TCs stromal progenitor cell analogy, which means that TCs can participate in immune surveillance and mesenchymal differentiation functions [37]. However, the classification and functional characterization of vasa-vasorum-associated perivascular progenitor cells in human aorta describe a subset of CD34+/CD31+/α-SMA− endothelial progenitor cells which are mainly abundant in aortic adventitia [29]. The functionality of EPCs is described in neovascularization of cardiovascular diseases [36,38]. Our current finding of the regulatory function of TCs and their markers’ similarity to HSCs such as EPCs, provide additional support that these unique cell populations may play a distinct and important role in aortic diseases [39]. Concordantly, we found that the onset of CD34−/ckit+-progenitor cells were decreased with disease progression, and CD133+/CD34+ double positive cells were detected in aortic media. However, the molecular identification of TCs and the distinction from endothelial progenitor cells (EPCs) are presented by negative staining of CD34+/ckit+/CD133− for TCs. Nevertheless, it remains open how an attraction of TCs from the adventitial layer to the intimal layer occurs. Is the attraction of EPCs in aneurysmatic tissue, the origin for TCs, or are they only involved in the induction of TCs? Further studies are required to clarify the relationships between the functionality of EPCs or the infiltration and differentiation of EPC-subsets to TCs, regarding the near identically expression profile. In recent years, the therapeutic effect of exosomes derived from TCs have been investigated intensely in multiple disease models and show that these exosomes exert functions similar to those of stem cells, including promoting tissue remodeling and expression of pro-angiogenic miRNA that regulate tissue repair via a paracrine-mediated mechanism in the vasculature [34]. However, to date, few studies have aimed to determine the functional role of exosomes derived from TCs in angiogenesis and tissue remodeling in vascular disorders [9,34]. In this study, we frequently observed exosomes in the immediate vicinity of pits, which suggests that the endocytosis of these vesicles may pass messages from one cell to another through exosomes. Thus, we isolated exosomes from TCs and evaluated their functions. Since it has been reported that TCs have proangiogenic functions, we hypothesized that exosomes of TCs would exert an influence on SMCs. The diversity of vSMC function is reflected in their contractile and synthetic phenotype, which are characterized by substantial differences in marker expression, morphology, and activity [34,40,41,42]. When TCs derived from aneurysmal human tissue were compared to those of healthy aortic tissue, we detected an increase of specific mRNA expression for a synthetic vSMC phenotype. Whereby markers for a contractile vSMC phenotype were downregulated in western blot. HTA-exosomes and TAA-exosomes are equally found in the vimentin and KLF-4 mRNA expression, their cell metabolism and proliferation, whereas TAA-exosomes interestingly show more disrupting features of diseased aortic tissue, collagen secretion, and regenerative potential (Figure 3), which correlates with previous findings of aortic cells found during aneurysm formation. The development of a TAA or HTA is a process of several pathomechanisms, still not clearly understood. Initial triggers release a destructive process of oxidative stress, apoptosis or dedifferentiation of vSMCs, and proteolytic fragmentation of the ECM. The same triggers are found to release TCs exosomes. The adverse environment now increases the reactivity and boosts oxidative stress by producing reactive nitrogen and oxygen species, which aggravates apoptosis or dedifferentiation of vSMCs leading to aneurysmal formation. Our findings, supported by a cell culture experimental subset, show that the treatment of vSMC with TC-exosomes leads to dedifferential-phenotype changes. Further investigations are needed to identify a direct link between oxidative stress and TCs exosome release leading to aneurysmal formation. The characterization and comparison of exosomes derived from HTA- and TAA-TCs revealed a high amount of VEGF-A and KLF-4 proteins in shed vesicles. Further, the human genome encodes 1048 miRNAs, which virtually regulate all biological processes [43]. Specific miRNA expression patterns have been previously described for TC-exosome treatment, where they were responsible for complex regulatory function driven by telocytes [44]. In our study, miRNA in aortic TC and TC-released exosomes showed an expression profile which is consistent to previous observations [38,45], but in TAA-samples we detected an increased shift of miRNAs involved in dedifferentiation of vSMCs (miR-146a, miR-221 and miR-222) [46]. Keeping in mind some of the roles attributed to the TC, such as the juxta/paracrine activity, the ability to remodel the collagen fibrils and to control tissue homeostasis [47], it could be speculated that the increased expression of vimentin, PDGFR-a and KLF-4 represents a potentiation of these functions. Specifically, KLF-4 is upregulated by shear stress [29,41], a typical EC differentiation stimulus found during aneurysm formation, and inhibits SMC maturation [48]. When we cultured vSMCs in the presence of exosomes isolated from TCs, we observed a dedifferentiation phenotype, which includes cell morphological changes, increased metabolism, and a significant increase of synthetic-phenotype related mRNA expression in vSMCs (KLF-4, vimentin, and collagen-I). The synthetic phenotype of vSMCs plays a crucial role in progressive aneurysm formation in human and is associated with high expression of VEGF-A, vimentin, KLF-4, and ECM degrading enzymes [37]. Besides our findings and others of vimentin and VEGF-A expression in TCs, most recently the expression of metalloproteinase-9 (MMP-9) was also attributed to TC [28,29]. MMP-9 is essential for degradation of ECM components [39]. In conclusion, the study shows for the first time that TCs are involved in development of TAA. Whereby the significantly high number of TCs found in TAA seems to be the decisive factor leading to an imbalance of homeostasis and to an uncontrolled remodeling of the tissue. The characterization of their exosome-related function and location in TAA, as well the observation of progenitor cell recruitment of EPCs-subsets, show the potential of aortic TCs for involvement in smooth muscle lineage progression during aneurysm formation. Our results provide preliminary evidence that aortic TCs have therapeutic potential for the treatment of TAA and the prevention of fatal progression of the disease. 4. Materials and Methods 4.1. Patient’s Specimens Human aortic tissue samples (52 samples) were obtained either during heart transplantation (23 samples), or during surgical procedure which involved aneurysm surgery of TAA (29 samples). Patients with ongoing endocarditis, sepsis, recent infectious disease, or genetic disorders (e.g., Marfan’s syndrome) were excluded. Additionally, the intake of immunomodulation therapy (e.g., cortisone) or anti-tumor therapy was an exclusion criterium. After receiving the specimens, aortic tissue was sliced, and one part was snap frozen and stored in liquid nitrogen, one part was fixed in 4.5% formalin, one part in 2.5% glutaraldehyde. The remaining tissue was subjected to cell isolation. This study was approved by the Ethical Committee of the Medical University of Vienna (EK 1280/2015). Written informed consent was obtained from all patients prior to inclusion in the study. The investigation conformed to the principles that are outlined in the Declaration of Helsinki regarding the use of human tissue. 4.2. Isolation and Sorting of Aortic Telocytes, Fibroblasts and vSMCs Isolation of human fibroblasts (n = 3), vSMCs (n = 4), and TCs (HTA, n = 10; TAA, n = 10) was performed according to our established protocol with few modifications as outlined in this section [28]. Briefly for isolation of TCs, aortic tissue was collected in RPMI-1640 cell culture medium supplemented with 10% fetal bovine serum [FBS], 25 mM HEPES as well as 100 IU/mL penicillin, and 100 UI/mL streptomycin (medium and all supplements were obtained from Gibco/Life Technologies Ltd., Pailey, UK). Aortic samples were dissected and minced into small pieces of about 1 mm3 and incubated for 3 h at 37 °C with mixture of collagenase type IV (Gibco) and elastase (porcine pancreas, Calbiochem/Merk, Darmstadt, Germany) dissolved in TC-cell culture medium (TC-CCM): high glucose (HG)-DMEM (Lonza Bioscience Solutions, Cologne, Germany) supplemented with 1.5 mM HEPES and 20% FBS (Gibco/Life Technologies Ltd., Vienna, Austria). The isolated cells were filtered through a cell strainer (100 µm), centrifuged and re-suspended in TC-CCM. 90 min after seeding, the supernatant, which mainly contains the majority of TCs, was removed and transferred into a new 24-well plate containing TC-CMM. Cells were cultivated at 37 °C in humidified atmosphere (5% CO2). The morphology of TCs was observed and pictures taken using a phase-contrast microscope (Olympus CKX41 with Olympus SC-20 camera, Olympus Life Science, Vienna, Austria). For CD34+/ckit+/CD90− TC-cell sorting to distinguish TCs from dedifferentiated vSMCs or fibroblasts., cultured aortic cells were collected in FACS buffer (PBS including 0.1% FBS), and 25 mM HEPES was added to the FACS buffer to prevent it from becoming basic and maintain the pH between 7.0–8.0, and 1 mM–5 mM EDTA to the buffer to prevent formation of aggregates. Cells were stained with 1× or 0.5× of the antibody concentration used for immunocytochemistry, followed by appropriate secondary antibody, if necessary (Supplement Table S1). Cells were re-suspended at a concentration of 2–3 × 107 cell/mL. Immediately before sorting, cells were filtered through a 70 µm mesh filter to prevent clogging and collected in HG-DMEM supplemented with 30% FBS afterwards. Cells were analyzed directly by western blot or cultivated in standard culture medium depending on the cell type (see above). Cell sorting was performed with the BD FACSAria™III Fusion (Software: BD FACSDiva Version 8.0.2, Becton, Dickinson and Company, San Jose, CA, USA). 4.3. Immunofluorescence Staining and Microscopy For immunocytochemical staining, cells were grown on 8 chamber slides (Falcon® glass slide with polystyrene vessel, Fa. Falcon/Szabo Scandic, Vienna, Austria) and fixed in 4% paraformaldehyde for 10 min. Followed by permeabilization in 0.1% saponine, and blocked with PBS (ThermoFisher Scientific, Waltham, MA, USA) containing 1% bovine serum albumin (BSA), 10% goat serum and 0.3 M Glycine for 1 h at 37 °C. Samples were incubated with 2–5 µg of primary antibody O/N according to the listed working dilutions (Supplement Table S1), followed by incubation with an appropriate secondary antibody including 1 µg/mL Dapi (ThermoFisher Scientific, Waltham, MA, USA) or 4 µg/mL Hoechst 34580 (Bio-Connect B.V., TE Huissen, The Netherlands), and mounted in Prolong Gold Antifade (Molecular Probes, ThermoFisher Scientific, Waltham, MA, USA). Negative controls were obtained following the same protocol, but omitting the primary antibodies, and the usage of purified anti-mouse and anti-rabbit IgG (Abcam, Cambridge, UK). For immunohistological staining, aortic tissue samples were fixed in 4% PBS-buffered formaldehyde. The tissues were embedded in paraffin, deparaffinized with HistoSAV and rehydrated in a descending series of ethanol. Following heat-induced antigen retrieval with citrate-buffer (pH 6), the sections were blocked (10% goat serum, 1% BSA, and 0.1% Tween-20 in PBS) at RT for 60 min. The antibody incubations corresponded to ICC staining protocol (see above). The density of TCs was calculated as the mean of total number of TCs/total number of DAPI stained nuclei per cross section. For confocal microscopy, we used a LSM700 Meta microscopy laser system, the appropriate filters, and a ZEN 2010 microscopy system (Zeiss, Inc. Jena, Munich, Germany). For spot counting and co-localization analysis images were analyzed with the CellProfiler™ cell image analysis software. 4.4. Transmission Electron Microscopy Samples of the aortic wall of approx. 2 cm2 were fixed immediately after surgery in 2.5% glutaraldehyde. After 6 h, samples were cut into smaller pieces of 1 mm3 and washed three times in 0.1 M cacodylate buffer. The secondary fixation was carried out either for 2 h. in 2% osmium tetroxide/0.1 M cacodylate buffer or for 2 h. in 1% reduced osmimum tretroxide, both at room temperature. Dehydration and embedding in Epon resin followed standard procedures. Ultrathin sections (70 nm) were cut with a Reichert UltraS microtome and contrasted with uranyl acetate and lead citrate. Images were acquired with a FEI Tecnai20 electron microscope equipped with a 4 K Eagle CCD camera and processed using the Adobe Photoshop software package. 4.5. Microvesicle and Exosome Isolation Microvesicle and exosome isolation was performed as previously described [28]. Briefly, HTA-TC-, TAA-TC-, and vSMC-cells were grown in FCS-free culture medium for 24 h. The cell suspension was centrifuged at 480× g at 4 °C for 5 min to remove any intact cells, followed by a 3200× g spin at 4 °C for 20 min to remove dead cells. To isolate shedding microvesicles (sMVs), the supernatant was centrifuged at 10,800× g at 4 °C for 20 min in an Optima L80 ultracentrifuge with a SW41Ti rotor (Beckman Coulter, Mississauga, ON, Canada). The pellet, containing sMV, was washed once with PBS−/− and ultracentrifuged at 10,800× g for 30 min. The pellet was dissolved in fresh medium for immediate use or stored at −80 °C for western blot analysis. The remaining culture medium was transferred to ultracentrifuge tubes and sedimented at 110,000× g at 4 °C for at least 75 min. The supernatant constituting exosome-free medium was removed and the pellets containing exosomes plus proteins from media were resuspended in PBS. The suspension was centrifuged at 100,000× g at 4 °C for at least 60 min to collect final exosome pellets. The quality of exosomes was confirmed by qNano analysis (Izon Science Ltd., Oxford, UK). Protein content of the exosome pellet was quantified using the Bradford protein assay kit (Biorad, Hercules, CA, USA). Pellet was dissolved in vSMC-specific cell culture medium for cell growth analysis and scratch assay, or the pellet was analyzed for miRNA and protein detection. 4.6. ELISA, Wound Healing Assay and EZ4U Measurements Collagen-I level was measured with a Soluble Collagen Assay Kit according to manufacturer instructions (ab241015, Abcam, Cambridge, UK). For wound healing experiments a scratch assay was used. Indicated cells were plated with ~80% intensity in 6-wells and after attachment, medium was changed after 24 h. Scratch was conducted in an appropriate size and cells were washed and treated with TC- or vSMC conditioned culture medium (CCM) from separate cell cultures, or with isolated exosomes resolved in appropriate cell culture medium. Images were conducted after 0, 12, and 24 h. Cell viability and cell proliferation was assessed using an EZ4U kit (Biomedica MP, Vienna, Austria) or cell counting kit (CCK)-8 assay (Sigma-Aldrich, Taufkirchen, Germany) according to manufacturer’s instructions. Cells were seeded in ~25% cell density and after attachment, cells were treated with indicated exosomes or vSMCs-control dissolved in vSMC culture medium. For EZU4 assay, the measurements were conducted 24 h. and 96 h. after treatment start. For CCK-8 assay, measurements were conducted 72 h. after treatment start. For cell growth analysis and collagen-I measurements, a recombinant human 20 ng PDGF-BB protein (ab79746, Abcam, Cambridge, UK) was used as positive control. 4.7. miRNA and mRNA Isolation and Real-Time PCR (RT-qPCR) For mRNA of all samples, RNA was isolated using Trizol (PeqGOLD TriFast, Peqlab, VWR, Vienna, Austria) followed by purification with the E.Z.N.A. Microelute Total RNA Kit (Omega Bio-Tek, VWR, Vienna, Austria), including the optional DNA digestion step (RNase-free DNase I Set, Omega Bio-Tek, VWR, Vienna, Austria) according to manufactures’ instructions. For RT-qPCR, RNA was reverse transcribed using the QuantiTect Reverse Transcription Kit (Qiagen, Hilden, Germany), followed by qPCR with the GoTaq RT-qPCR Master Mix (Promega, Mannheim, Germany) according to manufacturer’s instructions. For miRNA measurements, RNA was isolated from exosome pellet with miRNeasy kit (Quiagen, Hilden, Germany) according to manufacturer’s instruction. cDNA was generated with the miScript II RT Kit and was used as a template for real-time PCR with the miScript SYBR Green PCR Kit (Quiagen, Hilden, Germany) in accordance with the manufacturer’s protocol and a gene-specific probe in a 7500 Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). The relative expression level for each miRNA was computed using the comparative CT method [31]. miRNA expression was normalized to small nucleolar RNA U6. For mRNA analysis, samples were normalized to the geometric mean of two reference genes (GAPDH, RPLP0). Primer sequences are listed in Supplement Table S2. Acknowledgments The authors would like to thank Diyala Shihadih for careful English proofreading reading and constructive comments on this manuscript. Supplementary Materials The following are available online at https://www.mdpi.com/article/10.3390/ijms23094730/s1. Click here for additional data file. Author Contributions T.A. and O.A.: Conceptualization; Supervision; Methodology; Analysis; Data curation; Project administration; Software; Visualization; Funding acquisition; Writing—original draft; Writing—review & editing. K.S., E.E., F.K.E., B.W., Z.A., A.M., F.N. and B.M.: Methodology; Analysis; Data curation; Visualization; Resources; Writing—review & editing. B.K.P., G.L. and M.P.E.: Resources; Funding acquisition. M.G. and M.B.: Conceptualization; Resources; Funding acquisition; Writing—review & editing. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Medical-Scientific Fund of the Mayor of Vienna, grant number “AP15115”. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Medical University of Vienna (1280/2015 on 2015/02/06). Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflicts of Interest The authors confirm that there are no conflict of interest. Figure 1 Human aneurysmal tissue showed an increase of TCs and EPCs depending on aneurysm size. (A) Statistical analysis of TCs per cross-sectional (number of CD34+/ckit+ TCs/number of total cells) in healthy thoracic aortic (HTA, n = 23) or thoracic ascending aneurysm (TAA, n = 29) tissue. Specific layers of aorta are shown. (B) Representative image of double-positively TCs in all three aortic layers, adventitia (Adv; on the left), media (Med; in the middle), and transition zone of media, intima (Int), and endothelium (End; on the right), are presented. TCs are indicated by narrows. Magnification shows morphology of a TC in medial layer. Scale bar, 20 µm. CD34, red; ckit, green; cell nuclei, Hoechst, blue. (C) Correlation study through Pearson’s linear regression analysis of the aortic diameter in mm and percentage of TCs per cross sectional area. Location of TCs was separated into adventitial (adv, blue), medial (med, red) and intimal (int, green) layer. R and p values are given on the top. (D) Diagram of TCs detected depending on aortic diameter (size in mm) in healthy thoracic aortic (HTA, n = 23) or thoracic ascending aneurysm (TAA, n = 29) tissue. Data are mean ± SD. (E) Intimal thickness was reduced in TAA samples (n = 29) compared to HTA (n = 23). Intimal thickness is given in µm. (F) Representative images of immunostaining of EPCs (left) located in medial layer were double stained with CD133 (green) and CD34 (red) with TC-like morphology. CD34+/ckit+ TCs staining (CD34, red; ckit, green) is shown on the right. Single immunofluorescence images are presented on the right of each merged image. Nuclei were counterstained with Hoechst (blue). Scale bar, 50 µm. Statistical analysis in healthy thoracic aortic (HTA, n = 23) or thoracic ascending aneurysm (TAA, n = 29) tissue (G) and representative image (H) of ckit-positive and CD34-negative HSC-subset detected in human aortic tissue. CD34− EPCs were analyzed and given for intima/media and adventitia of aortic vessel separately (G). (H) Immunofluorescence showed ckit+ EPCs next to endothelial tube with double-positive TCs. Magnifications present morphological round to oval ckit+ EPC (right upon), double-positive TC (*) (right middle), and EPC close to endothelial tube with TCs (right bottom). CD34, red; ckit, green; nuclei, blue (Hoechst). Figure 2 Characterization of isolated and sorted TCs and exosomes from HTA (n = 23) and TAA (n = 29) specimens. (A) Representative image of isolated and CD34+/ckit+ sorted TCs from aortic tissue. In the magnification TC showed typical morphology with oval cell body (Asterisc), long thin processes including intermitted telopodes (arrows) shown by light microscopy 14 days after isolation. (B) mRNA expression profile of aortic TC marker genes (KIT, VIM, PDGFRA, PDGFRB, KLF4, and ITGB1) confirmed TC phenotype and showed differences between cells isolated from healthy aortic specimens (HTA-TCs, n = 10) compared to aneurysmal aortic specimens (TAA-TCs, n = 10). Bars indicate the relative expression of each mRNA normalized to GAPDH and RPLP0. Data are mean ± SD of three independent experiments. (C) Western blot analysis of TCs isolated from two individual HTA-aortic samples and two from TAA-aortic samples. Six protein markers which are involved in phenotype switching of SMCs were analyzed (ckit, SM-calponin, α-SMA, CD29/integrin ®-1, and KLF-4). ACTB was used as loading control; Primary antibodies and the observed molecular weight (kDa) are given on the left. Statistical calculation given by HTA/TAA ratio are given on the right. (D–F) Representative transmission electron micrographs of medial layer in human aortic specimens. The connections between TCs and (D-a) endothelial cell (EC), and (D-b) fibroblast (FB) embedded between collagen fibers (Col) is shown. Preparation artifact (Asterix). (E) Cell convergence of TC and vSMC from synthetic phenotype is shown. vSMC-phenotype was characterized by their found intracellular filament order. (F) Magnifications of TC telopode (Tp) to synthetic vSMC (a) revealed active intake of caveolae (b) and invaginated telopode (INV) and mitochondria (asterisks) by vSMC (c). MC, multivesicular cargos; ECM, extracellular matrix; TC, telocyte. (G) The quality, concentration (particle per ml) and particle diameter (nm) of exosomes was confirmed by qNano analysis (Izon instrument, UK) (n = 7). (H,I) Representative western blots of isolated HTA- and TAA-exosomes are shown. Cell lysate was used as control. Exosome-specific soluble factors (VEGF-A, KLF-4, CD34 and α-SMA) (H) as well as surface proteins (CD63, HSP90 and TSG101) (I) were analyzed. ACTB and GAPDH were used as loading control; Primary antibodies and the observed molecular weight (kDa) are given on the left of each blot. (J) Micro RNA (miR) expression profile in HTA (black, n = 23) and TAA (grey, n = 29) isolated exosomes. Expression of some miR which are involved in SMC-phenotype switching were measured by qRT-PCR. Bars indicate the relative expression of each miR normalized to U6 small nuclear RNA (RNU6B) and SNORD44. Data are mean ± SD of two independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001. Figure 3 Effect of exosomes isolated from aneurysmal telocytes (TCs) on proliferation, metabolism, and phenotype specific mRNA expression of aortic vascular smooth muscle cell (vSMC)-cell culture. Exosomes were isolated from isolated TCs from healthy aortic tissue (HTA, n = 10) or thoracic ascending aneurysm (TAA, n = 10). (A) Morphological changes shown by light microscopy and α-SMA reengagement by immunofluorescence in SMCs treated with TC-exosomes compared to SMC control in isolated aortic cell culture containing vSMCs and fibroblasts (FB). CD90 (FB-marker), red; α-SMA (vSMC-marker), green; DAPI (cell nucleus), blue. Scale bar of light microscopy 20 µm. (B) Quantitative RT-PCR of SMC-phenotype specific mRNA expression after HTA-exosomes or TAA-exosomes compared to vSMC-control. Synthetic phenotype mRNA expression (COL1A1, VIM, and KLF4) were increased, whereby contractile phenotype mRNA expression (MYH11, ACTA, and CNN1) showed no differences after treatment with TAA-exosomes. Bars indicate the relative expression of each mRNA normalized to GAPDH and RPLP0. Data are mean ± SD of two independent experiments. **, p < 0.01. (C) Effect of HTA- and TAA-exosomes compared to vSMC-control tested in aortic vSMCs by MTT assay after 1 and 4 days of treatment. PDGF-BB treatment was used as positive control. ns, non-significant. Data are mean ± SD of four independent experiments. (D) Dilution-depended exosome-induced cell proliferation tested in aortic SMCs. OD values resulting from CCK-8 assay, HTA- and TAA-exosomes were compared to vSMC control. OD, optical density. Data are mean ± SD of four independent experiments. *, p < 0.05; **, p < 0.01. (E) Collagen-I measurements after different exosome or control treatments after 3 days tested in vSMCs with PDGF-BB as positive control. Data are mean ± SD of three independent experiments. (F,G) Cell migration (scratch wound healing assay). (F) Values of percentage wound closure ± SEM (n = 3). Different exosome treatment or TC-conditioned medium (TCM) treatment were compared to vSMC medium as control group 24 h after treatment start. (G) Representative images are shown from three independent experiments at time points beginning (0 h), 12 h, or 24 h. Blue area defines the areas lacking cells, initial scratch line shown by dashed lines (wound area, ImageJ). Scale bar, 20 µm. ijms-23-04730-t001_Table 1 Table 1 Characteristics of the study population. Study Population HTA TAA p Value (n = 52) (n = 23) (n = 29) Demographic, risk factors, and comorbidities Age (years) (range) 58.6 (20–79) 52.2 (20–69) 63.8 (36–79) <0.01 female, n (%) 15 (28.8) 5 (18.9) 10 (34.5) 0.26 Body mass index (BMI), n (range) 26.8 (18–41) 24.9 (19–30) 28.3 (18–41) <0.01 Adipositas (BMI > 30), n (%) 11 (21.2) 2 (8.7) 9 (31.0) <0.05 Smoker, n (%) 10 (19.2) 0 (0) 10 (34.5) <0.01 Hypertension, n (%) 32 (61.5) 9 (39.1) 23 (79.3) <0.01 Dyslipidaemia, n (%) 24 (46.2) 10 (43.5) 14 (48.3) 0.42 Chronic renal failure, n (%) 10 (19.2) 8 (34.8) 2 (6.9) <0.01 Diabetes, n (%) 6 (11.5) 3 (13.0) 3 (10.35) 0.39 COPD, n (%) 9 (17.3) 2 (8.7) 7 (24.1) 0.07 Positive family history, n (%) 2 (3.8) 1 (4.4) 1 (3.5) 0.44 Ejection fraction (<50%), n (%) 30 (57.7) 23 (100) 7 (24.1) <0.01 Therapeutics Oral diabetes therapy, n (%) 3 (5.8) 2 (8.7) 1 (3.5) 0.23 Statins, n (%) 15 (28.8) 9 (39.1) 6 (20.7) 0.13 Aspirin, n (%) 16 (30.8) 10 (43.5) 6 (20.7) <0.05 Beta-Blocker, n (%) 25 (48.1) 16 (69.6) 9 (31.0) <0.01 ACE-Inhibitor, n (%) 20 (38.4) 9 (39.1) 11 (37.9) 0.47 COPD, chronic obstructive pulmonary disease. ijms-23-04730-t002_Table 2 Table 2 Correlation coefficients (r) of localization of TCs to patient’s baseline characteristics. % Telocytes in T. Adventitia % Telocytes in T. Media % Telocytes in T. Intima Correlation Coefficients (r) p Value Correlation Coefficients (r) p Value Correlation Coefficients (r) p Value Age 0.369 <0.01 0.283 <0.05 0.371 <0.01 Gender 0.317 <0.05 −0.026 n.s. 0.177 n.s. Body mass index (BMI) 0.344 <0.01 0.342 <0.01 0.134 n.s. Adipositas (BMI >30) 0.348 <0.05 0.316 <0.05 0.046 n.s. Smoker 0.416 <0.01 0.273 n.s. −0.113 n.s. Hypertension 0.373 <0.01 0.431 <0.01 0.334 <0.05 Dyslipidaemia 0.175 n.s. 0.121 n.s. 0.051 n.s. Statins −0.075 n.s. 0.092 n.s. −0.051 n.s. Chronic renal failure −0.152 n.s. −0.240 n.s. −0.240 n.s. Diabetes 0.072 n.s. 0.172 n.s. 0.079 n.s. Oral diabetes therapy 0.014 n.s. 0.006 n.s. 0.042 n.s. COPD 0.122 n.s. 0.333 <0.05 0.190 n.s. CVD −0.070 n.s. 0.136 n.s. −0.071 n.s. Ejection fraction (<50%) −0.420 <0.01 −0.439 <0.01 −0.360 <0.05 Aspirin −0.188 n.s. −0.260 n.s. −0.136 n.s. COPD, chronic obstructive pulmonary disease; CVD, coronary vessel disease; n.s., non-significant. ijms-23-04730-t003_Table 3 Table 3 Aneurysm size characteristics of the study population. Study Population HTA TAA p Value (n = 52) (n = 23) (n = 29) Thoracic aorta ascendens: Size < 35 mm, n (%) 23 (44.2) 23 (100) 0 (0) <0.01 45–54.9 mm, n (%) 14 (26.9) 0 (0) 14 (48.3) 55–64.9 mm, n (%) 9 (17.3) 0 (0) 9 (31.0) 65–74.9 mm, n (%) 3 (5.8) 0 (0) 3 (10.4) >75 mm, n (%) 3 (5.8) 0 (0) 3 (10.4) Intima thickness, µm (range) 60.7 (15–115) 81.7 (20–115) 44.7 (15–100) <0.01 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. El-Hamamsy I. Yacoub M. Cellular and molecular mechanisms of thoracic aortic aneurysms Nat. Rev. Cardiol. 2009 6 771 786 10.1038/nrcardio.2009.191 19884902 2. Billaud M. Hill J.C. Richards T.D. Gleason T.G. Phillippi J.A. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091889 polymers-14-01889 Article Finite Element Modeling of Debonding Failures in FRP-Strengthened Concrete Beams Using Cohesive Zone Model https://orcid.org/0000-0002-9193-0355 Al-Saawani Mohammed A. Al-Negheimish Abdulaziz I. https://orcid.org/0000-0002-1112-5883 El-Sayed Ahmed K. * Alhozaimy Abdulrahman M. Ito Hiroshi Academic Editor Center of Excellence for Concrete Research and Testing, Department of Civil Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia; malsaawani@ksu.edu.sa (M.A.A.-S.); negaimsh@ksu.edu.sa (A.I.A.-N.); alhozimy@ksu.edu.sa (A.M.A.) * Correspondence: ahelsayed@ksu.edu.sa; Tel.: +966-11-469-6345 05 5 2022 5 2022 14 9 188931 3 2022 29 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Intermediate crack (IC) debonding and concrete cover separation (CCS) are common types of debonding failures in concrete beams flexurally strengthened with fiber-reinforced polymer (FRP) composites. In this paper, a three-dimensional finite element (FE) model was developed to simulate the flexural behavior and predict the critical debonding failure in FRP-strengthened beams. The two critical debonding failures were considered in the FE model by implementing a cohesive zone model based on fracture mechanics considering the effect of the related parameters. The input values used for the cohesive zone model are modified in this study to obtain accurate and consistent predictions. The FE model was validated by comparison with experimental results tested by the authors for beams particularly prone to fail by either of the two critical debonding failures. The results obtained from the FE model agree well with the experimental results for both of the debonding failures and the corresponding capacities at failure. In general, the ratio of the experimental to numerical ultimate capacities was within 5%, and so was the ratio of the experimental to numerical mid-span deflections at debonding failures. The FE model developed in this study was then used to conduct a parametric study investigating the effect of shear span-to-depth ratio and spacing of steel stirrups on the ultimate capacity and type of debonding failure in FRP-strengthened beams. The results of the parametric study revealed that increasing the spacing of steel stirrups caused a significant decrease in the load capacity at concrete cover separation failure. In addition, varying the shear span-to-depth ratio was seen to have an important effect on the type of debonding failure and corresponding capacities for the FRP-strengthened beams having the same cross-section geometry and CFRP reinforcement. concrete beams FRP strengthening IC debonding concrete cover separation cohesive zone model shear span-to-depth ratio Center of Excellence for Concrete Research and Testing (CoE-CRT), King Saud UniversityThe authors extend their appreciation to the Center of Excellence for Concrete Research and Testing (CoE-CRT), King Saud University, for funding this research study. ==== Body pmc1. Introduction Fiber-reinforced polymer (FRP) is a composite material made of high-tensile-strength fibers glued by a polymer matrix. These fibers are usually made of carbon, glass, or aramid. The polymer used is usually an epoxy, vinylester, or polyester thermosetting plastic. FRP composites are lightweight, noncorrosive, easily constructed; higher in tensile strength; and can be applied to achieve the performance requirements. In particular, carbon FRP (CFRP) has excellent mechanical and fatigue properties compared to other types of FRP composites [1]. Due to these characteristics, FRP composites have gained wide acceptance and utilization in the rehabilitation of concrete structures. Strengthening of reinforced concrete (RC) members using FRP externally bonded (EB) to the concrete substrate is an excellent method to increase the load capacity of such members. Many experimental and analytical investigations have been conducted and their results have indicated the effectiveness of FRP strengthening in increasing the load-carrying capacity of RC members [2,3,4,5,6,7,8]. However, these studies have reported that only a small percentage of FRP tensile strength is utilized before the premature failure by FRP debonding. The most common debonding failures in FRP-strengthened beams are intermediate crack (IC)-induced interfacial debonding (IC debonding) and concrete cover separation (CCS) failure at the plate-end region. Figure 1 schematically shows these two common types of debonding failure modes in FRP-strengthened beams. The IC debonding failure could initiate at the toe of a flexural crack in the high-moment region at the middle of the beam, and then propagate along the direction of decreasing moment towards the FRP plate end, as shown in Figure 1a. The IC debonding failure may also initiate at the toe of a flexural-shear crack in the shear span, and then propagate towards the FRP plate end, as shown in Figure 1b. On the other hand, CCS failure, being the most common failure type that occurs at the plate-end region, initiates because of the combined interfacial shear and normal stresses that are generated at the FRP plate end, resulting in the formation of splitting cracks in the concrete. The cracks propagate from the bottom of the beam to the level of the tension steel, and then propagate horizontally onward along the level of the tension steel, eventually causing the separation of the concrete cover, as shown in Figure 1c. Many factors can affect the occurrence of a particular debonding failure mode, including the concrete cover thickness; the number and size of tension steel bars; the distance between FRP plate end and the beam support; FRP plate length, width, thickness, and modulus of elasticity; shear span-to-depth ratio; shear-to-moment interaction; section geometry; and concrete strength [6,9,10,11]. Among the various factors that affect the critical debonding failure in FRP-strengthened beams, Al-Saawani et al. [6] have conducted an experimental study that revealed the significant effect of shear span-to-depth ratio on the controlling debonding failure mode. The finite element (FE) method is a useful tool for improving the understanding of the behavior of RC beams strengthened with FRP composites. Many FE models have been developed for simulating the behavior of FRP-strengthened RC beams in which different approaches have been considered. In some FE studies, a full-bond assumption was considered between the FRP and concrete substrate in the simulation of the response of the strengthened members [12,13,14]. Other numerical studies have considered modeling the adhesive layer as a linear-elastic material [15,16]. To better describe the behavior at the interface between the FRP reinforcement and concrete, other FE studies used interface elements [17,18,19]. Among available bond-slip models, Lu et al.’s model [20] has received the most acceptance for describing the bond behavior of FRP-to-concrete interfaces [21,22,23,24]. Pham and Al-Mahaidi [25] used a bilinear model for the FRP-to-concrete interface. The parameters of the adopted bilinear bond-slip model were directly determined from tests conducted on bonded joints. Numerical studies using FE modeling are used for investigating debonding failures in FRP-strengthened members. Modeling of damage using FE analysis can be generally classified as discrete or continuous [26]. The two common modeling approaches are the fracture-energy-based cohesive zone model (CZM) and the continuum damage mechanics (CDM) approach. In the CZM approach, the adhesive is represented using interface elements in which their behavior is characterized using a traction-separation law. The CDM approach, on the other hand, is based on the stiffness degradation of the adhesive elements as imposed by a damage parameter. Both modeling approaches are able to predict both damage initiation and propagation. CZM can be used to represent delamination, whereas CDM accounts for matrix and fiber damage [26]. The increasing use of the CZM approach is attributed to its ability to simulate the initiation and progression of the debonding failure, and because of its ease of implementation, as it has been included as a built-in feature in many commercial FE software packages [27]. The cohesive zone model (CZM) is a recently applied method in the simulation of composite materials. This model assumes that the stress transfer capacity between the two separating faces of delamination is not completely lost at damage initiation [28]. It is rather a progressive event as governed by progressive stiffness reduction of the interface between the two separating faces. Existing FE models used for the prediction of IC debonding in FRP-strengthened beams based on the CZM derive the interface shear stress distribution law by defining a nonlinear interface bond-slip relationship [29,30,31]. This indicates the important role of the bond slip curve that is considered in the prediction of IC debonding failure. The constitutive relation of the FRP–concrete interface is mostly described by a bilinear model because of its simplicity and accurate prediction of the interface debonding [22,32,33]. Zidani et al. [30] presented an FE model to simulate the flexural behavior of initially damaged concrete beams repaired with FRP plates. The model used the bond stress-slip model proposed by Lu et al. [20] to characterize the interface elements between the EB FRP and concrete. Zhang et al. [34] proposed an FE model for the prediction of IC debonding in FRP-strengthened RC beams based on fracture mechanics and the cohesive zone model. The constructed model also relied on theoretical derivations and available experiments. Unlike FE modeling of IC debonding failure, numerical FE studies on beams failing by CCS failure are limited. Supaviriyakit et al. [35] used the smeared crack model to analyze FRP-strengthened RC beams. In their analysis, the FRP-to-concrete interface was assumed to be perfectly bonded, and the steel bars were uniformly distributed in the concrete elements without any additional nodes and elements. The model predictions showed close agreement with the test results; however, the predicted crack pattern was unclear. Pham and Al-Mahaidi [25] proposed an FE approach using a rotating smeared crack model for the concrete part under the level of tension steel, while a fixed smeared crack model was used for the concrete part above it. A perfect bond was assumed for the modeling of the steel-to-concrete interface. For modeling the FRP-to-concrete interface, a bilinear model was used, and the parameters considered in the bond-slip model were determined from shear tests of bonded joints. A more advanced model was proposed by Zhang and Teng [36], in which a two-dimensional FE approach was used for the prediction of end cover separation failure in concrete beams flexurally strengthened with FRP. The proposed approach considered the cracking of concrete, the bond behavior between steel bars and concrete and between FRP and concrete, and the radial stresses exerted by tension steel bars onto the surrounding concrete. Sakr [37] constructed FE models to analyze continuous beams strengthened with CFRP composites. The study took the two debonding failure modes into account by using cohesive surfaces. In this model, the relation between the concrete and the steel reinforcement was assumed to be perfectly bonded, as modeled by the embedded region constraint. However, no description of the cohesive zone model used for modeling CCS failure was provided. In addition, a clear presentation for the type of debonding failure was lacking. The conducted literature review indicates that most of the available numerical studies considered one of the debonding failure modes in the simulation of FRP-strengthened beams. The current numerical study is directed to consider both IC debonding and CCS failures in FRP-strengthened beams. In this paper, the development of a three-dimensional FE model using the commercial FE software ABAQUS [38] is presented. The FE model is developed to simulate the flexural behavior of FRP-strengthened RC beams and predict the controlling debonding failure mode. Nonlinear relations for the constituent materials are considered in this model. In addition, the interfacial shear and normal stresses at the level of the adhesive layer and also those at the level between the tension steel bars and concrete cover are considered using CZM based on nonlinear fracture mechanics. The FE model is validated using experimental data of beams previously tested by the authors, and then used to investigate the effect of shear span-to-depth ratio and spacing of steel stirrups on the behavior and mode of debonding failure in FRP-strengthened beams. 2. Development of FE Model A three-dimensional nonlinear FE model for simulating the flexural behavior of FRP-strengthened RC beams is developed in this study using the FE package ABAQUS [38]. Only one quarter of the FRP-strengthened beam was modeled in order to reduce the calculation time, and the FE results for the whole beam could be derived using the principle of symmetry. 2.1. Material Properties and Constitutive Models 2.1.1. Concrete Among the available approaches in ABAQUS to simulate the behavior of concrete, the concrete damaged plasticity (CDP) model was used in this study. Such model requires the values of elastic modulus, Poisson’s ratio, plastic damage parameters, and description of compressive and tensile behavior. The plastic damage parameters include the dilation angle (Ψ), the flow potential eccentricity (ϵ), the ratio of initial biaxial compressive yield stress to initial uniaxial compressive yield stress (fb0/fc0), the ratio of the second stress invariant on the tensile meridian to that on the compressive meridian (K), and the viscosity parameter that defines visco-plastic regularization. The values of plastic damage parameters are shown in Table 1 as recommended by ABAQUS documentation for the definition of concrete material. The Poisson’s ratio of concrete was chosen to be 0.2. To model the behavior of concrete in compression, the stress-strain curve of concrete for a given concrete characteristic compressive strength can be described using a suitable model, such as the one developed by Carreira and Chu [39], as follows:(1) fc = fc′βε/ε0β − 1 + ε/ε0β (2) β = 11 − fc′E0ε0 (3) E0 = fc′ε024.82fc′ + 0.92 (4) ε0 = 1680 + 7.1fc′ × 10−6 where fc is the concrete stress; f′c is the maximum stress; β is a material parameter; ε is the concrete strain; ε0 is the corresponding strain at maximum stress; and E0 is the initial tangent modulus of elasticity. The stress-strain curve can be defined beyond the ultimate stress into the strain-softening regime. The compressive inelastic strain, ε~0cin, is defined as the total strain minus the elastic strain, ε~0cin = εc − ε0cel, as illustrated in Figure 2a. The concrete behavior in tension is modeled using a linear elastic approach until cracking is initiated at its tensile strength. After crack initiation, the softening starts, and the post-failure behavior for direct straining is modeled with tension stiffening, which allows one to define the strain-softening behavior for cracked concrete. It is possible to specify tension stiffening by means of a post-failure stress-strain relation or by applying a fracture energy cracking criterion. The response of concrete to uniaxial loading in tension is shown in Figure 2b. Figure 2 Response of concrete to uniaxial loading [38]: (a) response of concrete in compression; (b) response of concrete in tension. For concrete under uniaxial tension, Hordijk [40] proposed the following tension-softening curve based on an extensive series of tension tests of concrete. This model is also considered in this study and is as follows:(5) σt = fct1 + c1wtwcr3e−c2wtwcr − wtwcr1 + c13e−c2 (6) wt = εcrhc (7) εcr = εt − εe (8) wcr = 5.14GFfct where wt and εcr are the crack opening displacement and cracking strain, respectively; εt and εe are the total strain and elastic tensile strain, respectively; wcr is crack opening displacement at the complete release of stress or fracture energy; σt is tensile stress normal to the crack direction; fct is concrete uniaxial tensile strength; GF is fracture energy required to create a stress-free crack over a unit area; and c1 = 3.0 and c2 = 6.93 are constants determined from tensile tests of concrete. In FE simulations, fct could be calculated by ACI 318 [41], and GF was considered from CEB-FIP [42] as follows:(9) fct = 0.33fc′ (10) GF = 0.0469da2 − 0.5da + 26fc′100.7 where da (in mm) is the maximum aggregate size, and f′c is the cylinder compressive strength (in MPa). The stress-displacement curve defined by Equations (5)–(8) can be transformed into a stress-strain curve according to the crack band model. In ABAQUS [38], the crack band width hc is defined as the characteristic crack length of an element. In this study, Rots’ [43] recommendation for estimating the crack band width is followed. For instance, the characteristic crack length of a plane stress four-node square element with four integration points is taken to be 2e, where e is the side length of the element. 2.1.2. Steel Reinforcement The steel reinforcements were modeled as an elastic perfectly plastic material, as shown in Figure 3. The input for the steel model includes elastic modulus, Poisson’s ratio, and yield stress. 2.1.3. FRP Reinforcement The CFRP laminate was modeled as a linear elastic behavior up to the brittle failure, where the CFRP composite is mainly stressed in the fiber direction. The elastic behavior was modeled as a lamina type in ABAQUS. The mechanical properties of the CFRP lamina include the ultimate tensile strength, fu, Poisson’s ratio, ν, and the Young’s modulus and shear modulus, E and G, that are associated with the material’s principal directions “1, 2, 3”, which represent the longitudinal, transversal, and normal directions, respectively. 2.2. Modeling of Debonding Failures FRP-strengthened beam could fail by two different debonding modes, either by interfacial debonding of FRP within a thin layer of concrete (IC debonding) or by the separation of concrete cover due to a combination of interfacial shear and normal stresses concentrating near the plate-end region. Therefore, two different failure modes were considered in the proposed FE model. This FE study takes the two possible types of debonding failure into account by considering two cohesive surfaces. The first cohesive surface is considered at the level of the adhesive layer to account for IC debonding failure, whereas the second cohesive surface is inserted between the tension steel bars and the concrete cover to account for CCS failure. Properties of both the adhesive and concrete are considered for the two cohesive surfaces. 2.2.1. Modeling of IC Debonding Using a perfect bond between CFRP and concrete results in overestimated predictions of the load capacity and stiffness compared to experimental results. Therefore, for IC debonding failure, the interface between CFRP laminate and concrete substrate needs to be modeled with an appropriate interaction. The cohesive model available in ABAQUS is a suitable choice for representing such interface behavior. The failure of the cohesive bond is characterized by progressive degradation of the cohesive stiffness, which is driven by a damage process. For this purpose, eight-node 3D cohesive elements (element COH3D8 in ABAQUS) were used to model the interface layer based on a proper bond-slip model. The definition of the cohesive zone model is characterized by parameters of initial stiffness, shear strength, fracture energy, and curve shape of the bond-slip model. In this study, the constitutive relation of the FRP–concrete interface is described by a bilinear model. Such a model is widely used to define the interface behavior of FRP-strengthened RC beams because it is convenient for use and gives accurate prediction of interface debonding [32,33]. A graphical representation of a bilinear traction-separation law is depicted in Figure 4. It can be observed from Figure 4 that the traction-separation law is defined by three parameters: initial stiffness (Ko), normal (σmax) or shear bond strength (τmax), and fracture energy (Gcr), which is equal to the area under the traction-displacement curve. The nominal traction stress vector consists of three components, σn, τt, and τs, which represent the normal and shear tractions, respectively. The constitutive equations for the bond-slip law are as follows:(11) τ = τmaxδoδ           0 ≤ δ ≤ δoτmaxδf − δoδf−δ     δo≤δ≤δf0          δ>δf where τmax is the shear strength of the interface, and 𝛿f is the bond separation slip when the interfacial shear stress is reduced to zero. The area surrounded by the bilinear curve represents the interface fracture energy Gcr, which can be calculated as Gcr = ½τmax𝛿f. For modeling the behavior of the FRP-to-concrete interface, the bond-slip interface model proposed by Lu et al. [20] was used. The considered interface model is based on traction-separation laws (cohesive behaviors). The equations of the bond–slip law model used in this study are summarized in Table 2, in which bf and b represent the width of the CFRP laminate and concrete beam, respectively. The tensile strength of concrete, fct, expressed in MPa, is calculated by Equation (9). The initiation of damage is assumed to occur when a quadratic traction function involving the nominal stress ratios reaches the value one. This criterion can be represented as follows:(12) σnσn02 + τsτs02 + τtτt02 = 1 where σn is the cohesive tensile strength; τs and τt are shear stresses of the interface; and n, s, and t refer to the direction of the stress component, as shown in Figure 5. Figure 5 Eight-node 3D cohesive element [38]. Modeling of damage allows for simulating both the degradation and failure of the bond between the two cohesive surfaces. The mechanism of failure includes the damage initiation criterion and a damage evolution law. The initial response is assumed to be linear elastic, and once a damage initiation criterion is met, damage can then occur according to a prior defined damage evolution law. In order to describe the damage evolution, the linear softening model is expressed in terms of fracture energy. The description of this model is available in the ABAQUS material library [38]. For describing the dependency of the fracture energy to the opening and sliding failure modes, the Benzaggah–Kenane (BK) and power law fracture criteria were used, which can be represented by:(13) GnGfnη + GsGfsη + GtGftη = 1 In the above expression, the quantities Gn, Gs, and Gt denote the work done by the interfacial stresses and its conjugate separation in the normal, the first, and the second shear directions, respectively; η is a cohesive property parameter; and Gnf, Gsf, and Gtf represent the critical fracture energies required to cause failure in the normal, the first, and the second shear directions, respectively. 2.2.2. Modeling of CCS Debonding For modeling CCS failure, a separate part that represents the concrete cover was first created. Then, the interface between the concrete beam and the concrete cover part was modeled by considering a cohesive surface at the level of tension steel reinforcement. Eight-node 3D cohesive elements (element COH3D8 in ABAQUS) were used to model the interface layer based on a proper bond-slip model. The input values for the cohesive model in this interface were developed in this study. In order to find the values of initial stiffness, shear strength, and fracture energy that yield the best fit, simulations were performed, and the FE results were compared with experimental results of beams that were tested by Al-Saawani et al. [6]. The following relations for initial stiffness, Ko, shear strength, τmax, and fracture energy, Gcr, as a function of the concrete properties, were proposed:(14) Ko = Gctc (15) τmax = fct (16) Gcr = 0.15fc′0.2 where tc is the interface thickness, Gc is the shear modulus of concrete in MPa, and fct is the tensile strength of concrete in MPa calculated by Equation (9). The initiation of damage and failure mechanism were modeled considering the same approach used in the case of modeling IC debonding described earlier. 2.3. Model Geometry and Element Types Three-dimensional simulations were performed to get an accurate approximation of the overall flexural behavior and type of debonding failure of the strengthened beams. The concrete was modeled using an eight-node reduced-integration linear brick element (C3D8R in ABAQUS). Steel reinforcements were modeled as embedded elements represented by two-node linear 3D truss elements (element T3D2 in ABAQUS). The truss elements have three degrees of freedom at each node, which include translations in the nodal x, y, and z directions. The truss element was defined by its cross-sectional area. The unidirectional CFRP reinforcement was represented by four-node reduced-integration shell element (element S4R in ABAQUS), because these elements consider the properties of orthotropic materials. In order to reduce the computational demand, the double symmetry of beam geometry and loading was utilized; thus, only a quarter of the strengthened beam was modeled upon applying appropriate boundary conditions. Figure 6 shows the three-dimensional model used, with the finite element mesh showing the geometry, different elements used, and applied load. 2.4. Mesh Size and Boundary Conditions For modeling concrete members, Bažant and Oh [44] recommended element sizes to be three times the size of the maximum coarse aggregate (3da). In his study, different mesh sizes were used in the FE simulation in order to obtain convergence as well as numerical results that are in good agreement with experimental measurements. A moderately fine mesh of 25 mm element size was adopted in this study as it provided good accuracy and was computationally less demanding compared to the 12.5 mm element size that was tried. Boundary conditions that represent the supports and specify values of displacement and rotation variables at appropriate nodes were applied. The boundary conditions for a quarter of the beam are shown in Figure 7. The element type in ABAQUS controls the element characteristics of the mesh. The mesh controls selected in the developed FE model are Hexahedron element meshing, while the type of mesh has been considered as structured. The reason for this selection of meshing type is because the geometry of elements used in modeling FRP-strengthened beams is not complex. In addition, the geometric order in ABAQUS controls the number of nodes on the selected elements. The selection between first-order or second-order elements is mostly a trade-off between computational expense and the accuracy of results. In addition, the class of problem being modeled dictates the proper choice of the element for FE modeling. For example, in case of modeling the contact with bending, the choice of first-order with reduced-integration elements is preferred, with a sufficient number throughout the thickness. 3. Model Calibration and Validation To validate the developed numerical model, FE simulations were conducted for seven FRP-strengthened beams that were tested by Al-Saawani et al. [6]. These beams were chosen because of the detailed test results that are available and easily accessible, including load-deflection curves, FRP strains, interfacial shear stress between FRP and concrete, and cracking patterns. In addition, these beams have experienced both debonding failures, namely IC debonding at the middle of the beam or CCS failure at the plate-end region. The analyzed beams were constructed with different clear-span lengths, which gave a wide range of shear span-to-depth ratios of 1.5 to 7.0. Table 3 shows details of the beams and lists values of the shear span, av, and the values of av/ds. The cross-section configuration and details of the steel and CFRP reinforcements for such beams are shown in Figure 8. The mechanical properties of steel and CFRP reinforcements used in FE simulation are shown in Table 4 and Table 5, respectively. Figure 8 Geometry and reinforcement details of the analyzed beams [6]. This part presents the calibration of the developed FE model through running FE simulations for beam S-3.5, which has failed by IC debonding [6]. The results from the FE model show that the predicted capacity for beam S-3.5 at IC debonding failure was overestimated when using the interface properties suggested by Lu et al. [20]. The predicted capacity at IC debonding for the beam S-3.5 is found to be 11.6% higher than the experimental value. The cause for this difference in the load capacity is related to the estimation of the behavior of the interface between CFRP and concrete using the bond-slip model proposed by Lu et al. [20]. To improve the accuracy of the predicted capacity at IC debonding failure obtained from the FE model, a modification is suggested for the properties of the interface between CFRP and concrete. This modification is mainly related to the maximum shear stress, τmax, and the fracture energy, Gcr. The maximum shear stress, τmax, calculated by Lu et al. [20] and shown in Table 2, provides an upper limit for τmax of 2.35 MPa for the analyzed beam S-3.5. The conducted FE analysis showed that this value is high because of the higher load capacity at IC debonding failure that was obtained considering Lu et al.’s model [20] compared to the test value, as shown in Figure 9. Therefore, different values of τmax were investigated, and a reduced value of 50% of that calculated gave better predictions of the load at IC debonding failure, as shown in Figure 9. This use of a reduced value of τmax was also observed by Sakr [37] to obtain better predictions. For the fracture energy, Gcr, the value obtained using Lu et al.’s model [20] for the analyzed beam S-3.5 is 300 J/m2. This small value of Gcr gives a lower capacity at IC debonding failure, as shown in Figure 10. In fact, previous research studies have indicated fracture energy values ranging from 300 to 1500 J/m2 [37,45,46]. To investigate the extent to which Gcr can affect the FE results, numerical simulations were performed on the beam S-3.5 considering Gcr values of 300, 500, and 900 J/m2. The simulations showed that Gcr has an influence on the ultimate capacity at IC debonding failure, as seen in Figure 10. In this study, the value 500 J/m2 was selected, as it provided a load capacity at IC debonding that is in good agreement with the experimental results. The input values for the bond-slip model that describes the CZM used to predict CCS failure were developed in this study. FE simulations were performed on the beam S-3.0, which experimentally failed by CCS [6], in order to find the values of initial stiffness, shear strength, and fracture energy that yield the best fit. As mentioned earlier in the model development section, the input values were suggested as a function of the concrete properties. Results from FE analysis were then compared with experimental results [6], which were in good agreement. The following subsections present details on the validation of the developed FE model by comparisons with the experimental results. 3.1. Ultimate Capacity and Mode of Failure The results obtained from the FE simulations for seven FRP-strengthened beams [6] are listed in Table 6. The results include the ultimate capacities and the two critical debonding modes of failure, as obtained from the FE simulations for the tested beams. The comparisons presented in Table 6 show that the FE model was capable of predicting the type of debonding failure (i.e., IC debonding or CCS failure mode) for the experimental beams that had various values of shear span-to-depth ratio. The comparisons also show a good agreement in the ultimate capacities obtained from the FE analysis as compared to the experimental results for the FRP-strengthened beams. In general, the ratios of the experimental to numerical ultimate capacities (Pu,exp/Pu,FEM) were within 5%, except for the beams S-2.0 and S-1.5, for which the ratio of Pu,exp/Pu,FEM was 0.94 and 0.92, respectively. These comparisons indicate that the developed FE model is valid and can be used as a tool to predict the flexural behavior and strength at debonding failure for RC beams strengthened with FRP composites. 3.2. Load-Deflection Curves Comparisons of the load versus mid-span deflection curves of the beams tested by Al-Saawani et al. [6] and the FE analysis are shown in Figure 11. It is shown that the finite-element model can predict the behavior of the FRP-strengthened beams at cracking, yielding of tension steel, and IC debonding. In addition, the load at cracking, at yielding of tension steel, and at failure, along with the corresponding displacements, are in good agreement with the experimental results. Figure 11 Load-deflection curves of analyzed beams obtained from experiments [6] and FE model: (a) beam S-1.5; (b) beam S-2.0; (c) beam S-2.5; (d) beam S-3.0; (e) beam S-3.5; (f) beam S-5.0; (g) beam S-7.0. 3.3. Strains in CFRP Reinforcement Comparisons of the axial tensile strain distribution along the CFRP laminate obtained from the FE analysis and the experimental study [47] were conducted and are shown in Figure 12. The predicted variations of strain along the CFRP laminates generally follow the bending moment variation along the beam length. In addition, the CFRP strains obtained from FE analysis are found to be quite similar to the measured strains obtained from the experimental results [47]. It can be seen from Figure 12 that the strain values increased linearly from the end of the CFRP laminates to the sections corresponding to the loading points and then stayed at a constant level within the constant-bending-moment region. This is particularly clear in the case of the beams with shear span-to-depth ratios ranging from 1.5 to 3.0, which failed by CCS at the plate-end region. In the case of beams with increased value of shear span-to-depth ratio, the strain values of the CFRP laminates were seen to further increase at the constant-moment region, with their highest values at the middle of the beam. Figure 12 Comparison of strain distribution along the CFRP laminates from FE analysis and experimental results [47]: (a) beam S-1.5; (b) beam S-2.0; (c) beam S-2.5; (d) beam S-3.0; (e) beam S-3.5; (f) beam S-5.0; (g) beam S-7.0. Comparisons of the CFRP strains at the middle of the beam obtained from the FE analysis and the experimental results [47] for the analyzed beams at debonding failures are shown in Figure 13. It can be seen from the figure that the CFRP strains obtained from the FE analysis are in good agreement with the strain values reported from the experimental study [47]. Figure 13 Comparisons of CFRP strains at the middle of the beam obtained from FE analysis and experimental results [47]. 3.4. Crack Patterns The concrete damage plasticity model does not have a notation for the development of cracks at the material integration point. Therefore, it was assumed that cracking initiates at the points where the maximum principal plastic strain is positive, following Lubliner et al. [48]. Figure 14a shows a comparison between plastic strain distribution obtained from the FE analysis and crack patterns obtained from the experimental study [6] for the strengthened beam S-3.5, which failed by IC debonding. Similarly, Figure 14b shows the same comparison for the beam S-3.0, which failed by CCS [6]. It can be seen from the figure that the cracking patterns correspond well to the experimental crack distributions until failure. Both experimental and FE analysis show similar cracking pattern for both of the strengthened beams. In the experimental study [6], adequate steel stirrups were used in order to prevent the occurrence of shear failure for the strengthened beams. The spacing of stirrups had an important role in the spacing of the developed cracks. At the earlier stages of loading, the developed cracks were flexural cracks propagating in a vertical direction. With increased loading, more cracks developed at the shear span, which were mainly flexural-shear cracks that started at a vertical direction then propagated in an inclined direction to the beam longitudinal axis. Figure 14 Comparison between plastic strain distribution from FE simulation and crack patterns from experimental results [6]: (a) beam S-3.5; (b) beam S-3.0. 4. Parametric Study 4.1. Effect of Shear Span-to-Depth Ratio on Debonding Failure Shear span-to-depth ratio (av/ds) is one of the main parameters that has an influence on the type of debonding failure in RC beams strengthened with externally bonded CFRP covering the entire span [6]. The developed FE model was used to simulate seven FRP-strengthened beams from the experimental study [6]. These beams had a wide range of av/ds from 1.5 to 7.0. Figure 15 shows the predicted capacities using the developed FE model for the beams with various values of av/ds compared to the experimental values [6]. It can be seen from the figure that the results obtained from the numerical analysis confirm the experimental findings on the effect of av/ds on shifting the failure mode from IC debonding (in the case of beams with av/ds of 3.5 and higher) to CCS failure at the plate end in the case of beams with av/ds values of 3.0 and lower. Figure 15 Comparison of FE analysis with experimental results [6] on the effect of av/ds on debonding failures. 4.2. Effect of Steel Stirrups on Debonding Failure The developed FE model was used to investigate the effect of stirrup spacing on the debonding failure load for the two common debonding failures in FRP-strengthened beams. FE simulations were conducted for two beams: one prone to fail by plate-end debonding, and the other prone to fail by IC debonding. In particular, two FRP-strengthened beams with av/ds values of 2.5 and 5.0 (beams S-2.5 and S-5.0) were chosen from the experimental study [6] in order to conduct the parametric study. For each beam, FE simulations were conducted where the variable was the spacing of stirrups: 100, 200, and 300 mm. These values represent a stirrup spacing to effective beam depth ratio of 0.28, 0.57, and 0.85, respectively. The diameter of stirrups of 8 mm was kept constant in all beams. The results of the FE analysis are shown in Table 7 and Figure 16, which include the predicted failure load and corresponding mode of failure. The analysis results indicated that increasing the spacing of the steel stirrups causes a reduction of the load capacity for the strengthened beams in either mode of debonding failure (IC debonding and CCS failure). The reduction in load capacity was more pronounced in the case of beams that are predicted to fail by CCS at the FRP plate-end region. The percentage decrease in load capacity was 12.9% and 15.9% for the beams S-2.5/S-200 and S-2.5/S-300 with steel stirrups spaced at 200 and 300 mm, respectively, compared to the beam S-2.5/S-100 with stirrups spaced at 100 mm. In fact, the reduction in load capacities at CCS failure with increased spacing of stirrups is attributed to the reduced efficiency of stirrups in controlling the widening of shear cracks that occur at the shear span close to the plate end. Such poor control of the widening of the shear cracks at the plate-end region increased the horizontal and vertical components of the relative displacement associated with such cracks. This, in turn, caused an increase in the interfacial shear and normal stresses at the plate end, which triggered the CCS failure at decreased load capacities of the strengthened beams. These numerical findings agree with the experimental results reported in the recent study conducted by Al-Negheimish et al. [8] that reported reduced capacities of 11.6% and 15.9% in the tested beams that failed by CCS when the stirrup spacing increased from 100 mm to 150 mm and 250 mm, respectively. In the case of beams that are prone to fail by IC debonding, the decrease in the load capacity was marginal in the case of the beam S-5.0/S-200 with stirrups spaced at 200 mm compared to the beam S-5.0/S-100 with stirrups spaced at 100 mm. This is because the IC debonding in these two beams initiated at the middle of the beam, close to the point load as induced by flexural cracks. In this area, the vertical stirrups have a limited role in restricting the widening of the flexural cracks, and this explains the marginal change in load capacity at IC debonding, which was basically induced by such flexural cracks in the middle of the strengthened beam. In contrast, a decrease in the load capacity of 10.4% was observed in case of the analyzed beam S-5.0/S-300 with steel stirrups spaced at 300 mm when compared to the beam S-5.0/S-100 with stirrups spaced at 100 mm. This is attributed to the occurrence of IC debonding at the shear span due to flexural-shear cracks rather than flexural cracks at the middle of the beam. Figure 17 shows the location of IC debonding failure in the beam S-5.0/S-300, which was initiated at a distance of 550 mm measured from the point load towards the shear span. The failure by IC debonding was evidenced by the damage of the cohesive elements that were used in the FE model to represent the IC debonding failure. This was checked by the maximum value of the quadratic nominal stress damage initiation (QUADSCRT) criterion and the overall scalar stiffness degradation (SDEG) variable in ABAQUS, which indicate that the initiation criterion and damage has been satisfied (as shown in Figure 17). Generally, the cracks at the shear span are inclined, and thus the steel stirrups have an important role in restricting the widening of such cracks. Therefore, increasing the spacing of stirrups to 300 mm (representing 85% of the effective depth of the beam S-5.0/S-300) reduced the effectiveness of stirrups in the shear span, and thus caused more widening of the cracks. This eventually triggered the failure by IC debonding at lower load capacity of the strengthened beam. It is worth noting that many design codes limit the maximum spacing of stirrups to the range of 0.5 to 0.7 the effective depth of the concrete beam. Therefore, within the allowable spacing of steel stirrups specified by design codes, the capacity at IC debonding failure should not affected by the spacing of steel stirrups. Comparisons of the load versus mid-span deflection curves of the analyzed beams using the FE model are shown in Figure 18a, in the case of FRP-strengthened beams with av/ds of 2.5, and Figure 18b, for the beams with av/ds of 5.0. The plots show no effect of the stirrup spacing on the flexural stiffness of the FRP-strengthened beams for the two values of av/ds. However, a decrease in the flexural stiffness was observed at the segment of the load-deflection curves before the IC debonding failure for the beams with increased stirrup spacing of 200 and 300 mm as compared to the beam with steel stirrups spaced at 100 mm. 5. Conclusions This paper presented the development of a finite-element model for simulating the two critical debonding failures in FRP-strengthened beams using the commercial software ABAQUS. The developed FE model used a concrete damaged plasticity model for concrete, an elastic perfectly plastic model for steel reinforcement, and a fracture-energy-based cohesive model to represent the interfaces related to the two critical debonding failures. The main findings of this investigation can be summarized as follows:The developed FE model in this study simulates the flexural behavior and predicts the critical type of debonding failure (IC debonding and CCS failure) and the corresponding capacity at failure for the FRP-strengthened beam. The FE results were validated by comparisons with the experimental results for FRP-strengthened beams having a wide range of shear span-to-depth ratios. The comparisons have confirmed the capability of the developed FE model to distinguish between the two critical debonding failure modes in FRP-strengthened beams and to predict the failure loads in close agreement with the experimental values. A parametric study was conducted using the developed FE model in order to investigate the effect of shear span-to-depth ratio and the spacing of steel stirrups on the debonding failures in FRP-strengthened beams. The FE analysis showed the effect of shear span-to-depth ratio on the type of debonding failure for RC beams strengthened with CFRP laminates covering the entire span to the supports. The results of the FE analysis showed that increasing the spacing of stirrups causes a reduction in the load-carrying capacity at CCS failure. In contrast, a marginal reduction in the load-carrying capacity in the case of the beams that fail by IC debonding was found if the allowable spacing of stirrups specified by design codes is not exceeded. However, increasing the stirrup spacing beyond the codes’ limit causes a change in the location of the IC debonding from the middle of the beam (induced by flexural cracks) to the shear span initiated by flexural-shear cracks at a reduced load-carrying capacity. Author Contributions Conceptualization, M.A.A.-S., A.I.A.-N. and A.K.E.-S.; methodology, M.A.A.-S. and A.I.A.-N.; validation, M.A.A.-S., A.K.E.-S. and A.M.A.; formal analysis, M.A.A.-S., A.I.A.-N. and A.K.E.-S.; investigation, M.A.A.-S., A.I.A.-N. and A.K.E.-S.; resources, A.I.A.-N. and A.M.A.; writing—original draft preparation, M.A.A.-S., A.I.A.-N. and A.K.E.-S.; writing—review and editing, M.A.A.-S. and A.K.E.-S.; visualization, A.I.A.-N. and A.M.A.; supervision, A.K.E.-S. and A.I.A.-N.; project administration, A.M.A. and A.I.A.-N.; funding acquisition, A.M.A. and A.I.A.-N. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Data from this study can be made available upon request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Common types of debonding failures in FRP-strengthened beams: (a) interfacial debonding induced by intermediate flexural cracks; (b) interfacial debonding induced by intermediate flexural-shear cracks; (c) concrete cover separation. Figure 3 Stress-strain behavior of steel reinforcement. Figure 4 Bilinear bond-slip curve. Figure 6 Geometry and elements used in the FE simulation. Figure 7 Symmetry boundary conditions used in the FE simulation. Figure 9 Comparison between experimental and FE results for different values of τmax. Figure 10 Comparison between experimental and FE results for different values of Gcr. Figure 16 Effect of stirrup spacing on the capacity at debonding failures: (a) beams with av/ds = 2.5; (b) beams with av/ds = 5.0. Figure 17 Failure of the beam S-5.0/S-300 used in the parametric study. Figure 18 Effect comparison of load-deflection curves for the beams with various stirrup spacings: (a) beams with av/ds = 2.5; (b) beams with av/ds = 5.0. polymers-14-01889-t001_Table 1 Table 1 Values of plastic damage parameters used for concrete model. Parameter Dilation Angle (Ψ) Eccentricity (ϵ) fb 0 /fc 0 K Viscosity Parameter Value 30° 0.1 1.16 0.667 0.0001 polymers-14-01889-t002_Table 2 Table 2 Parameters of bond-slip model used in the FE study. Parameter K0 (MPa/mm) τmax (MPa) Gcr (N/mm) βw Value τmax/δ0 δ0 = 0.0195 βwfct 1.5 βwfct 0.308 βw2fct 2.25 − bfb1.25 + bfb polymers-14-01889-t003_Table 3 Table 3 Details of the simulated beams that were tested by Al-Saawani et al. [6]. Beam Clear Span, L (mm) Shear Span, av (mm) Shear Span-to-Depth Ratio, av/ds S-1.5 2050 525 1.5 S-2.0 2400 700 2.0 S-2.5 2750 875 2.5 S-3.0 3100 1050 3.0 S-3.5 3500 1250 3.5 S-5.0 4500 1750 5.0 S-7.0 6000 2500 7.0 polymers-14-01889-t004_Table 4 Table 4 Properties of steel reinforcement used in FE simulation, tested by Al-Saawani et al. [6]. Property Elastic Modulus (GPa) Poisson’s Ratio Yield Stress (MPa) Tension steel 205 0.3 550 Compression steel 200 500 Stirrups 200 350 polymers-14-01889-t005_Table 5 Table 5 Properties of the CFRP reinforcement used in FE simulation, tested by Al-Saawani et al. [6]. Parameter E1 (GPa) E2 (GPa) ν 12 G12 (GPa) G13 (GPa) G23 (GPa) fu (MPa) Value 95.8 6.143 0.29 1.55 1.22 1.22 984.6 polymers-14-01889-t006_Table 6 Table 6 Ultimate capacities and failure modes obtained by experiments [6] and FE model. Beam Experimental Results FE Analysis Results Pu,exp/Pu,FEM 𝛿u,exp/𝛿u,FEM Pu,exp (kN) 𝛿u,exp (mm) Mode of Failure * Pu,FEM (kN) 𝛿u,FEM (mm) Mode of Failure * S-1.5 271.9 6.4 CCS 294.5 6.2 CCS 0.92 1.03 S-2.0 271.7 11.4 CCS 290.5 10.9 CCS 0.94 1.05 S-2.5 270.2 15.3 CCS 265.7 15.7 CCS 1.02 0.97 S-3.0 269.8 22.8 CCS 260.0 22.5 CCS 1.04 1.01 S-3.5 225.4 30.9 ICD 220.1 27.2 ICD 1.02 1.14 S-5.0 165.1 45.4 ICD 167.0 44.5 ICD 0.99 1.02 S-7.0 121.0 76.0 ICD 125.7 83.2 ICD 0.96 0.91 * CCS = concrete cover separation failure; ICD = intermediate-crack-induced debonding failure. polymers-14-01889-t007_Table 7 Table 7 Ultimate capacities and failure modes obtained by FE analysis on the effect of stirrup spacing. Beam Shear Span-to-Depth Ratio Sv (mm) Pu,FEM (kN) ΔPu,FEM (kN) Mode of Failure * S-2.5/S-100 2.5 100 271.7 - CCS S-2.5/S-200 200 236.7 −12.9% CCS S-2.5/S-300 300 228.5 −15.9% CCS S-5.0/S-100 5.0 100 173.9 - ICD S-5.0/S-200 200 168.8 −2.9% ICD S-5.0/S-300 300 155.8 −10.4% ICD * CCS = concrete cover separation failure; ICD = intermediate-crack-induced debonding failure. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Xian G. Guo R. Li C. Combined effects of sustained bending loading, water immersion and fiber hybrid mode on the mechanical properties of carbon/glass fiber reinforced polymer composite Compos. Struct. 2022 281 115060 10.1016/j.compstruct.2021.115060 2. Teng J.G. Smith S.T. Yao J. Chen J.F. Intermediate crack-induced debonding in RC beams and slabs Constr. Build. Mater. 2003 17 447 462 10.1016/S0950-0618(03)00043-6 3. Al-Negheimish A.I. El-Sayed A.K. Al-Zaid R.A. 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==== Front Nutrients Nutrients nutrients Nutrients 2072-6643 MDPI 10.3390/nu14091864 nutrients-14-01864 Review Bioelectrical Impedance Analysis for the Assessment of Body Composition in Sarcopenia and Type 2 Diabetes https://orcid.org/0000-0002-3795-2887 Sbrignadello Stefano 1 Göbl Christian 2 https://orcid.org/0000-0003-3466-5900 Tura Andrea 1* Figueroa Arturo Academic Editor 1 CNR Institute of Neuroscience, 35127 Padova, Italy; stefano.sbrignadello@cnr.it 2 Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090 Vienna, Austria; christian.goebl@meduniwien.ac.at * Correspondence: andrea.tura@cnr.it; Tel.: +39-049-829-5786 29 4 2022 5 2022 14 9 186425 3 2022 25 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Sarcopenia is emerging as a severe complication in type 2 diabetes (T2DM). On the other hand, it has been documented that nutritional aspects, such as insufficient protein or total energy intake, increase sarcopenia risk. The analysis of body composition is a relevant approach to assess nutritional status, and different techniques are available. Among such techniques, bioelectrical impedance analysis (BIA) is particularly interesting, since it is non-invasive, simple, and less expensive than the other techniques. Therefore, we conducted a review study to analyze the studies using BIA for body composition analysis in T2DM patients with sarcopenia or at risk of catching it. Revised studies have provided important information concerning relationships between body composition parameters (mainly muscle mass) and other aspects of T2DM patients’ conditions, including different comorbidities, and information on how to avoid muscle mass deterioration. Such relevant findings suggest that BIA can be considered appropriate for body composition analysis in T2DM complicated by sarcopenia/muscle loss. The wide size of the patients’ cohort in many studies confirms that BIA is convenient for clinical applications. However, studies with a specific focus on the validation of BIA, in the peculiar population of patients with T2DM complicated by sarcopenia, should be considered. sarcopenia type 2 diabetes bioelectrical impedance analysis body composition skeletal muscle mass appendicular muscle mass This research received no external funding. ==== Body pmc1. Introduction Sarcopenia is a syndrome characterized by low muscle mass, and either low muscle strength or low physical performance. The interest in the study of this syndrome has progressively increased, since it has become evident that sarcopenia not only determines poor quality of life, but also increases the risk for physical disability or even death [1,2,3,4]. In addition, it has been established that sarcopenia often coexists with typically chronic diseases or disorders, such as hypertension, obesity, and type 2 diabetes (T2DM) [5]. In fact, sarcopenia is emerging as a further severe complication in T2DM, in addition to those already well known, such as cardiovascular diseases [6]. In T2DM, the core pathophysiologic defects are insulin resistance in the muscle and in the liver, and pancreatic beta-cell dysfunction. However, it has been recognized that other factors play a relevant role in T2DM, especially accelerated lipolysis, gastrointestinal incretin hormones deficiency/resistance, hyperglucagonemia, increased glucose reabsorption, and brain insulin resistance (i.e., overall, the “Ominous Octet” [7]). Notably, these factors often present a common trait, that is, some degree of inflammatory condition. Inflammation, indeed, appears one of the factors which links T2DM and sarcopenia [8]. On the other hand, it has been documented that nutritional aspects, such as insufficient protein or total energy intake, or gastrointestinal diseases (malabsorption), increase the risk for sarcopenia or further worsen it after its onset [9,10], and of course, nutrition is particularly relevant in subjects with concomitant sarcopenia and T2DM [11,12]. In this context, the analysis of body composition is a relevant approach, as it helps to assess the overall nutritional status [13,14]. To this purpose, different techniques are available. Simple anthropometric measures, such as the body mass index (BMI), waist and hip circumference, or the waist-to-hip ratio, can be considered basic markers of body composition, whereas accurate and powerful techniques include dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI) [14]. However, these refined techniques may not be optimal for application in the clinical routine, due to factors such as cost, time required for the examination, and possible burden to the patient. Thus, an alternative to those techniques is the bioelectrical impedance analysis (BIA) [14]. Of note, in sarcopenia, one aspect of body composition is particularly important, i.e., the assessment of skeletal muscle mass. Indeed, muscle mass quantification is indicated as one of the criterion for the diagnosis of sarcopenia, according to the guidelines of the European Working Group on Sarcopenia in Older People (EWGSOP) [15,16]. Precisely, low muscle mass is required for sarcopenia diagnosis, whereas one further condition is sufficient between either low muscle strength or, alternatively, impaired physical performance [15,16]. It has to be noted that other guidelines for sarcopenia diagnosis exist, such as those from the Asian Working Group for Sarcopenia [17,18], or those from the Japan Society of Hepatology [19]. However, all guidelines essentially agree on the role of muscle mass and strength in sarcopenia. Among the indicated techniques for body composition assessment (and specifically for muscle mass quantification), BIA appears particularly interesting, since in our opinion it is likely that they are the most “cost-effective”. In fact, BIA may be more accurate than possible approaches based only on anthropometric measures [20], especially when the balance between fat mass and fat-free mass is abnormal (as it can happen in sarcopenia). In contrast, when compared to the refined techniques (DXA, CT, MRI), accuracy may be somehow lower; however, BIA has the advantages previously indicated. In summary, BIA may be the best compromise between cost and simplicity; hence, it appears particularly adequate for use in the clinical routine. Of note, EWGSOP explicitly indicates BIA as an appropriate/acceptable technique for muscle mass quantification in the clinical practice [15,16]. Accordingly, BIA has been used in several studies related to sarcopenia, as summarized by some recent reviews [21,22]. BIA has also been used in several studies on people with diabetes, mainly type 2, but also gestational and type 1 diabetes (including Wang et al. [23], Bai et al. [24], and Arpaia et al. [25]). Furthermore, BIA has been used in several studies related to concomitant sarcopenia and type 2 diabetes. The aim of the current review is to summarize those studies, with focus on the usefulness (but also possible limitations) of BIA for the goals and outcomes of the analyzed studies. To our knowledge, this is the first review study focused on BIA in sarcopenia and diabetes. 2. Methodology for Scientific Literature Search The search of scientific literature was performed in PubMed. Following testing of different search strings, we identified this final string: (bio-impedance[tw] OR bioimpedance[tw] OR ((bioelectric*[tw]) AND impedance[tw])) AND sarcopen*[tw] AND (diabet*[tw] OR prediabet*[tw] OR hyperglicem*[tw] OR hyperglycaem*[tw] OR (impaired[tw] AND glucose[tw])). According to PubMed guidelines, “tw” (“text word”) searches all main fields of PubMed records, i.e., in article titles, abstracts, MeSH terms, plus some additional fields. The symbol “*” searches for all variations of a word root, e.g., sarcopenia and sarcopenic, etc. Notably, to identify all possible articles of potential interest for our review, we searched for all possible variations for BIA indication, as well as all variations of type 2 diabetes. Indeed, for highest generalization, we also searched for prediabetes, hyperglycemia, or similar expressions, and we did not specify “type 2”, though this was the focus of our review. The indicated search strategy yielded 103 items (last check: 4 March 2022). We therefore analyzed each item, and ended with a set of 40 articles as pertinent for our review. In fact, we selected those articles actually including BIA data (i.e., not only mentioning BIA), and, similarly, explicitly presenting data in T2DM patients with sarcopenia (or at risk for it). Our search process is schematized in Figure 1. In the following sections, we summarize the main aspects of the selected studies, with special focus on BIA data and role. In each section, articles are presented in chronological order. Some synthetic information for each study are reported in Table 1. Some relevant methodological aspects related to BIA examination, explored when presenting the selected studies, are summarized in Figure 2. 3. BIA in Sarcopenia and T2DM: Cross-Sectional Studies In the present section, we describe cross-sectional studies where BIA was applied to subjects with T2DM and sarcopenia (or at risk for it). We include here those studies where there is no additional information of nutritional type, whereas we will describe studies where body composition data are complemented with explicit nutritional information in the following section, such as diet habits, etc. The present section is organized in two subjections: the “early” studies (until 2019), and the recent studies (i.e., in the last years: since 2020 onwards). 3.1. “Early” Studies The first study that we have identified as pertinent for our review was carried out in 2010, by Tajiri et al. [26]. Body composition was analyzed using BIA in 198 patients with T2DM, and the same number of healthy subjects were used for comparison, matched for gender, age, and BMI (107 men and 91 women in both groups). It is reported that BIA was performed by the INBODY720 device, without further details. The skeletal muscle amount (M) and its percentage related to body weight (M%) were assessed for the whole body and at isolated level (in each arm and leg). The fat mass (F) and the percentage for body weight (F%) were also estimated. Results showed that whole body M%, especially that of the lower limbs, was lower in T2DM compared to normal subjects. A progressive reduction of M% in the lower limbs was assigned to an increasing risk factor for cardiovascular disease. Regional sarcopenia (at lower limbs, named “leg sarcopenia”) was found in long-term diabetic patients with insufficient physical exercise. Somehow, similar to the Tajiri’s study, in 2013, Buffa et al. [27] investigated the characteristics of body composition in elderly subjects with T2DM, compared with healthy controls matched for age and BMI. A specific type of BIA analysis, called bioelectrical impedance vector analysis, was applied. T2DM patients were 144 free-living subjects (84 women and 60 men), 60 to 84 years old, whereas healthy controls were 209 (116 women and 93 men). Bioelectrical impedance vector analysis helped to calculate body resistance (R) and reactance (Xc), measured with a single-frequency impedance analyzer (BIA 101, Akern srl, Florence, Italy), and standardized by height. The phase angle was calculated as arctan (Xc/R) and the impedance vector as (R2 + Xc2)0.5. Individual vectors of the bioelectrical values in the healthy controls were exploited to plot the so-called “tolerance ellipses”. The vector position in the plane was then assumed as an indicator of body composition, the minor axis indicating the cell mass, and major axis referring to the hydration status. It was found that, compared to healthy subjects, T2DM female patients showed lower resistance, and male patients showed higher reactance. It was concluded that T2DM patients showed bioelectrical abnormalities that can be related to smaller appendicular muscular area and lower extracellular/intracellular water ratio, and such abnormalities can be risk factors for sarcopenia. In 2016, the study by Rizzo et al. [28] investigated the effects on sarcopenic parameters of antidiabetic agents, i.e., dipeptidyl peptidase 4 inhibitors (DPP4-I) or sulphonylureas (following at least 24 months of treatment). A group of 80 elderly T2DM patients was studied (42 males and 38 females, 65 years or older with minimum 5-year since T2DM diagnosis). Body composition was assessed using a BIA Handy device (DS Medica, Milan, Italy). Bioimpedance was measured (coefficient of variation of 16%); this helped to estimate fat-free mass (FFM), fat mass (FM), the FFM/FM ratio, the total body water, and extracellular and intracellular water. Skeletal muscle mass (SMM) was calculated using a BIA-based equation, i.e., SMM = 0.401 × (height2/resistance) + (3.825 × gender) − (0.071 × age) + 5.102, with height in cm, resistance in ohm, and gender equal to 1 for men and 0 for women. The SMM index was then computed as 100 × SMM, normalized to height squared, and it was considered abnormal if lower than 8.87 kg/m2 in men and 6.42 kg/m2 in women. It was found that those treated with DPP4-I showed better sarcopenic parameters compared with those under sulphonylureas. Specifically, the DPP4-I group had greater muscle mass, and better physical performance and strength, as compared to the sulfonylureas-treated group. However, it was reported that it was currently not known whether the action mechanism of DPP4-I on sarcopenic parameters is direct or indirect. In the same year, Hashimoto et al. [29] investigated the association between hepatic steatosis and the skeletal muscle mass index (SMI) in 145 Japanese T2DM patients (79 men and 66 women). The investigation was motivated by the fact that some previous studies revealed an association between sarcopenia and fatty liver diseases; however, specific analysis in T2DM patients had not been performed at that time. BIA was measured using a multifrequency impedance body composition analyzer (InBody 720, InBody Japan Inc., Tokyo, Japan). The SMI was defined as the skeletal muscle mass normalized to total body weight × 100. The visceral fat area was also assessed. Hepatic steatosis was evaluated with transient elastography. Results showed that the SMI was inversely associated with hepatic steatosis in T2DM men, likely due to the fact that reduced skeletal muscle mass is associated with insulin resistance; on the other hand, insulin resistance is typical of the hepatic steatosis and of the T2DM condition. In addition, the insulin-like growth factor 1 (IGF-1) was found decreased in patients with insulin resistance, and showed an important role on skeletal muscle mass. In 2017, Tuzun et al. [30] assessed the relationship between bioimpedance measurements and metabolic parameters in T2DM patients, at possible risk for sarcopenia. A group of 359 patients aged less than 65 years was studied (81 men, 278 women). BIA analysis was performed using a TANITA 48M device (no further details provided). Body fat mass, total muscle mass, and appendicular muscle mass were derived using BIA. The skeletal muscle index and percentage were calculated as the appendicular muscle mass normalized by body height squared and by body weight, respectively. Total muscle index and percentage were similarly calculated from the total muscle mass. It was found that, after adjusting for age and gender, there was no relationship between muscle-related parameters and fasting plasma glucose, as well as triglycerides and LDL-cholesterol; however, there was direct correlation with C-peptide, and inverse correlation with HDL-cholesterol. Several studies relevant for our review were performed in 2018 and 2019. In 2018, Osaka et al. [31] investigated the hypothesis that the ratio between serum creatinine and cystatin C (Cre/CysC) could be used as a surrogate marker for sarcopenia. A group of 285 patients with T2DM was studied (159 man, 126 women). BIA was measured using the InBody 720 (InBody Japan Inc., Tokyo, Japan). The skeletal muscle index (SMI) was calculated as the appendicular skeletal muscle mass divided by height squared. For sarcopenia definition, Japan Society of Hepatology guidelines were considered [19]; thus, sarcopenia was defined as an SMI of <7.0 kg/m2 in men and <5.7 kg/m2 in women, and handgrip strength was <26 kg/m2 in men and <18 kg/m2 in women. Results showed that 8.8% of the patients had sarcopenia. Authors suggested a cut-off value of 0.90 (higher value meaning higher sarcopenia risk) for the Cre/CysC ratio to detect sarcopenia, and concluded that the Cre/CysC ratio is usable as a simple screening tool to identify T2DM patients at high risk for sarcopenia, especially in patients also suffering for renal dysfunction. However, it was observed that possible causal relationship between sarcopenia and the Cre/CysC ratio needed further investigation. In the 2018 study, Tuzun et al. [32] assessed the prevalence of sarcopenia in T2DM patients using different criteria based on BIA. In total, 295 patients were involved in the study (176 females, 119 males), with age ≥ 18 years and BMI ≥ 30 kg/m2. BIA was performed in all participants using TANITA-48M device (TANITA, Tokyo, Japan). BIA measurements consisted of total fat mass, total muscle mass, and sum of the appendicular muscle masses (ALM) of the four limbs. Total muscle mass was calculated with the same formula previously reported for the study of Rizzo et al. [28], and the skeletal muscle index was assessed as the total muscle mass divided by the body height squared. The body muscle ratio was assessed as the total muscle mass divided by the body weight. Body fat percentage was calculated as the ratio of total fat mass to body weight, multiplied by 100. For the skeletal muscle index and body muscle ratio, some cut-off values were identified based on the analysis of such parameters distribution in young healthy people. Thus, sarcopenia of Class 1 was considered for the skeletal muscle index in the 8.51–10.75 range for men and in the 5.76–6.75 range for women (units: kg/m2). Accordingly, Class 2 sarcopenia was defined for the skeletal muscle index, i.e., <8.50 in men and <5.75 in women. Alternatively, Class 1 sarcopenia was defined as the body muscle ratio in the 31.5–37.0 range for men and the 22.1–27.6 range for women (units: %), whereas Class 2 required a body muscle ratio of <31.5 in men and <22.1 in women. Class 1 sarcopenia was also defined as a ALM/BMI ratio of <0.789 in men and <0.512 in women (units: kg/kg/m2). Results showed that sarcopenia was determined in 40 males using the body muscle ratio, in 15 using the ALM/BMI ratio, and in 1 participant using the skeletal muscle index. In females, sarcopenia was found in 61 participants using the body muscle ratio, in 1 participant using the ALM/BMI ratio, and in none using the skeletal muscle index. It was concluded that the prevalence of sarcopenia is low in obese T2DM when the skeletal muscle index or the ALM/BMI ratio are used, and definitely higher when the body muscle ratio is used. The study by Murai et al. [33] aimed to investigate the associations among visceral fat accumulation, skeletal muscle indices (mass, strength, and quality), and cardiovascular diseases in 183 hospitalized T2DM patients (126 men and 57 women, 33–88 years). The visceral fat area was measured using the EW-FA90 (Panasonic Corporation) instrument, while the muscle masses of the trunk, arms, and legs were measured using the InBody 720 (InBody Japan Inc., Tokyo, Japan) BIA device. The skeletal muscle index (SMI) was defined as the height-adjusted appendicular skeletal muscle mass, i.e., muscle mass of the arms and legs normalized to height squared. The grip strength was measured using a dynamometer, and muscle quality was calculated as the ratio of grip strength to the arm muscle mass, measured on both sides and then averaged. Several blood parameters possibly related to cardiovascular diseases were also measured, such as glucose, glycated hemoglobin (HbA1c), C-peptide, alanine transaminase, uric acid, cholesterol, triglycerides, creatinine, CRP, and brain natriuretic peptide. The prevalence of sarcopenia was defined by the criteria of the Asian Working Group [17]. Thus, cut-off values for the SMI were set to 7.0 kg/m2 for men and 5.7 kg/m2 for women. Cut-off values for grip strength were set to 26 kg for men and 18 kg for women. Sarcopenia was considered present when both the SMI and grip strength were below the respective cut-off values. Results showed that sarcopenia was present in 22% of the patients. Skeletal muscle mass was lower in the visceral fat accumulation group than in the other subjects’ group. Muscle quality was also significantly lower in patients with visceral fat accumulation. Furthermore, in the visceral fat accumulation group, patients with low muscle quality had higher prevalence of cardiovascular diseases, and longer duration of diabetes. Sex- and age-adjusted models showed significant association between low muscle quality and cardiovascular diseases in all subjects, but especially in patients with visceral fat accumulation. It was concluded that T2DM patients with visceral fat accumulation had low muscle quality, and patients with low muscle quality were more affected with cardiovascular diseases. In the 2018 study, Hashimoto et al. [34] investigated the association between sarcopenia and blood pressure parameters in elderly patients with T2DM. In total, 146 patients (86 men and 60 women), aged ≥ 65 years, were studied. Body fat mass, skeletal muscle mass, and appendicular muscle mass were obtained using the BIA InBody 720 (InBody Japan, Tokyo, Japan). The skeletal muscle mass index (SMI) was calculated as the appendicular muscle mass normalized to height squared. The definition of sarcopenia was based on the guidelines for sarcopenia of the Asian Working Group for Sarcopenia, requiring both the SMI and handgrip strength impairment [17]. The prevalence of sarcopenia was 14.4%. In patients with sarcopenia, the coefficient of variation of systolic blood pressure (SBP) was higher than that in patients without sarcopenia, although the average SBP was not different between sarcopenic and non-sarcopenic subjects. In addition, regression analysis showed that sarcopenia was associated with the coefficient of variation of SBP even after adjusting for covariates, whereas sarcopenia was not associated with the average SBP. It was concluded that sarcopenia is associated with blood pressure variability, but not with its absolute values. In 2019, Fukuoka et al. [35] investigated the prevalence of sarcopenia, its related factors, and the indicators of physical performance in elderly T2DM. A group of 267 patients (159 men, 108 women), aged > 65 years, was examined. Body composition was measured using InBody 770 (InBody, Japan Inc., Tokyo, Japan). The skeletal muscle mass index (SMI) was calculated by dividing the limb skeletal muscle mass by height squared, and a low SMI was defined as SMI < 7.0 kg/m2 in men and <5.7 kg/m2 in women, in agreement with previous studies, such as that by Hashimoto et al. [34] illustrated above. In addition to the SMI assessed by BIA, the grip strength and the usual gait speed were measured as indicators of physical performance. The prevalence of sarcopenia was 18.7%. Analyses showed that sarcopenia decreased as BMI increased, whereas sarcopenia tended to increase for higher body fat. In addition, sarcopenia was associated with the non-use of metformin and lower bone mineral content in men, as well as lower bone mineral content, lower serum levels of albumin, and older age in women. It was concluded that T2DM patients with high body fat percentage in addition to low BMI may develop sarcopenia. The study by Oh et al. [36] in 2019 aimed to evaluate muscle mass, strength, and physical performance in subjects with T2DM and assess whether diabetic peripheral neuropathy was a significant risk factor for sarcopenia. In total, 170 patients were studied, aged 50 years and older (93 males, 77 females). Sarcopenia was diagnosed, according to the Asian Working Group for Sarcopenia criteria [17]. To this purpose, in addition to BIA, a handgrip test and a gait speed test over 4 m walking distance were performed in all patients. BIA was performed using the InBody 770 device (InBody, Seoul, Korea). Of note, in the article, it is specified that participants were asked to wear light clothes for BIA measurements. The parameter considered as the muscle mass index was the appendicular skeletal muscle mass divided by the height squared. Various examinations of neuropathy were also carried out, including both small- and large-fiber neuropathy. In addition, neuropathy was also evaluated using an appropriate questionnaire (Michigan Neuropathy Screening Instrument Questionnaire). It was found that sarcopenia prevalence was at 14.1%. The questionnaire scores were higher in patients with sarcopenia, although other neuropathy examination results were not significantly associated with sarcopenia. It was concluded that active screening for sarcopenia should be performed in subjects with diabetic peripheral neuropathy. A similar study was performed in the same year by Yasemin et al. [37], but with a larger number of patients. Indeed, 602 T2DM patients were studied, from 18 years onwards, but with average age of 60.2 years (244 men, 359 women). Of them, 512 had diabetic neuropathy. BIA was performed with patients in light clothes using the Tanita body composition analyzer (no further details reported). Absolute skeletal muscle mass was converted into the percentage skeletal muscle mass (muscle mass/body mass × 100) and termed as the skeletal muscle index (SMI). Those who had a SMI less than a −1 standard deviation of the average SMI in young adults aged 18–40 years were defined as having low muscle mass. Thus, based on this, the cut-off level for SMI was taken as 37% for men and 28% for women. The handgrip test was also performed (cut-off of 30 and 20 for men and women, respectively). Those who showed only reduced handgrip strength were categorized in the s-presarcopenia group, whereas patients with only muscle mass (volume) loss were categorized in the v-presarcopenia group; those who had both defects were defined as sarcopenic. Sarcopenia that was accompanied by obesity was defined as sarcopenic obesity. It was found that sarcopenia prevalence was higher in in patients with diabetic neuropathy than in those without it (24.7% vs. 8.9%). On the other hand, diabetic neuropathy prevalence was 80.2% in those who had normal muscle mass and strength, 84.4% in s-presarcopenic patients, 82.1% in v-presarcopenic patients, and 94.1% in sarcopenic patients. Of note, diabetic neuropathy prevalence reached 95.9% in sarcopenic obese patients. Thus, the study revealed a clear relation between sarcopenia and diabetic neuropathy. The study by Su et al. [38] aimed to investigate the value of the skeletal-to-visceral ratio (SVR) in the prediction of non-alcoholic fatty liver disease (NAFLD) in patients with T2DM, possibly complicated by sarcopenia or sarcopenic obesity. In total, 445 T2DM patients were recruited (236 men, 209 women, aged 40–75 years). The lean body mass of arms and legs, the appendicular skeletal muscle mass (ASM, as a sum of the lean soft tissue masses in the arms and legs), and the visceral fat area (VFA) were calculated by BIA (InBody 720; Biospace, Land Seoul, Korea). Notably, the analyzer measured resistance at six frequencies (1, 5, 50, 250, 500 kHz, and 1 MHz) and reactance at three frequencies (5, 50, and 250 kHz). Of note, it was observed that good correlation was shown between VFA measured by BIA and that measured by abdominal computed tomography. SVR was calculated as ASM normalized to VFA, and assumed as an index of sarcopenic obesity. Hepatic steatosis for NAFLD diagnosis was assessed using the ultrasonic approach. Results showed that NAFLD prevalence increased with decreased SVR (significant differences were observed between the highest and lowest tertiles). Furthermore, SVR values in the lowest tertiles were independently associated with the presence of NAFLD in females. It was concluded that T2DM patients with lower SVR levels (who may also be sarcopenic) are associated with higher risks of developing NAFLD-related complications. 3.2. Recent Studies In this subsection, we include those studies that we have defined as “recent”, that is, from 2020 onwards. The study by Medeiros et al. [39] aimed to study patients on hemodialysis and determine the associations of sarcopenia with serum sclerostin concentrations, which is an osteoblast-inhibiting glycoprotein secreted mainly by osteocytes and regulated by hormonal changes and skeletal loading. A group of 92 hemodialysis patients (average age of 63.3 years) was studied—41 with T2DM (26 males, 15 females) and 51 without diabetes (32 males, 19 females). Multifrequency electric BIA (Biodynamics 310, Biodynamics Corporation, Shoreline, WA, USA) was performed immediately after hemodialysis, that is, with a weight considered to be “dry”. The fat-free mass index was calculated by dividing fat-free body mass by squared height. This was assumed as the skeletal muscle mass index. Values of 10.75 kg/m2 or less in men and 6.75 kg/m2 or less in women were considered to be low. Handgrip strength and physical performance tests were also performed for the diagnosis of sarcopenia. A low muscle mass index was identified in 65.2% of the individuals studied (76.7% were male and 36.7% diabetic), of which 10.9% were at the pre-sarcopenia stage, 23.9% had sarcopenia, and 30.4% had severe sarcopenia. Mean serum sclerostin was higher in men, in those individuals with the low muscle mass index, and in diabetic patients. After adjustments for potential confounders, high serum sclerostin was independently associated with the low muscle mass index and with presence of diabetes. It was concluded that serum sclerostin is directly related to diabetes and inversely related to muscle mass in hemodialysis patients. The study by Seo et al. [40] aimed to evaluate the association between skeletal muscle mass and carotid atherosclerosis in men and women with T2DM. In fact, it was observed that sarcopenia was already known to lead to metabolic and vascular abnormalities; however, in T2DM patients, little was known regarding the independent relationship between skeletal muscle mass and atherosclerosis. In total, 8202 patients with T2DM were recruited (4156 men, 4046 women), with age ≥ 19 years (average age equal to 57.7 years). Body composition was assessed using a segmental multifrequency BIA device (InBody 4.0; Biospace, Seoul, Korea). The skeletal muscle mass (SMM) was recorded in kilograms, while the skeletal muscle mass index SMI was calculated by dividing the SMM by total body weight × 100. Both carotid arteries were examined using B-mode ultrasound (carotid atherosclerosis was defined by having a carotid plaque or mean carotid intima-media thickness ≥ 1.1 mm). It was found that among the entire population, 52.4% of subjects had carotid atherosclerosis, and the prevalence of carotid atherosclerosis increased with decreasing SMI quartiles for both sexes. In addition, in men, the risk of atherosclerosis increased linearly with decreasing SMI quartiles. It was concluded that low skeletal muscle mass was independently associated with the presence of carotid atherosclerosis in T2DM. In the study by Jung et al. [41], the main aim was to investigate the relationship of sarcopenia with microcirculatory function, as assessed by skin perfusion pressure (SPP), in T2DM patients. In total, 102 T2DM patients (average age 55.9 years, 63.7% men), who underwent both SPP measurements and BIA, were enrolled in the study. BIA was used to determine the appendicular skeletal muscle mass (ASM), which was calculated by summing the lean mass in the arms and legs, primarily representing skeletal muscle mass in the extremities (the used BIA device was however not indicated). Sarcopenia was defined as low muscle mass, based on ASM divided by height squared, with cut-off values of 7 kg/m2 in men and 5.7 kg/m2 in women. SPP was assessed using the laser Doppler technique. Participants were divided into two groups based on SPP (≤50 and >50 mm Hg), and the low SPP group was considered as having impaired microcirculation. Based on such definition, 13.7% of participants were diagnosed with impaired microcirculatory function. The prevalence of sarcopenia in all subjects was 11.8%; however, the percentage of patients with low SPP who had sarcopenia was more than triple that of patients with normal SPP. In addition, a positive correlation was found between SPP and appendicular muscle mass adjusted for height. Thus, results suggested that sarcopenia may be significantly associated with impaired microcirculation in patients with T2DM, though it was acknowledged that the relatively small number of patients required cautious interpretation of such findings. Somehow, the study by Seo et al. [42] had similar aims to the study by Su et al. [38]; however, it covered a higher number of subjects. Indeed, in Seo’s study, association between sarcopenia and NAFLD was investigated separately in men and women with T2DM, in a cohort of 4210 patients (2160 men, 2050 women, average age of 57.4 years). BIA (by InBody 4.0, InBody Co., Ltd., Seoul, Korea) was used to determine the appendicular skeletal muscle mass (ASM), calculated as the sum of the lean mass in arms and legs, similar to the study by Jung et al. [41] illustrated above. The skeletal muscle mass index (SMI) was then calculated as ASM normalized to body weight. Sarcopenia was defined as a gender-specific SMI value > 2 standard deviations below the mean for healthy young adults. NAFLD was defined as the presence of hepatic steatosis on ultrasonography with no other causes of chronic liver disease. It was found that 29.5% of the patients had sarcopenia, whereas 30.4% had NAFLD, and the prevalence of NAFLD was significantly higher in those with sarcopenia, both in men and in women. In addition, sarcopenia was significantly associated with higher risk of NAFLD in men, while the association was attenuated in women after adjusting for clinical risk factors. Thus, in men with T2DM, sarcopenia appears independently associated with NAFLD, which suggests that sarcopenia may be a risk factor for NAFLD in T2DM men. The study by Low et al. [43] moved from the consideration that lower extremity skeletal muscle mass (LESM) in T2DM has been linked to several adverse clinical events; however, at the time of the study, it was not known whether it was associated with cognitive difficulties. Thus, the aim of the study was to investigate whether low LESM, possibly in parallel with a low upper extremity skeletal muscle mass (UESM) and a low total appendicular skeletal mass index (SMI), is associated with reduced cognitive function in people with T2DM. In total, 1235 T2DM patients were studied (641 males, 594 females, aged > 45 years). Body composition was assessed by tetrapolar multifrequency BIA device (InBody-S10; Biospace, Cerritos, CA, USA). Skeletal muscle masses in left and right lower limbs were added and divided by height squared to calculate LESM. UESM was calculated similarly by upper limbs masses. The skeletal muscle mass index (SMI) was calculated as the appendicular lean mass (mass of the four limbs) divided by height squared, which is also useful for the diagnosis of sarcopenia (which may share common underlying background with cognitive impairment). Cognitive function was assessed with appropriate tests, which helped to evaluate five domains of cognition (attention, language, visuospatial/constructional abilities, immediate and delayed memory); hence, an overall cognition score was derived from the five domains. Results showed that LESM, as well as UESM and SMI, were related to the cognition score; however, when examining the single cognition domains, such relationships were not always present. It was concluded that especially lower LESM may be a useful marker of possible co-occurring cognitive dysfunction. The study by Lin et al. [44] analyzed the association of body composition with T2DM, with focus on sarcopenic obesity. Body composition was measured through multifrequency BIA (MF-BIA; InBody 770, Cerritos, CA, USA). BIA was conducted in individuals with T2DM, who were aged ≥ 18 years. In total, 2404 patients were analyzed (1275 men, 1129 women), in a wide span of age (2.2% in 18–<35 years range, 49.8% in 35–<65 range, 27.4% in 65–<75 range, and finally 20.6% aged > 75 years). Fat-free mass (FFM, i.e., muscles, bones, organs, and body fluids), body fat mass (BFM, as total weight—FFM), percent body fat (PBF, as body fat mass/body weight × 100), visceral fat area (VFA, i.e., the estimated area of fat surrounding internal organs in the abdomen), appendicular skeletal muscle mass (ASM), and skeletal muscle index (SMI, as ASM/height squared) were collected for the analyses. Low muscle mass was defined using the SMI (male: <7.0 kg/m2; female: <5.7 kg/ m2), and sarcopenic obesity was defined as low SMI plus high PBF (male: low SMI and PBF ≥ 25%; female: low SMI and PBF ≥ 30%). The prevalence of overall low muscle mass and sarcopenic obesity was 28.0% and 18.7%, respectively, which increased with age. Interestingly, the normal BMI group exhibited a prevalence of low muscle mass of 55.6% and sarcopenic obesity of 34.8%. Thus, the prevalence of low muscle mass and sarcopenic obesity was higher in older adults and people with normal BMI. The study by Minohara et al. [45] moved from the consideration that several genetic loci related to lean mass were identified in healthy individuals; however, the contribution of these loci to body composition in T2DM remained to be investigated, which was the aim of the study. In total, 176 Japanese patients with T2DM across a wide age range (38–92 years, average of 67.4) were studied (106 men and 70 women). BIA was carried out to measure the total and segmental body composition using a commercial device (InBody770, Inbody Japan, Tokyo, Japan). The total lean mass was defined as the sum of the soft lean mass, except for lipids and bone minerals in the whole body, and appendicular lean mass was estimated as the sum of the soft lean mass of both arms and both legs. Body fat mass was defined as the sum of the lipids in the whole body. Body resistance was used to estimate the skeletal muscle mass in the whole body, according to an appropriate formula (the same formula used in the study by Rizzo et al. [28]). Relevant single-nucleotide polymorphisms were evaluated by genotyping and their contributions to body composition were examined considering known clinical determinants. Thus, one single-nucleotide polymorphism (IRS1 rs2934656) was identified as an independent predictor of skeletal muscle mass, and another one (ADAMTSL3 rs4842924) was an independent predictor of body fat mass and appendicular lean mass. It was concluded that the study findings clarified the contribution of genetic factors (IRS1 and ADAMTSL3) to interindividual variation in body composition in T2DM (independently from clinical factors), and such results can contribute to the establishment of effective methods for the prediction, prevention, and intervention for sarcopenia and frailty in diabetic patients. In the study by Jiang et al. [46], the aim was to investigate the association between muscle mass and function (of relevance for possible sarcopenia) and the use of different glucose-lowering drugs. Data of 1042 hospitalized patients (631 males, 411 females) with T2DM were included in this study. Skeletal muscle mass (SMM) was tested with multifrequency BIA (InBody 770, Seoul, Korea) and the skeletal muscle index was equal to the SMM divided by height squared × 100. Muscle strength was tested using the handgrip strength measurement, and taking the maximum reading of at least two trials using both hands in maximum-effort isometric contraction. Physical performance was assessed using six-meter gait speed. Sarcopenia was diagnosed according to the 2019 criteria of the Asian Working Group for Sarcopenia [18]. In such criteria, low muscle mass was defined as in the original set of criteria (briefly reported above, when illustrating the study by Murai et al. [33]), whereas one update was related to low muscle strength in men, defined as handgrip strength < 28 kg (whereas it remained as before for women). In addition, in the new criteria, low physical performance was defined as six-meter gait speed < 1.0 m/s. Sarcopenia was again defined as low muscle mass and either low muscle strength or low physical performance. It was found that in patients aged ≥ 60 years, 141 out of 491 were sarcopenic. Results also showed that the skeletal muscle index, handgrip strength, and gait speed decreased in patients using acarbose compared with the other patients, using different treatments, such as metformin, sulfonylureas, DPP-4 inhibitors, or insulin. Thus, it was concluded that acarbose treatment seems to contribute to decreased muscle mass and strength; hence the assessment of muscles condition, as well as proper exercising, may be extremely relevant in patients with long-term acarbose treatment. The very recent (2022) study by Kis et al. [47] moved from the authors’ consideration that using low handgrip strength cut-off points for the initial identification of sarcopenia, according to the 2019 guidelines of the European Working Group on Sarcopenia in Older People (EWGSOP2) [16], may mask the presence of sarcopenia. Thus, the relative knee extension strength test may help clinicians reduce false-negative results in sarcopenia diagnosis. A cohort of 100 T2DM elderly patients was studied (60% women, average age 74.5 years, mostly obese). Body composition measurements were obtained through segmental multifrequency BIA (InBody 770, InBody Co., Ltd., Seoul, Korea), by the same technician throughout the study. Measurements included fat mass, % body fat, total and segmental skeletal muscle mass (both legs, trunk, and both arms), and yielding appendicular skeletal mass index (ASMI, as the sum of skeletal masses of both arms and legs divided by height squared). In addition, patients underwent handgrip strength (HGS) and knee extension strength (KES) tests. Regression analyses were conducted to examine which variables can best predict ASMI, KES, and HGS. Results showed that using appropriate cut-off points for low KES helped to identify 24 patients with probable sarcopenia and 2 patients with confirmed sarcopenia. Conversely, using the EWGSOP2 cut-off points for low HGS, only one patient with probable sarcopenia was identified and none of the patients with confirmed sarcopenia were identified. Thus, it was concluded that KES cut-off points, attainable with a simple hand-held dynamometer, can assist in the identification of probable and confirmed sarcopenia, as identified by EWGSOP2 cut-off values for low muscle mass, especially in patients with a high BMI. 4. Adding Explicit Nutritional Information In this section, we review the studies in T2DM populations where some explicit information of nutritional type or interest has been reported, in addition to the BIA-derived information of direct relevance for sarcopenia. In 2014, the study by Akpinar et al. [48] assessed muscle mass and strength in T2DM patients, both elderly and younger, as well as in elderly and younger non-diabetic subjects. The specific aim was defining correlates of muscle mass and strength in these subjects. Sixteen elderly T2DM, sixteen younger T2DM, sixteen elderly non-diabetic, and eighteen younger non-diabetic subjects were studied. Fat-free mass (FFM) was measured by BIA using the BC-532 body analysis monitor for personal use (no further details reported). FFM was then corrected relative to height squared (CFFM). CFFM values with 2 standard derivations below the mean value of the younger non-diabetic group were defined as indicators of sarcopenic muscle mass. To assess muscle strength, isokinetic leg extension and flexion tests were performed using a dynamometer. Furthermore, during the exercise tests, in addition to functional capacity and maximum heart rate, the metabolic equivalent (MET) was recorded, which is of direct nutritional interest (though the methodology for MET measurement was not indicated). Results showed that muscle mass was similar between all groups, whereas muscle strength was significantly lower in diabetic and non-diabetic elderly subjects compared with younger diabetic and non-diabetic subjects. Muscle strength was correlated positively with MET, albumin, and hemoglobin, whereas it correlated inversely with age, HbA1c, functional capacity, and CRP. There were no clinically significant correlates of muscle mass. Of note, in this study, the presence or duration of diabetes were not found associated with muscle mass or strength. MET was found higher in younger T2DM compared to elderly T2DM subjects. The main study conclusions were that exercise test parameters may be useful markers in screening for sarcopenia; however, uncomplicated diabetes does not seem to accelerate aging-related muscle mass or strength loss. In 2015, the study by Hamasaki et al. [49] moved from the consideration that loss of muscle mass (sarcopenia) increases the incidence of obesity by reducing physical activity; on the other, hand sarcopenic obesity may become self-perpetuating, since obesity-related reduced physical activity, increasing the risk of sarcopenia worsening. Thus, the study investigated the associations of sarcopenic indices with metabolic parameters related to obesity, in patients with T2DM. Selected sarcopenic indices were the ratio of lower extremity muscle mass to body weight (L/W ratio), and the ratio of lower extremity muscle mass to upper extremity muscle mass (L/U ratio). A group of 26 T2DM patients with obesity (BMI over 30.0 kg/m2) but no physical disability was studied (10 men and 16 women, aged 27 to 76 years old). Body composition was analyzed using BIA (InBody720; Biospace Co., Ltd., Tokyo, Japan). In more detail, segmental body composition was estimated using a patented eight-point tactile electrode system. Similar to what reported in a study illustrated above [38], the device used 6 frequencies (1, 5, 50, 250, 500, and 1000 kHz) and produced 30 impedance values for five body segments, i.e., right and left upper extremities, trunk, and right and left lower extremities. Of note, it was clarified that previous validation studies showed that both fat mass and lean mass, evaluated by the indicated methodology, were highly correlated with those measured by dual-energy X-ray absorptiometry (with a correlation coefficient of 0.832 and 0.899, respectively). Furthermore, in this study, visceral and subcutaneous fat areas were measured using abdominal computed tomography. In addition, daily physical activity was measured by a triaxial accelerometer during a period of hospitalization. The used device differentiated 11 daily activities with almost 100% accuracy, and quantified the metabolic equivalent values (METs), which were strongly correlated with METs calculated from energy expenditure, as measured by indirect calorimetry. For METs, other parameters (such as the total energy expenditure) were derived. It was found that the L/W ratio was negatively correlated with BMI, body fat mass, subcutaneous fat area, and other parameters (such as serum free fatty acid concentration), whereas it was positively correlated with daily physical activity. The L/U ratio was positively correlated with serum HDL cholesterol. It can be concluded that high L/W and L/U ratios, indicative of preserved lower extremity muscle mass, were predictive of improved metabolic parameters related to obesity. Thus, in obese people with T2DM, preserved muscle fitness (especially of the lower extremities) may prevent sarcopenic obesity. In 2019, the study by Küçükdiler et al. [50] aimed to evaluate the relationship between sarcopenia and oxidative stress, as well as antioxidant status, in elderly patients with T2DM. In fact, it was noted that oxidative stress may play a role in the pathogenesis of both sarcopenia and T2DM; however, the relationship between sarcopenia oxidative stress and antioxidant status among the older T2DM population was not well investigated at the time of the study. In total, 60 T2DM elderly patients (≥65 years, BMI < 30 kg/m2) were enrolled (30 sarcopenic and 30 controls, with 19 females and 11 males in each group). Sarcopenia was assessed according to the EWGSOP criteria [15,16], which consider gait speed and handgrip strength, in addition to skeletal muscle mass. The latter was assessed by BIA, which was performed using the Quadscan 4000 body composition analyzer (Bodystat, P.O. Box 50, Douglas, Isle of Man, IM99 IDQ, British Isles). A resistance value at 50 kHz was also considered. The equation that was used to calculate skeletal muscle mass is the same already seen in some of the studied illustrated above, as in the study by Rizzo et al. [28]. After calculating the skeletal muscle mass, the absolute skeletal muscle mass was obtained by normalizing to height squared. Values less than 8.87 kg/m2 and 6.42 kg/m2 indicated low skeletal mass for men and for women, respectively. As regards the other sarcopenia-related parameters, a 4 m gait speed test was performed, with participants walking at their usual speed; in the case of speed ≤ 0.8 m/s, a low speed was classified. Handgrip strength was measured with a handheld digital dynamometer; two measurements were taken from the dominant hand and the highest value was recorded for each patient; and low handgrip strength was defined as <30 kg for men and <20 kg for women. Furthermore, within a comprehensive geriatric assessment, information on nutritional status were collected using an appropriate survey (the so-called “mini nutritional assessment short form”). In addition, several parameters reflecting oxidative stress and antioxidant status were measured, such as the plasma and the erythrocyte malondialdehyde, the glutathione peroxidase, the superoxide dismutase, the catalase, and xanthine oxidase. Results showed that plasma xanthine oxidase was independently associated with sarcopenia; thus, it can be important in the pathogenesis of sarcopenia in diabetes. The score derived by the mini nutritional survey was, however, not different between sarcopenic and control subjects. It was concluded that oxidative stress and antioxidant status might be associated with sarcopenia in older T2DM individuals; however, this association seems to be mediated by other factors. In 2019, the study by Okamura et al. [51] investigated the relationship between sarcopenia and energy intake in T2DM elderly patients, which is an important factor for the maintenance of muscle mass. In total, 391 physically active T2DM patients aged ≥ 65 years (205 men, 186 women) were studied. The body composition was evaluated using the InBody 720 BIA device (InBody Japan, Tokyo, Japan). Body fat mass, skeletal muscle mass, and appendicular muscle mass were obtained, and the skeletal muscle mass index (SMI) was then calculated by dividing the appendicular muscle mass by height squared. Sarcopenia was diagnosed with SMI and grip strength based on the criteria from the Asian Working Group for Sarcopenia [17]. In addition, a brief-type diet history questionnaire (BDHQ) was administered to assess patient’s habitual food and nutrient intake. BDHQ helped to estimate dietary intake for 58 items over the past month, especially the daily intake and type of rice and miso soup, the frequency of consumption of several food and both alcoholic and non-alcoholic beverage items, the usual cooking methods, and general dietary behavior information. Energy, carbohydrate, protein, and fat intake were calculated using the ad hoc computer algorithm, exploiting Japanese standard tables of food composition. Thus, dietary total energy (kcal/day) and carbohydrate, protein, fat, and alcohol intake (all in g/day) were estimated using these calculations. The patients who reported extremely low (under 600 kcal) or high (over 4000 kcal) energy intake were excluded. It was found that 55 patients (14.1%) had sarcopenia. Energy intake was significantly lower in patients with sarcopenia than without sarcopenia, and after adjusting for age, sex, exercise, smoking status, HbA1c, and BMI, the energy intake was negatively associated with the presence of sarcopenia. It was concluded that low energy intake is a risk factor for sarcopenia in T2DM elderly patients. In the same year, Okamura et al. [52] also investigated the association between sarcopenia and brain natriuretic peptide (BNP) in T2DM patients. In fact, it was noted that association between heart failure and sarcopenia had been previously reported; however, the possible association between sarcopenia and BNP, which is a biomarker of heart failure, was unclear. In total, 433 T2DM patients were studied (236 men, 197 women, average age of 65.4 years, BMI < 30 kg/m2). Similar to the previous study [51], body composition was evaluated using the InBody 720 device (InBody Japan, Tokyo, Japan). BIA-measured skeletal muscle mass, appendicular muscle mass, body fat mass, and the skeletal muscle mass index (SMI) were calculated as appendicular muscle mass normalized to height squared. Sarcopenia was diagnosed with the SMI and grip strength according to the Asian Working Group for Sarcopenia criteria [17], similar to the previous study [51]. Again, a brief-type self-administered diet history questionnaire was used to assess the habitual food and nutrient intake, assessing the dietary total energy (kcal/day), as well as the carbohydrate, protein, fat, and alcohol intake (g/day). Results showed that 32 out of 433 patients (7.4%) had sarcopenia. BNP levels were associated with the presence of sarcopenia, and the optimal cut-off point for BNP levels, indicating risk for sarcopenia, was determined as 27.3 pg/mL. It was concluded that in T2DM non-obese patients without heart failure, BNP levels are associated with sarcopenia. In 2020, the study by de Freitas et al. [53] aimed to establish the prevalence of sarcopenia and associated factors in elderly patients with T2DM, according to both 2010 and 2019 criteria of the European Working Group on Sarcopenia in Older People (EWGSOP [15] and EWGSOP2 [16], respectively). In total, 242 T2DM patients ≥ 60 years were studied (54% women, 78% white, average BMI of 29.5 kg/m2). Muscle mass was assessed by BIA (InBody 230, no further details reported), which provided total and segmental muscle mass through the arms and legs. These values were used to calculate appendicular skeletal muscle mass (ASM) as the sum of the muscle mass of arms and legs. The skeletal muscle mass index (SMI) was determined by ASM divided by height squared. Muscle strength was assessed using the handgrip strength test, and physical performance was assessed using the timed-up-and-go test. For the latter, EWGSOP and EWGSOP2 cut-off values were the same (impaired if higher than 20 s), whereas there were differences for the SMI and the handgrip test. Indeed, according to EWGSOP, a low SMI was below 8.5 kg/m2 in men and 5.75 kg/m2 in women; however, for EWGSOP2, it was below 7.0 and 6.0 kg/m2, respectively. As regards handgrip strength, for EWGSOP, low values were below 30 kg and 20 kg in men and women, respectively; however, for EWGSOP2, low values were below 27 and 16 kg, respectively. As regards nutritional information, a usual diet was assessed through a food frequency questionnaire specifically validated in T2DM. The reported intake of food groups was converted into daily consumption (also expressed as a percentage of the total energy), and a Brazilian food composition table was used to evaluate the nutritional composition of the questionnaire items. It was found that the overall prevalence of sarcopenia was 21% (i.e., with either EWGSOP or EWGSOP2 criteria). In more detail, EWGSOP sarcopenia prevalence was 17%, whereas it was 7% with EWGSOP2, suggesting 3% prevalence with both criteria. In addition, age and male sex were found to increase the prevalence of sarcopenia, whereas walking (>5400 steps/day) had a strong protective effect. In contrast, nutritional information did not appear to provide relevant information regarding sarcopenia, with only carbohydrate intake being higher in patients with sarcopenia. In a 2020 study, Okamura et al. [54] investigated the relationship between sarcopenia and omega-3 fatty acid intake in elderly T2DM patients, which is known to be important to maintain muscle mass. In total, 342 patients (180 men, 162 women) aged >65 years, with no evidence of physical inactivity, were studied. Body composition was determined by BIA (InBody 720, InBody Japan, Tokyo, Japan)to assess body fat mass, skeletal muscle mass, and appendicular muscle mass, and to calculate the skeletal muscle mass index, as carried out in previous studies by Okamura et al. [51,52], illustrated above. Diagnosis of sarcopenia was based on the Japan Society of Hepatology guidelines, similar to the study by Osaka et al. [31], as previously illustrated. For the assessment of habitual food and nutrient intake, the brief-type self-administered diet history questionnaire was used, similar to what described in some of the previous studies [51,52]. Results showed sarcopenia prevalence at 13.2% (45 patients). Patients with sarcopenia had a higher age and lower BMI than those without sarcopenia. In addition, omega-3 fatty acid intake in patients with sarcopenia was lower; on the other hand, omega-3 fatty acid intake was negatively associated with sarcopenia presence after adjusting for age, sex, exercise, smoking status, diabetes duration, and HbA1c, as well as energy, protein, and fat intake. Thus, it was concluded that omega-3 fatty acids increase muscle mass and improve skeletal muscle strength, likely by promoting neuromuscular function. In 2021, the study by Oguz et al. [55] aimed to evaluate sarcopenia and sarcopenic obesity in patients with T2DM, and the possible relationships of sarcopenia with serum irisin and myostatin levels. A group of 90 T2DM patients (20 males, 70 females), aged 18–70 years, with a BMI of 25–40 kg/m2, was studied. Body composition was measured by BIA (TANITA DC 360 ST, Tokyo, Japan). In the article’s methods, it is specified that patients were asked to not eat, drink, or undertake any physical activity at least three hours before the test, and to void the bladder immediately before the measurement. Fat mass, fat-free mass, and appendicular skeletal muscle (ASM) measurements were recorded. The skeletal muscle index (SMI) was calculated as ASM divided by height squared, and %ASM as ASM divided by body weight × 100. Handgrip strength tests and 6 m gait speed tests were also used; hence, sarcopenia was diagnosed according to the 2019 EWGSOP criteria [16]. Myostatin and irisin levels were measured using commercially available solid-phase sandwich enzyme-linked immunosorbent assay (ELISA) kits. As regards specific dietary information, it was stated that dietary compliance in the self-management of diabetes was recorded (though details were not provided). It was found that prevalence of sarcopenia and sarcopenic obesity was 25.6% and 35.6%, respectively. Irisin levels were lower in sarcopenic patients. Irisin remained an important predictor of sarcopenic obesity, even after adjusting for confounding variables, with an optimal cut-off value for sarcopenic obesity prediction by irisin equal to 9.49 ng/mL (specificity = 78.1%, sensitivity = 75.8%). Moreover, HbA1c was an independent risk factor for sarcopenic obesity. Conversely, the rate of dietary compliance was not different between sarcopenic and non-sarcopenic patients (about 48%). It was concluded that low irisin levels and poor glycemic control in T2DM patients are independent risk factors for sarcopenia, especially for sarcopenic obesity. 5. Longitudinal and Interventional Studies In this section, we review the articles related to investigations that included a follow-up period of the studied subjects, possibly with intervention, such as pharmacological treatment. In 2018, the study by Sugiyama et al. [56] aimed to investigate the effects on muscle mass and fat content of sodium–glucose co-transporter 2 inhibitor (SGLT2i) treatment (dapagliflozin). A group of 50 Japanese T2DM patients (HbA1c > 7%) was prospectively recruited, with BMI < 35 kg/m2 (72% males, average age of 56.1 years). Patients were divided into two groups: one treated with dapagliflozin (5 mg/day) and the other one with non-SGLT2i agents. Treatment lasted six months, with HbA1c improvement as primary outcome. Body composition including total fat mass, soft lean mass, and skeletal muscle mass was measured using a segmental multifrequency BIA device (InBody 770). Similar to what reported in studies illustrated above [38,49], the device used tetrapolar eight-point tactile electrodes and processed 30 impedance measurements at a different frequencies (1, 5, 50, 250, 500, and 1000 kHz) on five body segments (right arm, left arm, trunk, right leg, left leg), as well as 15 reactance measurements at three different frequencies (5, 50, and 250 kHz) on the same five body segments. The skeletal muscle mass index, calculated as quantitative indicator of sarcopenia, was computed as skeletal muscle mass normalized to height squared. In addition, the psoas muscle area (other indicator of total skeletal muscle mass) was assessed using abdominal computed tomography, and the psoas muscle index was computed by normalization again for height squared. After the follow-up period, it was found that HbA1c, body weight, and BMI were significantly decreased in both treatment groups, and the HbA1c decrease was not significantly different between groups. In addition, dapagliflozin treatment decreased total fat mass, without affecting the skeletal muscle mass index. In fact, the absolute change in soft lean mass and skeletal muscle mass was not different between groups. Moreover, dapagliflozin treatment did not decrease the psoas muscle index, and its absolute change was not different between groups. It was concluded that in T2DM patients, treatment with dapagliflozin for six months significantly improved glycemic control and reduced body weight without reducing muscle mass. Thus, dapagliflozin appears a proper agent in terms of the effects on the balance between fat and muscle mass. In 2021, the study by LeCroy et al. [57] aimed to determine whether loss of muscle mass is associated with risk for T2DM in Hispanic/Latino adults. Study participants were 6264 Hispanic/Latino adults (48.4% males, 18–74 aged years) without T2DM at baseline, which were prospectively followed on average for 6.1 years. At baseline and at the follow-up visit, participants underwent BIA (Tanita Body Composition Analyzer, TBF-300A; Tanita Corporation, Tokyo, Japan) to measure fat mass and fat-free mass (FFM), which was assumed as a marker of muscle mass. The relative FFM was then defined as FFM divided by body weight, and a percentage change in the relative FFM (%ΔFFM) was calculated as the difference between relative FFMbaseline and relative FFMfollow-up divided by relative FFMbaseline. In addition, sarcopenia was defined as having FFM ≥ 2 standard deviations below the sex-specific average FFM of healthy young (20–29-years old) Mexican Americans. Results showed that relative FFM declined by 2.1% between visits, and that %ΔFFM was inversely associated with changes in glucose and insulin levels, and with incident prediabetes. Associations were generally stronger for individuals with baseline overweight or obesity. The prevalence of sarcopenia was very low (less than 1%); however, in our opinion, this may be due to the assumed criterion for sarcopenia identification, which does not agree with those of the official recommendations. It was concluded that reducing loss of FFM during adulthood may reduce prediabetes risk (primarily insulin resistance), particularly among individuals with overweight/obesity. The 2021 study by Low et al. [58], examined among Asian T2DM patients, showed longitudinal relationships between changes in skeletal muscle mass and those in the estimated glomerular filtration rate (eGFR) and in albuminuria, since in previous cross-sectional analyses it was shown that sarcopenia is associated with lower eGFR. In total, 1272 T2DM patients were prospectively studied (616 men, 656 women, average age of 58.8 years, 693 subjects in in the 40–59 years range). Body composition was measured using tetrapolar multifrequency BIA at baseline (InBody-S20; Biospace, Cerritos, CA, USA) and at one follow-up visit (InBody-S10; Biospace, Cerritos, CA, USA) over a median period of 3.2 years, with a maximum of 7.8 years. The skeletal muscle mass index (SMI) was defined as total skeletal muscle mass/weight × 100. It was found that, at follow-up, about one third of participants had progression of chronic kidney disease (CKD), as shown by proper criteria based on eGFR decline, as well as albuminuria progression. The largest decrease over time from baseline SMI (first tertile) was associated with more than 60% higher risk of kidney disease progression, compared to that of the lowest tertile. Accordingly, every improvement in one standard deviation above baseline SMI was associated with about 20% lower risk of kidney disease progression and albuminuria progression. The main conclusion, therefore, was that low baseline skeletal muscle mass and its reduction over time is associated with increased risk for progression of CKD in Asian T2DM patients. A retrospective longitudinal study was carried out by Buscemi et al. [59], with focus on women. The main aim was to investigate whether body composition analysis predicts the development of impaired fasting glucose (IFG) or T2DM in a cohort of elderly women (≥65 years, average age of 71 years). In total, 159 women were included (with normal glucose tolerance at baseline) over a follow-up period of 94 months. Hand-to-foot BIA was performed (BIA-EFG, Akern srl, Florence, Italy) to estimate the body resistance, reactance, phase angle, percentage of fat mass (FM), and appendicular skeletal muscle mass (ASMM), according to the manufacturer’s equations (Akern, Bodygram Plus software). In agreement to EWGSOP2 guidelines [16], a cut-off value of 15 kg was used for diagnosing BIA-derived low ASMM. FM was exploited to define obesity (FM ≥ 35%). Sarcopenia was defined again in agreement with EWGSOP2 criteria, thus considering the presence of low muscle strength (handgrip strength < 16 kg) in addition to low ASMM. Furthermore, in this study, some explicit nutritional information was also collected. In fact, dietary intake was assessed via combination of a validated food frequency questionnaire and a 7-day food record, and it was calculated using proper nutritional software (MetaDieta 3.0.1, Metedasrl, San Benedetto del Tronto, Italy). The database used to calculate the nutrient intake was primarily derived from the National Institute of Food Research (INRAN) 2000 and the European Institute of Oncology (IEO) 2008. Results showed that participants with low ASMM had a higher IFG/T2DM incidence than those with normal ASMM over time, independently from BMI, fat mass, and habitual fat intake. The prevalence of sarcopenia at baseline was 9% and that of low ASMM was 45%. In the low ASMM subgroup, higher body resistance and lower body phase angle were also detected. Higher incidence of IFG/T2DM was observed in the subgroup with sarcopenia compared to those without sarcopenia, independently from BMI and fat mass. Thus, the study demonstrated that elderly women with low ASMM or sarcopenia had a higher probability of developing IFG/T2DM. In our opinion, this is in line with the hypothesized bidirectional relationship between sarcopenia and T2DM. Thus, not only is T2DM a risk factor for sarcopenia, but also sarcopenia (deteriorated muscle mass) is a risk factor for T2DM. The study by Lee et al. [60] aimed to examine the long-term longitudinal association between the loss of muscle mass and the presence of T2DM or CKD. This study had somehow similar aims compared to the study by Low et al. [58]; however, the recruited population was larger and the follow-up period was longer. Indeed, 6247 middle-aged adults (48.1% males, average age of 51.2 years) were studied, for a period up to 15 years (between 2001 and 2016). Patients were classified into four groups according to the presence or absence of T2DM and CKD. Body composition was assessed using multifrequency BIA (InBody 3.0, Biospace, Seoul, Korea), which was performed at baseline and biennially during the entire study period. Of note, it was explicitly clarified that multifrequency BIA has advantages compared to conventional BIA, since the latter relies on formulae to calculate the estimated mass of each body component, whereas the former assumes that the human body consists of five interconnecting cylinders and performs impedance measurements directly on these compartments (i.e., arms, trunk, and legs). Impedances were measured at four specific frequencies (5, 50, 250, and 500 kHz) using a tetrapolar eight-point tactile electrode system. The primary endpoint was de novo development of muscle depletion, which was defined as a decline in the BIA-derived fat-free mass index (FFMI) below the 10th percentile of the total population sample (for males < 16.9 kg/m2, and for females < 15.2 kg/m2). Secondary outcomes included the occurrence of cachexia (well known to have relationships with sarcopenia) and all-cause mortality. During 73,059 person-years of follow-up (median follow-up of 13.7 years), muscle depletion occurred in about 7% of the subjects. The risk of muscle depletion was higher in subjects with T2DM than in those without T2DM and CKD, and it was strongly pronounced in subjects with both T2DM and CKD. Accordingly, the annual decline rates in FFMI (but also in BMI) were the steepest in subjects with both T2DM and CKD. The secondary outcome analyses showed consistent results. It was concluded that T2DM and CKD are synergically associated with muscle loss over time, and the mortality risk is higher in individuals with muscle loss. The study by Sundar et al. [61] aimed to identify the association of sarcopenia and T2DM with clinical outcomes among hospitalized cardiac patients. This prospective observational study assessed 100 patients (76 males, 24 females, average age of 60.0 years, 82% overweight/obese, 50% with T2DM), which were followed-up until hospital discharge 90 days thereafter. Multifrequency BIA (Seca mVSA 531, Hamburg, Germany) was used to measure body composition, including skeletal muscle mass and fat mass. Patients were asked to void their bladder before the measurement, which was performed 10 min after the patients presumed in supine position on the bed, with arms and legs apart from the trunk. The appendicular skeletal muscle mass index (ASMI) was calculated as appendicular skeletal muscle mass divided by height squared. The handgrip strength test was also performed, and sarcopenia was diagnosed in both low ASMI and low handgrip strength cases, with cut-offs established by the guidelines of the 2019 Asian Working Group for Sarcopenia [18]. Prognosis of the patients was assessed using the prognostic nutritional index (PNI) calculated as 10 × serum albumin (g/dL) + 0.005 × lymphocyte count (per mm3). The outcome measures were the length of hospital stay (LOS), unplanned readmission within 90 days after discharge, and incidence of infections within 90 days after discharge. It was found that the prevalence of sarcopenia was 63%, and this was similar in patients with or without T2DM. After adjustments, sarcopenia was associated with 90-day unplanned readmission and LOS, whereas the condition of co-existence of sarcopenia and T2DM was associated with 90-day unplanned readmission and 90-day incidence of infections. It was concluded that sarcopenia with co-existent T2DM is associated with increased risk for hospital readmission and infections among cardiac patients. Thus, early identification of sarcopenia may be important for timely intervention to improve prognosis in cardiac T2DM patients. Koo et al. [62] aimed to assessed the determinants of glycemic control in elderly people with T2DM, including pancreatic beta-cell function, insulin resistance, muscle mass, and muscle quality. This was a prospective study in T2DM patients aged ≥ 60 years with a T2DM duration of ≥10 years. In total, 100 patients were studied (49% men), whose baseline average characteristics were age of 64 years, BMI of 24 kg/m2, and HbA1c of 7.1%. The median follow-up duration was 4.0 years. BIA was performed using the InBody 330 analyzer (InBody, Seoul, Korea). Muscle mass was expressed as a percentage (muscle/weight × 100) and similarly for fat mass, and their quartiles were calculated for each sex. The handgrip strength test was also performed, and muscle strength values are again stratified in sex-related quartiles. Low muscle mass and low muscle strength were defined according to the EWGSOP2 criteria [16]. Beta-cell function and insulin resistance were calculated from a 75 g oral glucose tolerance test (OGTT). Results showed that low muscle strength and insulin resistance at the baseline were independent determinants of the primary study outcome, i.e., HbA1c deterioration, following adjustment for age, sex, obesity, T2DM duration, antidiabetic medication use, and baseline HbA1c. In addition, sex stratification showed that, in women, both muscle strength and muscle mass were independent determinants of the primary outcome. The main conclusion was that, overall, low muscle strength and insulin resistance are the main risk factors for aggravated glycemic control among elderly patients with long-standing T2DM. The study by Hasegawa et al. [63] aimed to investigate the effect of COVID-19 pandemic restrictions on changes in muscle mass in older patients with T2DM. Data were obtained from patients who underwent BIA at least twice before April 2020 and at least once thereafter. Thus, 56 patients aged > 60 years were recruited (35 men and 21 women, average age of 75.2 years). Body composition was evaluated using a multifrequency impedance BIA (InBody 720, InBody Japan, Tokyo, Japan). Data on appendicular muscle mass and fat mass were collected. The skeletal muscle mass index (SMI) was determined using the appendicular muscle mass divided by height squared. Percent fat mass was calculated as fat mass divided by body weight × 100. Changes in the SMI (kg/m2/year) were calculated as (follow-up SMI—baseline SMI)/follow-up period (in years). Similarly, changes in body weight, appendicular muscle mass, and body fat and percent body fat were also calculated. In addition, low muscle mass was defined as SMI < 7.0 kg/m2 in men and <5.7 kg/m2 in women. Obesity was considered for BMI ≥ 25 kg/m2. Results showed a slight improvement in the SMI before the COVID-19 pandemic, whereas the SMI significantly decreased after the start of the COVID-19 pandemic. This decrease was particularly evident in men, in patients with poor glycemic control, and in those with a long diabetes duration. It was concluded that COVID-19 pandemic restrictions caused muscle mass loss in older patents with T2DM; thus, recommendations for exercise and adequate diet intake should be provided in such a population to prevent loss of muscle mass and, hence, risk for sarcopenia. The very recent (2022) study by Low et al. [64] aimed to study the profile of amino acids longitudinally associated with skeletal muscle mass loss in T2DM. This was a prospective study in 1140 T2DM patients (591 males, 549 females, average age of 56.6 years), followed for a period up to 7.9 years. Skeletal muscle mass was measured using tetrapolar multifrequency bioimpedance analysis at baseline (InBody-S20; Biospace, Cerritos, CA, USA) and at follow-up (Inbody-S10; Biospace, Cerritos, CA, USA). The skeletal muscle mass index (SMI) was defined as total skeletal muscle mass/weight × 100. Cut-off values for a low SMI were assumed as 30.8% in men and 24.3% in women, according to the guidelines from the 2019 Asian Working Group for Sarcopenia [18]. Changes in SMI were calculated as the follow-up SMI minus the baseline SMI. Amino acids were measured by mass spectrometry. Results showed that 43.9% subjects experienced skeletal muscle mass loss. Lower baseline valine, leucine, and isoleucine levels were associated with decreased SMI. Therefore, the main conclusion was that, in T2DM, branched-chain amino acids (valine, leucine and isoleucine) appear to have a preventive role in muscle mass loss over time, though it was acknowledged that further studies should be conducted to elucidate the pathophysiological mechanisms underlying the relationship between these amino acids and muscle mass loss. Another very recent study was carried out by Hoppe et al. [65], which aimed to investigate the impact of T2DM on selected indicators of protein–energy wasting in end-stage renal disease (ESRD) patients, undergoing maintenance hemodialysis (MHD). The study moved from the notion that ESRD is a deteriorating catabolic condition predisposing patients to protein–energy wasting, which is in contrast to T2DM, i.e., a systemic metabolic disease typically associated with overnutrition and consequent overweight/obesity. Based on this, the study intended to investigate the potential paradox of T2DM as a risk factor of protein–energy wasting development in ESRD patients under MHD. The multicenter, prospective, observational study was performed in a cohort of 515 ESRD patients (310 males and 205 females, median age of 67.3 years), followed for a period up to 5 years. Patients were divided into two subgroups, i.e., with and without T2DM (198 and 317, respectively). BIA was performed with a body composition monitor (Fresenius Medical Care Deutschland GmbH, Bad Homburg, Germany) to determine body composition parameters and hydration status. Precisely, the lean tissue mass and fat tissue mass, lean tissue index and fat tissue index (normalization to height squared), overhydration, and relative overhydration were measured. BIA was performed in supine position using disposable electrodes, which were attached to the hand and foot, contralateral to the arteriovenous fistula. Both BIA and the other study measurements were performed shortly before a mid-week hemodialysis session. Specific nutritional information was also detected. In fact, the quantitative evaluation of diet composition was performed in terms of the intake of energy and different nutritional components (proteins, lipids, cholesterol, carbohydrates, fiber, sodium, and potassium). Total metabolic rate, as an indicator of adequate nutritional requirements, was calculated based on body weight, height, age, sex, and activity level. Patients prepared a three-day food diary, including one hemodialysis day and two non-dialysis days. Food intake expressed in grams was converted to nutrient intake using a dietary calculator based on data from the National Nutrient Database for Standard Reference by the United States Department of Agriculture (USDA). In addition, a dialysis-modified questionnaire estimated the subjective global assessment (SGA) score, which is used to assess the protein–energy wasting on the basis of the patient’s history of nutrition quantity and quality, combined with anthropometric measurements of fat and muscle mass variations. SGA scores ranged from 7 (proper nutritional state) to 35 (severe malnutrition). Results showed that, compared to non-diabetic patients, T2DM patients had a higher SGA score, BMI, fat tissue mass and index, and overhydration/relative overhydration, whereas they had lower albumin, total cholesterol, and creatinine. Furthermore, increased morbidity and mortality was also observed in T2DM patients, for both cardiovascular diseases and other causes. It was concluded that hemodialysis patients with T2DM, on the one hand, show overnutrition, but a paradoxically higher predisposition to protein–energy wasting, expressed by a higher SGA score, on the other, as well as lower serum markers of nutrition. Of note, it was also suggested that higher protein–energy wasting, despite higher BMI and caloric intake, observed in T2DM, may be indicators of obese sarcopenia. However, in our opinion, it was however somehow surprising that the lean tissue mass and index were not different between T2DM and non-T2DM. 6. Concluding Comments In this review study, we summarized the studies in patients with T2DM and sarcopenia (or at risk for it), where BIA is used for body composition analysis. For easier readability, we grouped the revised studies into three main sections, i.e., one for the cross-sectional studies, one for cross-sectional studies also including specific nutritional information (that we have emphasized in our analysis), and one for the longitudinal studies, either observational or interventional. First, it is worth recalling that BIA is important for the diagnosis of sarcopenia, since it is among the techniques for the assessment of muscle mass (this being one of the sarcopenia factors) that are deemed acceptable by different international guidelines [15,16,17,18,19]. Of note, in the studies examined in this review, possible differences of sarcopenia prevalence in T2DM, according to different guidelines, has also been addressed. However, apart for sarcopenia diagnosis, in the context of T2DM-related sarcopenia, BIA appears useful for several purposes. In fact, our review shows that, in T2DM patients with sarcopenia (or at sarcopenia risk), BIA has proven relevant for several scientific and clinical goals, as it has been exploited in studies with very different outcomes. Some studied have contributed to the knowledge of the muscle mass condition in T2DM compared to healthy (or at least non-T2DM) control subjects, with analyses of the muscle mass either at whole body level or at specific body segments (especially lower limbs). Other studies have shown the associations between muscle mass deterioration (and possibly overt sarcopenia) in T2DM with different diabetes-related complications, such as atherosclerosis and microcirculation impairment, chronic kidney disease, hepatic steatosis and specifically non-alcoholic fatty liver disease, diabetic neuropathy, infections, and even cognitive dysfunction. Furthermore, some studies showed associations of T2DM-related sarcopenia/muscle mass loss with specific disorders or pathophysiological conditions, such as high levels of HDL-cholesterol, oxidative stress, high blood pressure variability, as well as genetic factors. In addition, abnormal values of some serum/plasma parameters were suggested as possible markers of muscle loss and possibly sarcopenia, such as the creatinine to cystatin C ratio, irisin, sclerostin, and brain natriuretic peptide. On the other hand, some studies showed the beneficial effect of nutraceuticals or dietary supplements in increasing muscle mass or at least preventing muscle mass loss, such as omega-3 fatty acids and branched-chain amino acids. Some studies also investigated the effects on muscle mass of some antidiabetic agents, such as DPP-4 inhibitors, sulphonylureas, SGLT2 inhibitors, and acarbose, showing that some agents appear beneficial, whereas others may be harmful. One study also showed the negative effects of the COVID-19 pandemic restrictions, in terms of muscle mass loss in elderly patients with T2DM. In summary, in patients with T2DM with sarcopenia or at sarcopenia risk, BIA has been extensively used for the study of body composition (especially for muscle mass assessment). Notably, due to non-invasiveness, simple execution, and relatively low cost, BIA appears adequate for use in clinical practice, and this is mirrored by the large number of patients analyzed in the studies addressed in this review. Figure 3 shows the percentage of studies at different size of the studied cohort (<50 subjects, 50–99 subjects, 100–499 subjects, 500–999 subjects, 1000–4999 subjects, ≥5000 subjects). It is interesting to observe that more than 20% of the studies included more than 1000 subjects (about 8%: more than 5000). Overall, in the 40 studies addressed in this review, almost 39,000 subjects were studied. In our opinion, this is a clear indication that investigators consider BIA as trustworthy for the study of body composition (and, specifically, muscle mass) in T2DM patients with sarcopenia or at risk of the disease. The acceptance of the scientific community for such approach is further indicated by the high number of citations of some of the revised articles, in relation to the typically recent publication year, as shown in Table 1 (on the other side, absence of citations for some articles is likely due to the very recent publication date). In our opinion, this further suggests that study methodologies were deemed reliable, obviously including the BIA approach for the analysis of muscle mass, which was a key issue in all examined studies. On the other hand, one may wonder: is current evidence in terms of BIA reliability totally satisfactory for investigations in T2DM patients with sarcopenia or at least with muscle mass loss? It has been reported that several studies demonstrated the validity of both single-frequency and multifrequency BIA, concluding that BIA may be used as an alternative to more complex/expensive techniques, such as DXA, for whole-body and segmental body composition assessment, especially in large cohorts [14]. However, it has also been documented that some studies demonstrated differences when comparing BIA to DXA, especially for segmental measures or with single frequency devices, with the inaccuracy increasing with higher BMI levels [14]. Interestingly, it was also reported that the accuracy of different BIA devices may vary, and may depend on the specific body composition parameters assessed, as well as on the equations used by the device in question, with many manufacturers using their own equations derived during the internal validity testing of the product [14,66,67]. This may result in equations that are general, with little specificity to varying populations. Based on these premises, in our opinion, it is worth noting that BIA accuracy should have been investigated in the specific population of patients with T2DM and concomitant sarcopenia. However, to our knowledge, no study focused on BIA validation in a T2DM sarcopenic population. Such kind of study may be relevant to clarify whether BIA is really sufficiently reliable (as we expect) in T2DM sarcopenic patients and, possibly, under what peculiar conditions/circumstances special caution should be required. In other words, such a study can draw the lines for “guaranteed” use of BIA in T2DM sarcopenic patients, leading to greater awareness in BIA use for such a population. Another question may be as follows: assuming that BIA can be deemed as reliable in T2DM sarcopenic/low muscle mass population, are there nonetheless limitations regarding the use of BIA in the studies analyzed in this review? In essence, the great majority of analyzed studies assessed the same parameters of body composition. Investigated parameters were typically fat mass and fat-free/skeletal muscle mass at a whole body level and sometimes at different body regions (e.g., arms, legs, and possibly trunk), usually normalizing to body weight or height squared (though not always motivating the preference for one or the other normalization). Just a few studies investigated other parameters that BIA can provide (at least, the more modern devices), such as intracellular, extracellular, total body water, and bone mineral content. The lack of analysis of such “advanced” body composition parameters is common to many of the analyzed studies. In addition, for improved accuracy, BIA measurements should be consistently taken in similar conditions among patients (for instance, in the morning at fasting, to limit the effect of differences in the state of hydration of the patients). However, surprisingly, the majority of the revised studies did not clarify under what specific conditions BIA was performed, thus this is another quite common limitation. On the other hand, it has to be acknowledged that the overall quality of the revised studies was acceptable in our opinion, and even remarkable in some cases. In fact, we analyzed the revised studies according to the “Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies” (see https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools; last checked: 15 April 2022). This tool suggests criteria to rate the articles and finally classify them in one of three quality categories, i.e., “poor”, “fair”, and “good”. Based on such criteria and indications, we did not rate any article as “poor”. As regards the 30 cross-sectional studies (see Section 3 and Section 4 of this review), we rated 15 of them as fair and 15 as good. For the 10 longitudinal studies (see Section 5), we rated 3 studies as fair and 7 studies as good. In conclusion, we examined the studies where BIA is used to analyze body composition in T2DM patients with sarcopenia or at risk of it. The revised studies have shown to provide several important pieces of information concerning the relationships between body composition parameters (mainly muscle mass) and other aspects of T2DM patients’ conditions, including different comorbidities, as well as information on how to oppose to muscle mass deterioration. Such relevant findings suggest that BIA can be considered generally appropriate for body composition analysis in T2DM complicated by sarcopenia/muscle loss. In addition, the wide size of the patients’ cohort in many studies indicates that BIA is particularly adequate and convenient for clinical applications. On the other hand, future studies may pay more attention to better clarify the operating conditions assumed for BIA measurement, and a larger battery of parameters may be studied for a more complete picture of the body composition state. Furthermore, in our opinion, studies should be carried out with focus on the specific validation of BIA performances, in the peculiar population of patients with T2DM complicated by sarcopenia. Acknowledgments Authors are grateful to Giovanni Pacini for his help and advice. Author Contributions Conceptualization, S.S. and A.T.; methodology, C.G. and A.T.; writing—original draft preparation, S.S. and A.T.; writing—review and editing, C.G. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 PRISMA flow diagram of the scientific literature search strategy. Figure 2 Methodological aspects related to BIA examination. Figure 3 Percentage distribution of the studies analyzed in the review in terms of the size of the study cohorts (from minimum of <50 subjects to maximum of ≥5000 subjects). nutrients-14-01864-t001_Table 1 Table 1 Main characteristics and information on analyzed studies. Each “Tweet” is 200 characters max. Number of citations (SCOPUS): last check: 4 March 2022; in parentheses: number of citations per year. No. of subjects field specifies the number of T2DM subjects and possibly of other populations, if any. BIA: bioelectric impedance analysis; T2DM: type 2 diabetes; BMI: body mass index. Ref. No. “Tweet” on Study Characteristics/Outcomes BIA Estimated/Calculated Parameters No. of Subjects Publication Year No. of Citations Cross-sectional studies “Early” studies [26] Regional body composition analysis in T2DM patients shows decreased leg muscle mass, leg sarcopenia, and increased risk for cardiovascular diseases Whole body and isolated (arms and legs) muscle mass (absolute and normalized to body weight), whole body fat mass (absolute and normalized) 198 T2DM, 198 healthy 2010 35 (2.9) [27] T2DM patients under bioelectrical impedance vector analysis show bioelectrical abnormalities, such as smaller appendicular muscular area, which can be risk factor for sarcopenia Body resistance (R) and reactance (Xc), phase angle as arctan(Xc/R), and impedance vector as (R2 + Xc2)0.5 144 T2DM, 209 healthy 2013 10 (1.1) [28] In T2DM, DPP4 inhibitors treatment improves sarcopenic parameters as compared to sulphonylurea treatment Fat-free mass (FFM), fat mass (FM), FFM/FM ratio, total, extracellular and intracellular water, skeletal muscle mass (SMM), SMM index 80 T2DM 2016 30 (5) [29] The skeletal muscle mass index is inversely associated with hepatic steatosis in T2DM men, likely due to factors such as insulin resistance, and abnormal levels of insulin-like growth factor 1 Skeletal muscle mass (normalized to total body weight), visceral fat area 145 T2DM 2016 47 (7.8) [30] In T2DM, there is direct correlation with BIA-derived parameters and plasma C-peptide, as well as inverse correlation with HDL-cholesterol, whereas no correlation is observed with glycemia and LDL Body fat mass, total muscle mass, appendicular muscle mass, skeletal muscle index and percentage, total muscle index and percentage 359 T2DM 2017 1 (0.2) [31] The serum creatinine to cystatin C ratio (Cre/CysC) is usable as a simple screening tool to identify T2DM patients at high risk for sarcopenia, with an optimal cut-off value for Cre/CysC equal to 0.90 Appendicular skeletal muscle mass, skeletal muscle index 285 T2DM 2018 49 (12.3) [32] In obese T2DM, the prevalence of sarcopenia is low when diagnosed by the skeletal muscle index or the appendicular muscle mass/BMI ratio, and is much higher when using the body muscle ratio Total fat mass, total muscle mass, sum of the appendicular muscle masses of the four limbs, skeletal muscle index, body muscle ratio 295 T2DM 2018 2 (0.5) [33] T2DM patients with visceral fat accumulation have low muscle quality, and patients with low muscle quality are more affected with cardiovascular diseases Trunk, muscle masses of arms and legs, muscle quality (ratio of grip strength to arm muscle mass), skeletal muscle index 183 T2DM 2018 26 (6.5) [34] In elderly T2DM patients, sarcopenia is associated with blood pressure variability, but not with its absolute values Body fat mass, skeletal muscle mass, appendicular muscle mass, skeletal muscle mass index (appendicular muscle mass /height squared) 146 T2DM 2018 20 (5) [35] Elderly T2DM patients are at higher risk for sarcopenia when having high body fat percentage but low BMI Limb skeletal muscle mass, skeletal muscle mass index 267 T2DM 2019 35 (11.7) [36] Neuropathy screening questionnaire scores are higher in T2DM sarcopenic than in non-sarcopenic patients, thus a questionnaire may be used for screening for sarcopenia in subjects with diabetic neuropathy Appendicular skeletal muscle mass (divided by height squared) 170 T2DM 2019 1 (0.3) [37] In sarcopenic obese patients, diabetic neuropathy prevalence reaches 96%, indicating a clear relationship between sarcopenia and diabetic neuropathy Absolute skeletal muscle mass, skeletal muscle mass index 602 T2DM 2019 6 (2) [38] In T2DM, lower values of skeletal muscle mass normalized to visceral fat area (skeletal-to-visceral ratio) are associated with higher risks of developing non-alcoholic fatty liver disease Lean body mass of arms and legs, appendicular skeletal muscle mass (sum of arms and legs lean masses), visceral fat area 445 T2DM 2019 5 (1.7) Recent studies [39] In hemodialysis patients, serum sclerostin is directly related to diabetes and inversely related to muscle mass Fat-free mass, skeletal muscle mass index (fat-free mass divided by height squared) 41 T2DM, 51 non-diabetic 2020 8 (4) [40] In T2DM, low skeletal muscle mass, which is typical trait of sarcopenia, is independently associated with presence of carotid atherosclerosis Skeletal muscle mass (SMM), skeletal muscle mass index (SMM divided by total body weight) 8202 T2DM 2020 8 (4) [41] In T2DM, sarcopenia appears significantly associated with impaired microcirculation, defined as low skin perfusion pressure Appendicular skeletal muscle mass (as a sum of lean mass in the arms and legs) normalized to height squared 102 T2DM 2020 _ 1 [42] In men with T2DM, sarcopenia appears independently associated with non-alcoholic fatty liver disease (NAFLD), suggesting sarcopenia as risk factor for NAFLD in that population Appendicular skeletal muscle mass (ASM, as a sum of lean mass in the arms and legs), skeletal muscle mass index (ASM normalized to body weight) 4210 T2DM 2020 6 (3) [43] In T2DM, low extremity skeletal muscle mass may be a marker of possible co-occurring cognitive dysfunction Skeletal muscle mass in legs and arms, appendicular lean mass (ASM, mass of four limbs), skeletal muscle mass index as ASM / height squared 1235 T2DM 2020 5 (2.5) [44] In T2DM, the prevalence of low muscle mass and sarcopenia may be found higher in older people and in people with normal BMI Fat-free mass, body fat mass, percent body fat, visceral fat area, appendicular skeletal muscle mass (ASM), skeletal muscle index (ASM divided by height squared) 2404 T2DM 2021 1 (1) [45] In T2DM, some genetic factors (IRS1 and ADAMTSL3) contribute to interindividual variability in body composition, and this can help to establish effective methods for the prediction and prevention of sarcopenia Total lean mass, appendicular lean mass, body fat mass, body resistance, skeletal muscle mass 176 T2DM 2021 0 (0) [46] Acarbose may contribute to decreased muscle mass and strength, thus muscle condition assessment and proper exercise may be important in T2DM patients using acarbose Skeletal muscle mass (SMM), skeletal muscle (SMM divided by height squared) 1042 T2DM 2021 0 (0) [47] In T2DM elderly patients, the knee extension strength test can assist in the identification of probable and confirmed sarcopenia, as diagnosed by EWGSOP2 criteria Fat mass, % body fat, total and segmental skeletal muscle mass (both legs, trunk, and both arms), appendicular skeletal mass index (sum of arms and legs masses/height squared) 100 T2DM 2022 _ 1 Cross-sectional studied with nutritional data [48] Uncomplicated T2DM does not seem to accelerate age-related muscle mass or strength loss, thus aging may be more relevant than diabetes for sarcopenia risk Fat-free mass (normalized to height squared) 32 T2DM, 34 non-diabetic 2014 11 (1.4) [49] In obese people with T2DM, preserved muscle fitness, especially of the lower extremities, may prevent sarcopenic obesity Fat mass and lean mass, at five body segments (right and left upper extremities, trunk, right and left lower extremities) 26 T2DM 2015 15 (2.1) [50] Oxidative stress and antioxidant status may be associated with sarcopenia in T2DM elderly individuals; however, the association is likely mediated by other factors Skeletal muscle mass, absolute skeletal muscle mass (normalizing to height squared) 60 T2DM 2019 4 (1.3) [51] In T2DM elderly patients, low energy intake is associated with sarcopenia Skeletal muscle mass, appendicular muscle mass, body fat mass, skeletal muscle mass index (appendicular muscle mass/height squared) 391 2019 31 (10.3) [52] In T2DM non-obese patients without heart failure, brain natriuretic peptide levels are associated with sarcopenia Skeletal muscle mass, appendicular muscle mass, body fat mass, skeletal muscle mass index (appendicular muscle mass/height squared) 433 T2DM 2019 8 (2.7) [53] In T2DM elderly patients, prevalence of sarcopenia is more than double when referring to the 2010 EWGSOP criteria, compared to revised 2019 criteria Appendicular skeletal muscle mass (ASM, sum of arms and legs muscle mass), skeletal muscle mass index (ASM divided by height squared) 242 T2DM 2020 17 (8.5) [54] In T2DM elderly patients, omega-3 fatty acids contribute to increase muscle mass and improve skeletal muscle strength, thus decreasing sarcopenia risk Skeletal muscle mass, appendicular muscle mass, body fat mass, skeletal muscle mass index (appendicular muscle mass/height squared) 342 T2DM 2020 7 (3.5) [55] In T2DM, low irisin levels and poor glycemic control are independent risk factors for sarcopenia, and especially for sarcopenic obesity Fat mass, fat-free mass, appendicular skeletal muscle (ASM), skeletal muscle index (ASM/height squared) 90 T2DM 2021 4 (4) Longitudinal (interventional) studies [56] In T2DM, treatment with dapagliflozin for six months improves glycemic control and reduced body weight without reducing muscle mass Total fat mass, soft lean mass, skeletal muscle mass at five body segments (arms, legs, trunk), skeletal muscle mass index (normalization to height squared) 50 T2DM 2018 36 (9) [57] In T2DM, reducing loss of fat-free mass over time may reduce insulin resistance and prediabetes risk, particularly among individuals with overweight/obesity Fat mass, fat-free mass (FFM), relative FFM (normalization to body weight), relative FFM percent change between baseline and follow-up 6264 T2DM 2021 3 (3) [58] In T2DM patients, low baseline skeletal muscle mass and its reduction over time is associated with increased risk for progression of chronic kidney disease Total skeletal muscle mass, skeletal muscle mass index (normalization to weight) 1272 T2DM 2021 2 (2) [59] Elderly women with low skeletal muscle or overt sarcopenia have higher probability of developing glucose intolerance or even diabetes Body resistance, reactance, phase angle, fat mass appendicular skeletal muscle mass 159 non-T2DM (at baseline) 2021 3 (3) [60] T2DM and chronic kidney disease are synergically associated with muscle mass loss over time, and mortality is higher in individuals with muscle loss Fat-free mass index (details not provided) 6247 subjects (some with T2DM) 2021 0 (0) [61] Sarcopenia with co-existent T2DM was associated with increased risk for readmission and infections among hospitalized cardiac patients Fat mass, appendicular skeletal muscle mass, appendicular skeletal muscle mass index (normalization to height squared) 50 T2DM, 50 non-T2DM 2021 0 (0) [62] In T2DM elderly people with long diabetes duration, low muscle strength and insulin resistance are the main risk factors for aggravated glycemic control Muscle mass, fat mass (both normalized to weight, and stratified in quartiles) 100 T2DM 2021 0 (0) [63] COVID-19 pandemic restrictions cause muscle mass loss in older patents with T2DM; thus, exercise and adequate diet intake are needed to prevent sarcopenia Appendicular muscle mass, fat mass, skeletal muscle mass index (SMI, as appendicular muscle mass/height squared), percent fat mass (fat mass/body weight), change in SMI per year 56 T2DM 2021 2 (1) [64] In T2DM, branched-chain amino acids (valine, leucine and isoleucine) appear to have preventive role in muscle mass loss Skeletal muscle mass, skeletal muscle mass index (normalization to weight) 1140 T2DM 2022 0 (0) [65] Hemodialysis patients with T2DM show overnutrition, but also paradoxically higher predisposition to protein–energy wasting (possible traits of obese sarcopenia) Lean tissue mass and fat tissue mass, lean tissue index and fat tissue index (normalization to height squared), overhydration and relative overhydration 198 T2DM, 317 non-T2DM 2022 _ 1 1 Not reported in SCOPUS (at the indicated date of last check). 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==== Front Nutrients Nutrients nutrients Nutrients 2072-6643 MDPI 10.3390/nu14091884 nutrients-14-01884 Article Whey Protein, L-Leucine and Vitamin D Supplementation for Preserving Lean Mass during a Low-Calorie Diet in Sarcopenic Obese Women https://orcid.org/0000-0002-1866-8223 Camajani Elisabetta 12 Persichetti Agnese 34 https://orcid.org/0000-0003-2225-8814 Watanabe Mikiko 3 Contini Savina 3 Vari Michaela 3 Di Bernardo Settimia 3 Faro Maria 3 https://orcid.org/0000-0001-8261-1451 Lubrano Carla 3 Gnessi Lucio 3 https://orcid.org/0000-0003-0722-7163 Caprio Massimiliano 25 Basciani Sabrina 3* Iacone Roberto Academic Editor 1 PhD Program in Endocrinological Sciences, University of Rome “La Sapienza”, 00161 Rome, Italy; elisabetta.camajani@uniroma1.it 2 Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy; massimiliano.caprio@sanraffaele.it 3 Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy; agnese.persichetti@gmail.com (A.P.); mikiko.watanabe@uniroma1.it (M.W.); savi.86@hotmail.it (S.C.); mvari86@gmail.com (M.V.); settimia.dibernardo@gmail.com (S.D.B.); maria.faro19@gmail.com (M.F.); carla.lubrano@uniroma1.it (C.L.); lucio.gnessi@uniroma1.it (L.G.) 4 Service of Pharmacovigilance, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy 5 Laboratory of Cardiovascular Endocrinology, IRCCS San Raffaele, Pisana, 00161 Rome, Italy * Correspondence: sabrinabasciani@yahoo.it 29 4 2022 5 2022 14 9 188427 3 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In sarcopenic obese subjects it is essential to reduce body weight and preserve lean mass, in order to avoid a worsening of muscle function. Several studies have shown that leucine supplementation can be useful to improve skeletal muscle mass in sarcopenic patients. The aim of our study was to evaluate the effectiveness of a short-term low-calorie diet (LCD) combined with supplementation with whey protein and leucine on weight loss, lean mass and muscle strength in sarcopenic, obese, hyperinsulinemic and post-menopausal women. Sixteen females with a mean age of 60 years (range: 50–70 years), BMI 37.6 kg/m2 (range: 31.7–44.1 Kg/m2), HOMA-index ≥ 2.5 (range: 2.9–12) were assigned to an LCD regimen (1000 kcal/day) with supplementation of 18 g whey proteins which 4.1 g of leucine for 45 days. Anthropometric indexes, blood and urine chemistry, body composition by DEXA, muscle strength by handgrip test and Short Physical Performance Battery (SPPB) were assessed at baseline and at the end of the treatment. A significant reduction in BMI (37.6 vs. 35.7 Kg/m2), waist circumference (107 vs. 102.4 cm), HOMA index (4.8 vs. 2.3) and fasting insulin (17.4 vs. 10.4 μIU/mL) was observed in all patients. Women preserved total lean body mass (55 vs. 5%) and significantly improved their muscle strength, as measured by handgrip (15.3 vs. 20.1 Kg), and their muscle function, as measured by SPPB (7.5 vs. 8.9). A significant increase in BUN was also observed (36.1 vs. 46.3). We conclude that LCD with adequate protein intake and supplementation with whey protein and leucine should be promoted to maintain muscle mass and improve muscle strength in post-menopausal women with sarcopenic obesity. carbohydrate restriction sarcopenia obesity New Penta s.r.l. (Cuneo, Italy)Financial support, as well as the meal replacement protein preparations, were kindly provided by New Penta s.r.l. (Cuneo, Italy). The funding source had no involvement in the study design, recruitment of patients, study interventions, data collection, or interpretation of the results. ==== Body pmc1. Introduction The term “sarcopenia” comes from the Greek ‘σαρξ’ (meat) and ‘πενια’ (lost). This term was first proposed by Rosenberg in 1988 and originally indicated only the loss of muscle mass caused by aging [1]. In 2010, the European Working Group on Sarcopenia in Older People (EWGSOP) defined sarcopenia as a syndrome characterized by progressive and generalized loss of skeletal muscle mass and strength, with a risk of adverse outcomes such as physical disability, poor quality of life and death [2]. Muscle loss related to aging is the result of the reduction in the size and number of muscle fibers, probably determined by multiple factors dependent on physical inactivity, inadequate nutritional intake, oxidative stress, systemic inflammation and hormonal changes [3,4,5,6]. In the updated definition of sarcopenia in 2019, EWGSOP2 not only refers to sarcopenia as the loss of muscle mass but focuses more on the loss of muscle strength [7]. EWGSOP2 indicates low foreground strength as a primary marker of probable sarcopenia, and sarcopenia is now considered a veritable disease of skeletal muscle (muscle failure), with low muscle strength that overcomes the role of low muscle mass as the main determinant. Muscle mass, strength and physical performance represent the measurable readouts to define sarcopenia [2]. In recent years, the literature has been referring to Sarcopenic Obesity (SO) which is a clinical condition characterized by an excess of fat mass and a reduction in muscle mass [8,9,10,11]. As reported in European Consensus on Definition and Diagnosis of Sarcopenia, SO is most often reported in older people, since its prevalence increases with age. Obesity exacerbates sarcopenia, increases the infiltration of fat in skeletal muscle, lowers physical function and increases the risk of mortality [3]. Moreover, as reported by Stoklossa et al., sarcopenia can be masked by obesity: a reduction in muscle strength and muscle function could therefore occur without any evidence of a reduction in muscle mass [12]. Currently, there is no univocal definition of SO in terms of diagnostic criteria and cut-offs; for this reason, it is still not possible to determine its prevalence [13]. Therefore, if sarcopenia has long been associated with aging and older people, it is now recognized that its development begins earlier in life and that the sarcopenic phenotype has several contributing causes beyond aging. Muscle and strength loss in women after menopause is the result of multiple factors, mostly dependent on physical inactivity, malnutrition, mitochondrial stress, systemic inflammation and hormonal changes that can also contribute to obesity [14]. For a sarcopenic obese subject, a lifestyle modification with adequate nutrition and proper physical activity is essential to counteract its progression. According to the Italian and International Guidelines, the administration of 0.8–1.1 g of protein per kg of body weight, depending on the age group, is sufficient to avoid muscle catabolism and support muscle mass [15,16]. According to Batsis and Villareal, strategies that optimize protein anabolism during weight loss, such as spreading protein throughout the day, can prevent weight loss-induced sarcopenia [9]. In addition, the increase in dietary protein intake also stimulates muscle protein synthesis. As reported by Ganapathy and Nieves, several studies have found an association between sarcopenia and protein intake, with lower protein intake associated with a loss of lean mass by DXA and a reduced grip strength [17]. The source of protein, the timing of intake, and specific amino-acid constitution also represent critical factors in increasing muscle mass and strength [5]. Recent studies have shown how protein supplementation, especially with high leucine content, can be effective in degenerative and end-stage diseases. Cancer cachexia induces a variety of metabolic disorders, including skeletal muscle imbalance. In this view, nutritional supplementation with leucine shows a modulatory effect over tumor-induced derangements in vivo and in vitro [18]. In fact, leucine directly affects skeletal muscle anabolism through activation of the mechanistic target of rapamycin complex 1 (mTORC1) signaling [19,20,21,22]. Xu et al. confirmed the efficacy of leucine on muscle protein synthesis, lean body mass and leg lean mass in older people [23]. On the basis of these considerations, we hypothesized that a low-calorie diet (LCD) with whey protein, leucine and Vitamin D supplementation is strictly linked to the improvement of sarcopenic obesity in post-menopausal women. The primary outcome was represented by the preservation of lean mass and muscle strength. 2. Materials and Methods 2.1. Study Design This was an open, nutritional intervention, uncontrolled, pilot study that enrolled sarcopenic, obese, hyperinsulinemic and post-menopausal women among those attending the Center for the Study of Eating Disorders and Obesity, Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology of the University of Rome “La Sapienza,” Italy. This trial was registered at clinicaltrials.gov, accessed on 26 March 2022 (NCT05287659). 2.2. Inclusion Criteria The inclusion criteria were as follows: women, age between 50 and 70 years, BMI between 30 and 40 kg/m2 with stable body weight (BW) in the previous 6 months, with hyperinsulinism (HOMA-IR ≥ 2.5) and sarcopenia. The presence of SO was considered when the following conditions were satisfied:- Fat mass > 38%, according to NHANES III [24] - Handgrip < 16 Kg, according to EWGSOP2 [7] - Chair stand test > 15 s, according to EWGSOP2 [7] - SPPB < 8, according to EWGSOP2 [7] 2.3. Patients We screened 350 subjects for eligibility; 334 were excluded (298 did not meet all the inclusion criteria and 36 declined to participate). Only 16 subjects were enrolled for this study. All patients had a very low level of physical activity and were sedentary. During this study, patients did not follow any scheduled physical activity. 2.4. Anthropometric Assessment BW, height, systolic and diastolic BP, waist circumference (WC), thigh circumference (TC), and hip circumference (HC) were measured at T0 and every 2 weeks for 45 days. Anthropometric measurements were recorded after an overnight fast under resting conditions using calibrated equipment. BW was measured using a balance-beam scale (Seca GmbH & Co, Hamburg, Germany) [25]. Systolic and diastolic BP were measured using a mercury-gravity manometer. The height was rounded to the closest 0.5 cm. BMI was calculated as weight divided by squared height in meters (kg/m2). WC was measured midway between the costal arch and the iliac crest, HC was measured at the symphysis-greater trochanter level to the closest 1 cm, and TC was measured at the middle of the right thigh. 2.5. Blood and Urine Chemistry Blood count (ADVIA 2120i Hematology System, Siemens Healthcare s.r.l., Milano, Italy), electrolytes (chloride, potassium, and sodium: indirect ion-selective electrode potentiometry; calcium, and magnesium: colorimetric assay), glucose (enzymatic colorimetric assay), insulin (electrochemiluminescence immunoassay), lipids (triglycerides, total, high-density lipoprotein, and low-density lipoprotein cholesterol; enzymatic colorimetric assay), total protein and albumin (capillary system), C-reactive protein (immunoturbidimetric assay), erythrocyte sedimentation rate (capillary photometric assay), plasma creatinine (kinetic colorimetric compensated Jaffé method), blood urea nitrogen (BUN), uric acid, alanine transferase, and aspartate transaminase (enzymatic colorimetric assay), and estimated glomerular filtration rate (eGFR) were determined at baseline and T45 [26]. All analyses were performed on a COBAS 6000 (Roche Diagnostics, Risch-Rotkreuz, Switzerland) and on CapillarysR Systems (Sebia, Evry, France). Insulin resistance was determined using HOMA-IR [27]. 2.6. Dual-Energy X-Ray Absorptiometry Measurement Body composition, total and regional body fat mass and fat-free mass were measured by dual-energy X-ray absorptiometry (Hologic 4500, Bedford, MA, USA) at baseline and at the end of the trial. Trunk fat was defined as the adipose tissue localized within the region below the chin, delineated by vertical lines within the left and right glenoid fossae bordering laterally to the ribs and by the oblique lines that cross the femoral necks and converge below the pubic symphysis. 2.7. Muscular Strenght and Functional Tests Handgrip strength (HG) was measured with a digital dynamometer (DynEx, Akern, Pontassieve, FI, Italy) at T0 and T45 with the patients seated, shoulder adducted, and forearms resting flat on the chair arms. Before starting, patients were asked to squeeze the dynamometer as hard as possible for at least 3 s. Three measurements were repeated with both the dominant and nondominant arms. The highest value measured for the dominant arm was recorded; the handgrip strength test was considered impaired when it was <16 kg [7]. The SPPB battery consists of three components of physical performance: (1) standing balance, (2) chair stands, and (3) gait speed. A score from 0 (poorest) to 4 (best) was assigned for each of these three components. The sum of the scores provided a composite score ranging from 0 to 12; physical performance was considered impaired when the total SPPB score was ≤8 [1,2]. 2.8. Dietary Intervention All patients followed an LCD dietary regimen (1000 kcal/day). Given the inadequate leucine content normally present in food (such as meat, fish and eggs) for this high-risk population, such as postmenopausal obese sarcopenic women, a protein supplement containing 18 g of whey protein was given, including 4 g of leucine, to satisfy the correct protein intake. In this way, it was possible to give 1.38 g protein/kg body weight/day. Hence, the dietary macronutrient composition was based on 28% protein (70 g/day), 32% fat (33.3 g/die) and 30% carbohydrate (97.7 g/die) for 45 days. The powdered drink preparation was characterized by whey protein (18 g), leucine (4.1 g) and vitamin D3 (5.01 mcg, corresponding to 200 IU), which was taken at 5 p.m. Therefore, the total amount of vitamin D provided to patients through diet and supplementation reached a total of 600 IU/day (as required by LARN 2014) [15]. It was recommended to drink no less than 1.5–2.0 l of water per day. Since in the program fruit consumption was limited, the patients were instructed to assume a multi-vitamin and multi-saline supplement (containing 365 mg magnesium) formulated to maintain the physiological acid/base balance and it was taken in the morning. 2.9. Data Management and Statistical Methods Data are expressed as the mean values ± standard deviations or percentages where appropriate. Comparisons between groups were evaluated using the Student’s t-test. The number of subjects was identified considering the number of subjects generally included in similar published pilot studies. Differences were considered statistically significant when p was ≤0.05. 2.10. Ethical Aspects The study protocol was approved by the Ethics Committee of the University of Rome “La Sapienza” (code 3920) and was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. All patients were informed about the possible risks and benefits of the proposed interventions. All patients signed an informed consent form in accordance with the General Data Protection Regulation (GPDR, 2016/679). 3. Results Sixteen females with a mean age of 60 years (range: 50–70 years) were enrolled in the study. All patients were post-menopausal, sarcopenic, insulin-resistant (HOMA index was 4.8 ± 2.2) and with medium degree obesity (average BMI was 37.6 ± 4.4 kg/m2). Three patients (18.7%) were dyslipidemic and were treated with statins and eight (37.5%) were on antihypertensive therapy, no one was diabetic. The differences in anthropometric measures, weight, blood pressure, clinical chemistry and blood count at the beginning and at the end of the study are shown in Table 1. At the end of the observation period, body weight was improved with a significant change in waist circumference and BMI (p = 0.04 and p = 0.042, respectively). The average weight loss was 4.6% (range 9.2–1.4%) in 45 days. Fasting insulin and HOMA IR were significantly improved at the end of the study (p = 0.001 and p = 0.001, respectively). Creatinine and eGFR did not undergo any significant change, conversely, BUN was increased (p = 0. 026). Lipid profile (total cholesterol, LDL cholesterol, triglycerides) and inflammatory markers were improved at the end of the study. Electrolytes did not vary significantly, except for Na and Mg which showed a slight reduction and increase (p = 0.030 and p = 0.002, respectively), however, all remained in the normal range. Body composition parameters underwent an overall improvement, with a significant decrease in total trunk fat (p = 0.049), as shows in Table 2. Figure 1 shows the results of the functional tests. Muscle strength measured by dynamometry improved significantly (average handgrip test at baseline was 15.3 ± 0.5 kg; average handgrip test after 45 days was 20.1 ± 0.9 kg; p = 0.000) from the baseline. The score of the SPPB test was significantly improved (range of total SBBP at T0 was 4–9; the range of total SPPB at T45 was 8–10; p = 0.000) after 45 days. None of the patients dropped out of the study and no significant adverse events were recorded. 4. Discussion Low physical activity, low protein intake and elevated oxidative stress, in association with a decline in estrogen, represent the greatest contributors to sarcopenia in postmenopausal women [28,29,30,31]. In addition, the simultaneous presence of both low muscle mass and obesity confers a higher risk of functional impairment and disability in this phase of a woman’s life. Few studies in the literature report nutritional interventions or physical rehabilitation for obese adults with sarcopenia [32,33,34,35]. According to National and International guidelines, the Recommended Daily Allowance (RDA) for daily dietary protein for adults is 0.8–1.1 g/kg of reference body weight/day; however, at present, there are many doubts that this regimen may be adequate for elderly subjects or for those with sarcopenia [36,37]. Although the RDA protein is sufficient to maintain weight, a low-protein and low-calorie diet can cause weight loss and muscle mass loss, especially if repeated. In the Health ABC study cohort, among participants who lost weight during the three-year follow-up period, a higher protein intake was associated with lean mass loss. Finally, among individuals who gained weight during the 3-year follow-up, an increase in protein intake was associated with higher lean body mass [38]. Moreover, as reported in the review by Trouwborst and colleagues, it is well established that the intake of dietary amino acids, and especially the essential amino acids, has a positive regulatory effect on the muscle protein synthesis in the muscle [33]. As reported by Wall and colleagues, whey protein has been shown to be very effective in stimulating postprandial muscle protein accretion in older men [39]. In addition, an intake of about 2.0–2.5 g/day leucine, mainly derived from animal sources, improves the post-prandial muscle protein synthesis in elderly men. Moreover, a recent review by Cereda and colleagues demonstrated that oral supplementation with whey protein, leucine and vitamin D is an effective therapy for older patients with sarcopenia and should be offered as a first-line treatment, not only to improve clinical outcomes but also to reduce healthcare resource consumption [40]. In our research, a high-protein diet (1.4 g/kg reference body weight/day) containing a supplementation with 18 g of high biological value whey protein and 4.1 g of leucine improved body composition by reducing absolute fat mass and preserving lean mass. Patients on a high protein diet showed a significant increase in muscle strength and function after 45 days of treatment. In addition, our data show that a 45-day-long LCD supplemented with whey protein rich in leucine causes a significant reduction in BMI and improves glycemic control in patients with obesity and insulin resistance. The LCD was safe and well-tolerated for 45 days; however, a significant increase in BUN was found, and a small increase in serum creatine and a mild reduction in eGFR were found, although not statistically significant. Therefore, this nutritional approach requires strict surveillance until definitively proven safe. Moreover, it could be advisable to vary the animal protein sources and increase the use of vegetable proteins [41]. Leucine supplementation substantially contributes to preserving muscle function and performance [39]. In line with the existing literature, we cannot exclude the fact that dietary magnesium may also support the preservation of skeletal muscle mass and the improvement of muscle function [17]. As reported by Welch et al., dietary magnesium is important for skeletal muscle function due to its direct role in muscle metabolism and indirect interaction with chronic low-grade inflammation, which represents a risk factor for loss of mass, skeletal muscle strength and function [42]. In line with this, Peterman-Rocha et al. revealed an inverse association between magnesium intake and sarcopenia [43]. It is evident that a correct diet supported by adequate supplementation of micro- and macro-nutrients seems to be relevant for the treatment of sarcopenic obese subjects. As remarked by Ganapathy et al., numerous studies evaluated the impact of supplementation with a combination of several macro- and micro-nutrients (whey protein, leucine, vitamin D, vitamin C, B-vitamin complex, calcium, magnesium, Omega 3 Fatty Acid) with regard to muscle mass and strength [17]. The aim of this pilot study was to identify an ideal macro- and micro-nutrient pattern that could be helpful for a high-risk population, such as postmenopausal obese sarcopenic women. It is also true that the primary treatment for sarcopenia is represented by physical exercise, which has been shown to produce the most beneficial preventive and therapeutic effects [44,45]. The contribution of both physical activity and diet are necessary for maintaining muscle function and endocrine function [46,47]. As regards physical exercise, not a single type of exercise appears to adequately address the requirements of therapeutic exercise in age-related sarcopenia, and therefore well-rounded exercise programs, consisting of aerobic and resistance exercises should be preferred. A limitation of this study is that it did not include daily physical activity for the patients. However, the study has numerous limitations represented by the number of patients, the duration of the study and the lack of follow-ups. Another limitation is the absence of a control arm, and for this reason, the current results must be considered with caution. In the future, longitudinal and randomized case-control studies will be performed to confirm the results obtained in this first exploratory study. 5. Conclusions We conclude that an LCD with adequate protein intake and a concomitant supplementation with whey protein, leucine and optimal micronutrient combination, such as Vitamin D, displays favorable effects in post-menopausal women with sarcopenic obesity, preserving muscle mass and improving muscle strength and function. Author Contributions Conceptualization, S.B. and L.G.; methodology, S.B. and L.G.; software, E.C., M.W. and A.P.; validation, E.C., S.B. and A.P.; formal analysis, E.C. and A.P.; investigation, E.C., S.B., A.P., S.C., M.F., M.V. and S.D.B.; data curation, A.P., E.C. and C.L.; writing—original draft preparation, E.C. and A.P.; writing—review and editing, M.C., L.G. and S.B.; visualization, E.C., A.P., S.B. and M.C.; supervision, L.G.; project administration, S.B.; funding acquisition, S.B. and L.G. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethical Committee of Sapienza University of Rome (cod. 3920) for studies involving humans. Informed Consent Statement Informed consent was obtained from the subject involved in the study. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Functional test: (A) Handgrip; (B) Short Physical Performance Battery (SPPB). nutrients-14-01884-t001_Table 1 Table 1 Participant characteristics of anthropometric measurements and blood test at baseline (T0) and at the end of the study (T45). T 0 T 45 p < 0.05 Weight (Kg) 96.8 ± 12.9 91.3 ± 13.1 0.162 Body Mass Index (Kg/m2) 37.6 ± 4.4 35.7 ± 4.2 0.044 * Waist Circumference (cm) 107 ± 7.4 102.4 ± 7.3 0.042 * Thigh Circumference (cm) 62.4 ± 2.8 60.4 ± 2.6 0.046 * Systolic Body Pressure (mmHg) 127 ± 11 120 ± 11 0.066 Diastolic Body Pressure (mmHg) 75 ± 7 70 ± 10 0.060 Fasting Glycemia (mg/dL) 112 ± 23.9 105.4 ± 14.2 0.145 Fasting Insulin (μIU/mL) 17.4 ± 7.7 10.4 ± 3.9 0.001 * HOMA Index 4.8 ± 2.2 2.3 ± 1.1 0.001 * Hb1AC (%) 5.96 ± 0.6 5.67 ± 0.3 0.053 BUN (mg/dL) 36.1 ± 7.8 46.3 ± 18.5 0.026 * Creatinine (mg/dL) 0.79 ± 0.2 0.86 ± 0.2 0.192 eGFR 124.2 ± 36.2 110.2 ± 37.2 0.140 Na (mmol/L) 142.1 ± 2.1 140.7 ± 2 0.030 * K (mmol/L) 4.4 ± 0.3 4.5 ± 0.3 0.280 Cl (mmol/L) 100 ± 1.7 99 ± 1.8 0.076 Ca (mg/dL) 9.6 ± 0.4 9.7 ± 0.5 0.230 Mg (mg/dL) 2.0 ± 0.1 2.2 ± 0.1 0.002 * P (mg/dL) 3.5 ± 0.5 3.7 ± 0.4 0.100 AST (U/L) 17.5 ± 5.4 19.5 ± 4.2 0.128 ALT (U/L) 21.2 ± 12.6 23.2 ± 11.4 0.321 Total Cholesterol (mg/dL) 224 ± 56 207 ± 47 0.177 LDL Cholesterol (mg/dL) 140 ± 53 128 ± 47 0.243 HDL Cholesterol (mg/dL) 57 ± 13 55 ± 11 0.294 Triglycerides (mg/dL) 132 ± 39 120 ± 35 0.192 Uric acid (mg/dL) 5.2 ± 1.4 5.3 ± 1.4 0.424 CRP (mcg/L) 7686 ± 6509 5643 ± 4973 0.166 ESR (mm/h) 36 ± 15 38 ± 18 0.412 Abbreviations: BUN, blood urea nitrogen; eGFR, estimated Glomerular Filtration Rate; AST, aspartate transaminase; ALT, alanine transferase; CRP, C reactive protein; ESR, erythrocite sedimentation rate. All values are presented as mean ± standard deviation. * p < 0.05. nutrients-14-01884-t002_Table 2 Table 2 Body composition by DXA at T0 and T45. T 0 T 45 p < 0.05 Left Arm Fat (%) 53 ± 5.2 49 ± 5.5 0.022 * Left Arm Lean (%) 43 ± 7.1 48 ± 4.9 0.013 * Left Arm Fat (g) 3040 ± 806 2733 ± 805 0.145 Left Arm Lean (g) 2015 ± 411 2199 ± 324 0.085 Right Arm Fat (%) 49 ± 5.3 47 ± 5.5 0.115 Right Arm Lean (%) 46 ± 4.7 49 ± 5.2 0.060 Right Arm Fat (g) 2905 ± 763 2618 ± 726 0.142 Right Arm Lean (g) 2197 ± 320 2166 ± 307 0.192 Left Leg Fat (%) 44 ± 4.4 41 ± 4.5 0.077 Left Leg Lean (%) 49 ± 13 56 ± 6 0.029 * Left Leg Fat (g) 6886 ± 1719 5727 ± 1863 0.038 * Left Leg Lean (g) 6967 ± 1206 7361 ± 1262 0.186 Right Leg Fat (%) 43 ± 5 41 ± 4.7 0.196 Right Leg Lean (%) 54 ± 4.9 56 ± 4.5 0.161 Right Leg Fat (g) 6812 ± 1754 6291 ± 1702 0.200 Right Leg Lean (g) 7488 ± 1243 7417 ± 1305 0.437 Trunk Fat (g) 21064 ± 4187 18517 ± 4266 0.049 * Trunk lean (g) 27872 ± 2871 27682 ± 3309 0.431 Trunk Fat (%) 42 ± 3.9 39 ± 4.5 0.026 * Trunk lean (%) 56 ± 3.8 59 ± 4.4 0.026 * Total Fat (g) 41797 ± 7705 37513 ± 7706 0.063 Total Lean (g) 53182 ± 5496 53020 ± 6088 0.468 Total Fat (%) 42 ± 3.3 40 ± 3.2 0.016 * Total Lean (%) 55 ± 3.2 57 ± 3.1 0.017 * * p < 0.05. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093047 materials-15-03047 Article Green-Engineered Cementitious Composite Production with High-Strength Synthetic Fiber and Aggregate Replacement Fu Chaoshu 12 Chen Mingzhao 12 https://orcid.org/0000-0002-2290-4911 Guo Rongxin 12* Qi Rongqing 123 Wang Xiao Yong Academic Editor Lin Run-Sheng Academic Editor 1 Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650504, China; fuchos@stu.kust.edu.cn (C.F.); 13056682443@163.com (M.C.); qrqing@swfu.edu.cn (R.Q.) 2 Yunnan Key Laboratory of Disaster Reduction in Civil Engineering, Kunming 650504, China 3 College of Civil Engineering, Southwest Forestry University, Kunming 650224, China * Correspondence: guorx@kmust.edu.cn 22 4 2022 5 2022 15 9 304723 3 2022 20 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Engineered cementitious composites (ECCs) are potentially useful structural reinforcement and repair materials. However, owing to their high costs and carbon emissions, they are not used extensively. To control these carbon emissions and costs, recycled fly ash cenospheres (FACs) and high-strength polyethylene (PE) fibers are used here to explore the possibility of developing green lightweight ECCs (GLECCs). A series of experiments was conducted to test the physical and mechanical properties of the developed GLECC and to evaluate the possibility of developing an GLECC. The crack width development of the GLECC was also analyzed using the digital image correlation method. The experimental results indicate the following: (1) The increase in FAC content and the decrease in PE content worsened the performance of GLECCs, but the resulting GLECCs still had significant strain-hardening properties; (2) The performance and costs of GLECCs can be balanced by adjusting the amount of FAC and PE. The maximum amount of FACs attainable is 0.45 (FAC/binder), and the required amount of PE fibers can be reduced to 1%. As a result, the cost was reduced by 40% and the carbon emission was reduced by 36%, while the compressive strength was greater than 30 MPa, the tensile strength was greater than 3.5 MPa, and the tensile strain was nearly 3%. (3) The width of the crack was positively correlated with the FAC content and negatively correlated with the fiber content. In the 0.8% strain range, the average crack width can be controlled to within 100 μm and the maximum crack width can be controlled to within 150 μm, with the performance still meeting the requirements of many applications. green lightweight engineered cementitious composites high-strength polyethylene fibers low-toughness matrix fly ash cenospheres costs carbon emissions ==== Body pmc1. Introduction Ordinary concrete is prone to brittle cracking during its service life, which compromises the safety and durability of the structure. Adding various types of fibers to concrete somewhat improves its toughness [1,2,3] and alleviates the cracking problem. However, fiber-reinforced concrete exhibits strain softening after cracking, meaning the structural durability problem remains unsolved. Under uniaxial tensions, engineering cementitious composites (ECCs) exhibit strain hardening behaviors, which strain can reach 3–8%, which is 300–800 times greater than that of ordinary concrete and fiber-reinforced concrete. Additionally, ECCs exhibit multiple cracking behaviors under tensile loads, with a high crack control ability. The crack width in ECCs is usually less than 100 μm. The unique highly tensile ductility and small crack width of ECCs mean they can overcome many of the challenges related to brittleness and crack damage in ordinary concrete, thereby improving the quality and durability of the infrastructure significantly [4]. To maintain the characteristics of strain hardening and multiple cracking, a small amount of fine quartz sand (<300 μm) is used in ECCs. Therefore, compared with ordinary concrete, the amount of cement in ECCs is usually high, which results in higher costs and carbon emissions [5]. Additionally, expensive synthetic polymer fibers should be added to ECCs to maintain their high tensile strain capacity. The price of synthetic fibers is high, and they are difficult to obtain, which also increases the costs and carbon emissions related to ECCs. In response to climate and resource issues, several studies [6,7,8,9,10,11,12,13,14,15] have been carried out within the building materials industry to improve material mechanics and green properties, and they have yielded encouraging results. Many studies have also been carried out on the carbon emissions and costs of ECCs. Regarding the cementitious materials, slag [16], glass pozzolans [17], sugarcane bagasse ash [18], and limestone calcined clay blend [19] have been used to partially replace cement in the development of green ECC, and geopolymers [20] have also been developed to completely replace cement in the production of ECC. Given the cost of quartz sand, researchers have used recycled rubber [21], recycled asphalt [22], recycled fine aggregate [23], and sea sand [24] as fine aggregates to produce green ECCs. The mechanical properties of the obtained green ECC are fairly weak but improve durability. In addition, low-cost unoiled polyvinyl alcohol (PVA) [4,25] and high-strength polypropylene fibers [26], recycled PET [27,28], and recycled tire polymer fibers [29] have also been used to partially replace PVA fibers in the production of low-cost ECCs. The obtained material had a tensile strain capacity of 1–3%, which is significantly lower than that of conventional ECCs, but it can still satisfy many engineering applications. The above results demonstrate that the partial or complete replacement of the components of ECCs with low-cost materials and recycled waste may weaken the mechanical properties to a certain extent but reduces material costs and carbon emissions. However, according to the research of Yu [27] and Leung [30], for a typical ECC-M45, the cost of PVA accounts for 80% of the total cost of ECCs, and it is difficult to achieve a substantial reduction in ECC cost and carbon emissions using only fiber substitution and aggregate substitution. According to the microscopic design principles of ECCs, high fiber bridging surplus energy and low matrix fracture toughness are beneficial to ECC multiple cracking. When the fiber bridging energy is greater than the matrix fracture toughness and has a certain surplus value, the stable multiple cracking of ECC can be realized. Therefore, when seeking ways to reduce the costs associated with ECC, this principle should also be observed. In recent years, high-tensile strength polyethylene (PE) fibers have been used to develop ECCs with ultra-high ductility (tensile strain of 8–12%) and ultra-high strength (compressive strength of 20–120 MPa) [31,32]. Owing to their hydrophobic nature, PE fibers have no chemical bonds with the mortar matrix, only relying on friction bonds, and PE fibers have a tensile strength of up to 2900 MPa, which is 1.8 times that of PVA fiber (1600 MPa). Therefore, when PE fibers are subjected to bridging stress, they can be easily degummed and pulled out instead of breaking up [33]. This characteristic of PE fibers is conducive to providing high complementary energy to ECCs. Therefore, ECCs prepared using PE fibers have high tensile ductility. However, not all engineering applications require such a high tensile strain capability. Under the premise of meeting engineering needs, we might reduce the content of PE fiber to balance the costs and ensure the mechanical properties of ECCs [34]. Recently, lightweight aggregates with a low density have been used to develop lightweight ECCs (LECCs) with a low density of 1200–1900 kg/m3 [35]. Because of their low specific gravity, LECCs have broad application prospects in bridges, high-rise buildings, and floating platforms that are weight-sensitive. The introduction of lightweight fillers ensures LECCs have lower matrix fracture toughness, which may be conducive to multiple cracking of ECC. Therefore, combining the high-strength properties of PE fibers and the low-toughness properties of LECCs may provide more space for balancing the number of ECC fibers with the cost and mechanical properties. Fly ash cenospheres (FACs) are an industrial waste produced by coal-fired power plants, and they have a composition similar to that of fly ash. They have a hollow spherical structure with a density of only 450–900 kg/m3. Using FACs as a lightweight filler of LECCs facilitates the balancing of the performance and costs of ECC, as well as the recycling of waste. This paper proposes the combination of high-strength fibers and recycled wastes to achieve a balance of ECC performance and cost with carbon emissions. Due to the introduction of lightweight fillers, the interface performance of the fiber/matrix in the ECC will be weakened, thereby reducing the capacity for fiber bridging, which may lead to a mismatch between the fiber bridging performance and the matrix fracture toughness, and the ECC may lose its multiple cracking characteristics. Therefore, we have undertaken a series of experiments to evaluate the possibility of using low-content PE fibers and lightweight recycled FACs fillers to develop green lightweight engineered cementitious composites (GLECCs). The mechanical properties of the materials developed were tested, and their cost and environmental impacts were evaluated. 2. Material Design Criteria The tensile strain hardening properties of ECC are related to its stable multiple cracking. When the ECC mortar matrix is stressed and reaches the cracking point, the matrix will crack. Here, the stress at the crack is carried by the fiber. The fibers rely on interfacial bonding with the matrix to transmit forces back to the surrounding uncracked matrix. As the stress elevates up to the cracking strength of the other parts of the matrix, new cracks will be formed, meaning new cracks are continuously formed, producing multiple cracking. To ensure multiple cracking, it is first necessary to ensure that the cracking stress of the matrix (σfc) is less than the maximum strength of the fiber (σ0)—the so-called strength criterion—and the greater the maximum bridging strength, the greater the likelihood of multiple cracking [36]. (1) σfc<σ0 Secondly, in order to protect the strain hardening properties of the ECC, the cracks must be stable. During cracking, the maximum opening displacement of the crack will not increase with its expansion, and the crack will be flat. To ensure the generation of flat cracks, the energy balance in the crack propagation process must be satisfied. According to Marshall and Cox [16], the steady-state cracking mode can be assessed using the fiber bridging stress–crack opening (σ-δ) curve. To ensure the stable expansion of the cracks, the fracture energy of the crack tip (Jtip) should be less than the complementary energy of fiber bridging (Jb′). (2) Jtip≤σ0δ0−∫0δ0σ(δ)≡Jb′ where δ0 is the crack opening corresponding to σ0. The Jb′ in the energy criterion establishes a relationship with the fiber/matrix interaction. The fiber–matrix interfacial bond is weak, and the fibers are easily pulled out of the matrix, which reduces the σ0 in the σ-δ curve. If the fiber matrix bond is too strong, the fibers will break easily, making the crack narrower. Both of these factors will lead to a reduction in Jb′, causing it to fall short of the requirements of steady-state cracking. A single-crack tensile test was used to quantify the σ-δ curve; the specific test method will be given in Section 3.4. The parameters σ0 and δ0 in formula (2) can be obtained from the curve, while σfc is yielded from the matrix tensile test. Jtip can be calculated by Equation (3). (3) Jtip=Km2/Em where Em is the modulus of elasticity, which can be derived from the matrix tensile test, and Km is the fracture toughness of the matrix, which can be determined via a three-point bending test of the notched matrix beam (test details are shown in the next section). Equation (4) is used to calculate and determine Km. (4) Km=1.5(FQ+mg2×10−2)×10−3⋅S⋅a01/2th2f(α) (5) f(α)=1.99−α(1−α)(2.15−3.93α+2.7α2)(1+2α)(1−α)3/2,α=a0h The meanings of parameters FQ, m, g, S, a0, t, h, and f(α) are described in [37]. The distribution of fibers and defects in the matrix is random. To achieve the stable multiple cracking of ECC, a certain margin of fiber bridging strength and energy is required. Kanda [38] proposed a strain hardening index (PSH) to estimate the strength and energy margins. (6) PSH(Strength)=σ0/σfc (7) PSH(Energy)=Jb′/Jtip 3. Materials and Test Procedures 3.1. Materials and Mix Proportions Ordinary silicate P.O.52.5 grade cement was used as the main cementitious material, and F-type fly ash was used as the supplementary cementitious material. FACs, which are a kind of waste generated by coal-fired power plants, were used as fine aggregates. The FACs had a diameter of 0.01–0.5 mm and a density of 530 kg/m3. FACs can be used to obtain GLECCs with low fracture toughness and low density. Moreover, the waste can be recycled to improve the cost-efficiency and green performance. The chemical compositions of the main raw materials used for the preparation of the GLECC are listed in Table 1, and the particle size distribution is shown in Figure 1. Since the introduction of FACs will reduce the workability of GLECCs, a high-performance water-reducing agent (WR) was used in this study. A previous study [13] has indicated that PE fibers with high strength and a high elastic modulus can provide more space for balancing the tensile strain and volume fraction of GLECCs. Hence, PE fiber was used to prepare the GLECCs in this study. The physical and mechanical properties of PE fiber are listed in Table 2. Figure 2 shows the optical microscope and SEM images of the PE fibers. Table 3 presents the GLECC mix proportions used in this study. First, by changing the FAC-to-binder mass ratio (FAC/binder: 0.15, 0.3, and 0.45), GLECC matrices were designed with three densities. Then, PE fibers of different volume fractions (Vf) were added to the GLECC matrix to explore the feasibility of developing GLECCs with high FAC and low PE fiber contents. Regarding the high-density mixture (FAC/binder: 0.15), six fiber contents (Vf: 1%, 1.25%, 1.5%, 1.75%, 2%, and 2.25%) were set to systematically study the effects of fiber content on the performance of GLECCs. For the low-density mixtures (FAC/binder: 0.3 and 0.45), only low fiber contents (Vf: 1%, 1.25%, 1.5%) were added to verify the feasibility of developing GLECCs with low fiber contents and high FAC contents. Previous studies [39] have shown that the dispersion of fibers is closely related to the workability of the matrix. As the FAC content increased, the workability of the matrix reduced significantly. It is difficult to meet the requirements pertaining to fiber dispersion for the workability of the matrix by only changing the amount of WR. Therefore, as the content of FACs increased, a larger W/B was adopted. Additionally, at each density of GLECC, the proportion of WR was kept constant in order to study the effects of fiber content on the workability of the matrix. The nomenclature of the GLECCs is twofold: the first part designates the mass ratio of FACs (FAC/binder), whereas the second part designates the fiber volume fractions (Vf). For example, the name of GLECC material FAC0.15−PE1 means that the mass ratio of FAC to binder is 0.15, and the fiber content is 1%. 3.2. Mixing, Casting and Curing Procedures The experimental procedures are shown in Figure 3, and the raw materials were weighed according to the mixing proportions shown in Table 3. The dry materials (including cement, FA, and FACs) were added to the planetary mixer and mixed at 128 r/min for 2 min. We then added the mixed solution of water and water reducing agent and continued to mix for 2 min, before mixing for 2 min at 180 r/min. Finally, the fibers were added and mixed at a 180 r/min mixing speed for 5 min to ensure their uniform distribution in the mortar. The fresh mixtures were cast into corresponding molds to be cured at room temperature and were demolded after one day. The specimens were cured to test age under standard curing conditions (temperature 20 ± 3 °C, relative humidity 95%) after demolding. 3.3. Workability of Mixture Test After the mixtures were mixed, their flow diameters were tested via the method described in standard GB/T2419−2005 [40] to quantify the workability of the mixtures; the larger the flow diameter, the better the workability. First, fresh mixtures were poured into the molds. The mold was then removed from above, and the jumping table was turned on to vibrate the mixture at a frequency of 1 Hz for 25 s. The average values of the maximum diameter of the fresh mixture along the two vertical directions were calculated to obtain the spread diameters of the mixtures. 3.4. Strain Hardening Index Test The strain hardening index is an effective tool for predicting the multi-cracking probability of ECC. Three-point bending fracture toughness tests and single-crack tensile tests were used in this paper to quantify the probability of multi-cracking in GLECC. The specific experimental procedures are as follows: Fracture Toughness Test Method. The fracture toughness of the GLECC matrix was tested with reference to the method of ASTM E399 [41], using a cuboid specimen of 354 × 75 × 40 mm3, as shown in Figure 4. Before the test, a notch 0.5 mm wide and 30 mm high was pre-cut into the middle of the specimen. The test was carried out with an electronic universal testing machine at a loading rate of 0.05 mm/min. During testing, a Linear Variable Differential Transformer (LVDT) was installed at the bottom of the specimen to measure its deformation, as shown in Figure 5. Test method of fiber-bridging complementary energy. Via the method of [23,31], the fiber-bridging complementary energy was obtained using a single-crack tensile test. The dimensions of the sample are shown in Figure 6a. Before the test, an opening was pre-cut into the middle of the specimen (as shown in Figure 6a), the width of which was 0.5 mm. The test was carried out on a universal testing machine at a loading rate of 0.5 mm/min. During the test, two LVDTs were arranged near the opening to record its width, as shown in Figure 6b. 3.5. Physical and Mechanical Performance Test Bulk density. The apparent density of the GLECC was tested on 50 × 50 × 50 mm3 cube specimens. After the samples were cured to the test age, the weight of the sample in the dry state of the surface was weighed, and three specimens were tested for each mix proportion. Compressive strength. The specimens used in the compressive strength tests were the same as those used in the bulk density test. The compressive strength test was performed after the bulk density test. Here, a loading rate of 0.6 MPa/s was used, and three specimens were tested for each mix proportion [42]. Uniaxial tensile test. Dog-bone specimens were used to test the tensile properties of the GLECC, and the dimensions of the specimens are shown in Figure 7a. The tensile test was carried out on a universal testing machine at a loading rate of 0.5 mm/min. During the tensile test, two LVDTs were fixed on either side of the specimen to record the deformation of the GLECC gauge length, as shown in Figure 7b. 3.6. Crack Characteristic Analysis The tensile crack characteristics of ECCs are of broad concern. In this study, digital image correlation (DIC) technology was used to analyze the crack characteristics of GLECCs. A conceptual introduction of the DIC method can be found in References [43,44]. Before the tensile test, white and black paints were sprayed onto the surface of the sample to produce random speckled patterns such that each subset had a unique gray-scale distribution. During the tensile test, a CCD camera was installed on a tripod in front of the testing device. Digital images were captured every 5 s. These images are correlated to the actual tensile strain values via the time recording. After the image was captured, the software GOM Correlate was used to calculate the deformation field and strain field in the plane. Based on the calculated results of DIC, crack development in GLECCs with different mix proportions can be analyzed. Taking the FAC0.15−PE1.5 GLECCs as an example, we assess the strain contours at different stages (initial crack state, multi-crack crack state, and ultimate state) on the stress–strain curve, as shown in Figure 8. Because the strain value of the cracked area is much greater than that of the uncracked area, a crack can easily be identified by the corresponding strain profile. As shown in Figure 8b–d, it is difficult to identify a crack from the original picture of the tensile specimen, but cracks are easily captured in the pictures of the strain field distribution calculated by DIC. After obtaining the corresponding displacement and strain fields, strain change at the cracking position can be observed according to the strain cloud diagram, and the strain change in the tensile direction can be used to infer the crack width. Figure 9 shows the displacement distribution and the strain distribution in the tensile direction, extracted from the central axis of the strain cloud of the specimen. When a crack occurs, the displacement in the loading direction will change in steps, while the strain will peak. The crack distribution and propagation behavior of the specimen can be monitored according to the strain and displacement distribution diagrams at different stages. Figure 10a–c show the strain and displacement distribution diagrams of the specimen in the initial crack state, the multiple crack state, and the ultimate crack state, respectively. When the applied stress reaches the cracking strength of the specimen, a crack propagates from the middle of the specimen. With the further increase in load, multiple cracks form on the specimen. When approaching failure, the cracks tend to spread across the gauge length of the specimen, as shown in Figure 10c. In addition, changes in crack propagation behavior at different stages can be analyzed according to the deformation distribution. Taking the first crack as an example, the variation in crack width at each stage can be obtained according to the displacement distribution diagram, as shown in Figure 10d. The crack width increased rapidly at the initial stage of loading. When reaching a certain width, the increase gradually slowed, demonstrating the excellent crack control ability of ECC. The crack width increased rapidly until failure occurred. In this paper, this method will be used to analyze the crack behavior of GLECCs with different mix proportions. 4. Results and Discussion 4.1. Workability Figure 11 shows the flow diameters of the GLECC mixtures. The fiber content had a significant impact on the workability of the GLECC mixtures. For any density of the GLECC mixtures, as the fiber content decreased, the flow diameter showed an increasing trend. This is because the randomly distributed fibers form a spatial network structure, which restricts the free flow of the fresh mixture. Thus, the higher the fiber content, the worse the workability of the mixture. 4.2. Bulk Density and Compressive Property The bulk density and compressive properties are shown in Figure 12. FACs were used as lightweight fillers with a hollow structure, the density of which was only 530 kg/m3, while the density of cement was 3180 kg/m3. For a unit volume of GLECCs, the addition of more FACs will reduce the amount of cement used. Therefore, adding FACs to GLECCs can significantly reduce the density of the GLECCs. The density of FAC0.45 series GLECCs was 1310 kg/m3, which is 20% lower than that of the FAC0.15 series GLECCs. Concrete with a density of less than 1950 kg/m3 can be defined as lightweight concrete [45]. The GLECCs developed in this study had densities between 1310 and 1650 kg/m3, and they can all be defined as lightweight. Inevitably, the increase in FAC content deteriorated the mechanical properties of the GLECCs. With the increase in FAC proportion, the compressive strength showed a decreasing trend. However, the compressive strengths of all mixtures remained greater than 30 MPa. For any density of the GLECC mixtures, with the decrease in PE fiber content, the compressive strength showed an increasing trend. For example, the compressive strength of FAC0.15−PE1 was 52.8 MPa, which was approximately 10% higher than the 47.5 MPa of FAC0.15−PE2. This is because an increase in fiber content results in a decrease in the compactness of the matrix [46]. The incorporation of fibers introduces bubbles in the mixture. The higher the fiber content, the greater the number of bubbles introduced. Moreover, the bubbles were difficult to eliminate. These bubbles persist in the material as defects after the cement hardens. Furthermore, as the fiber content increased, fiber entanglement occurred during the mixing process. The greater the fiber content, the greater the number of fiber clusters. The entanglement of fiber clusters not only precludes an enhancement of crack resistance, but these clusters also exist as defects in the matrix, thereby affecting the compressive strength. 4.3. Strain Hardening Index As introduced in Section 2, the strain hardening index can be used to evaluate the possibility of multiple cracking in ECC. In this study, the fracture toughness of three GLECC matrices with different FAC contents was obtained via a three-point bending test performed on notched beams to analyze the effects of lightweight fillers on the matrix fracture toughness of GLECCs. Table 4 presents the test results. As expected, the introduction of lightweight fillers significantly reduced the fracture toughness of the GLECCs matrix. With the increase in FAC content, fracture toughness and fracture energy decreased. Low fracture toughness is beneficial to the realization of multiple cracking in GLECCs [36]. The bridging stress–crack opening curves obtained from the single-crack tensile test are presented in Figure 13. The σ0 and δ0 values can be obtained from the curve, and the Jb′ value of GLECCs can be calculated using Equation (2). The results are listed in Table 4. σ0 and Jb′ reflect the crack-bridging performance achieved by the fiber. With the same fiber content, as the FAC content increased, the σ0 and Jb′ both decreased, which indicates that the introduction of lightweight fillers weakened the fiber’s bridging performance. This is because, as the FAC content increased, the proportion of cementitious material in the mixture decreased, which means that more FACs were present around the fibers, weakening the fiber bridging performance [37]. As the fiber content decreased, σ0 and Jb′ decreased. For example, for a GLECC with an FAC content of 0.15, with the reduction in fiber content from 2% to 1%, the σ0 was reduced from 9.43 MPa to 6.88 MPa (25.8%), and the Jb′ was reduced from 1295.6 to 380.1 J/m2, a 70% reduction. According to the above results, the introduction of lightweight fillers reduced the fracture toughness of the matrix while also weakening the fiber bridging performance, which is unfavorable to the balancing of fiber dosage and mechanical properties because a reduction in fiber dosage will also limit the fiber bridging performance. The degradation of fiber bridging performance caused by the combination of lightweight fillers and reduced fiber usage may prevent the strain hardening criterion from being met. We sought to comprehensively evaluate the possibility of GLECC multiple cracking when the amount of lightweight filler is increased and the amount of fiber is decreased. Based on the experimental results of fracture toughness and fiber bridging performance, the strain hardening index (PSH (strength) and PSH (energy)) were calculated using Equations (6) and (7). The results are listed in Table 4. As shown in Table 4, in the GLECC mixture with the same FAC content, with the decrease in fiber dosage, the PSH (strength) and PSH (energy) were greatly reduced, showing that fiber dosage had a significant effect on the stable multiple cracking of GLECCs. With the same value of fiber dosage, the increase in the FAC content had no obvious effect on PSH (strength) and caused the PSH (energy) to slightly increase. In general, reducing the amount of fiber and increasing the content of FAC will lead to a decrease in fiber bridging performance. However, thanks to the high strain hardening index surplus of high-strength PE fiber and the reduction in fracture toughness, the obtained material still had a large strain hardening index surplus value, indicating that the obtained material can achieve multiple cracking. For example, in the mixture FAC0.45−PE1, although the fiber content was reduced to 1%, it still had PSH (strength) and PSH (energy) values of 1.84 and 33.1. The evaluation of the strain hardening index suggests that it is feasible to develop a GLECC with a low content of high-strength PE fiber and a low-toughness matrix. The obtained material meets the strength and energy criteria and can realize multiple cracking, which is significant to balancing costs and mechanical properties. 4.4. Tensile Performance Figure 14 shows a tensile stress–strain relationship diagram for some GLECCs. The shapes of the GLECC stress–strain relationships for different fiber contents and FAC dosages are similar, and both contain an elastic section showing an increase in linear stress and a strain-hardening section with stress oscillation. However, the strength, strain, and stress shock amplitude exhibited were quite different. Decreases in fiber dosage and the increases in FAC proportion will weaken the tensile performances of GLECC, and a decrease in fiber dosage will increase the amplitude of stress shock. Figure 15 further compares the influence of fiber dosage and FAC proportion on the tensile performances of GLECCs. Ultimate tensile strength and ultimate tensile strain are useful parameters for evaluating the tensile properties of GLECCs. The ultimate tensile stress is obtained by dividing the maximum load that the specimen can carry throughout the entire tensile process by the cross-sectional area of the specimen. The strain of the specimen at the maximum stress is the ultimate tensile strain. As shown in Figure 15, as the FAC proportion increased, the ultimate tensile strength and ultimate tensile strain decreased for GLECCs with the same fiber content. The ultimate tensile strength and ultimate tensile strain are related to the bridging performance of the fiber. An increase in the proportion of FAC weakens the confinement effect of the matrix on the fibers, resulting in the weakening of the fiber’s bridging ability and finally the deterioration of the tensile properties of GLECCs. The fiber content had a significant effect on the tensile performances of GLECCs. When the FAC content was constant, as the fiber content decreased, the tensile properties decreased. We take GLECCs with an FAC proportion of 0.15 as an example. As the fiber content decreased from 2% to 1%, the ultimate tensile strength and tensile strain decreased from 7.2 MPa and 5.6% to 5.6 MPa and 2.5%, i.e., a decrease of 20% and 50%, respectively. This is because the decrease in fiber content was accompanied by an attenuation of the fiber bridging performance, which caused a reduction in tensile properties [34]. However, due to the high bridging energy surplus value of PE fiber and the contribution of FAC to the reduction in the fracture toughness of the matrix, although the fiber content was reduced to 1%, the resulting material still had excellent tensile properties, with a tensile strain of nearly 3%, which is comparable to conventional ECC-M45. The results of the above-mentioned tensile properties indicate that a reduction in fiber dosage can be achieved with high-strength PE fibers and a low fracture toughness matrix, which is of great significance for balancing the cost and tensile ductility of GLECCs. The developed GLECC mixtures still exhibit tensile strengths of up to 3.5–7.5 MPa and tensile strains of 2.5–5.5%, which values are close to those of the conventional ECC-M45, accompanied by densities as low as 1300–1650 kg/m3. As the tensile strain of an ordinary building during its normal service life rarely exceeds 3%, it is believed that the GLECCs we developed with low PE fiber contents and high FAC contents can still meet the requirements of ordinary building applications. 4.5. Analysis of Crack Characteristics 4.5.1. Cracking Pattern Figure 16 shows the final crack patterns of GLECC tensile specimens with different fiber contents at three different densities. All of the GLECC specimens exhibited the multiple cracking behavior. The fiber content affected the crack pattern significantly. When the fiber contents were 2% and 1.75%, the cracks were denser, finer, and saturated in the target area, while under high tensile strain, no crack localization was observed, indicating that the fiber content of GLECCs saturated with multiple cracks was 1.75%. As the amount of fiber decreased, the cracks gradually become more sparse and wide. When the fiber content was 1%, the crack spacing reached 50–70 mm, constituting an unsaturated state. 4.5.2. Crack Width Development Behavior The water permeability and self-healing ability of concrete are directly related to crack width. Studies [47,48,49,50] have shown that when the crack width is less than 100 μm, the water permeability and chloride ion diffusivity of concrete can be ignored. Moreover, concrete with a crack width of less than 150 μm also has a self-healing effect. Therefore, studying the relationship between the crack width and strain of GLECCs is of great significance when considering durability. Figure 17 shows the variation in the average and maximum crack widths of GLECC tensile specimens determined using the DIC method with increasing strain. The relationship curve between the average crack width and the deformation can yield cracking information for the material at a given strain, and the relationship curve between the maximum crack width and the strain provides the worst-case information [51]. As shown in Figure 17, the fiber content has a significant effect on the crack control ability. For GLECCs of the same density, with a decrease in fiber content, the average and maximum crack widths showed an increasing trend. For GLECCs with the same fiber content, the larger the FAC content, the worse the crack control ability. Furthermore, the maximum and average crack widths increased with increasing FAC proportions. This is because, in GLECCs with a high proportion of FACs, more FACs will be present around the fibers, thereby weakening the binding effect of the matrix with the fibers. Simultaneously, due to the addition of more FACs, the rheology of GLECCs will deteriorate, and more water will need to be added to meet the rheological requirements of fiber dispersion, resulting in a loss of strength. These two factors lead to a deterioration in fiber crack resistance. In GLECCs with a relatively high density (FAC/binder: 0.15), when the PE content was greater than 1.5%, the GLECCs had a strong crack control ability; i.e., for FAC1.5−PE1.75 and FAC1.5−PE2, the average crack width could be kept below 100 μm, whereas the maximum crack width was below 200 μm (as shown in Figure 17a). However, when the fiber content decreased, the crack width control ability decreased, thereby increasing the crack width; i.e., for GLECC mixtures with FAC/binder: 0.15, under the same strain level, as the fiber content decreased, the average crack width and the maximum crack width both increased (as shown in Figure 17a). Moreover, for FAC1.5−PE1, FAC1.5−PE1.25, and FAC1.5−PE1.5, with 1.0% tensile strain, the average crack width was less than 100 μm, whereas the maximum crack width was less than 150 μm. As shown in Figure 17b,c, for low-density GLECCs (FAC/binder: 0.3 and 0.45), in the 0.8% strain range, the average and maximum crack widths were less than 100 μm and 150 μm, respectively. This shows that although reducing the amount of fiber increases the crack width of GLECCs, their durability will not be affected within a certain strain range. For low-density GLECCs, when the fiber content was less than 1.5%, the durability was not affected in the strain range of 0.8%. However, for relatively high-density GLECCs (FAC/B: 0.15), the strain can be relaxed to 1%. It is believed that strain values of 0.8% and 1% can still meet the needs of many applications, because structures rarely exceed 1% tensile strain during their normal service life. In order to meet the requirements of an ordinary structure under normal use conditions, the fiber content in GLECCs can be reduced to 1–1.5%. 4.6. Cost and Carbon Emission Analysis To evaluate the green performance of GLECCs, their costs and CO2 emissions were calculated. The costs and CO2 emissions of the main raw materials used to produce GLECCs are listed in Table 5. The data were collected from the published literature and various material suppliers. Since FACs and FA are wastes, CO2 emissions were not generated by default in the calculation. According to the quantity, cost, and CO2 emissions of the raw materials listed in Table 3 and Table 5, the cost and CO2 emission index of producing a unit volume of GLECCs can be calculated. The results are shown in Figure 18. As a comparison, this study also outlines the cost and carbon emissions of ordinary concrete. The carbon emissions and costs of GLECCs were much higher than those of ordinary concrete. Owing to the application of more cement and fiber, the carbon emissions were mainly derived from cement, whereas the increase in cost was mainly derived from fiber. Therefore, as expected, increasing the use of FACs can significantly reduce the amount of cement, thereby reducing carbon emissions. Specifically, when the fiber content was constant, increasing the FAC content from 0.15 to 0.45 could result in the carbon emissions of GLECCs being reduced by 36%. Reducing the amount of fiber can significantly reduce the costs of the materials. For example, for GLECCs with an FAC content of 0.15, as the fiber content was reduced from 2% to 1%, the cost was reduced by approximately 40%. Increasing the FAC content and reducing the fiber content deteriorated the mechanical properties, but the developed GLECCs still had an ultimate tensile strain of approximately 3% and a tensile strength of more than 3.5 MPa, and the compressive strength was 30–50 MPa. For the standard ECC-M45, the compressive strength is 40 MPa, the tensile strength is 4.5 MPa, and the tensile strain is 3% [52]. Therefore, the performance of the GLECCs developed in this study is comparable to that of the standard ECC-M45, illustrating the availability of GLECCs. Moreover, in the 0.8% tensile strain range, the average and maximum crack widths of GLECCs were less than 100 μm and 150 μm, respectively. Since the penetration rate of water and chloride ions is very small when the crack width is less than 100 μm, it can be ignored in engineering [47,48]. In other words, the durability of GLECCs was not affected when the strain did not exceed 0.8%. The tensile strain of concrete is only 0.01%. GLECCs can function effectively in many scenarios in which concrete is ineffective. It can be concluded that GLECCs can meet many engineering needs, with significantly lower costs and carbon footprints. 5. Conclusions This study has aimed to explore the feasibility of using high-strength PE fibers and recycling FACs to develop GLECCs. The results show that by adjusting the FAC and PE contents in GLECCs, the production cost and carbon emissions, which are of practical significance when balancing the performance and costs of GLECCs, can be reduced considerably. The following conclusions can be drawn based on the results of this study. (1) FACs were effective in reducing the density of GLECCs. However, they also weakened the mechanical properties of GLECCs. With an increase in the FAC content, the compressive and tensile strengths showed a decreasing trend. However, the obtained compressive and tensile strengths of GLECCs were still greater than 30 MPa and 3.5 MPa, respectively. (2) The PE fiber affects the workability of GLECC adversely. As the fiber content increased, the flow diameter showed a decreasing trend. Additionally, it was observed that as the fiber content increased, the compressive strength of GLECCs showed a decreasing trend. In GLECCs with an FAC content of 0.15, the compressive strength of FAC0.15−PE2.25 was 44.2 MPa, which is approximately 16% lower than the 52.8 MPa of FAC0.15−PE1. (3) Reducing the amount of fiber will result in a decrease in the fiber’s bridging performance, resulting in a decrease in the tensile properties. All GLECCs exhibited obvious strain hardening, with a tensile strength of 3.5–7.5 MPa and a tensile strain of 2.5–5.5%. In GLECCs of any density, even when the fiber content was as low as 1%, the strain hardening behavior was still obvious. The tensile strain was close to 3%, whereas the tensile strength was greater than 3.5 MPa. (4) The width of the crack is positively correlated with the FAC content and negatively correlated with the fiber content. Although a change in FAC and fiber dosage resulted in a change in the crack width, the durability of GLECCs was not affected within a certain strain range. Even when the fiber content was as low as 1%, in the 0.8% strain range, the average crack width and maximum crack width remained at 100 μm and 150 μm, respectively, thus meeting the durability requirements of conventional applications. (5) The use of recycled FACs and PE can significantly reduce the cost and environmental impact of GLECCs. As the FAC content increased from 0.15 to 0.45, the carbon emissions of GLECCs were reduced by as much as 36%, and as the fiber content was reduced from 2% to 1%, the cost was reduced by approximately 40%. Nevertheless, the performances of the GLECCs are sufficient for many applications. This study provides a reference for the design and utilization of GLECCs. Crack width only offers a qualitative insight into durability. Further research should be undertaken to further evaluate the durability of GLECCs, including their resistance to carbonization, the chloride ion penetration, and the sulfate attack resistance. The width of the crack has a significant effect on the self-healing performance of GLECCs, which should be further evaluated quantitatively. In addition, FACs has certain pozzolanic capacities, the impacts of which on the long-term performance of GLECCs should be further considered. Author Contributions Conceptualization, C.F. and R.G.; methodology, C.F.; validation, C.F., R.G. and R.Q.; formal analysis, R.G.; investigation, C.F. and M.C.; resources, R.G.; data curation, R.G.; writing—original draft preparation, C.F.; writing—review and editing, C.F.; visualization, M.C.; supervision, R.G.; project administration, R.G.; funding acquisition, R.G. All authors have read and agreed to the published version of the manuscript. Funding This study was funded by the National Natural Science Foundation of China (NSFC) (Grant No.11962009). All the help and support are greatly appreciated. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this study are available from the corresponding author upon reasonable request. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Particle size distributions of main raw materials. Figure 2 The microscopic images of PE fibers. (a) Optical microscope image; (b) SEM image (SE 1500 kV ×100). Figure 3 Sample preparation procedure. Figure 4 The size of the specimen tested for fracture toughness (Unit: mm). Figure 5 Fracture toughness test device. (a) Photo of the test device; (b) diagram of the test device. Figure 6 Single-crack tensile test. (a) Diagram of specimen size (Unit: mm); (b) photo of the test device. Figure 7 Uniaxial tensile test. (a) Diagram of specimen size (Unit: mm); (b) photo of the test device. Figure 8 Strain field of GLECCs under different strains. (a) Tensile stress–strain curve of FAC0.15−PE1.5; (b) the strain contours of the initial crack state; (c) the strain contours of the multiple cracked state; (d) the strain contours of the ultimate tensile state. Figure 9 Displacement distribution and strain distribution of the central section. Figure 10 The strain and displacement distribution diagrams of the tensile specimen. (a) The initial crack state; (b) the multiple crack state; (c) the ultimate crack state; (d) the variation in the first crack’s width. Figure 11 Flow diameters of GLECC mixtures. Figure 12 Summary of density and compression of GLECCs. Figure 13 Fiber bridging stress–crack opening curves of GLECC mixtures. (a) FAC0.15−PE1.75; (b) FAC0.15−PE1.5; (c) FAC0.15−PE1.25; (d) FAC0.15−PE1; (e) FAC0.3−PE1.5; (f) FAC0.3−PE1; (g) FAC0.45−PE1.25; (h) FAC0.45−PE1. Figure 14 Tensile stress–strain curves of GLECC mixtures. (a) FAC0.15−PE1.75; (b) FAC0.15−PE1.5; (c) FAC0.15−PE1.25; (d) FAC0.15−PE1; (e) FAC0.3−PE1.5; (f) FAC0.3−PE1; (g) FAC0.45−PE1.25; (h) FAC0.45−PE1. Figure 15 Tensile performances of GLECC mixtures. (a) Tensile stress; (b) tensile strain. Figure 16 Cracking pattern of GLECC mixtures. (a) GLECCs with FAC content of 0.15; (b) GLECCs with FAC content of 0.3; (c) GLECCs with FAC content of 0.45. Figure 17 Crack development of GLECC mixtures containing different FAC content. (a) GLECCs with FAC content of 0.15; (b) GLECCs with FAC content of 0.3; (c) GLECCs with FAC content of 0.45. Figure 18 Cost and CO2 emissions of GLECCs. (a) Carbon emission; (b) cost [28,52,53,54]. materials-15-03047-t001_Table 1 Table 1 Chemical composition and physical properties of main raw materials. Chemical Analysis Cement (wt. %) FA (wt. %) FACs (wt. %) CaO 65.41 3.3 1.06 Fe2O3 3.2 8.09 1.96 MgO 1.58 1.34 - SO3 5.72 0.67 0.42 K2O 0.5 1.37 3.94 SiO2 19.47 53 73.1 Na2O - 0.34 2.42 Al2O3 3.86 24.19 16.7 TiO2 0.26 - 0.35 Others - 7.7 0.05 Particle size 15 µm 10 µm 0.01–0.5 mm Specific gravity (g/cm3) 3.18 2.68 0.53 Specific surface area (m2/g) 0.438 0.663 - materials-15-03047-t002_Table 2 Table 2 Physical and mechanical performances of PE fibers. PE Fiber Length/mm 12 Aspect ratio 460 Tensile strength/GPa 2.9 Young’s modulus/GPa 116 Density/g/cm3 0.97 materials-15-03047-t003_Table 3 Table 3 Mix proportions of GLECCs. Mix ID Cement (kg/m3) FA (kg/m3) FACs (kg/m3) Water (kg/m3) WR (kg/m3) PE (Vf) (%) FAC0.15−PEn 913 391.5 195.8 251.7 101.7 n FAC0.30−PEn 685.1 293.6 293.6 225 87.5 n FAC0.45−PEn 511.9 219.4 330 213.3 86.6 n For the GLECCs with an FAC content of 0.15, n = 1, 1.25, 1.5, 1.75 and 2. For the GLECCs with FAC contents of 0.3 and 0.45, n = 1, 1.25, and 1.5. materials-15-03047-t004_Table 4 Table 4 Strain hardening index analysis of GLECCs. Mix ID FQ (N) Km (MPa·m1/2) Jtip (J/m2) σ0 (MPa) PSH (Strength) Jb′ (J/m2) PSH (Energy) FAC0.15−PE2 751.7 0.6 18.63 9.43 2.48 1295.6 66.6 FAC0.15−PE1.75 8.48 2.23 1280.9 65.9 FAC0.15−PE1.5 8.25 2.17 930.8 47.9 FAC0.15−PE1.25 7.57 1.99 777.1 39.9 FAC0.15−PE1 6.88 1.81 380.1 19.5 FAC0.3−PE1.5 576.4 0.46 15.35 5.9 1.9 827.8 49.8 FAC0.3−PE1.25 5.82 1.88 703.2 42.3 FAC0.3−PE1 5.65 1.82 581.8 35 FAC0.45−PE1.5 481 0.39 13.85 4.44 1.96 594.8 44.9 FAC0.45−PE1.25 4.42 1.92 557.4 42.1 FAC0.45−PE1 4.24 1.84 438.9 33.1 materials-15-03047-t005_Table 5 Table 5 Costs and CO2 emissions of ECC raw materials. Chemical Analysis Cost (USD/t) CO2 Emissions (kg/m3) Cement 62.6 930 [28,53] FA 46.9 - FACs 469 - W 0.9 - WR 312 1667 [54] PE 35200 2671 Concrete 61.5 373 [52] Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Shi T. Lan Y. Hu Z. Wang H. Xu J. Zheng B. 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==== Front J Clin Med J Clin Med jcm Journal of Clinical Medicine 2077-0383 MDPI 10.3390/jcm11092629 jcm-11-02629 Review Neoadjuvant Chemo-Immunotherapy for Locally Advanced Non-Small-Cell Lung Cancer: A Review of the Literature Franzi Sara 1* Mattioni Giovanni 12 Rijavec Erika 3 Croci Giorgio Alberto 45 https://orcid.org/0000-0003-3767-0449 Tosi Davide 1 Lopci Egesta Academic Editor Roberto Gasparri Academic Editor 1 Thoracic Surgery and Lung Transplantation Unit, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; giovanni.mattioni@unimi.it (G.M.); davide.tosi@policlinico.mi.it (D.T.) 2 School of Thoracic Surgery, University of Milan, 20122 Milan, Italy 3 Medical Oncology Unit, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; erika.rijavec@policlinico.mi.it 4 Division of Pathology, IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; giorgio.croci@policlinico.mi.it 5 Department of Pathophysiology and Transplantation, University of Milan, 20122 Milan, Italy * Correspondence: sara.franzi@policlinico.mi.it 07 5 2022 5 2022 11 9 262904 3 2022 30 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Non-small cell lung cancer accounts for approximately 80–85% of all lung cancers and at present represents the main cause of cancer death among both men and women. To date, surgery represents the cornerstone; nevertheless, around 40% of completely resected patients develop disease recurrence. Therefore, combining neoadjuvant chemo-immunotherapy and surgery might lead to improved survival. Immunotherapy is normally well tolerated, although significant adverse reactions have been reported in certain patients treated with inhibitors of immune checkpoints. In this review, we explore the current literature on the use of neoadjuvant chemo-immunotherapy followed by surgery for treatment of locally advanced non-small-cell lung cancer, with particular attention to the histological aspects, ongoing trials, and the most common surgical approaches. In conclusion, neoadjuvant immunotherapy whether combined or not with chemotherapy reveals a promising survival benefit for patients with advanced non-small-cell lung cancer; nevertheless, more data remain necessary to identify the best candidates for neoadjuvant regimens. chemo-immunotherapy neoadjuvant non-small-cell lung cancer surgery overall survival Italian Ministry of HealthThe publication costs were supported by a fund dedicated to research derived from the Italian Ministry of Health donated to the IRCCS Foundation Ca’ Granda Ospedale Maggiore Policlinico. ==== Body pmc1. Introduction At present, lung cancer represents the main cause of cancer death in both men and women, constituting the most common type of cancer in men (22%) and the third most common type in women (8.4%) [1]. In Italy alone, more than 40,000 new cases of lung cancer were identified in 2020 [2]. The high mortality of lung cancer is mainly due to its late diagnosis; only about 10% of patients are discovered at an early stage, whereas the majority are diagnosed later, reducing the overall survival rate, which settles at about 15% after 5 years [1]. Non-small-cell lung cancer (NSCLC) accounts for approximately 80–85% of all lung cancers; its treatment depends on tumour histology, genetic subtype, performance status of the patient, and disease stage. Until now, surgery has been the cornerstone; nevertheless, around 40% of completely resected patients develop disease recurrence [3]. Therefore, combining neoadjuvant chemo-immunotherapy with subsequent surgery may lead to improved survival [1]. Following the results from the CHECKMATE-816 (NCT02998528) trial, the FDA recently approved nivolumab and platinum-based chemotherapy in the neoadjuvant setting for NSCLC [4]. In our review, we explore the current literature on the use of neoadjuvant chemo-immunotherapy followed by surgery to treat locally advanced (LA) NSCLC. In particular, we reviewed and discussed the current literature on the histopathological, oncological, and surgical aspects of NSCLC. 2. Immune Check-Points on Immunotherapy During chronic infections and in cancer, T lymphocytes are exposed to persistent inflammatory stimuli that lead cells to a deteriorating reversible process called “exhaustion”, which is associated with loss of T cell function and the expression of inhibitory receptors [5] such as Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4), programmed cell death-1 (PD-1), lymphocyte activation gene-3 (LAG-3), CD244, CD160, CD39, T cell immunoglobulin, and mucin domain-containing protein 3 (TIM-3) [6]. Exhausted T cells are not responsive to antigen-mediated T-Cell Receptor (TCR); they lack their ‘killing’ activity and secrete low amounts of the effector cytokines Tumour Necrosis Factor alpha (TNF-alpha) and Interferon-gamma (IFN-gamma) [7]. The receptor CTLA-4 is a member of the immunoglobulin family, which in normal conditions is weakly expressed in the haematopoietic compartment and increases following antigen stimulation. Its blocking might trigger a T-cell-mediated immune response against cancer and induce long-lasting immunological memory [8]. PD-L1 (programmed death-ligand 1) is a transmembrane protein that acts as an inhibitory factor of the immune response by binding PD-1, which is expressed on the T cell surface. PD-1 regulates the activity of T cells by activating the apoptosis of T effector cells and by inhibiting the apoptosis of T regulatory cells. PD-1/PD-L1 binding reduces the host immune response against cancer cells [5]. Importantly, exhausted T cells are not completely dysfunctional, and can therefore be reinvigorated and have their function restored [9]. Immune Checkpoints Inhibitors in Cancer The inhibitory function of both CTLA-4 and the PD-1/PD-L-1 axis makes them important therapeutic targets against cancer. CTLA-4 blockade provides a particularly long-lasting immunological memory, while PD-1/PD-L-1 blockade enhances tumour cytolysis and reduces metastases formation [8]. CTLA-4 and PD-1 are the most potent T cell regulatory molecules at different steps of the T cell lifespan. At present, CTLA-4, PD-1, and PD-L-1 represent the main targets in immunotherapy (Figure 1); indeed, most of the immune checkpoint inhibitors (ICIs) commonly used in immunotherapy act on these molecules [10,11]. 3. Histopathological Aspects According to the latest guidelines of the European Society of Medical Oncology (ESMO) [12], the availability of tumoral tissue is a mandatory requirement for the workup of NSCLC, particularly in LA NSCLC. In this context, the role of the anatomopathologist becomes crucial in determining the tumour histotype, assessing biomarkers, and addressing the neoadjuvant therapeutic strategy. Biomarkers The current approach to biomarker assessment includes two types of analysis: the evaluation of targetable alterations and/or markers of resistance inherent to the tumoral clone, and the evaluation of properties related to the interplay between neoplastic cells and the host. Of note, it is advisable to obtain tumoral tissue at any point in the clinical course to track molecular targets alongside disease progression. Among the inherent alterations in tumoral clones, it is recommended that recurrent mutations and/or chromosomal imbalances be investigated, especially in patients with advanced NSCLC, as this can allow identification of the adenocarcinoma (AC) component, the non-squamous non–small-cell histology, or any non-small cell histology with clinical features, indicating a high probability of an oncogenic driver (i.e., young age, no tobacco exposure) [13]. In such instances, it is mandatory to test for activating EGFR and BRAFV600E mutations, and when possible, it is recommended that such determinations be included within a comprehensive targeted panel containing mutations at ERBB2, MET (exon skipping), and KRAS (G12C mutation at exon 2). Recommended chromosomal imbalances to be tested comprise ALK (either via molecular-genetics approaches such as FISH or RNA-based assays or via immunohistochemistry (IHC) for ALK expression) as well as ROS1 and RET [12,13]. Though occurring in ~1% of lung AC, NTRK 1, 2, and 3 chromosomal imbalances emerged as targetable, and screening via IHC is recommended, along with further confirmation by FISH or Next Generation Sequencing (NGS) panel in NTRK+ cases [14,15]. Promising markers for squamous cell carcinoma (SCC) include FGFR1 and PDGFR amplification and PI3KCA, PTEN, and DDR2 mutations, although they have yet to be implemented in clinical practice [16]. Concerning the tumour–host interaction marker, the assessment of PD-L1 has been mandated by the ESMO guidelines, at least for unresectable cases, due to the growing evidence of improvement in clinical responses to checkpoint inhibition, and not only in advanced NSCLC [12]. It is assessed as tumour proportion score (TPS), i.e., the proportion (as a percentage) of tumoral cells showing membrane positivity, either partial and/or faint. Analysis of PD-L1 expression by ICH is feasible in the clinical routine and is reproducible [17]; indeed, most of the available assays have been proven to show highly comparable staining [18,19]. However, whichever assay is used in the laboratory, it is recommended that an internal validation be achieved. When available, PD-L1 analysis should be performed on a histologic specimen from surgical resection, although it can be determined with high reproducibility on small samples from fine-needle aspiration (FNA) with the sole requirement of measuring a minimum number of 100 tumour cells on the slide (Figure 2). When dealing with small specimens, reflex PD-L1 assessment coupled with sectioning for diagnostic purposes allows preservation of the tissue for further biomarker tests; thus, the main limitations reside in the potential heterogeneity of expression, which is missed on such samples, and in the greater challenges of separating tumoral from inflammatory cells, as the tissue architecture may be lost [20,21]. In addition to the current standards required for assessing immunotherapy eligibility, several studies have pointed out the predictive power of combined proportion score (CPS), i.e., including PD-L1+ immune cells in the scoring, as well as its concordance with TPS and most importantly the improvement in clinical response in NSCLS patients, with TPS < 1% in combination schemes [22,23]. Although yet to be implemented outside clinical trials, not routinely performed in clinical practice, and lacking in guidelines or recommendations for its assessment and reporting, the evaluation of tumour mutational burden (TMB) is gaining an increasing role as a predictor of response to immunotherapy and as a broad-spectrum tool in medical oncology [24,25]. TMB corresponds to the number of somatic non-synonymous mutations per coding area of the tumoral DNA and is hypothesized to correlate with the production of a higher amount of neoantigens inducing a stronger immune response which can be exploited by ICIs [26]. While the current approach to TMB relies on high throughput techniques such as whole-exome sequencing, targeted NGS panels are being developed and validated [27], and promising results have been found with cytological samples [28] and liquid biopsy [29] as well. It is thus conceivable that TMB, particularly in combination with PD-L1 assessment, will shortly become a feasible and robust predictive tool [30], particularly as the immunotherapeutic approach now represents the cornerstone of the management of NSCLC cases lacking demonstrable targetable lesions [22]. A crucial pitfall to be considered is that clonal heterogeneity, a frequent and challenging feature of NSCLC both intratumorally and inter-tumoral and at different sites (i.e., primary vs. metastatic) and at different timepoints of its clinical course, can affect response and/or development of resistance to immunochemotherapy [31,32]. Serial testing on specimens obtained at disease relapse is thus advisable, as it may reveal shifts in the molecular profile [31,33]. In addition, keeping in mind its biological and technical limitations, the analysis of tumour-derived circulating DNA/RNA via liquid biopsy may be able to capture multiple features of the molecular landscape of a tumour and may serve as a complementary tool in a comprehensive strategy [34]. 4. Immunochemotherapy in Oncology 4.1. Phase II Clinical Trials of Neoadjuvant Immunochemotherapy The NADIM trial was the first study aimed at investigating the combination of chemotherapy and immunotherapy as neoadjuvant treatment in resectable stage IIIA N2-NSCLC patients. In this single-arm phase II study in Spain, 46 patients received paclitaxel (200 mg/m2) and carboplatin (area under curve 6) plus nivolumab (360 mg) every three weeks for three cycles, followed by adjuvant nivolumab for one year (240 mg every two weeks for four months, followed by 480 mg every four weeks for eight months). Patients with EGFR mutations or ALK translocations were excluded. The primary endpoint of the study was progression-free survival (PFS) at 24 months in the modified intent-to-treat (ITT) population (all the patients treated with neoadjuvant treatment) and in the per-protocol (PP) population (all patients who underwent surgery and received at least one cycle of adjuvant nivolumab). Forty-one patients had tumour resection. At 24 months, PFS was 77.1% (95% CI 59.9–87.7) in the ITT population and 87.9% (95% CI 69.8–95.3) in the PP population. Two-year overall survival (OS) was 90%. Notably, 63% of patients who underwent surgery achieved a pathological complete response (pCR), defined as 0% of viable tumour cells in resected lung and lymph nodes, and 83% experienced a major pathological response (MPR), defined as <10% of viable tumour cells in resected lung and lymph nodes. The combination of chemotherapy and immunotherapy as neoadjuvant treatment was generally well-tolerated, and no surgery delays were reported. The most frequent grade ≥3 treatment-related adverse events (TRAEs) described were increased lipase (7%) and febrile neutropenia (7%) [35]. Zinner et al. evaluated the addition of nivolumab (360 mg) to cisplatin (75 mg/m2) plus pemetrexed (500 mg/m2) or gemcitabine (1250 mg/m2 on days 1 and 8) according to histology every three weeks for three cycles in thirteen patients affected by resectable stage IB (≥4 cm)-IIIA NSCLC, according to the 8th edition of the American Joint Committee on Cancer staging system (AJCC). Key exclusion criteria included EGFR mutations or ALK rearrangement. The primary endpoint of the study was an MPR. The study would be considered positive if at least 29% of patients achieved at least MPR. Eighty-five per cent of the patients achieved at least MPR; therefore, the study met its primary endpoint. Notably, 38% of patients experienced a pCR. The combination of chemotherapy and nivolumab demonstrated a manageable safety profile (Table 1); the most common grade 3 toxicities reported were haematological-associated (neutropenia and anaemia) and renal-related [36]. In a phase II study, Shu et al. investigated the administration of neoadjuvant atezolizumab (1200 mg) with carboplatin (area under the curve 5) and nab-paclitaxel (100 mg/m2 on days 1, 8, and 15) every three weeks for four cycles in 30 patients with resectable AJCC 7th stage IB-IIIA NSCLC. Patients were excluded from enrolment if they had never been smokers [37]. The primary endpoint of the study was MPR. More than half of patients (57%) experienced MPR. Notably, one-third of patients (33%) achieved pCR (Table 1). jcm-11-02629-t001_Table 1 Table 1 Results of neoadjuvant phase II clinical trials with chemotherapy and immunotherapy. M: Male; F: Female; PFS: progression-free survival; EFS: event-free survival; MPR: major pathological response; pCR: pathological complete response. Trial Patients (M/F) Age (Median) Stage Treatment Primary Endpoint Results NCT03081689 (NADIM) [35] 46 (34/12) 63 IIIA (N2) Nivolumab + paclitaxel and carboplatin PFS (at 24 months) PFS: 77.1% MPR: 83% pCR: 63% NCT03366766 [36] 13 (8/5) 69 IB (≥4 cm)–IIIA Nivolumab + cisplatin and pemetrexed or cisplatin and gemcitabine MPR MPR: 85% pCR: 38% NCT02716038 [37] 30(15/15) 67 IB–IIIA Atezolizumab + carboplatin and nab-paclitaxel MPR MPR: 57% pCR: 33% NCT02572843 (SAKK 16/14) [38] 67 (35/32) 61 IIIA (N2) Durvalumab + cisplatin and docetaxel EFS (at 12 months) EFS: 73.3% MPR: 60% pCR: 18.2% NCT04304248 (neoTPD01) [39] 33 (27/6) 61 IIIA–IIIB–(T3-4 N2) Toripalimab + carboplatin and pemetrexed or carboplatin and nab-paclitaxel MPR MPR: 60.6% pCR: 45.5% These results are even more remarkable considering that six patients had stage IIIA disease. The pathological response was observed regardless of PD-L1 expression. No surgical delays or postoperative complications related to neoadjuvant treatment were reported, and no new adverse events associated with the neoadjuvant regimen were described [37]. In the multicentre, single-arm, phase II SAKK 16/14 trial, a total of 68 patients were assigned to receive neoadjuvant treatment consisting of cisplatin (100 mg/m2) and docetaxel (85 mg/m2) every three weeks for three doses, followed by two cycles of durvalumab (750 mg) every two weeks. After surgery, durvalumab was continued for one year. The primary endpoint of the study was event-free survival (EFS) at 12 months. The hypothesis for statistical considerations was an improvement of EFS from 48% to 65% at 12 months. Key inclusion criteria included patients between 18 and 75 years of age and resectable AJCC 7th stage IIIA (N2) NSCLC [38]. At a median follow-up of 28 months, median EFS was not reached, and EFS at 12 months was 73% (90% CI 62.5–81.4). Ten patients (18.2%) experienced pCR, and 33 patients (60%) had MPR. Fifty-nine patients (88.1%) reported adverse effects (AEs) grade ≥ 3 [38]. These promising findings led to a prospective multicentre phase II SAKK 16/18 trial investigating the efficacy and safety of the combination between immune-modulatory radiotherapy and the SAKK 16/14 treatment regimen (Table 1) [40]. The single-arm phase II NeoTPD01 study evaluated the anti-PD-1 inhibitor toripalimab (240 mg) combined with carboplatin (area under the curve 5) and pemetrexed (500 mg/m2) or nab-paclitaxel (260 mg/m2) every three weeks for three cycles as a neoadjuvant treatment in 33 Asian patients with resectable stage IIIA or IIIB (T3N2) NSCLC. Patients with known sensitizing EGFR mutations or ALK translocations were excluded. After surgery, patients received adjuvant toripalimab monotherapy until month 12. The primary endpoint of the study was MPR. Of the 33 patients enrolled, 33 underwent surgery (PP population). The study showed remarkable pathological responses; the MPR rate was 60.6% in the ITT population and 66.7% in the PP population. The combination of toripalimab and chemotherapy showed tolerable results. The most common grade three TRAE observed was anaemia (6.2%) (Table 1) [39]. 4.2. Phase III Clinical Trials of Neoadjuvant Immunochemotherapy The combination of immunotherapy and chemotherapy as neoadjuvant treatment is being evaluated in five ongoing phase III trials (Table 2). jcm-11-02629-t002_Table 2 Table 2 Ongoing neoadjuvant phase III clinical trials with chemotherapy and immunotherapy. pCR: pathological complete response; EFS: event-free survival; OS: overall survival. Trial Stage Neoadjuvant Treatment Adjuvant Treatment Primary Endpoint Status NCT02998528 Checkmate816 [41,42] IIB–IIIA Platinum + vinorelbine/pemetrexed/gemcitabine/docetaxel/paclitaxel + nivolumab vs. Platinum + vinorelbine/pemetrexed/gemcitabine/docetaxel/paclitaxel NA pCR; EFS Active, not recruiting NCT04025879 Checkmate77T [43] IIA–IIIB (T3N2) Platinum + pemetrexed/docetaxel/paclitaxel + nivolumab vs. Platinum + pemetrexed/docetaxel/paclitaxel Nivolumab for 1year vs. placebo EFS Recruiting NCT03456063 Impower030 [44] II–IIIA–IIIB (T3N2) Platinum + pemetrexed/gemcitabine/nab-paclitaxel + atezolizumab vs. Platinum + pemetrexed/gemcitabine/nab-paclitaxel Atezolizumab for 48weeks vs. placebo EFS Active, not recruiting NCT03425643 KEYNOTE671 [45] IIA–IIIA–IIIB (N2) Cisplatin + pemetrexed/gemcitabine + pembrolizumab vs. Cisplatin + pemetrexed/gemcitabine Pembrolizumab for 39weeks vs. placebo EFS; OS Recruiting NCT03800134 AEGEAN [46] IIA–IIIA -IIIB(N2) Platinum + pemetrexed/gemcitabine/paclitaxel + durvalumab vs. Platinum + pemetrexed/gemcitabine/paclitaxel Durvalumab for 1 year vs. placebo pCR; EFS Recruiting Checkmate 816 is an international randomized phase III trial evaluating the addition of nivolumab to chemotherapy as a neoadjuvant treatment in 358 patients affected by resectable stage IB (with tumours with a diameter > 4 cm) to IIIA NSCLC (AJCC 7th edition) [41]. Patients with known sensitizing EGFR mutations or ALK translocations were excluded. Patients were randomized 1:1 to receive three cycles of platinum-based chemotherapy alone or in combination with nivolumab at a dose of 360 mg every three weeks. An exploratory arm investigating nivolumab plus ipilimumab was closed early. Patients underwent surgery within six weeks of the completion of neoadjuvant treatment. The primary endpoints of the study were pCR and EFS. In the ITT population, a statistically significant improvement in pCR (24% vs. 2.2%, p < 0.0001) was achieved in the combination arm compared to the chemotherapy alone arm. The improvement in pCR was observed regardless of the stage of disease, PD-L1 expression, or TMB assessment. No difference in terms of severe TRAEs (G3-4) was observed (34% in the combination arm and 37% in the chemotherapy alone arm) [41]. During the 2021 ASCO Annual Meeting, Spicer presented the surgical outcomes of the study. The addition of nivolumab to chemotherapy did not interfere with the feasibility and timing of the surgery. Indeed, the percentage of patients who underwent surgery was 83% in the experimental arm and 75% in the standard arm. Delays in surgery were similar between the two groups of treatments (31% vs. 24%, respectively). Patients treated with immunotherapy plus chemotherapy experienced more lobectomies (77% vs. 61%) and fewer pneumonectomies (17% vs. 25%) compared to those who received chemotherapy alone. Furthermore, the addition of anti-PD-1 to chemotherapy did not lead to an increase in toxicity or post-surgical complications [42]. The randomized phase III Checkmate 77T trial is evaluating neoadjuvant nivolumab in combination with chemotherapy followed by adjuvant nivolumab in resectable stage IIA–IIIB (T3N2 only) NSCLC patients. The primary endpoint of the study is EFS. Patients with EGFR/ALK mutations are excluded [43]. The safety and efficacy of atezolizumab in combination with platinum-based chemotherapy as a neoadjuvant treatment is being evaluated in resectable stage II-III NSCLC patients in the randomized phase III IMpower030 study [44]. The phase III KEYNOTE-671 study is investigating the administration of pembrolizumab and chemotherapy before surgery in early-stage NSCLC patients [45]. Lastly, the phase III AEGEAN trial in patients with resectable stage II-III NSCLC is assessing whether the addition of durvalumab to neoadjuvant chemotherapy followed by surgical resection and adjuvant durvalumab improves pathological and clinical outcomes compared to neoadjuvant chemotherapy plus placebo followed by surgical resection and adjuvant placebo. Patients with EGFR/ALK mutations are excluded [46]. 5. Surgery after Immunochemotherapy At present, interest in the application of molecular-targeted therapy or immunotherapy in lung cancer has increased, especially in the neoadjuvant setting, whether combined or not with chemotherapy. In the future, this could become the standard of care in resectable NSCLC, in particular for LA cases, thus modifying the current standard surgical approach. Surgery plays a role as part of a multidisciplinary strategy in LA NSCLC, which can be considered a systemic disease. Especially in N1-N2 cases, the goal is the radical resection of the local component of the disease, and established surgical principles can be identified. En bloc anatomical lung resection with removal of the involved structures (e.g., chest wall, pericardium) with or without proper reconstruction is the standard of surgical excision; lobectomy is the most common type of resection, while pneumonectomy, particularly on the right side, should be avoided when possible, and being replaced by sleeve lobectomy or bilobectomy where feasible [47,48,49,50]. In the case of neoadjuvant therapy, the main concerns are Effect on surgery timing and delay for adverse effects; Effect on cardiopulmonary function and performance status; Technical difficulty of surgical resection and potential complications; Necessity of surgery reconsideration in case of disease progression. Many studies have been conducted on neoadjuvant immunochemotherapy in LA NSCLC, and many are still ongoing. There are eleven available trials; of these, six provide a comparison between the chemotherapy arms [36,42,43,44,45,46], while two compare the actual results with the previous ones with neoadjuvant chemotherapy [38,51]. To date, the most extensive surgical data in these trials are from the NADIM study. In this Spanish study (Table 1), surgery was planned 21–28 days from the end of treatment, with no recorded delays, although with the exclusion from resection of five patients (11%); R0 resection was achieved in all the remaining cases. Surgical resections consisted of 35 lobectomies (85%, of which three were sleeves), three bilobectomies (7%), two right pneumonectomies (5%), and one left pneumonectomy (2%). The initial approach was Video-Assisted Thoracic Surgery (VATS) in 21 cases (51%) and the conversion rate to thoracotomy was 19%. Sixteen patients (39%) developed at least one perioperative complication. No perioperative deaths were reported [52]. In the NeoTPD01 trial (Table 1), surgery was planned 7–14 days from the last cycle, without any delays. Three patients (8%) were excluded from surgery at the end of medical treatment. R0 resection was achieved in 29 cases (97%). Surgical resections consisted of 22 lobectomies (73%), one bilobectomy (3%), six pneumonectomies (20%), and one wedge (3%). The initial approach was VATS in six cases (20%), and one patient (3%) was converted to thoracotomy. There were no perioperative deaths [39]. In the NCT02716038 study (Table 1), surgery was planned 3–15 days from treatment completion. Only one patient (3%) was excluded from resection, and R0 was achieved in 26 cases (87%). VATS was the preferred approach in twelve cases. (46%). One (3%) perioperative death was recorded [37]. In the CheckMate 816 trial (Table 2), surgery was planned within six weeks of treatment. In the two arms, six (4%) vs. nine (6%) patients had delayed surgery for adverse effects, while 30 (17%) vs. 44 (25%) patients were excluded from resection at the end of medical treatment. R0 resection was achieved in 100 (83%) vs. 87 (78%) patients, respectively. The initial approach was VATS in 36 (30%) vs. 24 (22%), and the conversion rate to thoracotomy was similar between the two arms (11% vs. 16%). Perioperative deaths were 2 (2%) vs. 0, whereas 0 vs. 3 (2.5%) patients died from adverse effects of systemic therapy. Perioperative complications were similar between the two arms [42]. In the Swiss SAKK 16/14 trials (Table 1), surgery was planned within 14 days after the treatment cycle completion. Twelve patients (18%) were excluded from surgery at the end of systemic treatment. R0 was achieved in 51 cases (93%). Forty-eight (87%) patients experienced perioperative complications and one (2%) died. Comparison with the preceding SAKK 16/00 trial with neoadjuvant chemotherapy demonstrated a one-year EFS increase of 25% (48 to 73%) [38]. The TOP1201 trial from Duke University enrolled 24 patients with resectable stage IIA-IIIA NSCLC (7th ed. TNM) [53]. Surgery was planned <12 weeks after treatment completion. Eleven patients (46%) were excluded from resection after induction therapy, and R0 was achieved in all the remaining cases. In two of them (15%), surgery was delayed for adverse effects. The initial approach was VATS in twelve cases (92%), and the conversion rate to thoracotomy was 23%. No perioperative deaths were recorded. Comparison with a previous cohort that received neoadjuvant chemotherapy with the addition of ipilimumab did not demonstrate a detrimental effect on surgical outcomes [51]. In several cases [54,55], macroscopical dense hilar and mediastinal tissues and higher frequent pleural adhesions were found after immunotherapy compared to chemotherapy alone. Current evidence has not proven these observations; nevertheless, after the combination of both treatments more technical difficulties are expected [56,57]. At present, the high heterogeneity of patients, the lack of detailed surgical and clinical outcomes, and short follow-up represent limits in determining whether neoadjuvant immunochemotherapy can become the best treatment strategy, and further data are needed. However, these preliminary reports suggest that surgery is feasible in LA NSCLC stages after neoadjuvant treatment, although with slight risks. VATS is the most commonly used surgical approach (in 20–51% of cases) while conversion to open surgery ranges from 3–19% of cases. Lobectomies were performed in 77–85% of cases, and pneumonectomies in 8–17% of cases. Globally, the complication rate is relevant at about 40% of cases; however, perioperative deaths are no more than 4%. 6. Discussion In this review, we provide an updated revision of the current literature on neoadjuvant treatments for NSCLC. Over the past decades, platinum-based chemotherapy has been the main systemic therapy option for LA NSCLC [58]. The discovery of driver mutations, such as EGFR in 2004, has led to the development of new molecular targeted therapies, which have shown an increase in survival and improved quality of life for patients carrying these mutations [59]. In this scenario, there is a growing interest in neoadjuvant immunotherapy, whether or not in combination with chemotherapy, for the treatment of LA NSCLC. More recently, ICIs have become a new strong approach against cancer; unlike chemo- and radiotherapy, which directly interferes with tumour growth and survival, immunotherapy addresses the tumour indirectly by increasing spontaneous immune responses. Immunotherapy works on ICs through action against CTLA-4 and the PD-1/PD-L1 pathway [60,61]. In advanced cases, the combination of chemotherapy plus immunotherapy has been demonstrated to be effective in terms of EFS and OS [62,63]. Several trials have investigated neoadjuvant single-agent ICI in NSCLC, with promising results. In the phase II Lung Cancer Mutation Consortium 3 (LCMC3) trial, the administration of two cycles of atezolizumab followed by one year of adjuvant treatment resulted in an MPR rate of 21% and pCR rate of 7% in stage IB-IIIB NSCLC patients [64]. Any unexpected toxicities were determined in the safety study PRINCEPS, which explored a single dose of atezolizumab in 30 resectable NSCLC patients [65]. In the phase II NEOMUN trial, two cycles of pembrolizumab before surgery were demonstrated to be safe and feasible; indeed, 27% of patients experienced MPR [66]. Gao et al. reported encouraging results with the anti-PD-1 inhibitor sintilimab in 40 stage IA-IIIB NSCLC patients, with fifteen (40.5%) patients achieving MPR [67]. The phase II IONESCO trial evaluating neoadjuvant durvalumab was stopped due to excessive 90-day postoperative mortality (9%); MPR was 18.6% [68]. Neoadjuvant nivolumab alone and in combination with ipilimumab demonstrated a 22% and 38% MPR rate, respectively, in the phase II NEOSTAR trial [69]. Results from clinical trials show promising results of both PD-1/PD-L1 inhibitors and immunochemotherapy as neoadjuvant treatments. Data on the anti-CTLA-4 antibody as a neoadjuvant single agent are not reported; however, promising results have been described with a dual-agent immune checkpoint blockade. A large number of ongoing phase III trials for ICI therapies are showing promising results, with longer overall survival and better response to treatment in both pre-operative and adjuvant settings (Table 2) [70]. Recently, Jiang et al. performed a systematic review and meta-analysis including data from sixteen studies of neoadjuvant immunotherapy with single/combined ICIs or chemo-immunotherapy and determined that histology does not significantly affect MPR and pCR rates [71]. In clinical trials investigating immunotherapy, women are always underrepresented compared to men. However, an individual’s sex is known to be an important modulator of the efficacy and toxicity associated with anticancer treatments, particularly for ICIs. Indeed, it is well established that sex-associated hormones can interfere with immune responses, with females showing stronger innate and adaptive immune responses compared to males. Further studies focused on improving the efficacy of ICIs in women are needed [72,73]. To date, there is no consensus on the use of PD-L1 expression as a predictive biomarker for neoadjuvant immunotherapy. In several trials, including LCMC3 and NCT02716038, MPR was described regardless of PD-L1 tumour expression. However, patients with elevated pre-treatment PD-L1 levels had a greater pathologic response in the NEOSTAR trial [74]. LA NSCLC is usually referred to as stage III, as stated by ESMO and the American College of Chest Physician (ACCP) [47,75]; however, this is not a standardized and widespread definition. Referring to cancer invading contiguous lung structures and limited to locoregional lymph nodes and no distant metastasis [75] should be described by T3-T4 and N0-N2 as stages IIB-IIIA (and T3-T4 N2 of IIIB). From a surgical point of view, when considering resectability criteria LA NSCLC should be defined as IIB-IIIA. At present, there is no consensus on whether neoadjuvant treatment should be used to achieve otherwise impossible resection or to increase survival in already potentially resectable patients [76,77]. Some concerns must be kept in mind when proposing induction treatment in LA NSCLC cases, especially if downstaging of the disease to achieve resectability is the goal. The aforementioned trials suggest that among LA NSCLC patients who are candidates for neoadjuvant immunochemotherapy, a variable number (up to 46%) are excluded from resection. This group can benefit from definitive chemoradiotherapy and eventually biological therapy [47,78]; moreover, in the absence of a neoadjuvant chemotherapy comparison arm in the different studies, it would be interesting to compare the outcomes of patients from the same populations in the same institutions subjected to standard neoadjuvant chemotherapy. However, further data are needed on the full comparison between no induction treatment and neoadjuvant chemotherapy-only groups regarding TRAEs, surgical outcomes, EFS, and OS. Immunotherapy is generally well tolerated, although significant toxicities have been reported in patients treated with ICIs [79]. The incidence of immune-related AEs (irAEs) with ICIs varies depending on the agent. To date, the most common irAEs described are dermatological toxicity, diarrhoea, hepatitis, endocrinopathies, and pneumonitis [80]. This is not a systematic review; however, it offers a comprehensive overview of the currently available literature. 7. Conclusions The main limitation of this study is the non-systematic nature of our review of the literature. In conclusion, neoadjuvant immunotherapy, whether combined or not with chemotherapy, appears to offer a promising survival benefit for patients with LA NSCLC. However, a definitive comparison with neoadjuvant chemotherapy remains to be found. Progress is being made in the identification of the best candidates for neoadjuvant regimens and immunotherapy. Of note, a variable percentage of patients obtain long-term survival; these findings could create a paradigm shift in NSCLC treatment. Nevertheless, the LA NSCLC treatment strategy is difficult to standardize, as it should generally be tailored to single patients and their particular context; a multidisciplinary discussion is mandatory in these cases. Alternative neoadjuvant therapies represent a relatively new and less explored field, and more studies are needed. Author Contributions Conception and design: D.T. and S.F.; Administrative support: D.T.; Provision of study materials or patients: S.F., G.M., E.R., G.A.C. and D.T.; Collection and assembly of data: S.F., G.M., E.R., G.A.C. and D.T.; Data analysis and interpretation: S.F., G.M., E.R., G.A.C. and D.T.; Manuscript writing: S.F., G.M., E.R., G.A.C. and D.T. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The literature search was conducted by the authors, to identify all published articles on the topic. PubMed, EMBASE, and Web of Science databases were consulted. The search was extended by consulting the listed references of each article. Conflicts of Interest S.F., G.M., G.A.C. and D.T. declare no conflict of interest. E.R. declares: Honoraria from Bristol Myers Squibb; Advisory Board for Sanofi. Figure 1 Schematic representation of the mechanisms of action of anti-CTLA-4 and anti-PD-1/PD-L-1. Figure 2 A representative panel depicting a case of squamous cell carcinoma (SCC) of the lung, diagnosed on core biopsy ((a), H/E, 200×) and confirmed by p40 positivity ((a), inset. 400×), featuring an inhomogeneous PD-L1 reactivity ((b), PD-L1, 22C3 clone, Dako, 200×) consistent with TPS ≥ 1% (>50%). On resection ((c), H/E, 40×; *: necrotic areas) after neoadjuvant therapy, the tumour specimen features the presence of a minor yet vital SCC component (arrows) alongside extensive necrotic areas; the former is characterized by intense and homogenous PD-L1 expression ((d), PD-L1, 22C3 clone, Dako, 40×). Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Kryczka J. Kryczka J. Czarnecka-Chrebelska K.H. Brzeziańska-Lasota E. Molecular Mechanisms of Chemoresistance Induced by Cisplatin in NSCLC Cancer Therapy Int. J. Mol. Sci. 2021 22 8885 10.3390/ijms22168885 34445588 2. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19094984 ijerph-19-04984 Article Anti-Inflammatory and Anti-Bacterial Potential of Mulberry Leaf Extract on Oral Microorganisms https://orcid.org/0000-0002-5154-9613 Kim Dokyeong 12 https://orcid.org/0000-0003-3439-1049 Kang Kyung-Hee 3* Jang Jong-Hwa Academic Editor 1 Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; dkkim2908@gmail.com 2 Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea 3 Department of Dental Hygiene, Konyang University, Daejeon 35365, Korea * Correspondence: dhkhkang@konyang.ac.kr; Tel.: +82-42-600-8448 20 4 2022 5 2022 19 9 498409 3 2022 18 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Mulberry leaves extract (Morus alba extracts; MAE) is known to have therapeutic potentials for numerous human diseases, including diabetes, neurological disorders, cardiovascular diseases, and cancers. However, there has not been sufficient research proving therapeutic effects on oral disease and its related oral risk factors. Thus, we investigated whether MAE has any anti-inflammatory and anti-bacterial effects on risk factors causing oral infectious diseases. To examine the anti-inflammatory response and bacterial inhibition of MAE, we measured intracellular reactive oxygen species (ROS) generation, production of pro-inflammatory cytokines, and the bacterial growth rate. Our study showed that MAE has anti-inflammatory activities, which inhibit the ROS generation and suppressed the production of pro-inflammatory cytokines (TNF-α and IL-6) in human monocyte THP-1 cells by stimulating lipopolysaccharide (LPS) and/or F. nucleatum, which are the virulent factors in periodontal diseases. Furthermore, MAE inhibited the bacterial growth on oral microorganisms (F. nucleatum and S. mutans) infected THP-1 cells. These findings suggested that MAE could be a potential natural source for therapeutic drugs in oral infectious disease. mulberry leaf anti-inflammatory agents anti-bacterial agents microorganisms oral diseases ==== Body pmc1. Introduction Over 700 species of microorganisms have been identified to exist in the oral cavity of healthy humans, and imbalance of host–microbial homeostasis can cause various oral diseases such as gingivitis, periodontitis, caries, and endodontic infections, and, in addition, further infectious diseases in various distant organ sites [1]. Among them, Fusobacteria nucleatum is a Gram-negative anaerobic bacillus and frequently isolated from both supra- and sub-gingival dental plaque [2]. It is pivotal in facilitating the structural bridge role in developing dental plaque and periodontal diseases, coaggregating with primary colonizers, including Streptococcus oralis, Streptococcus sanguinis, and Streptococcus mitis, etc., and anaerobic secondary colonizers (periodontitis-related keystone species) such as Porphorymonas gingivalis and Aggregatibacter actinomycetemcomitans [2,3]. Additionally, Streptococcus species are predominant in all oral niches. Out of them, S. mutans, a Gram-positive oral bacterium, has long been known to be the main pathogen of dental caries [4,5]. Natural resources have been used to treat various diseases and enhance human health. Mulberry (Morus alba), which belongs to family Moraceae, is multipurpose agroforestry plant that is native to China and widely distributed in many countries, including Asia, India, Africa, America, and Europe [6]. Mulberry leaves extract (Morus alba extracts; MAE) has various functions such as antitumor, antidiabetic, and anti-inflammatory activities, due to its bioactive compound that is rich in polysaccharides, polyphenols, alkaloids, and flavonoids [7,8]. Among its anti-microbial functions, its effect on some oral microorganisms such as S. mutans and P. gingivalis, has been reported in 2008 and 2015, respectively [9,10]. However, it is insufficient to explain anti-inflammatory and anti-microbial effects of MAE on oral microorganisms. Thus, we attempted to identify anti-bacterial and anti-inflammatory potentials of MAE on oral microorganisms (F. nucleatum and S. mutans), which are typical periodontitis and dental caries-related microorganisms, respectively. Additionally, we also examined the anti-inflammatory potentials of MAE in oral keratinocytes and fibroblasts. 2. Materials and Methods 2.1. Preparation of Extracts The mulberry leaf power (100%) was used for the research material and commercially purchased from Handsherb company (Yeoncheon-si, Korea). The mulberry leaf powder (60 g) and ethanol (600 mL) were mixed and left alone for 24 h at room temperature. Then, only the liquid component excluding precipitates was made to pass through a vacuum filter. Then, we concentrated the filtered extract using a decompression concentrator. The concentrated extract was stored to be analyzed after lyophilization. 2.2. Used Microorganisms and Culture Streptococuus mutans KCTC 3065 was acquired from the Korean Collection for Type Culture (KCTC) and cultured in the Brain Heart Infusion (BHI) liquid media at 37 °C. Fusobacterium nucleatum ATCC 25586 was purchased from American Type Culture Collection (Manassas, VA, USA) and cultured in the BHI liquid media at 37 °C under the anaerobic condition. Optical density (OD) of bacteria cultures was measured using the plate reader (BioTek Instruments, Inc., Winooski, VT, USA), with 0.5 and 0.6 OD representing ~109 CFU/mL for S. mutans and F. nucleatum, respectively. 2.3. Cell Culture THP-1 cells, human monocytic cell lines, were acquired from the Korea Cell Bank (Seoul, Korea) and cultured in RPMI 1640 media with 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 0.1 mg/mL streptomycin at 37 °C maintaining 5% CO2 condition. The tests were performed after seeding the cells onto 48-well plates (2 × 104 cells/well) or 6-well plates (2 × 105 cells/well). Immortalized human oral keratinocytes (IHOKs) [11] and gingival fibroblast (hTERT-hNOFs) [12] were provided by the department of the Oral Pathology in Yonsei University College of Dentistry. IHOK cells were immortalized by HPV16 E6/E7, and hTERT-hNOFs were immortalized by transfection with hTERT to be used in vitro. Both IHOKs and hTERT-hNOFs cells were cultured in DMEM/F12 media (3:1 ratio) with 10% FBS and 1% penicillin/streptomycin at 37 °C maintaining 5% CO2 condition. 2.4. Cell Viability Test The effect of MAE on THP-1 cell viability (>80% confluence of plates) was determined using a colorimetric EZ-CyTox Kit (Deaillab Service Co., Seoul, Korea) according to the manufacturer’s instructions. The absorbance was measured at 450 nm using a plate reader (BioTek Instruments, Inc., Winooski, VT, USA). IHOKs and hTERT-hNOFs were seeded onto 96-well plates by 1 × 104 and treated with MAE with serum-free media (DMEM/F12 media (3:1 ratio)) for 24 h. Cell viability was measured by MTT (Duchefa, Haarlem, The Netherlands) assay. In brief, MTT solution was added and incubated at 37 °C for 4 h. After incubation, DMSO (MTT solvent) was added into each cell. The absorbance was read at 590 nm by using a microplate reader. All experiments were performed in triplicate. 2.5. Flow Cytometry To check the generation of reactive oxygen species (ROS), THP-1 cells were seeded onto 6-well plates (2 × 105 cells/well). The cells were pretreated with MAE by changing concentration levels for 2 h and stimulated by LPS (InvivoGen, San Diego, CA, USA) for 24 h. The stimulated cells were washed with PBS and dyed with DCFDA (Abcam, Cambridge, MA, USA) for 30 min. Living cells (10,000 cells) were screened based on their forward- and side-scatter profiles. The samples were collected using BD FACSCalibur Flow Cytometer (BD Biosciences, San Jose, CA, USA) and the data were analyzed by BD CellQuest Pro Software (BD Biosciences, San Jose, CA, USA). IHOKs (5 × 105 cells/well) and hTERT-hNOFs (4 × 105 cells/well) were seeded in the 6-well plate and pre-treated with the MAE using different concentrations (5, 10, and 20 mg/mL) for 1 h, and applied with 10 ng/mL LPS for 24 h. Next, fluorescent probe 2′7′-dichlorofluorescin diacetate (H2DCFDA) dye (Molecular Probes, Eugene, OR, USA) was used. According to instruction, both cells were applied with 10 μM of H2DCFDA dye in the dark at 37 °C for 20 min. ROS were analyzed by flow cytometry (Becton Dickinson, Beckman coulter). 2.6. Enzyme-Linked Immunosorbent Assay (ELISA) THP-1 cells were pretreated with MAE for 2 h and then applied with LPS and F. nucleatum for 24 h. Cell culture supernatants were collected, and IL-6 and TNF-α were measured using the commercial ELISA kit (R&D Systems, Minneapolis, MN, USA). 2.7. Bacterial Growth MAE (20, 40, and 80 mg/mL) was added to the BHI liquid media in the test group while no MAE was added in the control group. S. mutans and F. nucleatum (1 × 107 bacteria) were inoculated and cultured in each media, then absorbance was measured using the optical density. Mean values were calculated by testing three times to gain the repeatable results in all test groups. 2.8. Statistical Analysis The differences among the mean values of different groups were assessed. Data is expressed as the mean ± standard deviation. Statistical significance tests were performed using one-way analysis of variance followed by the Tukey post-hoc test using GraphPad Prism version 5.00 (GraphPad Software, Inc., La Jolla, CA, USA). p < 0.05 was determined to indicate a statistical significance. 3. Results 3.1. MAE Decreased the Production of ROS in LPS-Stimulated THP-1 and Oral Cells To investigate the effects of MAE on cell viability, WST analysis was performed in THP-1 after treatment of MAE by various concentrations (10–80 mg/mL). Cells did not show significant cytotoxicity in the range of 10–40 mg/mL of MAE while the cell viability declined when the cells were treated with 80 mg/mL of MAE (Figure 1). Hence, MAE was used with 10, 20, and 40 mg/mL which proved to be non-cytotoxic. Next, we performed flow cytometry analysis to investigate whether MAE inhibits the increase in ROS by treatment with lipopolysaccharide (LPS), which is the richest component in the cell wall of Gram-negative germs. Results showed that LPS-induced ROS production was decreased in a dose-dependent manner when cells were pretreated with 10–40 mg/mL of MAE (Figure 2). In addition, we examined the effects of MAE in inflamed oral cells. Oral keratinocytes (IHOK) and fibroblasts (hTERT-hNOFs) were used to measure ROS generation in oral cells with MAE treatment. First, the MTT assay was performed to check the cytotoxicity of MAE. The cell viability was significantly reduced in IHOK with the treatment of 5, 10, 20, and 40 mg/mL of MAE. Meanwhile, the cell viability of hTERT-hNOFs declined significantly only when the cells were treated with 40 mg/mL of MAE (Figure S1). To perform the following test, the concentration levels of MAE were selected showing weak (within 80–60% of cell viability) or non-cytotoxicity (>80% of cell viability) in IHOKs and hTERT-hNOFs, respectively. Although ROS increased due to the inflammatory effect from LPS induction, MAE induced a decline in ROS by 3.8–4.8 times in LPS-stimulated IHOKs (Figure S2) and by 1.3–2.5 times in LPS-stimulated hTERT-hNOFs (Figure S3). Collectively, MAE could reduce ROS generation increased by LPS, indicating that MAE functions as an antioxidant in oral cells as well as monocytes. 3.2. MAE Decreased the Generation of IL-6 and TNF-α in LPS/F. nucleatum-Stimulated THP-1 Cells Next, we investigated whether MAE regulated production of inflammatory cytokines. First, we identified the effect of MAE in LPS-stimulated THP-1 cells. LPS induces the production of IL-6 and TNF-α, and both cytokines were markedly decreased after pretreatment with MAE in dose-dependent manner (Figure 3A,B). Next, we examined the effect of MAE in THP-1 infected by F. nucleatum, which is a typical germ involved in developing periodontal diseases. Infection of F. nucleatum led to generation of IL-6 and TNF-α and they were inhibited by pretreatment with MAE significantly (Figure 3C,D). Collectively, MAE could effectively reduce generation of pro-inflammatory cytokines in THP-1 cells stimulated by LPS and F. nucleatum. 3.3. MAE Has Antibiotic Effects against F. nucleatum and S. mutans Last, we investigated whether MAE showed the antibiotic effects against F. nucleatum and S. mutans, which is known to be a pathogen causing periodontal diseases and dental caries, respectively. After completing culture of F. nucleatum in BHI liquid media with different concentration levels of MAE, absorbance was measured using the plate reader. We confirmed that bacterial growth was significantly declined in F. nucleatum with MAE more than F. nucleatum without MAE (Figure 4). Consistently, the growth of S. mutans declined in a dose-dependent manner of MAE. Therefore, anti-bacterial effects of MAE were confirmed against oral bacteria, such as F. nucleatum and S. mutans. 4. Discussion Natural sources have been known to work as complementary and alternative medicines for centuries, with low levels of toxicity, therapeutic properties, and effects [13,14]. In contrast, some natural sources well known in traditional medicine have demonstrated that they have various toxicities [15]. However, Morus alba called mulberry has been evaluated as a safe herb showing no side effects [16,17]. The fruits, leaves, branches, and roots of mulberry possess a range of biological treatment effects on numerous human diseases [18,19], including diabetes, neurological disorders, cardiovascular diseases, and cancers [20,21,22,23,24]. However, there has not been sufficient research proving that mulberry can function as a therapeutic agent in oral diseases and its related oral risk factors. Inflammation is the process of recognizing and eliminating various harmful stimuli and initiating the healing process [25]. Pathogen infection can induce an inflammatory response through activation of pattern-recognition receptors (PRRs) [25,26]. The identification of PRRs by pathogens is mediated by various tissue-resident immune cells, leading to the release of inflammatory mediators, including chemokines and cytokines [27,28]. In addition, the antioxidant defense system affects oxidative stress. Increased oxidative stress can induce production of reactive oxygen species (ROS), which activate various transcription factors, including NF-κB, AP-1, p53, and STAT, followed by increased growth factors, inflammatory cytokines, and chemokines [29]. Based on such a background, we investigated inflammatory responses such as cytokines and ROS to identify the anti-inflammatory and anti-bacterial potentials of MAE. Monocytes and macrophages are critical cell types which recognize foreign pathogens using PRRs, and they secrete various pro-inflammatory chemokines and cytokines [30,31]. Thus, we mainly used THP-1 cell lines, which have been well known as human monocytes and macrophages in studies of inflammatory disease in vitro [31]. We applied LPS to induce inflammation in THP-1 cells because a key player in general bacteria-derived inflammation is LPS, also referred to as endotoxin [32,33]. First, we observed the effect of MAE as antioxidants in LPS-stimulated cells. MAE inhibited the increase in intracellular ROS production in LPS-stimulated THP-1 cells. Some researchers have consistently reported that MAE inhibits oxidative stress [34,35]. It means that MAE functions as an antioxidant in various cell types. However, there are few studies proving antioxidant effects of MAE in oral cells; there have been some studies showing anti-inflammatory effects in periodontal ligament cells (PDLs) [36,37]. Given that, we additionally examined ROS generation in oral cells. We selected two types of oral cells composed of oral mucosa; oral keratinocytes (immortalized human oral keratinocytes; IHOKs) [11] and fibroblasts (hTERT-transfected human normal oral fibroblasts; hTERT-hNOFs) [12]. Both cells were immortalized cell lines to use in vitro by transfecting HPV16 E6/E7 and hTERT, respectively. It was shown that LPS-stimulated IHOK and hTERT-hNOFs increased intracellular ROS, and then it was decreased by MAE. Thus, our findings suggested that MAE could function as antioxidants in oral inflammation. Periodontitis and caries are the major oral diseases, in relation to oral microbiota [38]. It is now well-established that S. mutans are associated with initiation of dental caries by acid production, sugar fermentation, and acid tolerance [4,39]. Periodontitis is an infectious and inflammatory disease characterized by progressive infiltration of bacteria and inflammatory cytokines into periodontal tissues, resulting in attachment loss, alveolar bone destruction, and the loss of teeth [40,41]. Among prime periodontal disease-related bacteria, P. gingivalis, Gram-negative, and anaerobic bacteria, are one of major causative pathogens which are found in > 85% of subgingival plaque of periodontitis patients [42]. Especially, P. gingivalis-LPS is a strong virulence factor in periodontitis and induces cytokine secretion (TNF-α, IL-6, and MCP-1) and inflammatory responses via TLRs [43]. The results of our study are consistent to various other research showing anti-inflammatory effects of MAE in P. gingivalis-infected THP-1 cells [44]. In addition to P. gingivalis, F. nucleatum is another key-stone periodontal pathogen. In dental plaque, F. nucleatum plays a structurally supportive and interconnecting role between commensal early colonizers and more pathogenic late colonizers, indicating that F. nucleatum acts as a backbone in progression of periodontal diseases [2]. Hence, we focused on F. nucleatum as the main causative bacteria of periodontitis more than P. gingivalis. To mimic the inflammatory response in periodontal tissues, we applied with LPS and F. nucleatum, the main causative factors in periodontal diseases. Our study showed that LPS-induced TNF-α and IL-6 were reduced by MAE in THP-1 cells. Additionally, F. nucleatum-infected THP-1 cells showed the increase in TNF-α and IL-6 and they were downregulated by MAE. Collectively, MAE was found to block the secretion of pro-inflammatory cytokines increased by stimulation of various periodontal pathogens, indicating that MAE might have potential for developing therapeutic herbal medicines in periodontal diseases. Even F. nucleatum can have a pathogenic role in extra-oral diseases such as colorectal cancer, inflammatory bowel disease, and rheumatoid arthritis [45,46,47]. Thus, MAE might be effective herbs in various human diseases caused by F. nucleatum, as well as periodontal disease. Lastly, we observed the effect of MAE in inhibiting bacterial growth. We selected two typical oral disease-related bacteria (S. mutans and F. nucleatum) and found that the growth of both bacteria was inhibited by MAE, varying in different doses. Our results are consistent with other studies showing that MAE have anti-inflammatory and anti-bacterial effects on oral bacteria. For example, the component (1-deoxynojirimycin (DNJ)) of MAE showed anti-adhesive effects by controlling the overgrowth and inhibiting biofilm formation of S. mutans [9]. Additionally, MAE have anti-inflammatory effects by suppressing MMPs, tissue destruction-related protein, in P. gingivalis LPS-infected THP-1 cells [44]. Taken together, MAE could function as anti-bacterial and anti-inflammatory agents in oral microorganisms. We demonstrated the anti-inflammatory and anti-bacterial effects of MAE on risk factors causing oral infectious diseases. However, there are some limitations of this study. THP-1 cells were used to investigate the anti-inflammatory response of MAE, while oral cells were only used to identify the antioxidant effects. However, one study has verified that LPS increases inflammatory cytokines such as IL-1β, IL-6, and IL-8 in hTERT-hNOFs and IHOKs which our study used [48]. Additionally, we need to further study whether F. nucleatum can increase cytokines, similar to results by LPS. Additionally, we did not observe the anti-inflammatory and anti-oxidant mechanisms of MAE in THP-1 cells, but only observed the anti-inflammatory phenomenon. Our findings are consistent with some other studies showing that IL-6 and TNF-α were increased by LPS in macrophages [49,50]. The inflammatory response can be orchestrated by pro-inflammatory cytokines such as TNF-α and IL-6 [25,51,52] and it can be mediated through NF-κB transcriptional factor [44,52]. Additionally, ROS can be regulated by NF-kB [53]. Based on other studies, we can speculate that anti-inflammatory signals of MAE might be involved in NF-kB-mediated signaling pathways [41]. Additionally, we need to identify what components of MAE can trigger anti-inflammatory and anti-bacterial effects. Phytochemical studies have revealed that MAE have many bioactive constituents, including flavonoids, cumarins, phenolic acid, alkaloids, and terpenoids [8]. In particular, flavonoids and phenolic acid in MAE have functions as antioxidants and anti-bacterial effects [18,19]. Considering that, we also speculate that flavonoids and/or phenolic acid might inhibit the ROS generation and production of pro-inflammatory cytokines in MAE. To solve these limitations, further studies are needed to clarify what components of MAE are considered in anti-inflammatory functions and their mechanism in detail. 5. Conclusions In conclusion, we demonstrated that MAE has anti-inflammatory effects, which can inhibit ROS generation and suppress production of pro-inflammatory cytokines in human monocyte THP-1 cells by stimulating LPS and/or F. nucleatum. Furthermore, MAE can inhibit the bacterial growth in oral microbes F. nucleatum and S. mutans. These findings suggested that MAE could be a potential natural source for prophylactic and therapeutic agents in oral infectious disease. Acknowledgments All authors have read and consent to publish this manuscript, and we thank all participants in the study. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19094984/s1, Figure S1: The cytotoxicity test by MAE in oral keratinocytes and fibroblasts; Figure S2: ROS generation by MAE in oral keratinocytes; Figure S3: ROS generation by MAE in oral fibroblasts. Click here for additional data file. Author Contributions Conceptualization, D.K. and K.-H.K.; methodology, D.K. and K.-H.K.; validation, D.K. and K.-H.K.; formal analysis, K.-H.K.; investigation, D.K. and K.-H.K.; resources, K.-H.K.; data curation, D.K. and K.-H.K.; writing—original draft preparation, D.K.; writing—review and editing, K.-H.K.; visualization, K.-H.K.; supervision, K.-H.K.; funding acquisition, K.-H.K. All authors have read and agreed to the published version of the manuscript. Funding This paper was supported by the Konyang University Research Fund in 2020 and the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Education) (No. NRF-2017R1D1A1B03035544). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. The cell lines used in this study are not required ethics approval for their use. Data Availability Statement All data are available on request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Figure 1 The cytotoxicity test of mulberry leaves extract (Morus alba extracts; MAE) in THP-1 cells. The cell viability of THP-1 in various concentration levels of MAE. THP-1 was treated with 10, 20, 40, and 80 mg/mL of MAE for 24 h. Data were analyzed by one-way ANOVA. * p < 0.05. Figure 2 MAE reduced generation of reactive oxygen species (ROS) in THP-1 cells stimulated by lipopolysaccharide (LPS). THP-1 was pretreated with MAE for 2 h and stimulated by LPS (100 ng/mL) for 24 h. ROS expression in the cell was shown by flow cytometry using a DCFDA antibody. The data were analyzed using one-way ANOVA. *** p < 0.001. Figure 3 The effect of MAE inhibiting generation of cytokines in THP-1 cells stimulated by LPS/F. nucleatum. THP-1 was pretreated with different concentrations of MAE for 2 h and stimulated by either LPS (100 ng/mL) or F. nucleatum (MOI 10) for 24 h. The concentrations of IL-6 (A,C) and TNF-α (B,D) were measured by ELISA in the culture supernatant. The data were analyzed using one-way ANOVA. *** p < 0.001. Figure 4 The effect of MAE in inhibiting growth of both F. nucleatum and S. mutans. MAE (0, 20, 40, and 80 mg/mL) was added in BHI liquid media, F. nucleatem (A) was inoculated (1 × 107 bacteria), and it was cultured for 24 h. S. mutans (B) was inoculated (1 × 107 bacteria) and it was cultured for 12 h. The absorbance was measured by optical density. The data were analyzed using a one-way ANOVA. ** p < 0.01, *** p < 0.001. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Dewhirst F.E. Chen T. Izard J. Paster B.J. Tanner A.C.R. Yu W.-H. Lakshmanan A. Wade W.G. The Human Oral Microbiome J. Bacteriol. 2010 192 5002 5017 10.1128/JB.00542-10 20656903 2. Brennan C.A. Garrett W.S. 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==== Front Cancers (Basel) Cancers (Basel) cancers Cancers 2072-6694 MDPI 10.3390/cancers14092079 cancers-14-02079 Systematic Review Comparison of Intraductal RFA Plus Stent versus Stent-Only Treatment for Unresectable Perihilar Cholangiocarcinoma—A Systematic Review and Meta-Analysis https://orcid.org/0000-0001-5068-8227 de Jong David M. 1 Fritzsche Jeska A. 234 Audhoe Amber S. 1 Yi Suzanne S. L. 1 Bruno Marco J. 1 https://orcid.org/0000-0002-9969-019X Voermans Rogier P. 234 van Driel Lydi M. J. W. 1* Kendall Tim Academic Editor 1 Department of Gastroenterology and Hepatology, Erasmus MC Cancer Institute, University Medical Centre Rotterdam, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands; d.m.dejong@erasmusmc.nl (D.M.d.J.); a.audhoe@erasmusmc.nl (A.S.A.); s.yi@erasmusmc.nl (S.S.L.Y.); m.bruno@erasmusmc.nl (M.J.B.) 2 Department of Gastroenterology and Hepatology, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands; j.a.fritzsche@amsterdamumc.nl (J.A.F.); r.p.voermans@amsterdamumc.nl (R.P.V.) 3 Amsterdam Gastroenterology Endocrinology Metabolism, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands 4 Cancer Center Amsterdam, Cancer Treatment and Quality of Life, De Boelelaan 1118, 1081 HV Amsterdam, The Netherlands * Correspondence: l.m.j.w.vandriel@erasmusmc.nl 21 4 2022 5 2022 14 9 207924 3 2022 18 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Simple Summary In patients with unresectable perihilar cholangiocarcinoma, adequate biliary drainage is essential. Stent patency remains a challenge in these complex patients, as both plastic and metal stent occlusion may occur, necessitating additional drainage procedures. Radiofrequency ablation (RFA) is a promising local treatment that has already proven its usefulness in other malignancies, such as hepatocellular carcinoma. In this meta-analysis and systematic review, we aimed to compare intraductal RFA with stent placement to stent placement alone in patients with unresectable perihilar cholangiocarcinoma. We found that RFA + stent treatment showed a significantly longer overall survival, in comparison to stent-only treatment. Further research is necessary in order to validate these findings to support the implementation of this promising strategy in clinical practice. Abstract Background: One of the cornerstones of palliative treatment for unresectable perihilar cholangiocarcinoma is biliary stent placement in order to restore biliary drainage. In this review, the potential added value of RFA with stent placement in comparison to stent placement alone in patients with unresectable perihilar cholangiocarcinoma is analyzed. Methods: We performed a comprehensive online search for relevant articles in November 2021 (PROSPERO ID: CRD42021288180). The primary endpoint was difference in overall survival. Secondary endpoints included overall survival, stent patency and complications. Only studies comparing survival after RFA + stent placement with stent placement alone were included in the meta-analysis. Non-comparative studies or comparative studies describing stent patency only were included in the systematic review. Results: A total of nine studies, including 217 patients with pCCA who underwent RFA + stent placement and 294 patients who underwent stent-only treatment, met the inclusion criteria for the primary endpoint analysis. Direct comparison between the two treatment groups showed a significantly longer overall survival for RFA + stent treatment, with a pooled HR of 0.65 [95% CI, 0.50–0.84, I2 = 38%]. When all eligible studies were included, RFA + stent treatment revealed an overall survival of 9.5 months [95% CI, 6.3–12.6], whereas survival for stent-only treatment was 7.0 months [95% CI, 5.7–8.2]. Due to the heterogeneity of the data, no pooled data analysis could be performed on stent patency or complications. Conclusions: RFA + stent placement displays promising potential to prolong survival. However, further research incorporating confounding factors like use of palliative chemotherapy is necessary in order to validate these findings. unresectable perihilar cholangiocarcinoma radiofrequency ablation endoscopic stent percutaneous stent biliary drainage ==== Body pmc1. Introduction Although perihilar cholangiocarcinoma (pCCA) is a relatively rare condition with an incidence of <6 cases per 100,000 people in most countries, its incidence is increasing across the globe [1,2,3]. Currently, surgical resection is the only curative treatment [4,5]. Unfortunately, only about one-fifth of patients qualify for curative resection at presentation [6]. Almost all patients with unresectable pCCA develop bile duct obstruction [7]. The mainstay of palliative treatment is the restoration of biliary drainage, by the endoscopic or percutaneous placement of plastic or (un)covered self-expanding metal stents (SEMSs) [8]. This treatment reduces or relieves jaundice, which not only improves quality of life but is also a prerequisite for the commencement of palliative chemotherapy (pCTx) in most clinical practices. Several studies have compared metal and plastic stents for palliative drainage in unresectable pCCA [9,10,11]. SEMSs are considered superior because of rapid and adequate biliary decompression, fewer re-interventions and a lower adverse event rate [12,13]. However, maintaining stent patency is a challenge as the tumor continues to grow and may cause obstruction of the biliary stent [10,14]. Intraductal radiofrequency ablation (RFA) is considered a promising treatment option to prolong stent patency and possibly survival in patients with malignant biliary obstruction [14,15]. RFA causes local tumor necrosis by the emission of heat generated using a high-frequency alternating current via a bipolar probe. This therapy has already been proven to be beneficial in patients with hepatocellular carcinoma and liver metastases [16,17]. In patients with unresectable intrahepatic CCA, percutaneous trans-hepatic RFA seems to prolong survival time as well [18]. Since the development of flexible catheters, RFA can be performed inside bile ducts by either an endoscopic or percutaneous approach. According to a recent meta-analysis, RFA can significantly improve stent patency and survival in patients with a malignant biliary obstruction [14]. In this study, however, malignant biliary obstruction in patients with distal cholangiocarcinoma, pancreatic head carcinoma and/or gallbladder carcinoma are also included. Studies solely focusing on the effect of intraductal RFA in patients with pCCA are sparse. This is of importance as the local anatomy of the liver hilum and the associated complexity of biliary drainage and survival are different compared to malignant biliary obstruction of the distal common bile duct. Additionally, the risk of obstruction of segmental side branches necessitates the placement of uncovered stents, which differs from distal obstructions in which covered stents are more commonly placed. We conducted a systematic review and meta-analysis to investigate the overall survival and stent patency of intraductal RFA in combination with a plastic stent or SEMS versus stent placement only for patients with unresectable pCCA. 2. Materials and Methods 2.1. Selection Criteria and Search Strategy The reporting of this systematic review follows the recommendations of the PRISMA guidelines [19]. Studies were identified by searching electronic EMBASE, Medline and Cochrane databases, and the last search was performed in November 2021 by two authors (D.J., J.F.). The study was registered in PROSPERO (CRD42021288180). The search terms are listed in the Supplementary files. Studies that evaluated at least either survival or stent patency in patients with unresectable pCCA were included. For the primary endpoint analysis, studies had to evaluate survival in intraductal RFA + plastic stent or SEMS placement in comparison to stent placement only in patients with unresectable pCCA by endoscopic retrograde cholangiopancreatography (ERCP) or by a percutaneous approach (PTC). Eligible studies were randomized clinical trials, case–control studies and comparative cohort studies. Exclusion criteria were reviews and studies containing results from a mixed group of CCA and non-CCA patients from which separate outcome data could not be extracted. For the secondary endpoint analysis, studies reporting median or mean survival and/or the stent patency of RFA+/− stent placement for pCCA patients specifically were included. For secondary outcome measures such as complications, single-arm studies were also included. There were no language, publication date or publication status restrictions. 2.2. Outcomes The primary outcome was difference in overall survival, expressed by hazard ratio. The secondary outcomes were (1) median overall survival, defined as the time from stent placement +/− RFA until death or end of follow-up; (2) stent patency, defined as the interval between the day of initial procedure and the recurrence of symptoms of biliary obstruction and (3) post-procedure complications in pCCA specifically (within 30 days after the procedure). Eligibility assessment and data extraction were performed independently in a standardized manner by two reviewers (D.J., J.F.). Disagreements between reviewers were resolved in consensus meetings. Authors were contacted for further information if needed. The following information was extracted systematically from each included study: (1) the characteristics of trial participants (including age, Bismuth–Corlette classification, time from diagnosis to intervention and concomitant pCTx); (2) type of intervention (including type of stent, endoscopic or percutaneous approaches and repeated interventions); (3) outcome measures (including survival time, stent patency and complication rate). The extracted data were cross-referenced between the two reviewers to rule out discrepancies. 2.3. Quality Assessment Two reviewers (D.J., J.F.) independently assessed the quality of the included studies, according to the Newcastle–Ottawa Scale (NOS) quality assessment tool for cohort studies and a modified Jadad score for randomized controlled trials (RCTs) [20,21]. For the single-arm cohort studies or case series, the Joanna Briggs Institute Critical Appraisal Tool was used [22]. 2.4. Statistical Analysis For the primary outcome measure, we performed a meta-analysis using the inverse variance method. Heterogeneity was evaluated using the Cochran Q-test and inconsistency index I2. Heterogeneity was classified as low (I2 = 0–30%), moderate (I2 = 30–50%), or substantial (I2 > 50%). Hazard rates, ratios and standard errors were calculated based on a normal distribution. Survival and stent patency data were converted from days to months if not reported as such. Statistical analyses were performed using R software version 4.0.1. 3. Results 3.1. Study Selection A total of 457 articles were identified. After duplicate removal, 415 articles were screened for relevance. A total of 40 articles remained, of which 24 were excluded for various reasons. Finally, nine articles were included in the meta-analysis for the primary endpoint analysis, as shown in Figure 1. For the secondary endpoint analysis, one additional comparative study reporting stent patency but not survival, and six additional single-arm studies were included. 3.2. Baseline Characteristics In the meta-analysis, a total of 511 patients were included across nine studies [23,24,25,26,27,28,29,30,31]. Of these patients, 217 received RFA + stent placement and 294 underwent stent placement without RFA. Six studies included all four Bismuth–Corlette types of pCCA [24,26,27,29,30,31], as specified in Table 1. Five studies were performed in Asia [24,27,28,29,31], two in the USA [26,30] and two in Europe [23,25]. Five studies used an endoscopic approach [25,26,28,30,31], three used a percutaneous approach only [23,27,29] and one did not specify the method [24]. The type of stents placed were plastic and SEMSs in three studies [25,30,31], SEMSs only in three studies [23,27,29], plastic stents only in two studies [24,28] and one study did not report the type of stent placed [26]. Four studies specified chemo(radio)therapy as additional treatment [23,25,29,30]. The characteristics of the studies included in the meta-analysis are further described in Table 1. The characteristics of the studies included for secondary endpoint analysis only are reported in Supplementary Table S1. In one study, the protocol included a planned re-intervention. Gao et al. performed a standard re-ERCP with plastic stent replacement +/− re-RFA three months after the initial intervention [28]. The remaining studies did not include a planned re-intervention, but in some studies patients were allowed to undergo re-RFA if indicated—for example, due to in-stent stenosis. The characteristics of the included studies are described further in Table 1. 3.3. Quality Assessment Details regarding quality assessment of the studies included in the meta-analysis are provided in Tables S2 and S3. We found that eight were of good quality [23,25,26,27,28,29,30,31] and one of fair quality [24]. For three studies, only an abstract was published [24,26,30]. Details on quality assessment of the studies included for secondary endpoint analysis only are reported in Tables S3 and S4. 3.4. Primary Outcome—Difference in Overall Survival Survival was adequately reported by nine studies [23,24,25,26,27,28,29,30,31]. The overall pooled HR was 0.65 [95% CI, 0.50–0.84, I2 = 38%] (Figure 2). These results were consistent after exclusion of the three abstract-only studies [24,26,30]. Bismuth–Corlette types seemed comparable across the two groups: type I (11 vs. 13%), type II (18% vs. 18%), type III (27% vs. 22%) and type IV (44% vs. 48%). However, data were missing for 270 patients across four studies [24,27,29,31]. Plastic stents were used in 47% of patients in the RFA + stent group compared to 50% of patients in the stent-only group, but data were missing or not specified in 238 patients across two studies [26,31]. 3.5. Secondary Outcomes 3.5.1. Median Survival When including the eight studies that reported mean or median survival in patients with pCCA, undergoing RFA with stenting showed a median survival of 9.5 months [95% CI, 6.3–12.6], as shown in Figure S1 [23,24,25,26,33,34,35,36]. For the five studies included in the meta-analysis that reported survival data on patients with pCCA in the stent-only group, median survival was 7.0 months [95% CI, 5.7–8.2], as shown in Figure S2 [23,24,25,26,31]. 3.5.2. Stent Patency Stent patency in pCCA specifically was reported by five comparative studies [23,27,29,37,38] and one single-arm study [39]. However, due to the heterogeneous reporting of stent type, placement techniques and the location of biliary obstruction in those studies, a meta-analysis was not possible. Three studies reported on percutaneously placed ucSEMSs in both groups [23,27,29], one study exchanged plastic stents for ucSEMSs by ERCP in both groups [37], one study placed either ucSEMSs or plastic stents by ERCP [39] and one study did not specify what stent was used or how it was placed [38]. Three of the comparative studies reported a significant improvement in stent patency [23,29,38], ranging from a 3.1 to 4.5 month increase. Two of these studies used ucSEMSs [23,27] and in the other study, the type of stent used remained unclear [38]. Two other studies did not find a significant difference [29,37]; one used ucSEMSs [29] and the other study standardly exchanged plastic stents for ucSEMSs [37]. The results of these studies are reported in Table 2. 3.5.3. Complications Complications were reported specifically for pCCA patients in four comparative studies [26,28,30,38] and three single-arm studies [33,35,36]. As shown in Table 3, none of the comparative studies reported significant differences between the two groups when only pCCA patients were included. The complications most reported on after RFA + stent placement in pCCA patients were cholangitis (0–44%), cholecystitis (10–28%), liver abscesses (10%) and abdominal pain (10–33%). Perforation or pancreatitis were not described after RFA in pCCA patients. All patients with cholecystitis post-procedure were treated with antibiotics or by percutaneous gallbladder drainage. Table S5 shows all reported complications in the included studies, although these were not specifically for pCCA patients only. 4. Discussion In this meta-analysis of 9 studies and systematic review of 16 studies, we compared the efficacy and safety of intraductal RFA + stent to stent-only treatment in patients with unresectable pCCA. The addition of RFA significantly improved survival with a pooled HR of 0.65 [95% CI, 0.50–0.84]. Due to the heterogeneity of the studies, no meta-analysis could be performed for secondary outcome measures, including stent patency and complication rates. These results are in line with previous reviews regarding intraductal RFA, including all types of malignant biliary obstructions [14,40]. A meta-analysis by Sofi et al., including 505 patients from nine studies, revealed a statistically significant survival advantage for patients treated with RFA as indicated by a pooled HR of 0.72 [95% CI, 0.59–0.87] [14]. Another meta-analysis by Cha et al., including 420 patients from eight studies, came to a similar conclusion with a pooled HR of 0.47 [95% CI, 0.34–0.64] in favor of RFA + stent treatment [40]. The survival of patients with unresectable pCCA is poor, however it varies between studies, with survival ranging from 3 to 10 months [41,42,43]. Therefore, the calculated pooled median survival rates in this systematic review cannot be compared with current literature on overall survival. Furthermore, most patients will not receive RFA early in the disease course, but only when the diagnosis of pCCA has been histologically confirmed and staging has been completed, which may take considerable time. Moreover, the study populations in most studies vary, and therefore it is difficult to compare results considering the potential for confounding factors such as systemic treatment. There are multiple factors that could influence survival which were inadequately described in the included studies or were not described for pCCA specifically, and hence could not be adjusted for in this meta-analysis. For example, systemic palliative treatment in the form of chemotherapy seems to be of paramount importance since this has been proven to have survival benefit, and in most guidelines the combination of cisplatin/gemcitabine is now presented as the best option for palliative treatment [43,44]. Other palliative treatments, such as radiotherapy or immunotherapy, are increasingly being studied and should therefore also be taken into account [45]. In addition to additional palliative treatment, other factors could also influence survival. A few studies included in this meta-analysis described age ≥ 65 years, number of ERCP procedures and TNM stage IV as poor prognostic factors [26,31]. These have also been reported in previous studies [6,42,43]. Unfortunately, this dataset lacked detailed information on such factors, which made it impossible to further analyze these in detail. The mechanism of improving survival after RFA is probably explained by improving stent patency due to local tumor ablation. Unfortunately, a pooled analysis was not possible due to the limited amount and heterogeneity of the data, with three of the five studies showing a benefit of RFA regarding stent patency [23,27]. Moreover, there have been preliminary reports on systemic immune mechanisms after RFA that may play a role by modulating circulating immune cells and cytokines. In a mouse model, a weak but detectable immune response was described after RFA. These findings were later confirmed in pancreatic cancer, hepatocellular carcinoma and colorectal liver metastasis [46,47,48]. However, these findings have not yet been confirmed in patients with biliary cancers. Regarding our other secondary outcome measure (i.e., complications), no pooled analysis could be provided. However, the treatment groups showed no major differences. A limitation present in many reviews on interventions is that the number of complications are routinely reported per patient or per intervention, making direct comparison difficult when patients undergo multiple interventions. Although two studies, including all types of malignant biliary obstruction, found a significantly higher percentage of patients with cholecystitis after RFA + stent placement, this was not reported in any of the other studies [28,31,38]. It is hypothesized that acute cholecystitis can be caused when the cystic duct is included in the RFA tract. This should therefore be avoided whenever possible. Despite the significant difference, the absolute number was very low, and all patients were treated successfully by percutaneous gallbladder drainage and/or antibiotics. Therefore, it can be concluded that RFA + stent placement seems safe, even when crossing of the cystic duct cannot be avoided. In a previous systematic review including all types of malignant biliary strictures, only abdominal pain seemed to occur significantly more often after RFA [14]. In the studies included in our systematic review, abdominal pain was heterogeneously reported and no individual studies reported a significant difference. This is probably partly due to underreporting, considering the large number of retrospective studies that were included. The main limitation of this systematic review was the inadequate reporting of confounding factors and complications in most of the included studies. Only two RCTs were included, and most studies were of a retrospective design. Although the quality assessment was good for most studies, the specific results of interest were sometimes lacking. Four studies published an abstract only, which included limited information. For example, the type of stent was not reported in two studies. Survival data was reported with significant variation, and manual calculation of the hazard ratios and standard errors was necessary. Furthermore, our findings are limited by a lack of unified treatment strategies in the included studies regarding RFA settings, treatment route and type of stents. Concerning stent patency, and presumably survival as well, the type of stent used is an important factor. Lastly, we excluded studies without a clear description and outcomes for pCCA. However, these studies could have had useful data because some, or even the majority, of the patients were diagnosed with pCCA. 5. Conclusions Despite the limitations and the lack of a clear definite conclusion based on the current literature, this systematic review does indicate the safety and potential benefits of intraductal RFA in patients with unresectable pCCA. Considering the limited palliative treatment options currently available for these patients and the large burden of recurrent jaundice, re-interventions, the concomitant risk of cholangitis and even impaired survival due to recurrent stent obstruction, we believe intraductal RFA could be of great value. Therefore, in order to be able to draw more definite conclusions regarding the benefit of intraductal RFA on survival and stent patency for pCCA patients, RCTs are warranted. Acknowledgments The authors thank W. Bramer, Librarian, and N.S. Erler, Statistician, Erasmus University Medical Center, for help with the systematic literature search and meta-analysis, respectively. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14092079/s1, Figure S1: Pooled median survival in months for RFA + stent treatment for studies reporting this in single arm studies specifically for pCCA in combination with articles included in the primary meta-analysis; Figure S2: Pooled median survival in months for stent-only treatment for all studies included in this primary meta-analysis, with stent type and method of stent placement; Search strategy; Table S1: Characteristics and outcome measures of studies describing survival and/or stent patency including the single-arm studies that were included for secondary endpoint analysis only; Table S2: Newcastle–Ottawa Quality assessment scale for cohort studies; Table S3: Modified Jadad scale for RCTs; Table S4: The Joanna Briggs Institute Critical Appraisal Tool for Case Series included in secondary endpoint analysis; Table S5: Adverse events reported in included articles. Click here for additional data file. Author Contributions Conceptualization, D.M.d.J., A.S.A., S.S.L.Y. and L.M.J.W.v.D.; data curation, D.M.d.J. and J.A.F.; Formal analysis, D.M.d.J. and J.A.F.; funding acquisition, not applicable; investigation, D.M.d.J., J.A.F., A.S.A. and S.S.L.Y.; methodology, D.M.d.J., J.A.F., R.P.V. and L.M.J.W.v.D.; project administration, D.M.d.J. and J.A.F.; resources, M.J.B.; supervision, M.J.B., R.P.V. and L.M.J.W.v.D.; validation, D.M.d.J. and J.A.F.; visualization, D.M.d.J. and J.A.F.; writing—original draft, D.M.d.J., J.A.F., R.P.V. and L.M.J.W.v.D.; writing—review and editing, D.M.d.J., J.A.F., A.S.A., S.S.L.Y., M.J.B., R.P.V. and L.M.J.W.v.D. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Conflicts of Interest M.J.B. is a consultant for Boston Scientific, Cook Medical and Pentax Medical, receives funding for industry-initiated studies from Boston Scientific and Cook Medical and receives funding or support from investigator-initiated studies from Boston Scientific, Cook Medical, Pentax Medical, Mylan, InterScope, ChiRoStim and 3M. R.P.V. is a consultant for, and receives funding for, investigator-initiated studies from Boston Scientific and Zambon Medical. The other authors declare that they have no competing interests. Figure 1 Flowchart of the selection process. Figure 2 Meta-analysis of the pooled survival hazard ratios. TE = treatment effect, seTE = standard error. cancers-14-02079-t001_Table 1 Table 1 Characteristics of included articles in meta-analysis for primary endpoint analysis. RFA setting = all included articles performed repeat RFA if segment was too long, R = retrospective, P = prospective, RCT = randomized controlled trial, ERCP = endoscopic retrograde cholangiopancreatography, PTC = percutaneous approach, W = watt, SEMS = self-expanding metal stent, pCTx = palliative chemotherapy, BTx = brachytherapy, RTx = radiotherapy, PDT = photodynamic therapy, HAIC = hepatic arterial infusion chemotherapy. * = Abstract only articles. Study Country Period Study Design CCA Type Intervention RFA Setting Stent Type N (RFA vs. Stent) Median Survival in Months Palliative Treatment R/P Design RFA Stent pCTx Other Andrasina [23] Czech Republic 2010–2019 P RCT Bismuth II–IV PTC 10 W for 90–120 s, Habib ucSEMS 21 vs. 22 12.3 12.3 14 vs. 13 BTx: 18 vs. 16 Bhadauria [24] * India NR P Cohort Bismuth I–IV NR 8–10 W for 120 s, Habib Plastic 10 vs. 7 15.8 7.1 NR NR Bokemeyer [25] Germany 2006–2011 controls, 2012–2017 cases R Case control Bismuth III–IV ERCP 8–10 W for 90 s, Habib Plastic 17 vs. 20 11.3 7.3 6 vs. 7 NR SEMS 3 vs. 2 Buerlein [26] * USA 2011–2018 R Cohort Bismuth I–IV ERCP NR NR 20 vs. 29 10.0 6.7 NR PDT: 2 vs. 0 Cui [27,32] China 2013–2015 R Cohort Bismuth I–IV PTC 10 W for 90 s, Habib ucSEMS 46 vs. 28 8.0 4.7 NR NR Gao [28] China 2013–2017 P RCT Bismuth I–III ERCP, repeat after 3 months 7–10 W for 90 s, Habib Plastic 25 vs. 22 HR: 0.414 NR NR Gou [29] China 2013–2018 R Cohort Bismuth I–IV PTC 10 W for 120 s, Habib ucSEMS 18 vs. 17 HR: 1.480 NR HAIC: 18 vs 0 Sampath [30] * USA 2010–2015 R Cohort Bismuth I–IV ERCP NR Plastic 8 vs. 10 11.8 4.7 8 vs. 11 (+/−RTx) NR SEMS 2 vs. 5 Xia [31] China 2012–2019 R Matched Cohort Bismuth I–IV ERCP 10–12 W for 60–120 s, Habib Both 47 vs. 132 10.5 6.0 NR NR cancers-14-02079-t002_Table 2 Table 2 Stent patency in pCCA patients. NR = not reported, NA = not applicable. * = In 6 of the 20 patients that received a stent (time to occlusion). † = In 8 of the 22 patients that received a stent (time to occlusion). Study Intervention Stent Type Group N Stent Patency p-Value or HR (95% CI) Andrasina [23] PTC ucSEMS RFA + stent 20 * Median 9.6 months [95% CI 5.2–11.2] 0.029 Stent-only 22 † Median 4.5 months [95% CI 0.8–10.3] Cui [27,32] PTC ucSEMS RFA + stent 25 Median 7.6 months [95% CI 6.8–9.2] 0.009 Stent-only 14 Median 4.3 months [95% CI 1.7–8.5] Gou [29] PTC ucSEMS RFA + stent 18 NR 1.173 [95% CI 0.685–2.011] Stent-only 17 Kang [37] ERCP Plastic exchanged for ucSEMS RFA + stent 13 Median 5.9 months [range 2.0–9.8] NR Stent-only 13 Median 4.0 months [range 3.4–4.6] Lee [38] NR NR RFA + stent 21 Median 8.0 months 0.01 Stent-only 21 Median 4.0 months Laleman [39] ERCP Both RFA + stent 9 Median 4.6 months [range: 1.7–11.2] NA cancers-14-02079-t003_Table 3 Table 3 Adverse events in pCCA specifically reported <30 days after the procedure in included articles. All adverse events were analyzed per patient. AE = adverse event, NA = not applicable, NR = not reported. Study No. per Group Overall AE Rate Cholangitis Cholecystitis Pancreatitis Liver Abscess Bleeding Abdominal Pain Perforation p-Value Buerlein [26] RFA 20 NR 40% NR NR 10% NR 10% NR >0.05 Stent-only 29 NR 41% NR NR 21% NR 6.9% NR Gao [28] RFA 25 NR NR 28% NR NR NR NR NR NR Stent-only 22 NR NR 0% NR NR NR NR NR Lee [38] RFA 21 NR NR NR NR NR NR NR NR >0.05 Stent-only 21 Sampath [30] RFA 10 NR 30% NR NR NR NR NR 0% (bile leak) NR Stent-only 15 NR 0% NR NR NR NR NR 7% (bile leak) Han [36] RFA 21 14.3% 0% 10% 0% NR 0% NR 0% NA Laquière [33] RFA 12 NR 8% NR NR NR 0% NR NR NA Wang [35] RFA 9 NR 44% NR 0% NR 0% 33% 0% NA Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Blechacz B. Cholangiocarcinoma: Current knowledge and new developments Gut Liver 2017 11 13 26 10.5009/gnl15568 27928095 2. Banales J.M. Cardinale V. Carpino G. Marzioni M. Andersen J.B. Invernizzi P. Lind G.E. Folseraas T. 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==== Front J Clin Med J Clin Med jcm Journal of Clinical Medicine 2077-0383 MDPI 10.3390/jcm11092660 jcm-11-02660 Review Lifestyle Modification and Atrial Fibrillation: Critical Care for Successful Ablation Fitzgerald John L. 12 Middeldorp Melissa E. 12 Gallagher Celine 12 Sanders Prashanthan 12* Knecht Sebastien Academic Editor 1 Centre for Heart Rhythm Disorders, University of Adelaide, Adelaide 5000, Australia; john.fitzgerald@adelaide.edu.au (J.L.F.); melissa.middeldorp@adelaide.edu.au (M.E.M.); celine.gallagher@adelaide.edu.au (C.G.) 2 Department of Cardiology, Royal Adelaide Hospital, Adelaide 5000, Australia * Correspondence: prash.sanders@adelaide.edu.au; Tel.: +61-883139000; Fax: +61-83622273 09 5 2022 5 2022 11 9 266024 3 2022 26 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Management of atrial fibrillation (AF) requires a comprehensive approach due to the limited success of medical or procedural approaches in isolation. Multiple modifiable risk factors contribute to the development and progression of the underlying substrate, with a heightened risk of progression evident with inadequate risk factor management. With increased mortality, stroke, heart failure and healthcare utilisation linked to AF, international guidelines now strongly support risk factor modification as a critical pillar of AF care due to evidence demonstrating the efficacy of this approach. Effective lifestyle management is key to arrest and reverse the progression of AF, in addition to increasing the likelihood of freedom from arrhythmia following catheter ablation. atrial fibrillation risk factor management catheter ablation risk factor modification lifestyle modification University of AdelaideNational Health and Medical Research CouncilNational Heart Foundation of AustraliaFitzgerald is supported by a Postgraduate Scholarship from the University of Adelaide. Middeldorp and Gallagher are supported by Postdoctoral Fellowships from the University of Adelaide. Sanders is supported by a Practitioner Fellowship from the National Health and Medical Research Council and also by the National Heart Foundation of Australia. ==== Body pmc1. Introduction With a rising incidence globally, atrial fibrillation (AF) continues to be the most common sustained cardiac arrhythmia encountered clinically [1,2]. Global data confirms trends of rising health-care utilisation related to AF and clear association with increased all-cause mortality, stroke and heart failure [2,3]. The key pillars of evidence-based AF management, as described in international guidelines including those of the European Society of Cardiology, American Heart Association/American College of Cardiology, the National Heart Foundation of Australia and others include anticoagulation for stroke risk-reduction, rate control, rhythm control and risk factor management [4,5,6]. Risk factors predisposing to AF contribute to changes in left atrial (LA) structure and function through haemodynamic, electrophysiologic and metabolic effects on the atrial myocardium, with inadequate management contributing to increasing AF burden, progression and resultant complications related to AF [7,8,9,10,11,12]. The standard trajectory of atrial fibrillation with progression to persistent, then long-standing persistent and finally permanent AF can be arrested and reversed by risk factor management as part of comprehensive care delivery from the time of AF diagnosis [13,14,15]. In this review the evidence for AF management strategies and mechanisms of influence of major modifiable risk factors will be discussed. We will then focus on evidence supporting risk factor modification in conjunction with ablation for improving AF outcomes. Finally, we will review practical aspects of delivery of cardiovascular risk factor modification to the AF population. 2. Rhythm Control for Atrial Fibrillation Management of atrial fibrillation with rhythm control has been hampered by low efficacy and deleterious side-effect profiles of anti-arrhythmic medication, leading to recommendations for prioritisation of rhythm control only in patients with ongoing symptoms despite efforts at adequate rate control [16,17]. The CABANA clinical trial of catheter ablation compared with anti-arrhythmic drug therapy in 2204 patients with a mean of 4 years’ follow-up failed to show a difference in the composite primary outcome of death, disabling stroke, serious bleeding, or cardiac arrest, though there were lower than expected event rates and significant rates of between-group cross-over tempering interpretation of these results [18]. Recently, the EAST-AFNET 4 trial randomised 2789 patients with AF duration ≤ 1 year to early rhythm control compared with usual care, where rhythm control was reserved for symptom management [19]. This trial showed that patients randomised to early rhythm control (19% had AF ablation) had a lower risk of the composite outcome of death from cardiovascular causes, stroke, hospitalisation with worsening heart failure or acute coronary syndrome (hazard ratio 0.79; 96% CI 0.66 to 0.97, p = 0.005), and also had lower risk of individual secondary endpoints or death from cardiovascular causes and stroke. Event rates were low overall in the trial, likely reflecting contemporary use of medical management for cardiovascular comorbidities and risk factors. Overall, this trial supports the use of an early rhythm control strategy to reduce adverse clinical outcomes related to AF. The success of atrial fibrillation ablation demonstrates attrition over time. In a meta-analysis of long-term success-rates, 80% success at 5 years was demonstrated, allowing for multiple procedures (though this was only 50% after a single procedure) [20]. In a single-centre study of outcome in 255 patients at 10 years post ablation (43% paroxysmal), 52% remained arrhythmia free and 10% progressed to permanent AF [21]. Multivariate predictors for freedom from AF recurrence were no rise in blood pressure, BMI or fasting glucose over follow-up, highlighting the importance of cardiovascular risk factor management in this population. There is evidence for progression of atrial cardiomyopathy, and therefore substrate for AF, despite successful catheter ablation. Despite demonstrated reduction in left atrial size following catheter ablation for AF, a contact-mapping study demonstrated atrial electroanatomic abnormalities were greater in AF patients than a control group at baseline and progressively worsened over 10 ± 14 months following AF ablation [22]. This indicates there is a progressive atrial substrate in AF which is not addressed by ablation alone. Additional substrate ablation approaches at the time of initial AF ablation compared with pulmonary vein isolation alone have so far failed to improve post-ablation outcomes, suggesting these do not significantly impact the atrial changes occurring progressively post-ablation. Risk factors for the development of AF have been associated in various studies with development and progression of this AF substrate. 3. Modifiable Risk Factors for Atrial Fibrillation Obesity, physical inactivity, hypertension, obstructive sleep apnoea, diabetes mellitus, alcohol consumption and smoking have all been implicated in increasing the risk of AF development [23,24,25,26,27,28,29,30,31]. Mechanisms for these risk factors increasing atrial fibrillation have been explored in animal and human studies and include increased LA size, pressure, blood volume, increased central blood volume and systemic vascular resistance, left ventricular hypertrophy and stiffening, reduced diastolic filling, adverse changes in the renin-angiotensin-aldosterone system, increased atrial fibrosis, inflammatory and prothrombotic changes and alterations in left atrial voltage, refractoriness and conduction velocities [7,8,9,10,11,12,32,33,34,35,36,37]. Modifiable cardiovascular risk factors play a critical role in the development of AF (See Table 1 for a summary of risk factors for AF). Similarly, poor control of these risk factors is associated with a heightened risk of AF recurrence post-ablation. The presence of individual risk factors and poor control of these at the time of AF ablation has been associated with poorer outcomes in multiple studies. 4. Risk Factor Modification before and in Conjunction with AF Ablation Several studies have highlighted the effectiveness of comprehensive cardiovascular risk factor management in reducing AF burden and progression [13,14,15]. Whilst some studies of isolated risk factor modification, including alcohol abstinence and bariatric surgery for morbid obesity have demonstrated improved arrhythmia freedom [50,51], a comprehensive approach is likely to be more sustainable and translatable, given the interdependence and co-existence of modifiable cardiovascular risk factors. The cost-effectiveness of comprehensive risk factor management delivery has also been demonstrated [52]. Management of isolated risk factors, such as obesity, in the context of AF ablation have demonstrated variable success. Some studies have shown improvement in outcomes, particularly with treatment of severe hypertension and OSA, but to a lesser degree than that of comprehensive risk factor management programs (Figure 1). 5. Obesity and Ablation In patients undergoing AF ablation, increased BMI is associated with increased AF recurrence. Although earlier studies showed somewhat conflicting outcomes [53,54], the greatest risk of AF recurrence occurs with BMI of >30 kg/m2 [55]. In a study of 771 paroxysmal AF patients undergoing AF ablation with pulmonary vein isolation, the recurrence of AF at up to 1 year progressively increased with higher BMI categories, and was up to 58% if BMI was >40 kg/m2 [56]. Similarly, each five-unit increase in BMI has been associated with a 13% increase in likelihood of AF recurrence post ablation [23]. The impact of a weight loss intervention on AF recurrence after catheter ablation was evaluated in the recently published SORT-AF randomised trial [57]. In this multi-centre study, risk factors were assessed in both groups with a sleep study undertaken in all patients. Hypertension and new diabetes were managed according to guidelines pre ablation. The intervention group participated in a structured weight-loss program with twice monthly medical attendance, regular nutrition advice and assistance with physical training for 6 months. A specialised obesity department with endocrinology oversight was the setting of weight-loss guidance following guidelines from the Medical Society for Treatment of Morbid Obesity. The intervention group achieved a BMI reduction from a mean of 34.9 to 33.4 kg/m2 with on average 4.6 kg weight loss (3.9% initial body weight) and 33% non-compliance demonstrated at 12 months, whereas the control group lost an average of 2 kg from BMI 34.8 to 34.5 kg/m2. There was no significant difference between groups in AF recurrence (as detected by implantable loop recorder) in the primary intention to treat analysis, but ancillary analysis showed reduced AF burden was associated with BMI reduction, where this was achieved, particularly in participants with persistent AF. The small degree of weight-loss achieved is likely to account for the outcomes observed in this study. An observational study of weight loss in 90 patients with long-standing persistent AF showed improved quality of life, but no improvement in symptom severity or AF recurrence at 1 year of follow-up following a single AF ablation procedure which involved pulmonary vein antral isolation, posterior wall isolation and non-pulmonary vein trigger ablation [58]. In 58 patients who achieved a significant 24.9 (IQR 19.1–56.7) kg weight loss from a mean BMI of 38 ± 4, there was no significant difference in freedom from AF at 1 year (63.8%) compared to a control group of 32 patients with similar BMI of 37 ± 5 who achieved no significant weight loss, with 1-year freedom from AF in 59.3%. Although alcohol reduction and smoking cessation were strongly encouraged in both groups, dietician-guided calorie reduction and encouragement to exercise with a daily diary kept of these were the main interventions studied. Reasons proposed for this failure of effect of weight loss are the more advanced AF substrate of long-standing persistent AF, likely with less reversibility potential compared with paroxysmal AF and perhaps closer follow-up for increased AF detection. It is of note that only a single procedure was performed in this study, whereas it is known that multiple procedures are often required for successful elimination of AF, particularly in the setting of persistent or long-standing persistent AF [59]. 6. Physical Inactivity and Ablation Data for the effects of physical inactivity in isolation following AF ablation are limited, though the effect of the level of cardio-respiratory fitness (a surrogate for physical activity) at the time of AF ablation has been studied. In a single-centre study of 591 patients with a mean follow-up of 32 months, lower cardio-respiratory fitness, as assessed on exercise stress test within 12 months pre-ablation, was associated with increased AF recurrence, with 79% in the low (<85% predicted) versus 28% in the high (>100% predicted) cardiorespiratory fitness groups experiencing arrhythmia recurrence post AF ablation, respectively [60]. A cardiac rehabilitation model of intervention after AF ablation was tested in the CopenHeart RFA trial [61]. In this study, 210 patients (72% paroxysmal AF) undergoing AF ablation were randomised to cardiac rehabilitation with usual care or usual care alone. Cardiac rehabilitation involved 12 weeks of physical exercise sessions and four psycho-educational consultations. Physical capacity increased in the cardiac rehabilitation group as measured by VO2 Max, though there was not a statistical difference in the METs of exercise done regularly by each group, and there was no difference in mental health components of the SF-36 questionnaire between groups. A difference in VO2 max was sustained at 12 months in the cardiac rehabilitation group and a lower proportion of patients had high anxiety at 24 months in this group. No difference in hospitalisation or mortality was seen in the rehabilitation group at longer-term follow-up of 24 months [62]. While some fitness effects were long-lasting, the duration of input following ablation was relatively short and not as broadly focussed as a comprehensive risk-factor modification approach. 7. Hypertension and Ablation The effect of hypertension on atrial fibrillation recurrence after ablation is influenced by the level of control of this risk factor. Hypertension associates with increased age, more cardiovascular comorbidities, greater likelihood of persistent AF, and was an independent predictor of increased recurrence of AF post-ablation in earlier studies [63,64,65,66]. A more recent study of 626 patients from 55 centres in the German Ablation Registry showed that AF recurrence rates, freedom from antiarrhythmic medication and repeat ablation were not different with versus without hypertension diagnosis at the time of ablation, though there were more reports of dyspnoea, angina and more re-hospitalisations in those with hypertension [66]. A further multi-centre study of 531 consecutive patients undergoing AF ablation showed again that hypertension itself was not associated with a higher recurrence rate of AF following AF ablation, but poor control of hypertension despite medical therapy pre-procedure did lead to higher recurrence [65]. In a randomised study of renal artery denervation for drug-resistant hypertension in patients referred for pulmonary vein isolation, a treatment group of 13 patients showed significant BP reduction from 181/97 to 156/87 mmHg compared with no significant change in the control group [67]. In the treatment group, 69% remained free of AF at 12 months compared with only 29% in the control group. In the recent ERADICATE-AF study, patients undergoing ablation for paroxysmal AF with hypertension despite taking at least 1 antihypertensive medication were randomised to renal artery denervation in addition to ablation or ablation alone (PVI) in 154 versus 148 individuals [68]. Freedom from AF, atrial flutter or tachycardia at 12 months was significantly reduced in the renal artery denervation group (72% versus 56%) without any increase in complications. A limitation of both of these renal artery denervation trials is the absence of sham-procedure control for the renal denervation component, a feature that has significantly altered outcomes in recent renal denervation trials for hypertension alone [69]. Aggressive treatment of isolated mild hypertension has not demonstrated reduced likelihood of AF recurrence post AF ablation. In the SMAC-AF randomised open-label trial of aggressive blood-pressure control, targeting < 120 mmHg versus < 140 mmHg in 184 patients undergoing AF ablation, no significant difference in AF recurrence or symptoms were observed at 14 months’ median follow-up. Blood pressures achieved in the two study groups were 123 mmHg vs. 132 mmHg for intervention versus control, respectively [70]. 8. Diabetes Mellitus and Ablation Diabetes mellitus is associated with a greater likelihood of AF recurrence post-ablation, particularly in the setting of persistent AF [71,72]. However, a meta-analysis including 1464 patients in earlier studies and review of a large 8175 patient dataset from the German Ablation Registry showed no significant increased atrial arrhythmia recurrence associated with diabetes, although an increased rate of repeat procedures was seen [73,74]. Glycaemic control pre-AF ablation has been studied in a retrospective observational study of 298 patients [75]. A higher glycated haemoglobin (HbA1c) at the time of AF ablation (>9%) was associated with increased recurrence over 25.92 ± 20.26 months (68.75%) compared with an HbA1c < 7% which was associated with 32.4% recurrence. The 12-month pre-ablation trend in HbA1c was a significant predictor of recurrence on multi-variate analysis, with 10% improvement in HbA1c associated with only 2% recurrence, compared with 91% recurrence in patients with a worsening HbA1c trend pre-ablation. This increase in AF recurrence with poorer glycaemic control was also seen in a meta-analysis including data from 1464 patients and presents a strong argument for strict glycaemic control in patients undergoing AF ablation [73]. 9. Obstructive Sleep Apnoea and Ablation Initial studies of OSA as a predictor of AF recurrence following AF ablation have yielded conflicting results [54,76,77,78,79]. Meta-analysis of these early studies shows polysomnography-confirmed OSA diagnosis confers independent predictive value for AF recurrence (risk ratio 1.40, 95% CI 1.16–1.68, p = 0.0004) but Berlin Questionnaire-based diagnosis does not (risk ratio 1.07, CI 0.91 to 1.27) [80]. Continuous positive airway pressure (CPAP) treatment of known OSA significantly improves outcomes following AF ablation. Meta-analyses of outcomes following ablation for AF in patients with OSA with or without CPAP have incorporated mostly observational studies in the absence of RCTs [76,78,79,81,82,83,84]. One analysis of five observational studies, including 3743 patients showed increased relative risk (1.31, p = 0.00) of AF recurrence with a diagnosis of OSA. Untreated OSA was associated with increased recurrence (RR 1.57, p = 0.00), whereas treated OSA showed no increase in risk compared to patients without OSA (RR 1.25, p = 0.37), with similarity maintained after removal for study heterogeneity [81]. A subsequent meta-analysis comparing CPAP versus no CPAP treatment of OSA following AF ablation showed a 42% decreased risk for AF recurrence in a random effects model, with a meta-regression model showing particular benefit for younger, obese male patients [82]. 10. Alcohol Consumption and Ablation Ongoing alcohol consumption has been demonstrated to lead to increased AF recurrence following AF ablation, an effect particularly evident in heavy drinkers. A Japanese study of 1361 patients undergoing AF ablation showed that AF recurrence after the initial procedure was increased in drinkers compared to non-drinkers, though there was no significant difference following repeat procedures [85]. A study of 122 consecutive patients undergoing ablation for paroxysmal AF showed moderate and particularly heavy drinking (defined as >15 g/day for women and >30 g/day for men) was associated with more extensive low-voltage zones and increased AF recurrence [86]. These studies and others are somewhat hampered by reliance on self-report and individual recall of alcohol intake. Objective data using ethyl glucuronide in hair as a long-term alcohol consumption marker, demonstrated an increased likelihood of recurrence of AF in men who met or exceeded a 7 pg/mg cut-off, consistent with ongoing alcohol consumption [87]. 11. Smoking and Ablation Little data exists examining the impact of smoking on AF recurrence after ablation. One study of 59 patients (predominantly paroxysmal AF) undergoing pulmonary vein isolation found that ongoing smoking versus non-smoking was independently associated with 43% vs. 14% AF recurrence (p < 0.05) at 306 ± 95 days of follow-up. AF recurrence was increased in current and former smokers compared with never smokers [88]. A retrospective study of 201 patients undergoing persistent AF ablation did not show an increased AF recurrence in smokers, but more extensive non-pulmonary vein AF triggers were demonstrated in smokers. Patients with right-atrial non-pulmonary vein triggers seen on mapping at the index procedure had increased recurrence compared to those without RA triggers, pointing to harmful effects of nicotine in promoting these specific non-pulmonary vein triggers and further AF recurrence [89]. 12. Comprehensive Risk Factor Modification Comprehensive management of cardiovascular risk factors has been associated with the greatest likelihood of freedom from arrhythmia recurrence post ablation. (Table 2) This was demonstrated in the ARREST-AF study, where in 149 consecutive patients undergoing AF ablation with BMI ≥ 27 kg/m2 and ≥1 cardiac risk factor, 61 chose to undertake aggressive risk factor modification whilst 88 declined [90]. In the risk factor modification group blood pressure, lipids (as guided by overall cardiovascular risk), blood glucose levels, and sleep apnoea were all intensively managed, with lifestyle interventions to achieve weight loss and target exercise volume, as well as alcohol reduction/cessation and tobacco abstinence. Significant improvements were observed in multiple risk factors including weight, blood pressure management, glycaemic control and lipid profiles. There was a greater likelihood of single procedure and multiple-procedure drug-unassisted AF-free survival in the risk factor managed group with 16% requiring ongoing anti-arrhythmic drugs compared with 42% in the control group at a mean follow-up of 42 months. AF symptoms were also markedly reduced in the treatment group compared to controls. Overall arrhythmia-free survival in the risk factor management group was 87% compared to 17.8% in the control group. These results provide clear evidence for the importance of continuing to address the underlying substrate progression with AF risk factor management, even after attempts at a curative catheter ablation procedure, if optimal AF-free survival and symptom reduction are to be achieved. While there is little to no data on risk factor management following ablation for AF during open cardiothoracic surgical procedures, it is reasonable to assume that the same substrate progression and AF recurrence can be impacted by comprehensive risk factor modification, given the underlying disease process is the same. Figure 2 summarises the effects of AF ablation with or without risk factor management. 13. Structure of Risk Factor Modification Clinics A structured, co-ordinated, and goal-directed approach to AF management was employed in the LEGACY and ARREST-AF trials and is best fitted to managing cardiovascular risk factors in AF, as it is to many other chronic diseases. Enhanced patient compliance and improved clinical outcomes have been demonstrated with specialised AF clinics [94]. Surrounding the individual patient with AF, a team involving the primary care doctor, heart rhythm specialist, specialised nurse, and where applicable, exercise physiologist, sleep physician, endocrinologist/diabetes care team and pharmacist are best placed to gain optimal control of all risk factors and maximise the likelihood of reduced AF burden and recurrence. The clinic model employed successfully in the above studies involves an independent risk factor management clinic, which is separate and additional to usual medical care. Physicians and specialist nurses deliver this separate clinic with support from other multidisciplinary team members. Due to the demonstrated success of this model, it is strongly supported as a key pillar of AF care in major current international guidelines including those of the European Society of Cardiology, American Heart Association/American College of Cardiology, and others [4,5,6,95]. Critical components of the risk factor clinic are:Patient centred, individually tailored care; A lifestyle journal including regular recordings of blood pressure, exercise and dietary intake, which is regularly reviewed, with feedback provided; Incremental, goal-directed approach; Flexible follow-up, with access to care between appointments; Surveillance and titration of medication and other treatments; Screening of all patients with AF for the presence of OSA and review of any CPAP treatment data to ensure efficacy. Where access to a specialised AF risk factor management clinic is not available, strong consideration should be given to the establishment of a dedicated clinic where significant volumes of patients with AF are treated. Another potential solution is to harness chronic disease management models or plans, where these are facilitated by local government and funding models to utilise primary care nurses, doctors and allied health to deliver the components of risk-factor management and forge the necessary relationships with a network of other specialists required to deliver optimal AF care. 14. Conclusions The importance of risk factor modification as a critical pillar of AF care has developed a significant evidence base and is essential to reduce the risk of AF burden and progression. Regardless of treatment strategy employed, it is critical to optimise risk factor profiles to manage this condition. Patient engagement and a unified team approach are required to modify obesity, physical inactivity, hypertension, dyslipidaemia, diabetes mellitus, OSA, alcohol and tobacco use, and this approach is cost effective. The strong evidence for improved short and long-outcomes and ongoing increase in the burden of AF on health-care systems globally point towards the urgency of optimising the management of patients with this arrhythmia. Furthermore, in those undertaking ablation for AF, it is critical to optimise risk factor profiles to maximise the likelihood of successful outcomes from this procedure. The widespread implementation and evaluation of risk factor management clinics is critical to stem the growing burden of AF and associated health care impact. Author Contributions Conceptualization, P.S., J.L.F., M.E.M., C.G.; methodology, P.S., J.L.F.; data collection and analysis, J.L.F., M.E.M., C.G.; writing—original draft preparation, J.L.F.; writing—review and editing, J.L.F., M.E.M., C.G., P.S.; supervision, C.G., P.S. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest P.S. reports having served on the advisory board of Medtronic, Abbott Medical, Boston Scientific, CathRx, and PaceMate and that the University of Adelaide has received on his behalf research funding, and lecture and/or consulting fees from Medtronic, Abbott Medical, Boston Scientific, and Microport. All the other authors declare no conflict of interest. Figure 1 Effects of risk factor management (RFM) for AF including the benefit of undertaking risk factor management as a primary method of care for atrial fibrillation (AF) patients. Benefits are also seen in those who require ablation, with lower benefit for AF ablation alone in those who present with risk factors. Figure 2 This figure highlights the additive benefit of risk factor management with AF ablation. Whether partial (some risk factors) or comprehensive (all risk factors) a benefit for risk factor management is seen in patients with AF who require ablation. jcm-11-02660-t001_Table 1 Table 1 Outline of studies which demonstrate the effects of individual risk factors on atrial fibrillation (AF). Risk Factor Effect Obesity Increased LA size, pressure and blood volume, increased central blood volume and systemic vascular resistance, increased epicardial and pericardial fat deposition, conduction slowing [7,8,9,32]. 29% increased risk for developing AF for each 5-point increase in BMI [23]. Physical Inactivity Higher risk of developing AF and association with poorer cardiovascular health and increasing obesity [38]. Up to 28% reduction in the risk of AF was associated with moderate-intensity physical activity in the Cardiovascular Health Study [24]. Hypertension Left ventricular hypertrophy and stiffening, reduced diastolic filling, increased left atrial volumes all associated with hypertensive increased afterload. Left atrial dilatation, adverse electrophysiological changes and circulating hormones such as angiotensin II linked with fibrosis and perpetuation of these changes [10,11,12]. 40 to 50% increased likelihood of developing AF in the Framingham Heart Study [25]. Blood pressure above 130/80 mmHg associated with significantly higher risk of major adverse cardiovascular events in a large cohort of nearly 300,000 patients with known atrial fibrillation [39]. Obstructive Sleep Apnoea (OSA) Repetitive interruption of ventilation via recurrent pharyngeal collapse leads to hypoxaemia-related atrial electrophysiological changes and altered haemodynamics that increase LA pressures, leading to left atrial enlargement [33,34]. Chronic OSA is associated with inflammatory and prothrombotic systemic changes [35]. The risk of developing AF doubles with the presence of OSA [26]. Degree of symptoms is a very poor indicator of the presence or severity of OSA [40]. Considerable night-to-night variability shown in the severity of sleep-disordered breathing [41]. Repeat testing in the case of an unexpectedly negative overnight oximetry study should be strongly considered where clinical suspicion remains high [41]. Diabetes mellitus One-third increase in the risk of developing AF has been independently attributed to diabetes mellitus [27]. In patients with AF, the presence of diabetes has been associated with increased thromboembolism and stroke risk [42,43]. Alcohol consumption Binge-drinking has been associated with increased risk of AF episodes, termed ‘Holiday Heart’ for many years [28]. Chronic alcohol use is associated with increased incidence and burden of AF, and regular moderate alcohol consumption is associated with reduced voltage and slowed conduction on electro-anatomical mapping at the time of AF ablation [29,36]. Chronic alcohol consumption is strongly associated with hypertension, obesity and OSA [29]. While more than seven standard drinks per week was associated with increased risk of developing AF, in a recent study of UK Biobank data, this risk may vary with the type of beverage consumed, with an increased risk of AF seen with reported beer or cider consumption, compared to wine or spirits [30]. Comprehensive review of previous data on alcohol and AF risk suggests that any alcohol at all increases the risk of AF and there is no benefit on AF from light alcohol consumption, which has been seen in studies related to ischaemic heart disease risk [29,44]. Smoking Observational cohort studies, such as the ARIC study, show up to double the risk of developing AF associated with smoking [31]. Mechanisms proposed include increased sympathetic tone, oxidative stress, inflammation and atrial fibrosis. In the presence of AF, smoking increases the risk of thromboembolism and mortality [45]. Cholesterol levels Observational studies have not shown a correlation with lower cholesterol, or particularly LDL and reduced AF, with, in fact a paradoxical relationship where less AF was seen with higher LDL levels [46,47]. The significance of this data is unclear though, and many patients with AF and hypercholesterolaemia have associated increased risk for cardiovascular events related to comorbidities such as hypertension or prior cardiovascular events. Adverse cardiovascular events, rather than AF-risk per se are associated with raised lipids in these instances [48,49]. jcm-11-02660-t002_Table 2 Table 2 Studies demonstrating evidence for risk factor management in patients undergoing AF ablation. Study Study Type Number of Patients Intervention or Risk Factor Studied * Population Change in Risk Factor(s) Average Follow-Up Duration (Months) Number of Procedures Outcomes Pathak et al., 2014 (ARREST AF) [90] Cohort 149 Aggressive comprehensive risk factor management AF patients, BMI > 27 kg/m2 61 RFM 88 standard care RFM group More weight loss Improved BP control Improved blood sugar control Reduced sleep apnoea 41.9 1.6 ± 0.7 Reduced AF symptom burden (p < 0.001) Improved arrhythmia-free survival: 87% arrhythmia free treatment group vs. 17% in control group (p < 0.001) Gessler et al., 2021 (SORT-AF) [57] Randomised controlled trial 133 6 months of structured weight loss program AF patients, BMI 34.9 67 weight loss 66 usual care Weight loss group lost more weight (3.91%) 12 1 17% had >1 ablation AF burden reduced in both groups post-ablation (p < 0.001) but no difference between groups Mohanty et al., 2018 [58] Cohort 90 1 year of weight loss intervention Long-standing persistent AF patients, BMI 38 58 weight loss 32 standard care Weight loss group Lost 24.9 kg cf control group 0.9 kg (p < 0.001) 12 1 No difference in AF symptoms by AFSS Improved physical (p = 0.013) and mental (p < 0.02) component scores of SF-36 in weight loss group compared to usual care Pokushalov et al., 2012 [67] Randomised controlled trial 27 Renal denervation in addition to pulmonary vein isolation versus pulmonary vein isolation alone AF patients refractory to 2 AADs with drug-resistant hypertension, BMI 28 14 PVI only 13 PVI + renal denervation Intervention group: BP improved from 181/97 to 156/87 12 1 Intervention group: 69% arrhythmia-free Control group: 29% arrhythmia-free (p = 0.033) Pokushalov et al., 2014 [91] Meta-analysis of combined data from 2 randomised controlled trials 80 Renal denervation in addition to pulmonary vein isolation versus pulmonary vein isolation alone AF patients BMI not stated 39 PVI only 41 PVI + renal denervation Intervention group: BP 12 1 Intervention group: 63% AF-free Control group: 41% AF-free (p = 0.014) Steinberg et al., 2020 (ERADICATE-AF) [68] Randomised controlled trial 302 Renal denervation in addition to pulmonary vein isolation versus pulmonary vein isolation alone Paroxysmal AF patients, BMI not stated, 16.8% obese 154 PVI + renal denervation 148 PVI alone Intervention group: mean BP reduced 150–135 mmHg vs. control group 151–147 mmHg (p < 0.001) 12 1 Greater freedom from AF recurrence (72%) in treatment vs. (57%) control group (p = 0.006) Parkash et al., 2017 (SMAC-AF) [70] Randomised controlled trial 184 Aggressive BP treatment (target <120 mmHg) vs. standard BP treatment (target <140 mmHg) AF patients, BMI 32, (57% paroxysmal) 92 aggressive BP treatment 92 standard BP treatment Aggressive BP treatment group mean BP reduced 143–123 mmHg vs. control group 142–135 mmHg (p < 0.001) 14 1 Intervention group recurrence of AF/atrial tachycardia/atrial flutter not different to control group (both 61%) (p = 0.763) Fein et al., 2013 [79] Retrospective cohort 62 Treatment of obstructive sleep apnoea vs. non-treatment AF patients, BMI 30, 53% persistent AF 32 with OSA on CPAP 30 with OSA no CPAP Not specified 12 Not specified Higher atrial tachyarrhythmia-free survival rate with CPAP than without (72% vs. 37%) (p = 0.01) Patel et al., 2010 [84] Retrospective cohort 3000 Treatment of obstructive sleep apnoea vs. non-treatment AF patients, BMI 27, 53% paroxysmal 315 with OSA on CPAP 325 with OSA no CPAP CPAP vs. no CPAP 32 1 Higher AF-free survival rate with CPAP than without (79% vs. 68%) (p = 0.001) Naruse et al., 2013 [83] Prospective case–control 153 Treatment of obstructive sleep apnoea vs. non-treatment AF patients, BMI 25, 54% paroxysmal 82 with OSA on CPAP 34 with OSA no CPAP CPAP vs. no CPAP 19 1 Lower AF recurrence with OSA + CPAP vs. OSA no CPAP (30% vs. 53%) (HR 0.41, CI 0.22–0.76, p < 0.01) Jongnarangsin et al., 2008 [76] Retrospective cohort 324 Treatment of obstructive sleep apnoea vs. non-treatment AF patients, BMI 30, 72% paroxysmal 18 with OSA on CPAP 14 with OSA no CPAP CPAP vs. no CPAP 7 1 Lower AF recurrence with OSA + CPAP vs. OSA no CPAP (50% vs. 71%) (underpowered for this outcome, p = 0.289) Donnellan et al., 2019 [75] Retrospective cohort 298 Pre-procedure HbA1c control <7% vs. poor control AF patients with diabetes, BMI 34, 40% paroxysmal n = 298 HbA1c controlled to <7% compared to >9% 26 Not specified AF recurrence lower with HbA1c <7% (32.4%) vs. >9% (69%) (p < 0.0001) HbA1c trend in 12 months prior to ablation: 10% improvement showed lower (2%) recurrence vs. HbA1c worsening trend (91%) (p < 0.0001) Donnellan et al., 2019 [92] Retrospective observational cohort 239 Bariatric surgery vs. no bariatric surgery pre-AF ablation AF patients, BMI 41, 39% paroxysmal 51 Bariatric surgery vs. 188 no Bariatric surgery Bariatric surgery vs. no Bariatric surgery 36 1.3 Lower AF recurrence in surgery group vs. non-surgery group (20% vs. 61%) (p < 0.0001) Lower repeat procedure requirement with surgery group vs. non-surgery group (12% vs. 41%) (p < 0.0001) Donnellan et al., 2019 [93] Retrospective observational cohort 255 Bariatric surgery for morbid obesity pre-ablation vs. non-obese AF patients, BMI 35, 41% paroxysmal 51 Bariatric Surgery BMI 37 vs. 102 no surgery BMI 43 vs. 102 non-obese BMI 25.6 Bariatric surgery vs. no Bariatric surgery vs. non-obese 29 Not specified Comparable AF recurrence in surgery group (20%) to non-obese group (24.5%), both significantly lower than non-surgery group (55%) (p < 0.0001) Risom et al., 2016 (CopenHeartRFA) [61] Randomised controlled trial 210 12-weeks of cardiac rehabilitation AF patients, BMI 28, 72% paroxysmal 105 Cardiac Rehabilitation vs. 105 usual care Cardiac rehabilitation group improved VO2 max at 4 months compared with usual care 6 Not specified VO2 max increased in cardiac rehabilitation group vs. controls, no significant difference in mental health or other SF-36 score components (p = 0.20) * Unless otherwise specified, study intervention groups had intervention + usual care, control group had usual care alone. 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==== Front Sensors (Basel) Sensors (Basel) sensors Sensors (Basel, Switzerland) 1424-8220 MDPI 10.3390/s22093433 sensors-22-03433 Article Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning https://orcid.org/0000-0002-4914-3921 Choi Won-Hyuk https://orcid.org/0000-0002-9042-0599 Choi Yong-Suk * Marín Sergio Toral Academic Editor Artificial Intelligence Laboratory, Hanyang University, Seoul 04763, Korea; gandet09@hanyang.ac.kr * Correspondence: cys@hanyang.ac.kr 30 4 2022 5 2022 22 9 343317 2 2022 27 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). With the increase in the performance of deep learning models, the model parameter has increased exponentially. An increase in model parameters leads to an increase in computation and training time, i.e., an increase in training cost. To reduce the training cost, we propose Compositional Intelligence (CI). This is a reuse method that combines pre-trained models for different tasks. Since the CI uses a well-trained model, good performance and small training cost can be expected in the target task. We applied the CI to the Image Captioning task. Compared to using a trained feature extractor, the caption generator is usually trained from scratch. On the other hand, we pre-trained the Transformer model as a caption generator and applied CI, i.e., we used a pre-trained feature extractor and a pre-trained caption generator. To compare the training cost of the From Scratch model and the CI model, early stopping was applied during fine-tuning of the image captioning task. On the MS-COCO dataset, the vanilla image captioning model reduced training cost by 13.8% and improved performance by up to 3.2%, and the Object Relation Transformer model reduced training cost by 21.3%. compositional intelligence image captioning feature mapping layer pre-training method transfer learning transformer Korea government (MSIT)No. 2020-0-01373 National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)No. 2018R1A5A7059549 National Research Foundation of Korea (NRF) No. 2020R1A2C1014037 This work was supported by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)), National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2018R1A5A7059549), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1014037). ==== Body pmc1. Introduction Recently, the parameters of deep learning models have increased exponentially. The parameter of GPT-3 [1] has a very large size of 175B. This is more than 500 times larger than the BERT large [2] model. As a result, the training cost increased significantly. In the case of GPT-3, the training cost of 3.14 × 1023 flops is almost 590 times that of the BERT large model. This is hundreds of years of work with a single NVIDIA V100 GPU and it costs over $4.6 million. Training these large models every time is expensive. Therefore, research to reduce the training cost is needed. A common way to reduce training costs is transfer learning using pre-trained models. Transfer learning is solving one problem and then applying it to a different but related problem. In computer vision, a pre-trained feature extractor such as a VGG network [3] is used to fine-tune the target tasks [4,5,6,7]. In NLP, pre-trained BERT [2] is used to fine-tune the target tasks [8,9,10]. However, the previous methods take a pre-trained network and use it as part of the entire model, e.g., connect the pre-trained feature extractor and Region Proposal Network to detect objects in the image [5], and connect the pooling layer and softmax calssifier to the pre-trained BERT to perform sentence embedding [8]. So, part of the model is still training from scratch. The more parts to train from scratch, the higher the training cost. The Compositional Intelligence (CI) method, on the other hand, constructs the entire model by combining pre-trained models. The advantage of using pre-trained models is that training converges quickly and performance can be improved. A representative model to which CI can be applied is the encoder–decoder structure. Since the encoder and decoder are connected with a hidden layer in the middle, they can be connected if the shape of the hidden vector is the same. It can be applied to various target tasks by combining a pre-trained encoder–decoder. Previous studies of CI have been done to combine two pre-trained models in the same domain. In the study by Yoo et al. [11], the image style transfer task was performed through a combination of encoder and decoder, trained with different style images. Oh et al. [12] applied CI to machine translation. First, the Transformer is pre-trained with monolingual task, and then the machine translation task is trained by connecting an encoder and decoder trained in different languages. Yoo et al. [11] applied the CI to the image domain and Oh et al. [12] applied the CI to the text domain. Since the same domain data is used, there is no need to consider the problem of domain differences when using CI. We extended CI to a dual domain; image and text. Two models trained in each domain were used for the image captioning task. As shown in Figure 1, the typical image captioning model has an encoder–decoder structure. Most encoders use pre-trained feature extractors [3,5,13]. In the decoder, NLP models such as LSTM [14,15] and Transformer [16] are used as caption generators. After the announcement of the Transformer [16], which showed higher performance than the existing NLP model, a model using the Transformer as a caption generator was studied [17,18,19,20]. Since the model size of the Transformer [16] is much larger than that of LSTM [14], the training cost increased along with the performance improvement. Usually, the caption generators are trained from scratch, so training is expensive. Since Image Captioning has an encoder–decoder structure, the CI method can be applied. Applying CI can reduce training costs while maintaining or improving performance. We used the pre-trained image feature extractor [21] commonly used in image captioning models. We devised and trained a proper pre-training task that works well as a caption generator. Connecting two pre-trained models on data from different domains requires feature mapping layer (FML) to mitigate differences in data distributions. We compared the training cost and metric scores [22,23,24,25,26] of our CI model that uses a pre-trained caption generator and the From Scratch model that trains the caption generator from scratch. For comparison of training cost, we checked the training time by applying early stopping when fine-tuning the image caption task. Compared to the From Scratch model, our CI model significantly shortened the training time, and all metric scores showed a slight improvement. In summary, our contributions of this study as follows. We extended our CI study from single domain to dual domain; We devised a pre-training task for caption generator; We showed in the image captioning task that the CI model is effective in reducing training costs. 2. Related Work 2.1. Image Captioning Image recognition has come a long way in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) using ImageNet [27]. A CNN-based Deep Neural Network has been developed from AlexNet [28]. AlexNet won the ILSVRC-2012, significantly improving the top-5 test error rate of 26%, which was recorded by 2nd place, to 16%. Subsequent models, VGGNet [3], GoogLeNet [29], and ResNet [13], have better performance and deeper layers. The CNN network, which has been sufficiently trained in the challenge, has been used in several vision applications. Image captioning generates captions with visual information extracted through the CNN network. The CNN network fine-tuned VGGNet, GoogLeNet, and ResNet without training from scratch [30,31,32]. However, the aforementioned CNN network extracted one feature vector from the image. Since only one vector information was extracted from the image, the information entered into the caption generator was limited. With the advent of object detection models [33,34], it was possible to utilize the rich information by extracting object feature vectors from images [21]. The object detection model is used not only for image captioning but also for many vision applications [35,36,37]. All of the previous methods used a pre-trained CNN network, but all caption generators were used from scratch. Recently, models using Transformer [16] structure in image captioning are increasing [19,20,38]. If the Transformer structure is used, the parameters of the model inevitably increases, which leads to an increase in training cost. 2.2. Text Pre-Training Method The field of NLP has been developed along with the study of various self-supervised pre-training methods. GPT-3 [1] trained using the decoder structure of Transformer [16]. It showed good performance with fine tuning in NLP tasks such as NLI, QA, reasoning, semantic similarity, classification, and text generation. The GPT-3 [1] also performed well for zero or few-shot learning, but it required a huge model size and a lot of data. BERT [2] uses the encoder of Transformer. BERT performed well with two objective functions: masked token prediction and next sentence prediction. However, BERT has poor text generation performance due to limitations in model structure and pre-training methods. Bart [39] has a standard Transformer architecture and trained the model using five pre-training methods: token masking, token deletion, sentence permutation, document rotation, and text infilling. These transformer models are expensive to train from scratch each time. Therefore, these models are fine-tuned to the target task using pre-trained models. We used the original Transformer [16] as a pre-training model so that both the encoder and decoder can be reused. We also devised a pre-training method to predict a sentence with keywords suitable for caption generator. 2.3. Compositional Intelligence Method (CI) CI is used to connect models learned in different domains or tasks and apply them to a new task. It is a type of transfer learning in that it uses pre-trained models. However, transfer learning can use a part of the model as a pre-trained model, but CI uses only pre-trained models except for a simple FML. CI utilizes a pre-trained model and can expect effective performance with minimal training cost. The model structure in which CI can be easily applied is the encoder–ecoder structure. Various combinations of models can be created with multiple encoders and decoders. Yoo et al. [11] performed well on style transfer tasks using Auto-encoder. In Auto-encoder learned with Style-A data, encoder extracts A style’s presentation, and decoder regenerates image from the presentation. If the encoder learned in A style and the decoder learned in B style are connected to the feature mapping layer, the image of Style A is converted into the image of Style B. Similar studies create one shared latent space of multiple style data, such as UNIT [40] and CD-GAN [41]. As shown in Figure 2, the Transformer has encoder and decoder structures. Therefore, CI can be applied by connecting the encoder trained with Data A and the decoder trained with Data B. Oh et al. [12] conducted a study on translating languages by combining multiple Transformers trained on monolingual data. We extended the CI method to dual domains. We applied CI to the image captioning task by reusing the feature extractor and caption generator. 3. Approach A typical image captioning model uses a pre-trained image feature extractor, but a caption generator is trained from scratch. Unlike this, we pre-trained the caption generator and conducted image captioning training using CI. A pre-training method suitable for the target task to which CI is to be applied is required. Inappropriate pre-training methods can cause performance degradation during fine-tuning. Therefore, we devised and applied a pre-training method suitable for image caption generator. 3.1. Pre-Training Model Architecture We used the Transformer [16] base model as the pre-training model. As shown in Figure 2, the Transformer is divided into encoder and decoder structures, and the decoder is responsible for generating text. So, we reused the pre-trained decoder as a caption generator. We used keywords as input to the pre-training, but there is no need for positional information between the keywords. This is because, regardless of the order of keywords, the model completes the sentence by putting the keyword in the appropriate position when generating the sentence. Therefore, Positional Encoding (PE) used in the original Transformer encoder was removed. As shown in Figure 3, the pre-training model was constructed. We extract keywords from sentences and use them as input to the encoder. The decoder calculates the attention score between the keywords passed through the encoder and the tokens predicted previously (Token0,Token1⋯Tokent−1). The decoder updates the values of the tokens with the attention score and predicts the next token (Tokent). As shown in Figure 4, the CI model requires FML because of the different vector distributions of the two pre-trained models. On the other hand, the From Scratch model does not need FML because it trains the caption generator from scratch. 3.2. Pre-Training Method It is impossible to unconditionally increase good performance by connecting pre-trained models to each other. Therefore, in order to harmoniously connect the models, it is necessary to devise a pre-training method suitable for the target task. We used two pre-trained models for the image captioning task. As shown in Figure 1, the image captioning model consists of two parts, the first part is the image feature extractor and the second part is the caption generator. As shown in Figure 4, Faster R-CNN composed of ResNet-101 was used as an image feature extractor [21]. Between 10 and 100 feature vectors of 2048-dimension are created for each image. Each object feature vector extracted from the image feature extractor is fed into the caption generator. That is, object information is used to create captions. The caption generator learns to insert verbs or adjectives into relationships between objects when there is no information other than objects. As shown in Figure 5, it can be seen that the object detected in the image and the keyword of the sentence play the same role. Therefore, we assumed that if the caption generator was pre-trained to construct a sentence with keywords, it would be suitable for working with the image captioning task. Figure 6 shows the training process and input of the pre-training model. The process of generating the input is divided into two parts, as shown in Figure 6b. First, we extract keywords from the sentence. Keywords were created by dividing sentences into word units and excluding stopwords [42]. Then, we select some of the keywords and shuffle to make them input data. Training was conducted using only 70% of the keywords made in one sentence. The use of 70% of keywords was determined through experiments, in Section 4.3.1. Table 1 is an example of the input actually made from the sentence. As shown in Figure 6a, the generated input is fed into the encoder of the pre-training model, and the decoder is trained to generate the original sentence. A word tokenizer was used instead of sub-words when tokenizing keywords to match object feature units in the image to be used for fine-tuning. We used the pre-trained Transformer-xl tokenizer [43] of Huggingface [44]. Since our pre-training method creates input keywords from a single sentence, an Input-Output data pair is not required. Therefore, it has the advantage of being able to easily obtain the data required for pre-training. We used WikiText-103 dataset [45] for pre-training. In order to have a distribution similar to the MS-COCO caption, which is an image captioning dataset, several preprocessing steps were performed. The maximum length of the MS-COCO caption was 250 and the minimum length was 21. Therefore, wiki sentences with a length of less than 21 characters or more than 250 characters were excluded. Pre-training was performed for 5 epochs with about 730,000 data. 3.3. Feature Mapping Layer (FML) We pre-trained the model in the image domain and text domain. As shown in Figure 7, images and words of the same meaning trained in different domains have different vector values. This is because different models have different distributions of feature spaces they create. So we need a layer that smoothly maps the different feature spaces. Previous studies also showed that the model with FML outperformed the model without FML [11,12]. From the experimental results in Section 4.3.2, the Overcomplete FML showed the best performance. FML consists of two fully-connected layers. Using ReLU, a non-linear mapping function can be created. 3.4. Compositional Intelligence (CI) As shown in Figure 4, a CI method was used to connect the pre-trained image feature extractor and caption generator with FML. We used a model composed of Faster R-CNN [5] and ResNet-101 [13] as a pre-trained image feature extractor. However, we did not train the model and used the extracted image vectors provided by Anderson et al. [21]. Anderson et al. pre-trained the image feature extractor with the Visual Genome dataset [46], and extracted feature vectors of objects from the images of the MS-COCO dataset [47]. The image of the bounding box created through the Region Proposal Network (RPN) of Faster R-CNN was given as input to ResNet-101. An intermediate feature map of ResNet-101 was used. For each bounding box, 2048-dimensional vector values were extracted. Before proceeding with CI, we reduced the 2048-dimensional vector to 512-dimensional using an auto-encoder. The reason is that it was not possible to use a 2048-dimensional vector as it is in our experimental environment. Through the test, it was confirmed that there was no performance degradation due to dimensional reduction. The vector is input as the Key and Value of the second Multi-Head Attention of the Transformer decoder, which is a caption generator, through FML. The caption generator gets its weights from a Transformer model that has been pre-trained with keywords. The completed model has a structure in which Faster R-CNN with ResNet-101 and Transformer decoder are connected through FML. Then, fine-tuning of the image captioning task is performed on the MS-COCO dataset. 4. Experiment 4.1. Dataset and Metrics For the image captioning task, we used the MS-COCO 2014 captions dataset [47]. Training and evaluation were conducted with Karpathy validation and test splits [48]. The splits divide the MS-COCO dataset, and as shown in Table 2, it consists of 113 K train data and 5 K each of validation and test data. Instead of training an image feature extractor, we used image feature vectors extracted by Anderson et al. [21] from the MS-COCO dataset. Anderson et al. provides between 10 and 100 2048-dimensional object feature vectors in an image. We mentioned in Section 3.4 that we reduced the 2048-dimensional feature vector to 512-dimensional. This is because we had an issue with the GPU memory in our experimental environment (we used one Nvidia GeForce RTX 2080 Ti). As model evaluation indicators, CIDEr-D [22], SPICE [23], BLEU [24], METEOR [25], and ROUGE-L [26] are used. It is common to evaluate image captioning models with these metric scores. To compare the training cost, the training time was measured by applying early stopping. There was a difference applying the CI, but other training environments (epochs, batch size, model hyperparameters, and hardware environment) were the same (to be exact, the parameters of the CI model are a little more because of the FML. However, the CI model overcame this weakness and showed better performance). Therefore, the shorter the training time, the lower the training cost. 4.2. Vanilla Image Captioning Model As mentioned in Section 3.4, we experimented with two methods: training the image captioning model from scratch and applying CI to the pre-trained model. As shown in Figure 4, in the CI model, the image feature vector goes into the Transformer decoder through FML, and in the From Scratch model, the image feature vector goes directly into the Transformer decoder without FML. As a result of the experiments in Section 4.3.1 and Section 4.3.2, we used a pre-trained model with 70% keyword input and an Overcomplete FML in this experiment. We used the decoder of the Transformer base model as the caption generator. It has 6 decoder layers (N), 8 heads (h), 512 embedding vectors (dmodel), and a size of 2048 feed-forward network (dff). The batch size is 20. We use Adam optimizer without the learning rate scheduler (Learning rate = 9×10−5, β1=0.9, β2=0.999 and ϵ=1×10−6). Except for FML, the training conditions of the CI and From Scratch models were the same. As shown in Table 3, the CI model has 2.1M more parameters than the From Scratch model due to FML. Both models were trained 8 times. 4.2.1. Evaluation Metric Scores and Training Cost The advantage of the CI model is that it can reduce the training cost by using a pre-trained model. Therefore, when training the CI and From Scratch models, we applied early stopping to measure the time the training ends. Since the training conditions were the same, the time spent on training equals the training cost (as can be seen in Table 3, although the CI model has 3.74% more parameters due to FML than the From Scratch model, but had a greater advantage in training time than that). Early stopping conditions are as follows:Lval(t)>Lval(t−2), where Lval is the loss for the validation data after each epoch training and the t is the epoch. After each epoch, the validation loss was measured and the training was set to end if the loss was greater than before 2 epochs. For the CI model, it exhibits the lowest validation loss at 4 epochs, so it overfits at early stopping conditions greater than 2. When the model was continuously trained, both the CI model and the From Scratch model showed the lowest values around 4–6 epochs, and it was confirmed that they continued to increase. Figure 8 shows the measurement of validation loss of each model. Early stopping was applied to stop training when the current validation loss was higher than before 2 epochs. It can be seen that the tendency is similar, depending on the model. For the CI model, training was finished at 6 epochs during a total of 8 training sessions, and the From Scratch model was trained for 7 epochs and 8 epoch. As shown in Table 3, comparing the time used for actual training, the CI model saved about 13.8% of the time compared to the From Scratch model. It was confirmed that the CI model has the effect of reducing the training cost compared to the From Scratch model. Table 4 is the result of generating captions for the test dataset and measuring the metric scores. Mean and standard deviation metric scores were calculated for the model trained 8 times. We confirmed that the CI model has a small performance improvement in all metric scores compared to the From Scratch model. In particular, in the case of CIDEr-D, there was a performance improvement of 3.2%. Therefore, the CI model can significantly reduce the training cost while being similar or slightly better in the metric score. 4.2.2. Qualitative Analysis Table 5 shows the qualitative results of the From Scratch model and the CI model. We selected two images from the test dataset and performed inference (1 step = 1 batch). Looking at the inference results before training (0 step), both the From Scratch model and the CI model repeat the same word. In the case of the CI model, before the start of training, the image and text mapping is not done, so it generates words that have nothing to do with images, but unlike From Scratch, it tries to make a sentence. In the case of 200 steps, the From Scratch model still creates only the word ‘a’, but in the case of the CI model, as the mapping layer is learned, it starts to generate sentences similar to images. In the case of the From Scratch model, the sentence generation for the image above started at step 2600, and the sentence generation for the image below started at step 1600. As learning progresses, it can be seen that both the From Scratch and CI models generate sentences of similar quality. It can be confirmed that the CI model learns faster than the From Scratch model, but both models quickly generated sentences that fit the images. Since the captions of the MS-COCO dataset consist of relatively simple sentences, it can be considered that the learning is fast. If CI is applied to complex or long sentence data, better results can be expected. 4.3. Ablation Study 4.3.1. Comparison Input Keyword Percentage We pre-trained the models with different keyword input ratios. We thought that if 100% of the keyword extracted from the sentence is used, the pre-training model can easily predict the original sentence. If only 30% of the keyword is used, the original sentence cannot be easily predicted, but it creates a creative sentence. Therefore, a model with a low percentage of keywords is more effective when the input information is scarce. When fine-tuning the image captioning task, the information of image objects received as input may not be sufficient. Therefore, this experiment aimed to find the optimal pre-training model. The training was carried out by setting the keyword input of the pre-training model to 30%, 50%, 70%, and 100%. As in Section 4.2, we applied CI to the image captioning task. We then evaluated which pre-trained model had the best metric scores. Each model was trained 8 times and the mean and standard deviation were calculated. As shown in Table 6, it was confirmed that the metric performance of the 70% model was good overall. When comparing the training epoch by applying early stopping, the 70% model and the 50% model showed good performance with an average of 6 epochs. We used the keyword 70% pre-training model to evaluate the vanilla image captioning models in Section 4.2 considering the metric score and the training cost. 4.3.2. Comparison Feature Mapping Layer In order to find an effective FML, we devised four FMLs, as shown in Figure 9. To see which performed best, we experimented with a model without FML and four FML models. The model without FML is a model to check whether FML is effective in our study as in the experiments of previous studies [11,12]. The without FML model has the same structure as the From Scratch model in Section 4.2, but the caption generator performed fine-tuning by loading the weights of the pre-trained model. For Dense Layer 1, the FML equation is FML(x)=xW1+b1, where W1∈R512×512. Dense Layer 1 has a single dense layer, but the activation function is not applied. Therefore, it is an FML that acts as a linear transform. The other FML expressions are as follows:FML(x)=max(0,xW1+b1)W2+b2. For a Dense Layer 2, W1∈R512×512 and W2∈R512×512. It consists of two dense layers, and a non-linear transformation was performed by applying a ReLU activation function in the first layer. For a Undercomplete, W1∈R512×128 and W2∈R128×512. This structure is similar to the auto-encoder. For a Overcomplete, W1∈R512×2048 and W2∈R2048×512. It has the same structure as Transformer’s Feed-forward Block. The ReLU activation function was applied to the first layer for both Undercomplete and Overcomplete. As shown in Table 7, early stopping was applied to each model, and evaluation was performed after training seven times to obtain the mean and standard deviation. The performance of the model using Overcomplete FML showed better performance in all metrics than other models. In addition, the training cost was lower compared to other models. The w/o FML model had a small training cost, but the metric scores were all lower than other models. In the case of Undercomplete, it was confirmed that the performance was the lowest and the training cost was large. It can be inferred that as the size of the vector in the intermediate layer becomes smaller, the information loss increases. 4.4. Apply CI to Object Relation Transformer We experimented with whether CI was applicable to the model of the previous paper. We applied CI to Object Relation Transformer (ORT) [38]. It is an image captioning model using Transformer. As shown in Figure 10a, the ORT model generates captions after passing the image feature vectors and geometry features, which are the bounding box information, through the encoder. Unlike the vanilla image captioning model in Section 4.2, since the ORT model is a structure in which encoders exist, both the CI and the From Scratch models had to train the encoder from scratch. This is because the encoder architecture of our pre-training model is different from the encoder architecture of the ORT. Unlike the original Transformer encoder, the ORT encoder computes the relation between appearance features and geometry features. The From Scratch model was trained using the ORT model as it is, as shown in Figure 10a. When learning the image captioning task, both the encoder and decoder of ORT were trained from the scratch. The CI model has a model structure in which FML is connected between the ORT encoder and the decoder, as shown in Figure 10b. The Dense Layer 2 FML mentioned in Figure 9 was used. Because the encoder of the ORT model has a different architecture from the pre-trained model encoder in Section 3.1, pre-trained weights could not be used. However, in the case of the decoder, since the model structure is the same, fine-tuning was performed using pre-trained weights (we used the weights of the 70% keyword pre-trained model). Therefore, in the case of the CI model, when fine-tuning, the encoder and FML were trained from scratch. Except for the structure of the model, other training conditions were equally applied to both the From Scratch model and the CI model. We used 6 encoder and decoder layers (N), 8 heads (h), 512 embedding vectors (dmodel) and a size of 2048 feed-forward network (dff) for the ORT model. The batch size is 16. We use Adam optimizer without learning rate scheduler (Learning rate = 4×10−4, β1=0.9, β2=0.999 and ϵ=1×10−8). As shown in Table 8, the CI model has 0.6M more parameters than the From Scratch model due to FML. Each model was trained 7 times by applying early stopping, and the mean and standard deviation were measured by evaluating the metric scores. As shown in Table 9, although the CI model has lower scores for all metrics compared to the From Scratch model, the value is around 1%, so it can be seen that there is no significant difference in performance. However, the training cost results are impressive. As shown in Table 8, the CI model was able to save 21.3% of the training time of the From Scratch model. ORT’s CI model is not the original CI method because the encoder must be trained from scratch. This is because the original CI approach was to fine-tune two pre-trained models by connecting them with a simple FML. For this reason, the metric scores may not have the same results shown in Table 4. However, there was an impressive performance improvement in the training cost. We mentioned that the training cost can be reduced with the CI method as the parameters of the model are increased. Compared to the vanilla image captioning model in Section 4.2, the parameters of the ORT model is about 5.5 times larger. The training cost save rate increased from 13.8% to 21.3%. If the pre-trained model was used even for the encoder of the ORT model, the performance would have been better not only in the metric scores but also in the training cost. 5. Conclusions In this paper, we conducted a study by extending mono-domain CI [11,12] to dual-domain CI. CI uses pre-trained models to create a model for a new target task. We applied CI to the image captioning task by combining a model trained on images and a model trained on text. The previous image captioning models only use a pre-trained feature extractor and the caption generator is trained from scratch. We devised a pre-training method of caption generator that extracts keywords, puts them as input, and predicts original sentences. Since the objects of the image and the keywords of the sentence play the same role, it was effective when CI was applied to the image captioning task. We show that training the CI model is faster compared to the From Scratch model. The metrics maintained similar scores to the From Scratch model. That is, the CI model trained quickly and the performance was the same. The advantage of the CI method is that it uses the pre-training model multiple times. We applied CI to the vanilla image captioning model in Section 4.2 and the ORT model in Section 4.4 with the same pre-trained captiong generator. The more the pre-trained model is used for multiple target tasks, the more the cost can be reduced. As a future study, we will conduct research by expanding the domain to voice-to-text, video-to-text, and text-to-image. After that, the study will be expanded from dual-domain to multi-domain. As the name suggests, we will conduct research for Compositional Intelligence. Author Contributions Conceptualization, W.-H.C. and Y.-S.C.; data curation, W.-H.C.; formal analysis, W.-H.C.; funding acquisition, W.-H.C. and Y.-S.C.; investigation, W.-H.C.; methodology, W.-H.C. and Y.-S.C.; software, W.-H.C.; supervision, Y.-S.C.; writing—original draft, W.-H.C.; writing—review and editing, W.-H.C. and Y.-S.C. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Publicly available datasets were analyzed in this study. This data can be found here: Bottom-up-attention image features, https://github.com/peteanderson80/bottom-up-attention (accessed on 7 January 2022); WikiText-103, https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip (accessed on 7 January 2022). Conflicts of Interest The authors declare no conflict of interest. Figure 1 Typical image caption architecture. It consists of image feature extractor (encoder) and caption generator (decoder). Figure 2 Original Transformer architecture. The Transformer consists of encoder and decoder structures. Figure 3 Pre-training model architecture. Figure 4 Two models used in the image captioning task. (a) Image captioning model using pre-trained caption generator. (b) Image captioning model with a caption generator trained from scratch. Figure 5 The relationship between the objects in the image and the keywords in the sentence. Figure 6 Input generation and training method in pre-training phase. (a) When keyword data is fed into a pre-training model encoder, the decoder learns to generate the original sentences. (b) Shows how to create input data from a sentence. After extracting keywords, we select some and then shuffle. Figure 7 Compare vector values of images and words. Figure 8 Vanilla image captioning model validation loss. The solid line are the From Scratch models and the dotted line are the CI models (ours). Each model was trained 8 times and is marked with different colors. Figure 9 Four Feature Mapping Layers used in the experiments in Section 4.3.2. Figure 10 Object Relation Transformer (ORT) model architecture. (a) The ORT model devised a new encoder block to process image features and geometry features. (b) ORT model structure using FML to apply CI method. We used the Dense Layer 2 FML. sensors-22-03433-t001_Table 1 Table 1 Input and output for the pre-training phase. Keywords are extracted from a sentence. Input (Keywords) Output (Sentence) [Democrats, without, 1920, Beckham] The Democrats renominated Beckham without opposition in 1920. [Route, 1, interchange, northbound, entrance] Route 1 at a partial interchange with a northbound exit and southbound entrance. [Two, 1963, 1964, rerouted, western, highway] Two realignments in 1963 and 1964 rerouted the western end of the highway again. sensors-22-03433-t002_Table 2 Table 2 Statistics of MS-COCO 2014 dataset. Train Valid. Test 113 K 5 K 5 K sensors-22-03433-t003_Table 3 Table 3 Training cost evaluation of vanilla image captioning models. Training eight times for each model. Model Train Epoch Training Time Params (Mean) From Scratch 7.125 17 h 52 m 56.13 M CI (Ours) 6 15 h 24 m 58.23 M sensors-22-03433-t004_Table 4 Table 4 Metric evaluation of vanilla image captioning models. Training eight times for each model. Model BELU-1 BLUE-4 ROUGE-L METEOR SPICE CIDEr-D (Mean ± St. Dev.) From Scratch 71.78 ± 0.50 29.06 ± 0.31 53.08 ± 0.20 25.58 ± 0.12 19.0 ± 0.06 97.12 ± 0.65 CI (Ours) 72.34 ± 0.27 29.62 ± 0.46 53.46 ± 0.24 26.04 ± 0.16 19.46 ± 0.15 100.24 ± 1.26 sensors-22-03433-t005_Table 5 Table 5 Qualitative analysis of the vanilla image captioning model. The From Scratch model and the CI model were trained while performing inference on the test data image. 1 batch is 1 step, and the batch size is set to 20. 20,705 steps is 1 epoch. (...repeat...) means that the previous word is repeated until it reaches the maximum length (180 words). Steps Inferences From Scratch model CI model 0 ound congregations mound adverse mound mound congregations mound congregations mound mound (...repeat...) he chapel of the chapel of the chapel was a chapel of the recently to the stag’s stag’s for the <unk>of the <unk>of the <unk>of the <unk><unk><unk>. 200 a a a a a a a a a a man riding a surf board on a sandy beach. 400 man on a on a on a on a on a on a on a on a on a (...repeat...) man is riding a surfboard on a surfboard. 600 man is is is on a beach. man is walking on the beach with a surfboard. 2600 man standing on a beach with a surfboard. man holding a surf board on the ocean. 10,000 man walking on a beach with a surfboard. man walking on a beach with a surfboard. 20,705 man carrying a surfboard on the beach. man holding a surfboard on top of a beach. 32,906 man holding a surfboard on top of a beach. man holding a surfboard standing on a beach. Steps Inferences From Scratch Model CI Model 0 slam retreat dilidilidilidilidilidilivietnamese vietnamese vietnamese credits dilicredits dilidil (...repeat...) he <unk>and <unk><unk>were the <unk>of the <unk>and (...repeat...) 200 a a a a a a a a a large double decker bus on a street. 400 is is is on a on a street. red and white bus on a street. 600 train on a train on a train on a train. red bus is parked on a street near a building. 1600 bus is parked on a street with a city street. red and white bus parked on a street. 10,000 bus is parked on the side of a street. bus is parked on the side of a road. 20,705 bus is parked on the side of the road. bus is driving down the street near a building. 32,906 red and white bus driving down a street. bus is stopped at a bus stop. sensors-22-03433-t006_Table 6 Table 6 Performance comparison according to keyword input percentage. Training eight times for each model. Input BELU-1 BLUE-4 ROUGE-L METEOR SPICE CIDEr-D Train Epoch (Keywords) (Mean ± St. Dev.) (Mean) 30% 72.36 ± 0.29 29.72 ± 0.42 53.44 ± 0.27 26.02 ± 0.15 19.28 ± 0.07 99.80 ± 0.93 6.125 50% 72.73 ± 0.32 29.96 ± 0.27 53.62 ± 0.12 26.04 ± 0.05 19.36 ± 0.17 100.66 ± 0.29 6 70% 72.67 ± 0.14 29.82 ± 0.36 53.62 ± 0.20 26.12 ± 0.16 19.44 ± 0.12 100.90 ± 0.71 6 100% 72.52 ± 0.24 29.76 ± 0.27 53.50 ± 0.25 26.1 ± 0.21 19.36 ± 0.16 100.18 ± 0.96 6.125 sensors-22-03433-t007_Table 7 Table 7 Performance comparison according to FML. w/o applies CI without FML. DL 1 is Dense Layer 1, DL 2 is Dense Layer 2, UC is Undercomplete, and OC is Overcomplete. Training seven times for each model. FML BELU-1 BLUE-4 ROUGE-L METEOR SPICE CIDEr-D Train Epoch (Mean ± St. Dev.) (Mean) w/o 71.60 ± 0.15 29.24 ± 0.31 53.06 ± 0.21 25.78 ± 0.12 19.04 ± 0.08 97.86 ± 1.01 6 DL 1 72.26 ± 0.26 29.52 ± 0.25 53.36 ± 0.19 25.82 ± 0.17 19.28 ± 0.10 99.14 ± 0.70 6.71 DL 2 72.40 ± 0.23 29.68 ± 0.17 53.44 ± 0.14 25.98 ± 0.17 19.28 ± 0.13 99.92 ± 0.80 6.71 UC 72.04 ± 0.21 29.48 ± 0.20 53.28 ± 0.12 25.80 ± 0.11 19.10 ± 0.18 98.32 ± 0.19 7.43 OC 72.76 ± 0.40 30.10 ± 0.50 53.66 ± 0.19 26.08 ± 0.10 19.40 ± 0.14 100.96 ± 1.03 6 sensors-22-03433-t008_Table 8 Table 8 Comparison of training cost and parameters of ORT structure for two models, From Scratch and CI. Training seven times for each model. Model Train Epoch Train Time Params (Mean) From Scratch 16.4 19 h 24 m 319.6 M CI (Ours) 12.6 15 h 15 m 320.2 M sensors-22-03433-t009_Table 9 Table 9 Comparison of metric scores of ORT structure two models, From Scratch and CI. Training seven times for each model. Model BELU-1 BLUE-4 ROUGE-L METEOR SPICE CIDEr-D (Mean ± St. Dev.) 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==== Front Int J Mol Sci Int J Mol Sci ijms International Journal of Molecular Sciences 1422-0067 MDPI 10.3390/ijms23095272 ijms-23-05272 Article The Benefits of Fibrinolysis Combined with Venous Systemic Oxygen Persufflation (VSOP) in a Rat Model of Donation after Circulatory Death and Orthotopic Liver Transplantation Kröger Nadja 12 https://orcid.org/0000-0003-0373-3210 Czigany Zoltan 134 Jiang Jipin 1 Afify Mamdouh 15 Paschenda Pascal 1 Nagai Kazuyuki 1 https://orcid.org/0000-0001-7465-5761 Yagi Shintaro 1 Tolba René H. 1* Thuillier Raphaël Academic Editor 1 Institute for Laboratory Animal Science and Experimental Surgery, Faculty of Medicine, RWTH-Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany; nadja.kroeger@rwth-aachen.de (N.K.); zczigany@ukaachen.de (Z.C.); jpjiang@tjh.tjmu.edu.cn (J.J.); mafify@ukaachen.de (M.A.); ppaschenda@ukaachen.de (P.P.); kaznagai@kuhp.kyoto-u.ac.jp (K.N.); shintaro@kuhp.kyoto-u.ac.jp (S.Y.) 2 Department of Plastic, Reconstructive and Aesthetic Surgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937 Köln, Germany 3 Department of Surgery and Transplantation, Faculty of Medicine, University Hospital RWTH-Aachen, 52074 Aachen, Germany 4 Department of Surgery, Campus Charité Mitte/Campus Virchow-Klinikum, Charité-Universitätsmedizin, 13353 Berlin, Germany 5 Department of Pathology, Faculty of Veterinary Medicine, Cairo University, Giza 12211, Egypt * Correspondence: rtolba@ukaachen.de; Tel.: +49-(0)241-80-89882 09 5 2022 5 2022 23 9 527223 3 2022 06 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Organ shortage has led to the increasing utilization of livers retrieved from donors after circulatory death (DCD). These pre-damaged organs are susceptible to further warm ischemia and exhibit minimal tolerance for cold storage. The aim was thus to examine the effects of fibrinolysis combined with Venous Systemic Oxygen Persufflation (VSOP) on the preservation of DCD livers in vivo. Livers of male Lewis rats were explanted after 45 min of warm ischemia, cold-stored for 18 h, and transplanted into a recipient animal. Livers were left untreated or underwent either VSOP or fibrinolysis via Streptokinase (SK) or received combined SK and VSOP. Combined treatment exhibited improved microvascular flow at 168 h (p = 0.0009) and elevated microperfusion velocity at 24 h post-transplantation (p = 0.0007). Combination treatment demonstrated increased portal venous flow (PVF) at 3 and 24 h post-transplantation (p = 0.0004, p < 0.0001), although SK and VSOP analogously achieved increases at 24 h (p = 0.0036, p = 0.0051). Enzyme release was decreased for combination treatment (p = 0.0002, p = 0.0223) and lactate dehydrogenase (LDH) measurements were lower at 24 h post-transplantation (p = 0.0287). Further supporting findings have been obtained in terms of serum cytokine levels and in the alterations of endothelial injury markers. The combination treatment of SK + VSOP might provide improved organ integrity and viability and may therefore warrant further investigation as a potential therapeutic approach in the clinical setting of DCD. orthotopic liver transplantation organ preservation ischemia-reperfusion injury VSOP oxygen cold storage DCD donors after circulatory death warm ischemia fibrinolysis streptokinase microsurgery START program of the Faculty of Medicine RWTH Aachen#108/21 Z.C. was in part supported by the START program of the Faculty of Medicine RWTH Aachen (#108/21). Otherwise this project has received no external funding. ==== Body pmc1. Introduction Due to continuous improvements in patient selection, surgical techniques, perioperative care and the development of modern immunosuppressants liver transplantation (LT) became the treatment of choice for patients suffering from end-stage liver disease or acute liver failure [1,2,3]. As clinicians are faced with worldwide organ shortages due to insufficient numbers of organ donors, this challenge has led to the increasing utilization of marginal or extended criteria allografts [4,5,6,7]. Organs from donors after circulatory death (DCD) are exposed to warm ischemia and suffer a concomitant injury, which is linked to primary graft dys- or non-function and a high incidence of biliary complications after transplantation, which ultimately causes poor transplantation outcomes [8]. Molecular mechanisms associated with warm ischemia and therefore involved in graft damage include thrombosis, apoptosis, cytotoxic repercussion, the release of proinflammatory mediators, and Kupffer cell activation [9]. Multiple attempts have been made to thoroughly understand the process of ischemic injury and improve graft integrity after an ischemia-reperfusion injury [2,5,10,11,12,13,14]. The beneficial effect of graft reconditioning via Venous Systemic Oxygen Persufflation (VSOP) was already demonstrated in a porcine orthotopic LT model by Minor et al., as VSOP-treated grafts were able to show a sustained liver function and improved survival during an entire week after transplantation, whereas no survival could be achieved for recipients of non-treated grafts [15]. Subsequently, the principle of fibrinolysis prior to cold storage was shown to significantly improve tissue integrity and thus mitigate graft injury, which was validated, amongst other settings, in a DCD rat LT model by Yamauchi et al. [16]. The synergistic value of both treatments, namely fibrinolysis and hypothermic aerobic organ preservation via VSOP was finally shown by Tolba et al. in a DCD rat in vitro reperfusion model. Reduced portal venous pressure (PVP), less enzyme release, increased bile production, and less hepatocellular apoptosis upon reperfusion was demonstrated for liver grafts preserved with fibrinolysis and VSOP, suggesting improved viability after the aforementioned combined treatment [17]. Up until today, the effects of coupled VSOP and fibrinolysis on graft function and viability after LT are yet to be explored, therefore the goal of this study was to examine graft integrity and surgical outcome in a clinically relevant and complex microsurgical model of orthotopic LT following DCD in rats. 2. Results 2.1. Evaluation of Microcirculatory Perfusion Figure 1A shows the microcirculatory flow measured using the O2C (O2C-Oxygen to see device and LF1 surface probe, LEA Medizintechnik GmbH, Giessen, Germany) technique. The control group exhibited the lowest microcirculatory flow at each timepoint. The combination treatment of Streptokinase (SK) and VSOP achieved the highest microcirculatory flow measurements at 24 h and 168 h post-transplantation (SK + VSOP 110.85 au ± 26.13, p < 0.0001; 101.06 au ± 18.53, p = 0.0009), whereas at 3 h post-transplantation, the sole treatment via VSOP displayed the highest values of microcirculatory flow (p = 0.0021). The treatment with SK only exhibited increased microcirculatory flow in comparison to the control group, but performed worse compared to the other treatments, except at 168 h post-transplantation, where higher absolute microcirculatory flow values were achieved in comparison to the treatment with VSOP only. Nevertheless, significant differences between the control group and the groups treated via SK solely could only be demonstrated at 24 h post-transplantation (p = 0.0025). The peak of microcirculatory flow was measured at 24 h post-transplantation for rats treated with combined SK and VSOP (p < 0.0001) and rats treated with VSOP only (p < 0.0001), indicating improved graft microcirculation after reperfusion for named treatments. Figure 1B displays the microperfusion velocity measured via the O2C measurement technique. The control group exhibited the lowest microcirculatory velocity at 3 h and 24 h post-transplantation, respectively, whereas at 168 h post-transplantation, no significant differences in microperfusion velocity could be shown for either of the groups. Rats that had received livers subjected to fibrinolysis only exhibited higher microperfusion velocity in comparison to the control group solemnly at 24 h post-transplantation (p = 0.0212). Significant differences in microperfusion velocity in comparison to the control groups could be demonstrated for rats that had received livers treated with VSOP only at 3 h and 24 h post-transplantation, respectively (p = 0.0311, p = 0.0041). The overall highest microperfusion velocity was measured at 24 h post-transplantation for recipients which had received livers treated with combined SK and VSOP (SK + VSOP 16.60 au ± 2.11, p = 0.0007), hereby indicating improved microcirculation after transplantation, particularly in comparison to the other treatment groups. Figure 1C demonstrates tissue oxygenation. The highest absolute values for tissue oxygenation were measured at 168 h post-transplantation for rats which had received livers treated with a combination of SK and VSOP, although no overall significant differences could be demonstrated for either of the groups at any given time point. Figure 1D shows the amount of intravascular hemoglobin measured within the grafts using O2C. All groups at all time points exhibited similar values. No significant differences could be demonstrated for either of the groups at any given time point. 2.2. Evaluation of Portal Circulation Figure 2A shows the portal venous flow (PVF) measured via a transit-time perivascular flowmeter. The control group exhibited the lowest portal flow at all time points. Combined SK and VSOP resulted in the highest absolute portal flow values at all post-transplantation timepoints, whereas significant differences could be demonstrated only at 3 h and 24 h post-transplantation (SK + VSOP 13.6 mL/min ± 4.92, p = 0.0004; 18.8 mL/min ± 1.16, p < 0.0001), nevertheless indicating improved reperfusion. PVF increased over time reaching threshold values at approximately 18 mL/min at 168 h post-transplantation for each of the groups without exhibiting any significant differences between the experimental groups. Significant differences in comparison to the control group could also be demonstrated for recipients who have received livers preserved via VSOP at 3 h and 24 h post-transplantation sacrifice (p = 0.0481, p = 0.0051) and for recipients of livers treated via fibrinolysis only at 24 h post-transplantation sacrifice (p = 0.0036). Figure 2B displays the PVP measured via direct puncture of the portal vein. The overall highest values for PVP were measured at 168 h post-transplantation for all groups. The highest absolute PVP value was measured at approximately 13 mmHg within the control group, although no overall significant differences could be demonstrated for any of the groups at any given time point. At one week, the SK and VSOP group showed a tendency towards a lower PVP compared to the control group, however, it did not reach the level of statistical significance. 2.3. Serum GOT/AST, GPT/ALT, and LDH Measurements Figure 3A shows the serum aspartate aminotransferase (GOT/AST) release measured at sacrifice. AST values peaked at 24 h post-transplantation in most groups. Strikingly, the combination of VSOP and SK led to strongly decreased AST levels compared to the other experimental groups (SK + VSOP, 645.8 IU ± 267.29, p = 0.0002 vs. control, p < 0.0001 vs. SK, p < 0.0001 vs. VSOP) which demonstrates a mitigated hepatocellular injury. Overall AST release decreased to nearly zero at 168 h post-transplantation in all groups. Figure 3B shows the serum alanine-aminotransferase (GPT/ALT) release. ALT values peaked at 3 to 24 h post-transplantation, depending on the experimental group, and decreased over time until overall ALT release reached nearly zero at 168 h post-transplantation for all groups. Combination SK and VSOP treatment showed significantly reduced ALT levels in comparison to the control group at 3 h and 24 h post-transplantation (SK + VSOP 1837 IU ± 254.33, p = 0.0268; 691.6 IU ± 117.44, p = 0.0223), thus indicating decreased hepatocellular damage for recipients of liver grafts pre-treated via fibrinolysis and VSOP. Rats that had received livers treated via combined SK and VSOP also displayed significantly reduced ALT release at 3 h and 24 h post-transplantation in comparison to the groups treated via SK alone (p = 0.0096, p = 0.0347). At 3 h post-transplantation, the group which had obtained livers treated via VSOP only exhibited the most pronounced decrease in ALT release in comparison to the control group, even with respect to the group which had received livers treated via combined SK and VSOP (p = 0.0025). Figure 3C shows the lactate dehydrogenase (LDH) release. Overall LDH values were measured highest at 3 h post-transplantation and decreased over time until LDH release reached nearly zero at 168 h post-transplantation among all groups. Most importantly, animals that had received livers treated via combined SK and VSOP demonstrated significantly reduced LDH release in comparison to animals of the control group, this becoming especially evident at 24 h post-transplantation (SK + VSOP 2208.6 IU ± 1458.87, p = 0.0287), hereby indicating markedly decreased mitochondrial graft damage. At 3 h post-transplantation, animals that had received livers preserved via VSOP solely exhibited significantly lower LDH release in comparison to the animals of the control group (p = 0.0033), whereas no significant difference could be demonstrated for animals that had obtained organs treated with combined SK and VSOP. 2.4. Histopathological Analysis Table 1 shows the semi-quantitative histological scoring of livers of each group at 3 h and 24 h post-transplantation. At 3 h post-transplantation, the control group demonstrated an average general score of 1.8. The SK, as well as the SK + VSOP groups, demonstrated an average general score of 2.2, respectively. The group treated by VSOP amounted to an average general score of 2.8. Thus, at 3 h post-transplantation, the histological analysis did not indicate any hepatoprotective effects of named treatments. On the contrary, higher scores of the treated groups, namely the group which had received VSOP treatment, indicated a certain degree of parenchymal damage. At 24 h post-transplantation, the VSOP and SK + VSOP combination groups demonstrated markedly less damaged livers, reflected by average general histological scorings of 2 and 2.2, respectively, whereas the control group and the group treated via SK expressed hepatic damage, reflected by average general scores of 3.2 and 4, respectively. Generally speaking, the overall amount of completely unaffected tissue of full integrity was higher predominately among the groups which had received combination VSOP and SK treatment. Representative histological slides of the evaluated liver tissue of the different groups at different time points, as well as a graphical depiction of the general histological scores of the groups, are provided in Figure 4 (3 h/24 h: Control 1.8 ± 0.84, 3.2 ± 1.44; VSOP 2.8 ± 0.84, 3.8 ± 0.84; SK 2.2 ± 0.45, 4 ± 0.82; SK + VSOP 2.2 ± 0.84, 2.2 ± 0.45). 2.5. Serum TNF-α, IL-6, sICAM-1, HA and Immunohistochemical Analysis of RECA-1 Staining Figure 5A shows tumor necrosis factor-alpha (TNF-α) release. TNF-α values were low or merely detectable at 3 h post-transplantation within all groups. At 24 h post-transplantation, the SK group exhibited the second highest TNF-α release following the control group. Nevertheless, no significant differences could be demonstrated for either of the groups. Figure 5B shows interleukin-6 (IL-6) release. IL-6 release was highest within the control and VSOP groups at 3 h post-transplantation, whereas at 24 h post-transplantation, IL-6 release was overall lower among all groups, except for the SK group. Analogously to TNF-α release, no significant differences could be demonstrated for either of the groups in terms of IL-6 release. Figure 5C shows soluble intercellular adhesion molecule-1 (sICAM-1) release. The highest sICAM-1 values were detected for the SK group at 3 h post-transplantation, whereas the least release was detected for the SK and VSOP combination group, resulting in a significant difference (p = 0.0288) between both groups. At 24 h post-transplantation, sICAM-1 release was similar for all of the groups. Figure 5D shows hyaluronic acid (HA) release. The highest HA release was measured within the control group at both, 3 h and 24 h post-transplantation. HA release slightly increased within the SK group at 24 h post-transplantation. Overall, no significant differences could be demonstrated for either of the groups. In sum, cytokine release seemed elevated especially within the control and SK groups. In contrast, the VSOP solely, as well as the SK and VSOP combination groups exhibited an overall diminished cytokine release, thus indicating a reduced proinflammatory response, although significant differences were not detected throughout the experiment. Figure 5E shows a graphical depiction of the histological scoring after immunohistochemical rat endothelial cell antibody-1 (RECA-1) staining of the liver samples. No significant difference in scoring was observed for either of the groups. Observed histological changes were within the moderate range at 3 h post-transplantation for all groups, whereas at 24 h post-transplantation, overall changes were less severe within all of the groups. The SK and VSOP combination group exhibited the overall lowest scoring at 24 h post-transplantation, thus suggesting the least endothelial damage. 3. Discussion In this study, the effect of fibrinolysis combined with VSOP on the preservation of DCD livers was examined in the setting of liver transplantation in an isogenic rat model. Combining VSOP and fibrinolysis yielded beneficial effects, namely improved graft integrity with respect to microvascular perfusion and portal venous flow, as well as enzyme release. Histologically assessed graft damage was reduced in comparison to the other groups, although no improvements were achieved with respect to tissue oxygenation and portal venous pressure. Furthermore, this study highlights the timely shift of the treatment effect, as most outcomes started to become evident at 24 h post-transplantation and were mostly not measurable immediately after transplantation. The growing gap between the availability of donor allografts and the increasing number of patients registered for organ transplantation calls for new solutions to expand the available donor pool [18]. Due to improvements in organ preservation and retrieval techniques, DCD organ viability and transplantation outcomes for kidneys and livers have been continuously improved, which has led to the increasing use of DCD allografts in the battle against severe global organ shortage [19]. However, up until today, graft and patient survival for DCD LT remains inferior in comparison to donation after brain death (DBD) LT, thus emphasizing the continuous need for enhanced allograft treatment and organ preservation techniques to improve graft and patient outcomes [20]. The crucial factor for post-transplant viability lies in the period of warm ischemia, where sufficient perfusion before organ retrieval cannot be warranted. Further graft damage is then caused by the combination of subsequent cold ischemic storage and reperfusion injury following transplantation [21,22,23]. Livers retrieved from DCD are prone to severe perfusion deficits due to the formation of microthrombi in the setting of cardiac arrest, thus additionally hampering the appropriate equilibration of the graft microvasculature with the preservation solution [24,25]. The beneficial effects of temporary fibrinolysis, namely the improvement of graft microcirculation and oxygen supply have been demonstrated in multiple experimental animals, predominantly rat models, where a fibrinolytic preflush with SK resulted in a relevant and significant improvement of structural graft integrity as well as functional and metabolic graft recovery [25]. Analogously, the technique of VSOP has been shown to be an effective tool for resuscitating pre-damaged porcine livers after warm ischemic insult, thus allowing for successful transplantation after hypothermic storage [15]. In a rat transplantation model by Minor et al., preservation via VSOP lead to more homogenous aerobic conditions within the liver parenchyma and prevented the emergence of energy deficits during cold storage and therefore reduced parenchymal tissue injury upon reperfusion [26]. By combining VSOP and temporary fibrinolysis, Tolba et al. effectively preserved rat livers subjected to up to 90 min of warm ischemia, achieving results comparable to livers from heart-beating donors (HBD) with regards to enzyme release and bile production in an in vitro reperfusion model without LT [17]. Nevertheless, to the best of our knowledge, the in vivo effects of this combination therapy (SK + VSOP) on liver transplantation has never been demonstrated before. In this study, the previously reported results suggesting improved graft preservation via combined preservation strategies could be validated in a technically complex and elegant in vivo orthotopic LT model in rats: The combination of both strategies, VSOP and fibrinolysis, led to significant improvements in microvascular and portal venous flow, approximately doubling the amount of flow in comparison to the control groups and achieving the overall highest absolute flow values among all experimental groups. Although no significant differences in microperfusion velocity could be shown for either of the groups at 168 h post-transplantation, this is presumably due to a selection bias towards the animals who survived 168 h in the first place. Animals that had received grafts treated via combined VSOP and fibrinolysis exhibited markedly decreased AST and ALT enzyme release in comparison to the other animals, this becoming especially evident at the peak of reperfusion injury at 24 h post-transplantation. Furthermore, LDH release returned to near baseline at 24 h post-transplantation in the SK and VSOP group. These results altogether suggest improved blood circulation, enhanced graft integrity, and less hepatocellular injury. Histologically and immunohistochemically verified diminished parenchymatic and endothelial damage further supports the thesis of improved graft integrity after above mentioned combined treatment. The combination of VSOP and fibrinolysis thus represents a feasible and encouraging therapeutic approach for the transplantation of livers in the setting of DCD. Further research needs to be conducted in order to overcome the limitations of the animal model utilized and establish a transfer to clinically relevant liver storage and transplantation in human patients: Main drawbacks of the study, among others, include the differences of species and the respective differences in anatomy, molecular pathways, and operating procedure, the standardized cold-storage and warm ischemic times within the study, which does not reflect the clinical reality and the use of only male rats thus disrespecting gender diversity in transplant patients. Future research must therefore focus on further adaption to the current clinical setting and validation in other models and species to ultimately improve patient outcomes, as recipients of DCD livers still face poorer outcomes in comparison to recipients of DBD livers. 4. Materials and Methods 4.1. Animals All experiments were performed in adherence to institutional guidelines, the EU Directive 2010/63, and the German federal law on the protection of animals. The respective authorities approved the ethical proposal of the study (LANUV Recklinghausen ID: 84-02.04.2012-A017). All animals received humane care conforming to the principles of the “Guide for the Care and Use of Laboratory Animals” (8th Edition, National Institutes of Health (NIH) Publication, 2011, USA). The study was designed, executed, and reported in line with the “Animal Research: Reporting of In Vivo Experiments” (ARRIVE) guidelines [27,28]. Male Lewis rats (Strain name: LEW/OrlRj, Janvier Labs, Le Genest Saint Isle, France; n (total) = 136 (68 recipient and 68 donor animals, body weight range: 175–200 g) were used in this study. As defined by the “Federation for Laboratory Animal Science Associations” (FELASA, www.felasa.eu, accessed on 4 August 2021), all animals were housed under specific pathogen-free (SPF) conditions. The barrier environment was temperature- and humidity-controlled and subjected to a 12 h light and dark cycle. Water and standard pellet food supply for laboratory rats (V1534-000 diet, Sniff GmbH, Soest, Germany) was provided ad libitum at all times. 4.2. Experimental Groups and Design Liver transplantation was performed on a total number of 68 animals which was based on an a priori sample size calculation. Before the liver grafts were orthotopically transplanted into the recipient animal, they underwent 45 min of warm ischemia in situ to mimic a DCD situation and a subsequent cold storage for 18 h in 4 °C cold Histidine-tryptophan-ketoglutarate (HTK) solution supplemented with 20 mM of N-acetylcysteine (NAC, Hexal AG, Holzkirchen, Germany) in order to simulate a prolonged clinical transport and storage time of the graft. Animals were randomly allocated into four experimental groups. The control group (n = 20; 5 (3 h), 5 (24 h), 10 (168 h)) received livers subjected to cold storage in HTK without any additional treatment. Group 2 (n = 16; 5 (3 h), 5 (24 h), 6 (168 h)) received livers treated via VSOP during the time of cold storage: medical grade gaseous Oxygen was insufflated into the livers at a rate of 0.2 L/min with pressure being limited to a maximum of 18 mmHg via the suprahepatic vena cava (SHVC). Each liver lobe was punctured with two to three small pinpricks utilizing a customary acupuncture needle in order to let the gas emerge from the microvasculature. Group 3 (n = 16; 5 (3 h), 5 (24 h), 6 (168 h)) received livers that underwent a fibrinolytic preflush at room temperature (RT) via the portal vein containing 7500 international units (IU) of SK diluted in 20 mL of Sodium chloride (NaCl) immediately after warm ischemia and just before cold storage in HTK solution. Group 4 (n = 16; 5 (3 h), 5 (24 h), 6 (168 h)) received livers treated with both, VSOP and fibrinolytic preflush, as mentioned above. Recipient rats were sacrificed using lethal dose of anesthesia, organ retrieval, and subsequent exsanguination by blood drawing via direct puncture of the vena cava at 3, 24, and 168 h after reperfusion, respectively. All recipients survived 3 and 24 h after transplantation. In the control group, only 5 out of 10 recipients survived 168 h post-transplantation. In groups 2, 3, and 4, 5 out of 6 recipients survived 168 h. Based on our experience, regeneration of the transplanted liver grafts is usually almost complete after 168 h in all surviving animals independently of the treatment groups. Therefore, this subgroup was only used for the evaluation of recipient survival and further analyses were omitted in this particular subgroup. After surgery, all animals were inspected at least every 12 h by an experienced veterinary technician blinded for the experimental design. Their clinical condition was concomitantly evaluated via a humane-endpoints score sheet designed specifically for liver transplantation research based on the previous works of Morton and Griffiths and Kanzler et al. [29,30]. Animals were euthanized when humane endpoints were reached. An overview of the study design is provided within the study design flowchart in Figure 6. 4.3. Surgical Techniques All experiments were performed at the same time of the day in order to circumvent the possible influence of the circadian rhythm. Rats were allowed an acclimatization period of one week in our facility. All procedures were carried out according to the previously published recommendations on surgical procedures for rat models of LT by Nagai and Czigany et al. [28,31]. For a more detailed technical description, we, therefore, refer to the cited video manual and previous publications of our group. Inhalation anesthesia was applied utilizing 4 Vol% Isoflurane (Forane, Abbott GmbH, Wiesbaden, Germany) in 100% Oxygen at a flow rate of 4 L/min for induction and 1.5 Vol% Isoflurane in 100% Oxygen at a flow rate of 2 L/min for maintenance. A total of 0.1 mg/kg Buprenorphine (Temgesic, EssexPharma, Haar, Germany) was injected subcutaneously for analgesia. Rats were placed on a heating pad. All surgical procedures were carried out by the same surgeon. All surgical steps were performed under a surgical microscope. All intravenous injections were administered via the penile vein unless otherwise mentioned. 4.3.1. Donor Procedure Donor rats underwent midline laparotomy with bilateral extensions. The liver was freed from ligamentous attachments. The left phrenic vein was then ligated and cleaved. The paraoesophageal vessels were coagulated via bipolar forceps and subsequently dissected. After isolation of the infrahepatic vena cava (IHVC) from the retroperitoneal tissue, the right adrenal vein was ligated. According to the anatomical situation, either a 22- or 24-gauge catheter stent was proximally inserted into the bile duct via a small incision after ligation of the duct at the level of division of the gastroduodenal artery. For a detailed description of catheter stent preparation and insertion we again refer to the manuscript published by Nagai et al. [31]. The portal vein was then liberated from the pyloric and splenic veins. After ligating and cleaving the gastroduodenal artery, the common hepatic artery (CHA) was isolated from the pancreatic head to its root. The dorsal hepatic ligamentous attachments were then dissected. After approximately 3 min, the surgical field was re-exposed, cardiac arrest was induced via phrenotomy and the CHA was ligated proximal to its root. The IHVC and the portal vein were then both clamped. An 18-gauge catheter was then carefully inserted into the portal vein via a small incision and after 45 min of warm ischemia in situ, the liver was at first flushed with 20 mL of NaCl and subsequently perfused with 60 mL of 4 °C cold HTK solution supplemented with 20 mM of NAC at a hydrostatic pressure of 20 cm H2O in order to achieve an equilibration of the membrane potential. The diaphragm was then immediately cut, the intrathoracic vena cava transected and the anterior wall of the IHVC was surgically opened in order to allow the perfusion solution to be rinsed out of the liver. The IHVC was then clamped and the organ was then excised by dissection of the IHVC, the portal vein, the diaphragm, the remaining dorsal ligamental attachments, the right adrenal vein, and the CHA. For a detailed description of the dissection margins of the respective vessels, we refer to the manual previously published by Nagai et al. [31]. The retrieved graft was placed into 4 °C cold HTK solution supplemented with 20 mM of NAC and stored in a metal cup mounted in a plastic box filled with crushed ice. 4.3.2. Ex Vivo Graft Preparation after Retrieval Ex vivo graft preparations were all performed which included the placement of a 14-gauge cuff to the portal vein, the insertion of a 24-gauge catheter stent into the CHA, and the placement of a 12-gauge cuff to the IHVC. For a more detailed and depicted description of cuff and catheter stent preparation and placement, we refer to our manual published by Nagai et al. [31]. The liver was subsequently flushed with 5 mL of 4 °C cold HTK solution via the arterial catheter stent. In order to adequately prepare the SHVC for latter transplantation, two 7-0 polypropylene sutures were placed as stay sutures for later anastomosis at both corners of the vein, respectively. For two animals, placement of a 12-gauge cuff to the IHVC was not feasible. In these cases, 14-gauge cuffs were utilized for IHVC reconstruction. Organs were then stored in a cold-water bath containing HTK solution at a temperature of 4 °C for 18 h in order to simulate transportation time of the graft. 4.3.3. Recipient Procedure Rats underwent midline laparotomy. The liver was then freed from ligamentous attachments. The left phrenic vein was then ligated and cleaved. The IHVC was then carefully isolated from the retroperitoneal tissue. Next, the right adrenal vein was ligated and cut. The dorsal hepatic ligamentous attachments and the bile duct were dissected. Subsequently, the gastroduodenal artery and proper hepatic artery were ligated and dissected at 3 mm distance from their origination of the CHA. Remaining dorsal ligamentous attachments were cleaved. After intravenous injection of 2 mL lactated Ringer solution, the IHVC was clamped. The portal vein was then also clamped, as well as the SHVC together with the corresponding diaphragm. During anhepatic, portal, and arterial cross-clamping time, Isoflurane anesthesia was reduced to 0.4 Vol%. The native liver was subsequently excised and the graft was then placed orthotopically into the surgical site. Transplantation was commenced by anastomosis of the SHVC. Next, the portal vein was anastomosed via cuff technique. Clamping of the portal vein and SHVC was then released, leading to liver reperfusion. Isoflurane inhalation anesthesia was then again increased to 0.8 Vol%. The procedure was continued by reconstructing the CHA by catheter stent technique. The previously placed clamp was then released. The recipient IHVC was then reconstructed by cuff technique. After declamping, Isoflurane inhalation anesthesia was again increased to 1.0 Vol% and 0.5 mL of 8.4% NaCl-Bicarbonate solution together with 1 mL of lactated Ringer solution were administered intravenously. Afterward, the reconstruction of the bile duct by catheter stent technique was performed. Upon thorough completion of reconstruction, 1 mL of 5% Glucose solution was administered intravenously. Animals were supplied with a subcutaneous injection of Cefuroxime-NaCl (16 mg/kg) and Buprenorphine (0.1 mg/kg) diluted in a total of 1.5 mL of regular Saline solution immediately after surgery. Rats were then placed in a specialized intensive care unit cage with heated air (30–35 °C) and Oxygen supply for 1 h. Buprenorphine (0.1 mg/kg) was injected subcutaneously every 12 h for 3 d. Rats were then transferred to their normal cages and provided food and water ad libitum again. Animals were sacrificed after 3, 24, and 168 h after transplantation, respectively. For a more detailed description of suturing techniques and reconstruction techniques via cuff or catheter stent, we repeatedly refer to the manual published by our group [31]. Please note that in the present study the IHVC was reconstructed via cuff, rather than suture technique. 4.4. Anhepatic Time and Survival Estimate Anhepatic time (Figure 7A) within the recipient procedure was registered for each surgical case in order to ensure comparability of the performed measurements and investigated results between the different groups. No significant differences in anhepatic time between the groups could be demonstrated (mean anhepatic time in all groups: 17.75 min). A Kaplan-Meier estimate of recipient survival for better understanding and transparency in drop-out and survival for each group is provided in Figure 7B. 4.5. Evaluation of Microcirculatory Perfusion Hepatic microcirculatory perfusion was evaluated at sacrifice, just before the measurement of portal circulation and the collection of blood and tissue samples. Relative microcirculatory blood flow, blood flow velocity, tissue oxygen saturation, and relative hemoglobin amount were evaluated with O2C measurement technique via a corresponding surface probe in order to investigate graft perfusion. Measurements were performed at four standardized reference points of the liver surface and the mean value was calculated in order to characterize microcirculation, as previously described by Czigany et al. [10]. The captured signals were then transferred to an integrated computer, where software enabled the real-time display of the data with an accompanying visualization of the blood flow pattern (Integrated factory software, LEA Medizintechnik GmbH, Giessen, Germany). 4.6. Evaluation of Portal Circulation PVF and PVP were evaluated at the time of sacrifice immediately before collecting blood and tissue samples, as previously proposed by Yagi et al. [32]. PVF was therefore measured with a transit-time perivascular flowmeter (T403, Transonic Systems Inc., Ithaca (NY), USA) utilizing a transonic flow probe (MA2PSB, Transonic Systems Inc., Ithaca (NY), USA) and PVP was measured via direct puncture of the anterior wall of the portal vein with a 27-gauge needle (BD Microlance 3, Becton Dickinson GmbH, Heidelberg, Germany) and subsequent recording with a corresponding monitoring device (Sirecust 404, Siemens, Erlangen, Germany). PVF and PVP were investigated to assess gross vascular perfusion. 4.7. Serum GOT/AST, GPT/ALT, and LDH Measurements Blood samples were collected from the IHVC by direct puncture with a customary 20-gauge needle during sacrifice. After centrifugation at RT for 10 min at 2500 rpm, serum levels of AST and ALT as markers for hepatocellular injury were measured via an automated analyzer (Vitros 250, Johnson and Johnson, Neuss, Germany). Furthermore, serum levels of lactate dehydrogenase (LDH) as an index for mitochondrial graft damage and cell death were measured analogously. 4.8. Histopathological Analysis Retrieved livers were macroscopically examined and immediately fixed in 10% neutral buffered formalin and consecutively embedded in paraffin. Microtome slides of 4 μm thickness were cut and subsequently stained via hematoxylin and eosin (HE) dye and then investigated in a blinded fashion by a senior pathologist. The histological examiner was blinded for the experimental set-up, respective treatments, and animal grouping. Livers of animals sacrificed at 3 h and 24 h post-transplantation were investigated. Ten randomly chosen and non-overlapping fields at a 400-fold magnification were chosen in order to be evaluated via light microscopy (Leica Digital Microscope 2500, Leica Microsystems GmbH, Wetzlar, Germany). A semi-quantitative scoring system based upon a formerly published scoring assessment by Streidl et al. in 2021 was employed for the analysis of hepatic injury [33]: Histological signs of injury, namely hemorrhage, congestion, inflammatory cell infiltration, necrosis, degenerative changes, biliary epithelial proliferation, and Kupffer cell activation were assessed and graded on a scale ranging from 1–5, whereas a score of 1 represented either no histological changes at all or overall negligible lesions, a score of 2 = rather mild lesions, a score of 3 = moderate lesions, a score of 4 = moderate to severe lesions and a score of 5 resembled severe lesions. A general score for each slide was then calculated and the general scores for each respective group were then averaged. The approximate amount of tissue not affected at all within each section was also assessed. 4.9. ELISA Measurements of TNF-α, IL-6, sICAM-1, HA, and Immunohistochemical Analysis of RECA-1 Staining Commercially available rat enzyme-linked immunoassay (ELISA) kits (R and D Systems, Minneapolis, MN, USA) were utilized and carried out according to the manufacturer’s instructions: Serum samples were, first of all, stored at −80 °C and subsequently used for TNF-α, IL-6, sICAM-1, and HA assessment. In addition, liver samples at 3 h and 24 h post-transplantation were immunohistochemically stained with RECA-1 according to the manufacturer’s instructions and then forwarded to the respective pathologist for further examination. All slides were investigated in a blinded fashion by a senior pathologist. Six vessels were randomly chosen per slide and further investigated at a 400-fold magnification via upright light microscopy (Leica Digital Microscope 2500). A semi-quantitative scoring system was employed for the analysis of vascular integrity, whereas a score of 0 represented either no histological changes at all or overall negligible lesions, corresponding to thin and intact endothelium without any disruption and normal lining, a score of 1 represented rather mild lesions with slightly thicker endothelium and only few disruptions, a score of 2 represented moderate lesions and a score of 3 represented rather severe lesions. A score for each slide was then calculated and the scores for each respective group were then averaged. 4.10. Statistical Analysis All results are expressed as mean values ± standard error of the mean (SEM) or respectively, standard deviations (SD), for each group. Two-way analysis of variance (ANOVA) and Tukey post hoc tests were performed to analyze changes in time-dependent parameters and differences between each group at each time point. Kaplan-Meier survival estimate and the log-rank test were utilized to analyze and depict recipient survival. Differences were considered significant when p < 0.05. Data analysis and plotting were performed using the GraphPad Prism 8 software package (GraphPad Prism 8 software package, GraphPad Software Inc., San Diego, CA, USA). Acknowledgments The authors would like to thank Mareike Schulz for her skillful assistance. Author Contributions Conceptualization, R.H.T., K.N., S.Y. and J.J.; methodology, R.H.T., J.J., P.P., K.N., S.Y. and M.A.; formal analysis, N.K. and M.A.; investigation, J.J., P.P., M.A., K.N., S.Y. and Z.C.; resources, R.H.T.; data curation, N.K., J.J. and Z.C.; writing—original draft preparation, N.K.; writing—review and editing, Z.C. and R.H.T.; visualization, Z.C.; supervision, R.H.T.; project administration, R.H.T. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The study was conducted according to the institutional guidelines (Institute for Laboratory Animal Science and Experimental Surgery, Medical Faculty, RWTH-Aachen University) and the German federal law for the protection of animals. The ethical proposal of the study was approved by the responsible authorities (LANUV-Recklinghausen, Germany, ID: AZ-LANUV: 84-02.04.2012-A017). Conflicts of Interest The authors declare no conflict of interest. Figure 1 (A) Microvascular flow of the liver grafts at 3, 24 and 168 h post-transplantation in arbitrary units (au, see text). The control group exhibited the least microvascular flow. Significant differences could be demonstrated particularly for the groups treated either with Streptokinase (SK) and Venous Systemic Oxygen Persufflation (VSOP) or only VSOP at sacrifice 24 h post-transplantation (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Standard error of the mean (SEM). (B) Microvascular velocity of the liver grafts at 3, 24 and 168 h post-transplantation in arbitrary units (au, see text). Significant differences could be demonstrated particularly for the groups treated either with Streptokinase (SK) and Venous Systemic Oxygen Persufflation (VSOP) or only VSOP at sacrifice 24 h post-transplantation (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Standard error of the mean (SEM). (C) Tissue Oxygen saturation of the liver grafts at 3, 24 and 168 h post-transplantation in percent (%, see text). There was no significant difference (* = p < 0.05) in tissue Oxygen saturation between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). (D) Relative intravascular amount of hemoglobin of the liver grafts at 3, 24 and 168 h post-transplantation in arbitrary units (au, see text). There was no significant difference (* = p < 0.05) in relative intravascular amount of hemoglobin between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). Figure 2 (A) Portal venous flow (PVF) of the liver grafts at 3, 24 and 168 h post-transplantation in milliliter/minute (mL/min, see text). The control group exhibited the least PVF. Significant differences could be demonstrated particularly for the groups treated with Streptokinase (SK) and Venous Systemic Oxygen Persufflation (VSOP) at sacrifice 3 h and 24 h post-transplantation, respectively (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Standard error of the mean (SEM). (B) Portal venous pressure (PVP) of the liver grafts at 3, 24 and 168 h post-transplantation in millimeter of Mercury (mmHg, see text). There was no significant difference (* = p < 0.05) in PVP between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). Figure 3 (A) Glutamic oxaloacetic transaminase (GOT/AST) release of the liver grafts at 3, 24 and 168 h post-transplantation in international units (IU, see text). AST release peaked at sacrifice 24 h post-transplantation. Significant differences could be demonstrated particularly for the group treated with Streptokinase (SK) and Venous Systemic Oxygen Persufflation (VSOP) at sacrifice 24 h post-transplantation, as this group exhibited unanticipated low AST release in comparison to the other groups at named timepoint. (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Standard error of the mean (SEM). (B) Glutamic pyruvic transaminase (GPT/ALT) release of the liver grafts at 3, 24 and 168 h post-transplantation in international units (IU, see text). ALT release peaked at sacrifice 3 h post-transplantation. Significant differences could be demonstrated particularly for the groups treated either with Streptokinase (SK) and Venous Systemic Oxygen Persufflation (VSOP) or only VSOP at sacrifice 3 h post-transplantation, as these groups exhibited lower ALT release in comparison to the other groups at named timepoint. (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Standard error of the mean (SEM). (C) Lactate dehydrogenase (LDH) release of the liver grafts at 3, 24 and 168 h post-transplantation in international units (IU, see text). LDH release peaked at sacrifice 3 h post-transplantation. Significant differences could be demonstrated particularly for the group treated with Venous Systemic Oxygen Persufflation (VSOP) at sacrifice 3 h post-transplantation, as this group exhibited lower LDH release in comparison to the other groups at named timepoint. Significant differences could also be demonstrated for the group treated with Streptokinase (SK) and VSOP at sacrifice 24 h post-transplantation, as this group exhibited unanticipated low LDH release in comparison to the other groups at named timepoint. (* = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Standard error of the mean (SEM). Figure 4 (A) Histopathologic example of the control group at 3 h post-transplantation. Congestion (arrow). Macrovesicular fatty vacuolization (star). (B) Representative histopathology of the Streptokinase (SK) group at 3 h post-transplantation. Micro vesicular fatty vacuolization (triangle). (C) Example of histopathology of the Venous Systemic Oxygen Persufflation (VSOP) group at 3 h post-transplantation. Congestion (arrow). Micro vesicular fatty vacuolization (triangle). (D) Exemplary histopathology of the SK and VSOP group at 3 h post-transplantation. Congestion (arrow). Macrovesicular fatty vacuolization (star). (E) Exemplary histopathology of the control group at 24 h post-transplantation. Congestion (arrow). Macrovesicular fatty vacuolization (star). (F) Exemplary histopathology of the SK group at 24 h post-transplantation. Micro vesicular fatty vacuolization (triangle). Confluent necrotic areas (square). Mononuclear cell infiltration (dot). (G) Exemplary histopathology of the VSOP group at 24 h post-transplantation. Confluent necrotic areas (square). Overall less parenchymatic damage. (H) Exemplary histopathology of the SK and VSOP group at 24 h post-transplantation. Micro vesicular fatty vacuolization (triangle). Overall less parenchymatic damage. (I) Histological general score of the liver grafts at 3 and 24 h post-transplantation. At 24 h post-transplantation, the SK and VSOP group exhibited an overall lower score than all of the other groups, thus indicating less parenchymatic damage, although no significant difference could be detected in comparison to the control group. (* = p < 0.05). Standard error of the mean (SEM). Figure 5 (A) Tumor necrosis factor alpha (TNF-α) release of the liver grafts at 3 and 24 h post-transplantation in picogram/milliliter (pg/mL, see text). There was no significant difference (* = p < 0.05) in between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). (B) Interleukin-6 (IL-6) release of the liver grafts at 3 and 24 h post-transplantation in picogram/milliliter (pg/mL, see text). There was no significant difference (* = p < 0.05) in between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). (C) Soluble intercellular adhesion molecule-1 (sICAM-1) release of the liver grafts at 3 and 24 h post-transplantation in picogram/milliliter (pg/mL, see text). At 3 h post-transplantation, the Streptokinase (SK) group exhibited an overall higher release of sICAM-1 than the other groups, thus resulting in a significant difference in comparison to the SK and Venous Systemic Oxygen Persufflation (VSOP) group. (* = p < 0.05). Standard error of the mean (SEM). (D) Hyaluronic acid (HA) release of the liver grafts at 3 and 24 h post-transplantation in nanogram/milliliter (ng/mL, see text). There was no significant difference (* = p < 0.05) in between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). (E) Rat endothelial cell antibody-1 (RECA-1) immunohistochemical staining scoring of the liver grafts at 3 and 24 h post-transplantation. There was no significant difference (* = p < 0.05) in between the four groups at any timepoint of sacrifice after transplantation. Standard error of the mean (SEM). Figure 6 Flowchart of the study design. Created with BioRender.com. Figure 7 (A) Anhepatic time of the liver grafts as part of the recipient procedure in minutes (min, see text). There was no significant difference in anhepatic time between the four groups at any timepoint of sacrifice after transplantation. Mean +/- Standard error of the mean (SEM). (B) Kaplan-Meier estimate of recipient survival for each group under consideration of the three investigated timepoints at 3 h, 24 h and 168 h post-transplantation. Streptokinase (SK) and Venous Systemic Oxygen Persufflation (VSOP) combination group exhibited increased survival with the least drop-outs, whereas the control group displayed lower survival especially within the first 40 h post-transplantation. The log-rank test showed no significant differences (p = 0.27). ijms-23-05272-t001_Table 1 Table 1 Semi-quantitative histological scoring of livers of each group at 3 h (top half) and 24 h (bottom half) post-transplantation. Approx. areas without lesions = Approximate percentage of areas without significant lesions in the section. Scoring ranging from 1–5, whereas a score of 1 represents either no histological changes at all or overall negligible lesions, a score of 2 = rather mild lesions, a score of 3 = moderate lesions, a score of 4 = moderate to markedly severe lesions and a score of 5 = severe lesions. General scoring is provided on the far right. Averages of the general scores and respective standard deviations (SD) are provided beneath each group package, as indicated above. Group Approx. Areas without Lesions Hemorrhage Congestion Inflammatory Cell Infiltration Necrosis Degenerative Changes Biliary Epithelial Proliferation Sinu. Endothelial/ Kupffer Cell Activation General Score Granulocytes Macrophages Mononuclear Cells (Other) Fatty Degeneration Granularity of Cytoplasm 3 h Control 50 1 4 1 1 1 1 1 2 1 1 2 70 1 3 1 1 1 1 1 1 1 1 2 95 1 2 1 1 1 1 1 1 1 1 1 85 1 2 1 1 1 1 1 2 1 1 1 60 1 4 1 1 1 2 3 3 1 1 3 Mean/SD 72 +/− 18 1 3 +/− 1 1 1 1 1.2 +/− 0.44 1.4 +/− 0.89 1.8 +/− 0.84 1 1 1.8 +/− 0.84 3 h SK 40 1 3 1 1 1 1 2 3 1 3 2 60 1 3 1 1 1 1 2 4 1 1 3 75 1 3 1 1 1 1 1 3 1 1 2 60 1 2 1 1 1 1 1 2 1 1 2 75 1 2 1 1 1 1 2 3 1 1 2 Mean/SD 62 +/− 14 1 2.6 +/− 0.55 1 1 1 1 1.6 +/− 0.55 3 +/− 0.71 1 1.4 +/− 0.89 2.2 +/− 0.45 SK + VSOP 40 2 4 2 1 1 2 2 2 1 1 3 3 h 70 1 3 1 1 1 2 2 2 1 1 3 85 1 2 1 1 1 1 2 2 1 1 2 80 1 3 1 1 1 1 3 2 1 1 2 90 1 2 1 1 1 1 2 2 1 1 1 Mean/SD 73 +/− 19 1.2 +/− 0.45 2.8 +/− 0.84 1.2 +/− 0.45 1 1 1.4 +/− 0.55 2.2 +/− 0.45 2 1 1 2.2 +/− 0.84 VSOP 90 1 3 1 1 1 1 2 2 1 1 2 3 h 60 1 3 1 1 1 2 2 2 1 1 3 60 2 4 3 1 1 4 2 3 1 1 4 60 1 3 1 1 1 3 2 3 1 2 3 85 1 2 1 1 1 1 2 2 1 1 2 Mean/SD 71 +/− 15 1.2 +/− 0.45 3 +/− 0.71 1.4 +/− 0.89 1 1 2.2 +/− 1.30 2 2.4 +/− 0.55 1 1.2 +/− 0.45 2.8 +/− 0.84 Control 95 1 2 1 1 1 1 2 2 1 2 1 24 h 60 1 4 3 2 1 3 3 3 2 3 4 60 1 3 2 2 1 3 2 3 3 2 3 55 2 4 5 3 2 4 3 3 3 2 5 75 2 3 2 2 2 3 2 2 3 3 3 Mean/SD 69 +/− 16 1.4 +/− 0.55 3.2 +/− 0.84 2.6 +/− 1.52 2 +/− 0.71 1.4 +/− 0.55 2.8 +/− 1.10 2.4 +/− 0.55 2.6 +/− 0.55 2.4 +/− 0.90 2.4 +/− 0.55 3.2 +/− 1.44 SK 40 2 4 3 2 2 5 3 3 3 2 5 24 h 60 2 4 4 2 2 4 3 3 3 2 4 85 1 3 3 2 2 3 2 3 3 2 3 65 1 4 3 2 2 4 2 3 3 2 4 Mean/SD 63 +/− 18.48 1.5 +/− 0.58 3.75 +/− 0.5 3.25 +/− 0.5 2 2 4 +/− 0.82 2.5 +/− 0.58 3 3 2 4 +/− 0.82 SK + VSOP 90 1 2 1 1 1 2 2 2 2 1 2 24 h 90 1 2 2 1 1 2 2 2 2 2 2 85 1 2 4 2 1 4 2 2 2 2 3 90 1 2 2 2 1 2 2 2 2 2 2 95 1 2 2 2 1 2 2 2 2 2 2 Mean/SD 90 +/− 3.54 1 2 2.2 +/− 1.1 1.6 +/− 0.55 1 2.4 +/− 0.9 2 2 2 1.8 +/− 0.45 2.2 +/− 0.45 VSOP 75 1 3 3 2 1 4 2 3 2 2 4 24 h 40 2 4 3 2 1 4 2 3 2 2 5 60 2 4 3 2 1 4 3 3 2 2 4 85 1 3 2 2 1 3 2 3 2 2 3 90 2 3 2 2 1 3 2 3 2 2 3 Mean/SD 70 +/− 20.31 1.6 +/− 0.55 3.4 +/− 0.55 2.6 +/− 0.55 2 1 3.6 +/− 0.55 2.2 +/− 0.45 3 2 2 3.8 +/− 0.84 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 10.3390/ijerph19095363 ijerph-19-05363 Systematic Review Effect of Rural Clinical Placements on Intention to Practice and Employment in Rural Australia: A Systematic Review https://orcid.org/0000-0003-1565-4130 Seaman Claire Ellen * https://orcid.org/0000-0002-7291-6419 Green Elyce https://orcid.org/0000-0001-5932-741X Freire Kate Rasiah Rohan Academic Editor Lawn Sharon Academic Editor Three Rivers Department of Rural Health, Charles Sturt University, Wagga Wagga, NSW 2678, Australia; elgreen@csu.edu.au (E.G.); kfreire@csu.edu.au (K.F.) * Correspondence: cseaman@csu.edu.au 28 4 2022 5 2022 19 9 536317 2 2022 22 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Background: Supporting the provision of clinical placement (CP) experiences in rural areas is a strategy used worldwide to promote the rural health workforce. While there is international evidence for this intervention in medicine, there is limited understanding of the influence of rural CP for nursing, midwifery, allied health, and dentistry health professions in Australia, which have received substantial federal investment. This review examined the relationship between rural CP and non-medicine health students’ future rural practice intentions and workforce outcomes. Methods: Four databases were systematically searched; papers were screened using defined criteria and appraised using the mixed-methods appraisal tool (MMAT). Findings were synthesized using a critical narrative approach. Results: The methodological quality of the 29 eligible studies (13 quantitative non-randomized, 10 mixed method, 4 qualitative, 2 quantitative description) was appraised. Ten high-quality studies were identified. The review found that positive CP experiences may influence intention to practice rurally amongst undecided students and serve as a reinforcing experience for those students already interested in rural practice. There were mixed findings regarding the influence of CP length. The review also found that there is currently only evidence for the short-term effects of CP on students’ future practice outcomes in rural areas with focus thus far on early practice outcomes. Conclusions: Those looking to use rural CP to promote the rural health workforce should focus on supporting the quality of a large number of CP experiences that are undertaken in rural areas, as there are currently differing findings on the role of rural CP length. Future studies of rural CP should consider greater use of social and educational theories to guide them. higher education policy health education clinical placements rural training Australian GovernmentThe authors of this review are employees of Three Rivers Department of Rural Health, funded by the Australian Government under the Rural Health Multidisciplinary Training (RHMT) Program. ==== Body pmc1. Introduction Health profession student clinical placement (CP) opportunities in rural communities are commonly integrated into tertiary education curricula to provide students with exposure to rural health skills and practice opportunities [1]. Rural CP experiences are also part of a global effort to recruit and retain health staff in rural locations [1] and are a central component of rural workforce strategy in Australia towards equitable health service delivery for rural people [2]. There is some evidence of this intervention positively contributing to the recruitment of medical students to the rural health workforce [3]; however, the WHO has previously highlighted the limited evidence worldwide of CP influence on rural practice interest or workforce outcomes for other health professionals [1]. More recently, a review of national policies to address rural workforce maldistribution in select OECD countries found Australia has produced the best evidence for recruitment impact from rural CP, likely due to national funding of rural CP [4], described below. This systematic review critically appraises the current body of evidence focused on the relationship between Australian rural CP undertaken by health professions students in disciplines of nursing, midwifery, dentistry, oral health, and allied health (all other tertiary-level health professions degrees), and their rural workforce intentions or employment. The terms ‘rurality’ and ‘CP’ both arouse different understandings and expectations, including across disciplines and nations. This multidisciplinary review therefore focuses only on rural CP undertaken in Australia as a leading national context for evidence of rural CP effects. For this study, CPs are defined as set blocks of time during tertiary study where health students experience training in a clinical, health, or other organisational settings (i.e., schools, community centres, government agencies) for the purpose of work-integrated learning. This excludes community immersions that do not involve practice-based learning. CP supports authentic engagement with industry to develop the occupational skills required as students transition towards becoming accredited practitioners [5,6]. CP experience also enable students to engage in a multitude of different practice communities and therefore to discover and assess possibilities for their future professional practice aligned with their developing professional identity [5,6]. Rural CPs are expected to support students to positively discover and assess the possibilities of rural health practice through meaningful engagement with associated communities [7]. In Australia, the federal government has supported rural CPs for nursing and midwifery, allied health, and dentistry for more than twenty years via University Departments of Rural Health (UDRHs) [2]. Since 2016, UDRHs have been distinctly funded under the same Rural Health Multidisciplinary Training (RHMT) Program as Rural Clinical Schools (RCS) which are specific to medicine, with investment in the RHMT Program costing approximately AUD 200 million per annum [7]. Key aims of UDRHs and RCSs include enhancing the future rural health workforce through providing health students with positive, longer-term CP experiences, as well as supporting the recruitment of students from rural areas into health professions degrees [7]. These aims are supported by substantial international and Australian evidence that students’ prior rural living is a predictor of rural workforce intention and uptake [1,3,8]. There is also some evidence from medicine that longer-term rural CP can increase the likelihood of future rural practice [3]. Despite the ongoing investment in UDRHs, the effectiveness of rural CP for attracting non-medicine health professionals to rural practice is not well understood [7,8]. Finally, there are calls for enhanced, meaningful engagement with educational and sociological theories in health education research, including studies of work-integrated learning experiences, to better inform health education strategies [9,10,11]. In rural health, health professions education, and workforce research, there is increasing use of place-based understandings and situated learning frameworks to critically interrogate the socio-political contexts of interventions and inform best practice [11,12,13,14]. The funding allocated to multidisciplinary rural CP in Australia suggests this could be a site for innovative pedagogical practices. Such innovations could inform rural workforce recruitment efforts in other nations, as well as broader health education practices through testing and developing relevant theories. It was therefore pertinent in this review to also assess the existing use of theory in studies of rural CP for allied health, dentistry, nursing, and midwifery students. The aim of this systematic review was to examine the research on non-medicine CP and rural practice intentions and rural workforce outcomes. The following research questions were used to guide the review:What is the influence of rural CP on intention to practice rurally? What is the influence of rural CP on future rural practice? What theoretical frameworks are used in research to explore these relationships? 2. Materials and Methods 2.1. Search Strategy A preliminary search of PROSPERO, the Cochrane Database of Systematic Reviews, and the JBI Database of Systematic Reviews and Implementation Reports was conducted, and no current or in-progress systematic reviews on the topic were identified. The review followed the JBI methodology for systematic reviews and the protocol was registered with PROSPERO (CRD42021235448). Database searches were conducted using EBSCOhost (health) (inclusive of CINAHL Plus), PubMed, Web of Science, and Scopus with combinations of keywords related to rural health CP and the outcomes of recruitment, retention, intention, workforce, employment, or career. An example of keyword combinations and database searches are shown in Appendix A. Citation tracking of included studies was also used to identify potential studies for inclusion in the review. Searches were first conducted in January 2020 and were repeated in January 2021 and April 2022. 2.2. Article Selection All identified citations were uploaded into EndNote X8 and the duplicates removed. Titles and abstracts were screened by two independent reviewers against the inclusion/exclusion criteria set for this review (Table 1). Disagreements were resolved by a third reviewer. Full texts of potentially relevant articles were then assessed against the inclusion criteria by two independent reviewers. Disagreements were again resolved by a third reviewer. Reasons for exclusion of articles at full text were recorded using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) as a guide [15]. For this review, the definition of rurality was kept broad as there is a variety of approaches to rurality used in studies in this area [16]. Where information on the location of the rural CP is available, ‘rural’ is defined as outside of Australia’s ‘major city areas’ per the Australian Bureau of Statistics’ remoteness area classification [17]. Otherwise, the study meets the inclusion criterion if the study has defined placements as ‘rural’ inclusive of ‘regional’ and ‘remote’ terminology. 2.3. Assessment of Methodological Quality All included studies were critically appraised by two independent reviewers for methodological quality using a modified Mixed Methods Appraisal Tool (MMAT) [18]. The MMAT was chosen to assess methodological quality because it can facilitate examination of different methods. For this review, studies were allocated to a study type based on the approach described in-text. With this approach, one alteration to the MMAT was made with the fifth quality criterion for non-randomised quantitative studies—‘During the study period, was the intervention administered (or exposure occurred) as intended?’ replaced by the descriptive survey study criterion, ‘Is the statistical analysis appropriate to answer the research question?’. This substitution was made given the prevalence of survey-based studies, as well as the variation in ‘the intervention’ under study—the characteristics and contexts of multidisciplinary rural CP [7,8]. The reviewers agreed that appropriate statistical analysis is therefore a more relevant indicator of study quality than attempting to assess whether the intervention was administered as intended. Reviewer-agreed definitions of low-, medium-, and high-quality papers (meets 0–2, 3, and 4–5 criteria, respectively) were applied to the screening. Disagreements on researchers’ ratings were resolved by discussion amongst the three authors. In accordance with the MMAT, no article was excluded from the review based upon its score; however, review results place greater emphasis on the findings of studies from articles that were rated as having high methodological quality [18]. 2.4. Data Extraction and Synthesis Data from the included studies were extracted into a spreadsheet, including reported study design, research question or aim, student discipline, placement length, setting, rural origin, rural origin accounted for in analysis, analysis, rural practice intention outcomes, rural employment outcomes, and results. Two reviewers extracted data independently which was then combined into one dataset by a reviewer, with discrepancies resolved by discussion among the reviewers. Due to the heterogeneous nature of the study designs, data were synthesised by a critical narrative summary guided by the results of the quality screening. 3. Results 3.1. Identification and Selection of Articles The results of the search and the article inclusion process are reported in Figure 1. Database searching resulted in 514 potential papers once duplicates were removed, and 233 papers were excluded through abstract screening. Full-text retrieval and full-text screening resulted in the exclusion of a further 252 papers. Twenty-nine articles were included in the review. 3.2. Characteristics of Included Studies Seven studies were published between 2000 and 2009, thirteen from 2010 to 2019, and nine from 2020 to 2021. The studies investigated students’ CP across several health professions, listed in Table 2. The most common rural CP duration was one month (n = 7, 24%). Where one CP experience was examined, CP duration ranged from one week to one year. Multiple CP experiences were examined cumulatively as total weeks or total number of CP. Five studies (17%) did not report the duration of the CP under examination. 3.3. Methodological Quality The included reports used four different study designs: quantitative non-randomised (n = 13), mixed method (n = 10), qualitative (n = 4), and quantitative descriptive (n = 2) (Table 3). Nearly half of the studies were assessed as low quality (n = 14, 48%), with five (17%) assessed as medium quality, and ten (34%) as high-quality studies. All four qualitative studies were rated as low (n = 1) to medium quality (n = 3). The 13 studies that employed a non-randomised quantitative design were largely rated as high quality (low n = 3, medium n = 1, high n = 9). The 10 studies that used a mixed method study design were evaluated as predominately low quality (low n = 8, medium n = 1, high n = 1). Five papers were found to have met the criterion regarding the rationale for using mixed methods design; however, no papers met the criterion adhering to the quality criteria of each tradition of the methods. There was a higher rate of medium- or high-quality papers that examined rural practice (n = 10, 63%) than rural intention (n = 6, 40%) outcomes. In conclusion, critical appraisal found the overall methodological quality of the included papers was mixed. 3.4. Review Findings A summary of the review findings is found in Table 4. 3.4.1. Rural CP Influence on Intentions and Attitudes towards Rural Practice Fifteen studies examined the relationship between students’ rural CP experiences and their intentions and attitudes towards future rural practice, including two that also examined employment. All six medium–high-quality papers considered the role of rural background or existing rural interest on rural practice intentions or attitudes [25,36,42,44,45,47]. Five of these studies relied on cross-sectional post-placement evaluation, with only one [42] using longitudinal, pre-post evaluation. Synthesis of the studies showed mixed findings on the relationship between rural CP and interest in future rural practice. There was no significant difference in practice intention associated with different placement durations in one multidisciplinary study [25], while another found all placement lengths were associated with encouraging consideration of rural practice [47]. Study results point to the importance of prior rural living, experience, or interest when assessing placement impact on intentions or attitudes towards rural practice. Among pharmacy students, rural CPs were significantly associated with increased likelihood of intending to practice rurally in students’ final year relative to their first year [42]; however, this only approached significance when controlling for the positive effect of rural background [42] (p. 308). A study of allied health students [47] found that rural CPs were only significantly associated with increased intention to practice rurally among city-background students. However, the survey item asked whether the rural CP made students ‘reconsider their future’ towards rural practice, and there was already high pre-placement interest in rural practice among rural background students. Similar findings were reported in studies of nursing students [26] and dentistry students [20,29,30]. Johnson and Blinkhorn [29,30] also found a rural CP experience positively encouraged ‘undecided’ dental students towards interest in rural practice. Somewhat distinctly, a multidisciplinary study of whether the CP ‘encouraged’ consideration of rural practice found that only placement quality (satisfaction with educational resources and CP overall) and prior interest in working rurally had a significant and positive association, while rural background and differing placement lengths did not [25]. A study where intention change was qualitatively examined distinctly for city and rural background students also found evidence that a rural CP more positively affected the intention of city-background students [45]. In more remote settings, a study found that quality rural CP can highlight the professional benefits of rural practice and develop students’ cultural capabilities working with First Nations peoples to encourage work in this area [44,45]. Despite this, there was low interest in long-term remote practice, attributed to feelings of social isolation [44]. Finally, rural CPs may also serve to discourage students from rural practice. For instance, a study of final year nurses found rural CPs gave students a better understanding of what their graduate year might look like in a rural hospital, including being dissuaded from rural practice by demanding workloads and lack of graduate support [37]. Overall, this indicates that positive CP experiences may influence intention most among those for whom rural practice has not previously been a major consideration, as those from a rural background or already highly interested in rural practice may seek out rural placement opportunities. Concrete findings are limited by variation of intention measures across studies, as well as the very limited statistical or theory-informed consideration of item reliability and validity. 3.4.2. Rural CP Influence on Rural Practice Sixteen papers examined the effect of students’ rural CP experiences on their subsequent working location, including two [19,44] that also examined rural intention. Ten of the papers were rated as medium or high quality, eight of which considered the effect of rural background on rural practice [24,28,33,38,39,40,41,44]. This review found some evidence that quality placements are positively associated with early practice outcomes; however, evidence for long-term effects was limited and findings more broadly were limited by the self-selection of students into rural training experiences. It is not possible to reach a clear conclusion from this review on the effect of rural CP length on rural practice. Contrary to policy expectations, Playford et al. (2006) [38] found longer rural CP duration (more than one month) to be negatively associated with rural practice compared to CP of less than one month, controlling for rural background. Volunteering for the CP and perceived excellence in professional development gained through the CP was also significantly and positively associated with rural practice. The authors suggest that shorter lengths of CP may contribute to metropolitan and other travelling students’ positive experience through reducing financial burden and social dislocation when in accommodation away from paid work and family and friends. Another study attempted to control for this by asking questions about barriers to undertaking CP, although it is not clear whether these were rural CP-specific [46]. In this study, propensity score matched analysis of survey data from multidisciplinary health professionals 1–14 years after graduation found that professionals in the highest quintile of cumulative placement length worked significantly more hours rurally [46]. Quality-rated studies have also found some evidence that rural CP length is positively associated with future rural practice, using data from the recently developed, multi-institutional Nursing and Allied Health Graduate Outcome Tracking (NAHGOT) study [48]. NAHGOT is a longitudinal study that is currently in its early stages, but which aims to comprehensively track individuals’ health professional education, CP, and practice journey through linking institutional and professional registration data [48]. Using university data, two NAHGOT papers each computed a polytomous variable of total time spent on rural CP across students’ degree [28,41]. Both found that likelihood of rural practice increased the time a student spent in rural CP; however, this was non-significant in multivariable analysis of the one that examined only medical radiation students, potentially due to small sample sizes [28]. The other study was multidisciplinary and found that students with less than 20 cumulative days of rural CP were not any more likely to be practicing rurally from those who did no rural CP, while 21–40 days of rural CP had double the likelihood and more than 40 days had 4.5 times the likelihood [41]. A third study similarly used cumulative CP data to compute three variables: total rural CP, total metro CP, and a ratio of rural-to-metro CP, with the aim of simultaneously examining the effect of metropolitan exposure [40]. Broadly, the study found more rural CP is positively associated with rural practice, while metro CP is negative [40]. Few studies examined the role of prior rural interest when describing the relationship of rural CP to practice; however, the potential confounding role of self-selection into rural CP or associated surveys has been noted as a limitation in this literature [24], including in a study using NAHGOT data [28]. This is exemplified in results from a multidisciplinary study from Campbell and Moore (2021) [24]. Health professionals who indicated they were ‘already committed’ when asked about rural CP impact on their rural practice had the highest rates of rural practice. They also had the highest mean intention to be in rural work in five years’ time [24]. The evidence for rural CP affecting long-term rural practice is limited and, at most, suggestive of an indirect effect through early-career rural practice. In a follow-up study, Playford et al. (2020) [39] found that having previously lived rurally was no longer significantly associated with rural practice 15–17 years after graduation. Instead, the only significantly positive variable was the rural location of the respondents’ first job after graduation. Unlike in Playford et al. (2006) [38], multivariate analyses in this later study found only rural background to be significant, with the effect of ‘excellent’ placements only approaching significance at the 5% alpha level [39]. However, it should be noted that the follow-up study also included an indicator of whether the student was considering future rural practice at the end of their graduate year, which had the largest coefficient but was only approaching significance. Another multidisciplinary study that found a positive effect for rural CP on rural practice also found that years since graduation was inversely associated with rural practice; however, an interaction effect of the two was not examined [40]. Findings from dentistry are mixed on the long-term effect of a voluntary rural CP, with rural CP students significantly more likely to be working rurally than non-CP participants at the initial follow-up 2–6 years later, but not at a second follow-up after an additional two years [33]. However, it was students with positive pre-CP rural practice intentions and ‘prior rural experience’ who were significantly more likely to be working rurally at both touch points [33] (pp. 186–187). Thus, those from a rural background and prior interest in working rurally are more likely to be in the rural workforce in the long term [24,33]. While there is evidence around rural CP which can support early rural practice uptake, this effect appears to diminish with time. Additionally, there is evidence that rural CP can have both a negative and positive effect on students’ practice location choice. Among occupational therapy professionals in a qualitative study [21], a prior rural CP attracted some of the rural-practising graduates towards rural practice, while the non-rural-practising graduates reported a dissuading effect. Additionally, a study of dentists found those who indicated a rural CP influenced their work location were significantly more likely to be working rurally; however, descriptive statistics also showed 50% of these respondents were working in a metropolitan area [34] (p. 219). Similarly, in a study of allied health graduates, one-third of those who reported their rural CP was influential in their graduate practice location were employed in city locations [23]. 3.4.3. Use of Theoretical Frameworks to Inform Study Design Low numbers of papers used a theoretical framework to inform their study. Most papers (n = 26, 90%) did not situate their research with a discernible theoretical framework. Three papers (10%) engaged with the following theories or concepts: situated learning n = 3 [27,44,45], experiential learning n = 2 [44,45], and place-based social processes n = 1 [45]. A qualitative paper also identified ‘social capital’ as a theme describing the importance of students’ CP relationships to their supervisor, colleagues, and community for a positive CP experience [36]. 4. Discussion This review included twenty-nine studies that examined rural CP in non-medical health professions and their influence upon intention to practice rurally and future rural practice. The main findings of the systematic review are that rural CPs: (1) Are an avenue for reinforcing or transforming students’ views of rural practice, though this may not necessarily be positive, (2) When high-quality, can positively influence students to undertake rural practice early in their career, although evidence is limited by self-selection of students into rural training experiences, and (3) Have scarcely been examined through explicit engagement with theoretical frameworks to inform study methodology. Situated learning theory was the most common theoretical framework employed. The review found that rural CP experiences can influence students’ intentions or attitudes regarding rural practice, as well as their early-career practice locations. This effect is, however, inconsistently found and appears contingent on student background and prior interest and CP quality. Rural CPs can positively reinforce or increase rural practice intention, particularly among students who are undecided or who do not come from a rural background [36,45,47]. Quality evidence for a positive CP effect on rural practice intention or employment was found in studies where students had rated the rural CP as being of high quality [25], or as providing ‘excellent’ professional development opportunities [38]. There is also some evidence that those who self-select into rural CP are more likely to rate their experience favourably and to report feeling encouraged towards rural practice [25]. However, rural CPs are highly varied experiences, and there was mixed quality evidence indicating that rural CP can negatively influence students’ rural practice intentions [37] and be associated with non-rural practice location choice [21,23,34]. Overall, while those from a rural area are most likely to practice rurally, rural CP experiences are an opportunity to reinforce the values of working rurally, as well as to attract those who are undecided, or whose rural living and working conditions have not yet been a consideration. The review found no evidence for a positive effect of a rural CP experience on rural practice in the long term, partly attributable to few and only recently commenced longitudinal study designs. It is outside the scope of this review to examine non-CP-related factors in long-term rural workforce commitment; however, one reviewed study of nursing students’ graduate practice intentions reported that exposure to negative aspects of rural practice during CP dissuaded some students from rural practice [37]. Multidisciplinary research indicated that rural CP experiences may support early uptake of rural practice [38,40,41,46], with only early rural practice associated with long-term rural practice outcomes [39], and with likelihood of practising in rural areas being significantly negatively associated with years from graduation [40]. This suggests rural CP may have a ‘foot-in-the-door’ effect [45] for rural employment but not as a sufficient driver in individuals’ decision to stay rural in the long term [44]. See Cosgrave (2020) [49] for further discussion of this issue. Contrary to policy expectations, this review found high-quality evidence varied on whether longer-term placements foster greater interest in, or realisation of, rural practice for non-medicine health professionals [24,25,28,38,40,41,46]. This may be attributable to the influence of CP quality being more easily examined through student self-reports, highlighting the importance of more rigorous quantitative studies for informing best practice on CP length. Several studies did find that high cumulative rural CP length is positively associated with early-career rural practice [24,40,41,46]. Despite this, some were wary of a focus solely on long-term CP given practical constraints. For example, Campbell and Farthing [24] found longer placements to be positively associated with remote practice. They subsequently recommended, given the “increasing competitiveness in securing clinical placements”, that longer and immersive CP should be offered in the final year, while early years of study should look to incorporate shorter and high-quality rural experiences [24] p. 955. While high-quality long-term rural CPs are supported through substantial funding in Australia [2], the volume of CPs required for the large number of non-medicine health students in Australia means students are also likely to experience CPs outside of quality placement models [6,7], which may contribute to a dissuading influence away from rural practice [37]. These mixed findings add further weight to national and international calls for more disciplinary- and context-specific work to understand the positive and negative mechanisms at play in rural CP experiences [1,7,8,50]. The available evidence suggests that those interested in increasing the rural health workforce should focus on high-quality rural CP experiences, including enhancing rural CP more broadly. What ‘quality’ rural CP means, however, requires greater conceptual attention to support meaningful evaluation [50]. Evaluation must also explicitly connect experiences of quality to the impetus of the associated CP model and funding [50]. The overall methodological quality of the included studies was found to be mixed, consistent with a recent multidisciplinary scoping review [8]. Application of the MMAT to assess study quality across different attributes combined with the narrative review found positive study findings were often over-generalised, with methodological limitations largely inadequately addressed. Additionally, controlling for rural background [38] or prior rural experience and pre-CP interest in rural practises appears key for mitigating potential selection bias, which can lead to over-estimation of positive rural CP effects, given that students are more likely to choose a rural placement or end up practising rurally [26,32,38]. A further source of selection bias may be from students with existing rural interest who may be more likely to participate in studies focused on rural placements and provide positive feedback related to these CPs. While longitudinal studies are promising, the reviewed longitudinal studies were conducted with small samples that inhibit generalisation [33,34], do not have a control group for comparison [39], or do not take into account placement preferences and self-selection into rural CP [40,41,46]. Conversely, when examining change in rural practice interest, findings from this review indicate that ceiling effects may occur among students who have high pre-CP interest in rural practice [47], masking the reinforcing potential of rural CPs. Systematically accounting for these factors in quantitative designs will provide more accurate estimates of rural CP effects on intentions and employment, particularly for assessing the influence of placement length. Finally, this review found limited engagement with theoretical frameworks among included studies, consistent with critiques on engagement with theory in health education research [10]. Theoretical models support critical interrogation of existing assumptions, expectations, and researcher bias prior to analysis which would enhance methodological rigor [10,11]. ‘Social capital’ has been identified as a key factor in students’ rural CP experience [36], while several studies have described how rural CP may not be an influential factor in practice location decision-making because of other interests and constraints, including existing familial, social, and cultural ties [29,36,44,45]. This indicates there is unrealised potential for social theories to be utilised for informing methodologies and enhancing current understandings of effective mechanisms and contexts for rural CP to affect future practice. Specifically, situated learning theory and practice-based learning frameworks could be used to inform CP design to meet students’ professional development needs and best promote rural practice interest (see: [6,9,11,13]). These approaches are commonly employed in health education research [10,11], and could be incorporated into recent critical work examining place-based understandings of rural communities, health access, and workforce, as well as social accountability frameworks that have informed rural-based health education partnerships (for example [12,14,49]). Limitations The findings of this review were limited by the focus on empirical studies of rural CP for non-medicine health professions students in the Australian context. It excluded studies where medical students were inseparable from other health students on the key measures; however, this was deemed appropriate for the Australian context where rural medical study is distinctly resourced from other health disciplines. This approach, however, means the review encompasses a broad spectrum of non-medicine health disciplines where curricula, placement opportunities, and student characteristics are likely to differ. As noted, this study has used adapted assessment criteria for quantitative non-randomised studies to best fit the characteristics of the studies within scope. Although the authors have taken all reasonable steps to apply the MMAT tool appropriately, it is acknowledged that different tools or assessors may yield different assessments of quality. 5. Conclusions This study found that high-quality Australian rural CP experiences can have a positive effect on rural practice intentions and early practice choices for non-medicine health professionals. The reviewed evidence indicated that providing professional development opportunities on rural placements that students view as meaningful and relevant to their practice is important for rural practice intention and early-career employment. Rural CP can also have a dissuading effect due to issues such a social and cultural isolation and poor resourcing of the CP and health services. There were mixed findings on the value of longer rural CP duration. This indicates that those seeking to promote the rural health workforce should focus on lifting the quality of practice experiences overall. The evidence base is currently limited by methodological factors such as varied measures of rural practice intention, potential selection bias, questionable interpretations of statistical analyses, limited long-term data, and low engagement with educational or social theory. There is capacity and need to better inform best-practice policy and CP design for supporting the rural health workforce. Acknowledgments The authors would like to acknowledge Melissa Nott for providing feedback on a version of this manuscript. Author Contributions Conceptualization, C.E.S. and E.G.; methodology, C.E.S., E.G. and K.F.; screening, reviewing, and analysis, C.E.S., E.G. and K.F.; writing—original draft preparation, C.E.S., E.G. and K.F. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest The authors declare no conflict of interest. Appendix A Exemplar of search strategy. (AB ((rural*) OR (regional*) OR (remote))) AND (AB ((clinical placement) OR (student placement))) AND (AB ((health*) OR (medic*) OR (nurs*) OR (clinical) OR (social work))) AND (AB ((recruitment) OR (retention) OR (intention*) OR (workforce) OR (employment) OR (career))). Figure 1 PRISMA flow chart of included articles, based on Page et al., 2021 [15]. ijerph-19-05363-t001_Table 1 Table 1 Article inclusion and exclusion criteria. Inclusion Criteria Exclusion Criteria Students in tertiary-level health professions degrees including medical radiation science, occupational therapy, dentistry, speech pathology/therapy, physiotherapy, nursing, pharmacy, nutrition and dietetics, podiatry, social work, oral health, audiology, orthotics and prosthetics, midwifery, paramedics, psychology, optometry, chiropractic, exercise physiology, and other health courses Sample included medical or non-health students, where practice intention or employment results were not separable for non-medicine health students Australian rural, regional, or remote CP studied Research was not about CP experiences (e.g., simulation, community visits) CP were not defined as rural, regional, or remote. CP location was outside of Australia Report outcomes included rural practice intentions (including ‘interest’ or ‘attractiveness’), and/or rural employment directly attributed to a rural, regional, or remote CP experience Outcomes were not specific to rural CP. Other outcomes examined only (e.g., placement enjoyment, course progress) Peer reviewed journal articles of all original study designs Literature reviews and theses, grey literature, text, and opinion papers Published between 2000 and 2022 Papers published outside the stated publication range English language papers Papers in languages other than English ijerph-19-05363-t002_Table 2 Table 2 Disciplines of health placements included in the literature. Discipline Total Number of Studies Occupational therapy 14 Dentistry 12 Physiotherapy 11 Medical radiation science (incl. radiography, nuclear science, radiation therapy, medical imaging) 10 Nursing 10 Pharmacy 10 Nutrition and dietetics 9 Speech pathology/therapy 9 Podiatry 7 Oral health 5 Social work 5 Audiology 4 Midwifery 4 Paramedicine 4 Psychology 4 Chiropractic 2 Exercise physiology 2 Other health courses 8 ijerph-19-05363-t003_Table 3 Table 3 Article characteristics and results of quality screening. Studies Duration of Rural CP Disciplines of CP Study Design Report Quality MMAT Rating MMAT Criteria Met A mixed-method study of chiropractic student clinical immersion placements in nonmetropolitan Western Australia: influence on student experience, professional attributes, and practice destination [19] 1–2 weeks Chiropractic Mixed methods Low None An innovation in Australian dental education: rural, remote, and Indigenous pre-graduation placements [20] 3 weeks Dentistry Quantitative descriptive Low 4.3 Four years after graduation: occupational therapists’ work destinations and perceptions of preparedness for practice [21] NR Occupational therapy Mixed methods Medium 5.2, 5.3, 5.4 Longitudinal tracking of workplace outcomes for undergraduate allied health students undertaking placements in rural Australia [22] Short-term CP (<8 weeks), medium-term (8 to 18 weeks), or long-term (semester long or full year) CP Diagnostic radiography, nuclear science, nutrition and dietetics, occupational therapy, physiotherapy, radiation therapy, and speech pathology Mixed methods Low 5.1 Preparing graduates to meet the allied health workforce needs in rural Australia: short-term outcomes from a longitudinal study [23] Short-term (<8 weeks), medium-term (8 to 18 weeks), or long-term (semester long or full year) CP Diagnostic radiography, nuclear science, nutrition and dietetics, occupational therapy, physiotherapy, and speech pathology Mixed methods Low 5.1 Workplace locations of allied health and nursing graduates who undertook a placement in the Northern Territory of Australia from 2016 to 2019: an observational cohort study [24] Shorter (2–10 weeks), or longer (10–47 weeks) CP Nursing, midwifery, audiology, dentistry/oral health, dietetics/nutrition, disability, medical imaging, occupational therapy, optometry, orthotics and prosthetics, paramedicine, pharmacy, physiotherapy, podiatry, psychology, radiation science, social work, speech pathology, and other Quantitative non-randomized High 3.1, 3.3, 3.4, 3.5 # Characteristics of nursing and allied health student placements in the Northern Territory over time (2017–2019) and placement satisfaction [25] 1 ≤ 2 weeks, >2–4 weeks, >4–12 weeks, >12 weeks Nursing, midwifery, audiology, dentistry, oral health, dietetics/nutrition, disability, medical imaging, occupational therapy, optometry, orthotics and prosthetics, paramedicine, pharmacy, physiotherapy, podiatry, psychology, radiation science, social work, speech pathology, and other Quantitative non-randomized High 3.2, 3.3, 3.4, 3.5 The impact of rural clinical placement on student nurses’ employment intentions [26] 4 weeks Nursing Quantitative non-randomized Low 3.1, 3.3 # Rural placements in Tasmania: do experiential placements and background influence undergraduate health science student’s attitudes toward rural practice? [27] NR Nursing, pharmacy, audiology, nutrition and dietetics, occupational therapy, podiatry, physiotherapy, speech therapy, prosthetics, and social work (also medicine). Quantitative descriptive Low 4.1, 4.2 Factors influencing medical radiation science graduates’ early-career principal place of practice: a retrospective cohort study [28] 0 days, 1–25 days, 26–50 days, 51+ days Medical radiation science Quantitative non-randomized High All Student opinions on a rural placement program in New South Wales, Australia [29] 1 month Dentistry Qualitative Low None Assessment of a dental rural teaching program [30] 1 month Dentistry Quantitative non-randomized Low 3.1, 3.3 # The influence of a clinical rural placement program on the work location of new dental graduates from the University of Sydney, NSW, Australia [31] 1 month Dentistry Quantitative non-randomized Medium 3.1, 3.2, 3.3 # A longitudinal evaluation of the Rural Clinical Placement Program at the University of Sydney Dental School [32] 1 month Dentistry Mixed methods Low 5.4 A longitudinal workforce analysis of a Rural Clinical Placement Program for final-year dental students [33] 1 month Dentistry Quantitative non-randomized High All # The workforce outcomes of dental graduates from a metropolitan school ‘Rural Clinical Placement Program’ versus a ‘Rural Clinical School’ [34] 1 month Dentistry Mixed methods Low 5.1 Pharmacy students’ rural career intentions: perspectives on rural background and placements [35] 2 or 12 weeks Pharmacy Mixed methods Low None What do dental students value about their rural placements—Is clinical experience enough? [36] 5 weeks Dentistry and oral health Qualitative Medium 1.3, 1.4, 1.5 The lure of the bush: do rural placements influence student nurses to seek employment in rural settings? [37] NR Nursing Mixed methods Low 5.4 Going country: rural student placement factors associated with future rural employment in nursing and allied health [38] >2 weeks Dietetics, environmental health, health information management, health promotion, medical imaging, nursing, occupational therapy, occupational health and safety, physiotherapy, podiatry, social work, and speech therapy Quantitative non-randomized High 3.1, 3.2, 3.4, 3.5 # Factors associated with rural work for nursing and allied health graduates 15–17 years after an undergraduate rural placement through the University Department of Rural Health program [39] 2–18 weeks Dietetics, environmental health, health promotion, health information management, health promotion, medical imaging, nursing, occupational therapy, occupational health and safety, pharmacy, physiotherapy, podiatry, social work, and speech therapy Quantitative non-randomized High All # Does undertaking rural placements add to place of origin as a predictor of where health graduates work? [40] Duration NR: cumulative days and number of placements calculated, ratio to metro placement days Dentistry, midwifery, nursing, oral health, occupational therapy, paramedicine, pharmacy physiotherapy, podiatry, and psychology. Nursing analyzed separately. Quantitative non-randomized High 3.1, 3.2, 3.3, 3.4 # Destinations of nursing and allied health graduates from two Australian universities: a data linkage study to inform rural placement models. [41] Cumulative placement days (0 days, 20 days or less, 21–40 days, more than 40 days) and number of placements calculated Nursing, occupational therapy, pharmacy, physiotherapy, and medical radiation science Quantitative non-randomized High All # Pharmacy students’ intention to practice in a rural setting: measuring the impact of a rural curriculum, rural campus, and rural placement on a predominantly metropolitan student cohort [42] 2 or 12 weeks Pharmacy Quantitative non-randomized High All Rural pharmacy workforce: influence of curriculum and clinical placement on pharmacists’ choice of rural practice [43] NR Pharmacy Mixed methods Low 5.1 Up close and real: living and learning in a remote community builds students’ cultural capabilities and understanding of health disparities [44] 2–5 weeks Speech pathology, occupational therapy, social work, exercise physiology, and generalist health science Qualitative Medium 1.3, 1.4 1.5 Learning from follow-up of student placements in a remote community: a small qualitative study highlights personal and workforce benefits and opportunities [45] 3–5 weeks Occupational therapy, speech pathology, and generalist health science Qualitative Medium 1.1, 1.3, 1.4 Rural placements during undergraduate training promote future rural work by nurses, midwives, and allied health professionals [46] Zero weeks of rural CP compared to 19.4 (nursing)/20.6 (allied health) weeks or greater Nursing and midwifery, and allied health (analyzed separately) comprising physiotherapy, occupational therapy, social work, speech pathology, dietetics, pharmacy, exercise physiology, psychology, paramedicine, podiatry, radiography, medical laboratory science, audiology, radiation therapy, sonography, optometry, dentistry, oral health, other allied health Quantitative non-randomized Low 3.4, 3.4 # Immersive placement experiences promote rural intent in allied health students of urban and rural origin [47] 1 week to 12 months Medical radiation science, nutrition and dietetics, occupational therapy, physiotherapy, and speech pathology Mixed methods High 5.1, 5.2, 5.3, 5.4 # Using adapted MMAT criterion 3.5, ‘Is the statistical analysis appropriate to answer the research question?’. CP: clinical placement, MMAT: Mixed Methods Appraisal Tool, NR: not reported. ijerph-19-05363-t004_Table 4 Table 4 Review findings presented by outcome (intention and/or employment) studied. Studies Theory or Conceptual Framework Rural Background/Interest Incl. in Analysis Intention and/or Employment Examined Outcome Indicator Results A mixed-method study of chiropractic student clinical immersion placements in nonmetropolitan Western Australia: Influence on student experience, professional attributes, and practice destination [19] NR No Intention and employment More likely to consider practicing in a rural or remote setting as a result of CP and rurality of employment locations Graduates who were working in a rural location were more likely to have voluntarily undertaken a rural CP during their degree. An innovation in Australian dental education: rural, remote, and Indigenous pre-graduation placements [20] NR No Intention Consideration of rural practice Most students reported considering rural practice prior to the CP at similar levels to post-CP, despite most not having experienced rural living before placement. Four years after graduation: occupational therapists’ work destinations and perceptions of preparedness for practice [21] NR No Employment Described influence of rural CP experiences on rural or metro employment location Rural CP enticed some of the rural-practicing graduates towards rural practice, while all seven of the non-rural-practicing graduates reported their rural CP had a dissuading effect. Longitudinal tracking of workplace outcomes for undergraduate allied health students undertaking placements in Rural Australia [22] NR Yes Intention Pre- and post-CP rural work intentions “38.3% positive change” between allied health students’ retrospective self-assessment of rural practice intention pre-CP to their post-CP rating, with 55% of students with no rural background having a higher rating. Preparing graduates to meet the allied health workforce needs in rural Australia: short-term outcomes from a longitudinal study [23] NR Yes Employment Influence of CP on current rural or metro employment, employment location one year after graduation Students from a non-rural background were significantly more likely to have indicated that their CP influenced their decision and be practicing rurally. No significant difference was found among rural students. One-third of allied health students who indicated that their CP experience influenced them to take up their graduate position were employed in a city location. Workplace locations of allied health and nursing graduates who undertook a placement in the Northern Territory of Australia from 2016 to 2019: an observational cohort study [24] NR Yes Employment Whether employed in a rural area ^, whether rural placement influenced consideration of rural practice, intention to work as a rural or remote health professional within the first 5 years following graduation (0 = no intention; 50 = 50/50 probability; 100 = absolute certainty) Graduates who spent more than 10 weeks on CP were more likely to be working rurally. Three-quarters of respondents reported that placement influenced their consideration of rural practice and were more likely to be practicing rurally. Those who indicated they were ‘already committed’ to rural practice had highest rates of rural practice and highest mean intention of being in rural work in five years’ time. Characteristics of nursing and allied health student placements in the Northern Territory over time (2017–2019) and placement satisfaction [25] NR Partially. Prior consideration of rural work examined Intention Placement has encouraged consideration of living and working in a rural or remote location Those who reported prior consideration of rural living and working were more likely to be encouraged by CP, as were those who reported satisfaction with educational resources and overall. Rural background and placement length not significant in univariate tests. The impact of rural clinical placement on student nurses’ employment intentions [26] NR Partially Intention Intention to seek work in a rural area Proportion of students intending to practice rurally was significantly higher among those who chose a rural CP than among those who chose a metro CP. This difference in intention between groups was significantly higher both pre- and post-CP. No significant change in the proportion of students intending to practice rurally post-CP. Rural placements in Tasmania: do experiential placements and background influence undergraduate health science student’s attitudes toward rural practice? [27] Situated learning Yes (but not separated by discipline) Intention Pre- and post-CP rural work intention ratings Participants of all disciplines areas and geographical backgrounds increased their mean rural work intention ratings. This was significant in all groups except pharmacy and rural classification areas, which had sample sizes of seven or less Factors influencing medical radiation science graduates’ early-career principal place of practice: a retrospective cohort study [28] NR Yes Employment Rural employment 2 years after graduation Multivariate analysis found rural background the sole predictor of rural practice; neither number of CP nor cumulative CP days were significant beyond univariate models. Student opinions on a rural placement program in New South Wales, Australia [29] NR Partially Intention Intention to work rurally, pre- and post-CP, description of how CP raised interest Factors of the rural CP experience reported to raise interest in rural practice, including positive experiences with the rural community and patients, a broader range of clinical procedures, a shorter commute to work, quality supervision, and clinical mentors. Assessment of a dental rural teaching program [30] NR Yes Intention Intention to work rurally, pre- and post-CP 55% of pre-CP participants were interested in working rurally after they graduate with the rest undecided. All post-CP participants, apart from one, were interested (97%). The influence of a clinical rural placement programme on the work location of new dental graduates from the University of Sydney, NSW, Australia [31] NR No Employment Rural employment location two and three years after graduation Participants in the voluntary rural CP were significantly more likely to be practicing rurally. A longitudinal evaluation of the Rural Clinical Placement Program at the University of Sydney Dental School [32] NR Partially Employment Rural employment location, whether rural CP had a positive influence on employment location Participants in the voluntary rural CP had higher likelihood of being employed rurally at follow-up, approaching significance at the 5% alpha level. Respondents who agreed their rural CP experience influenced their working location were more likely to be working rurally. A longitudinal workforce analysis of a Rural Clinical Placement Program for final year dental students [33] NR Yes Employment Rural employment location at two time points Participants in the voluntary rural CP were significantly more likely to be working rurally than non-participants at initial follow-up but not at the second follow-up two years later, although these graduates were significantly more likely to be retained rurally between years. The workforce outcomes of dental graduates from a metropolitan school ‘Rural Clinical Placement Program’ versus a ‘Rural Clinical School’ [34] NR Yes (for quantitative component only) Employment Rural employment location, positive influence of rural CP on employment location Participants in the voluntary rural CP had higher likelihood of being employed in rural practice, approaching significance at the 5% alpha level. Graduates who indicated that the rural CP influenced their work location were significantly more likely to be working rurally; 50% of those who responded that it did impact their work location were working in a metropolitan area. Pharmacy students’ rural career intentions: perspectives on rural background and placements [35] NR Yes Intention Rating whether rural CP increased likelihood of working in a rural area High agreeance from students who have undertaken at least one rural CP reporting it as a valuable learning experience and made them more likely to work in a rural area. No difference by whether they had a rural background or not. What do dental students value about their rural placements—is clinical experience enough? [36] NR Partially (predominately metro sample) Intention Interest in rural practice post-placement Most would consider rural living and working after CP predominately because of the practice opportunities, with responses focused on their positive CP experience. Those who did not described social connections to city and city lifestyle as reasons. The lure of the bush: do rural placements influence student nurses to seek employment in rural settings? [37] NR Partially Intention Whether would consider working rurally, whether would consider working rurally on graduation Rural CP gave students “good insight” on what a graduate year might look like in a rural hospital, although this could have both negative and positive impacts on practice considerations. No clear numbers reported. Going country: rural student placement factors associated with future rural employment in nursing and allied health [38] NR Yes Employment Rural employment one year after graduation The two CP factors that were significantly, positively associated with future rural practice when controlling for rural background were where the CP was rated by students as ‘excellent’ for their professional development as well as those whose rural CP was for four weeks or less. Factors associated with rural work for nursing and allied health graduates 15-17 years after an undergraduate rural placement through the University Department of Rural Health program [39] NR Yes Employment Rural employment at one and 15–17 years after graduation No rural CP characteristics significantly associated with long-term rural practice, only whether the first job after graduation was in a rural location. Rural background also non-significant. Does undertaking rural placements add to place of origin as a predictor of where health graduates work? [40] NR Yes Employment Employment in metropolitan, regional, rural, or remote areas in early years of practice Higher ratio of metro to rural CP significantly, negatively associated with rurality of practice. Rural background was significant and positive, accounting for the greatest amount of variance in rural practice. Destinations of nursing and allied health graduates from two Australian universities: a data linkage study to inform rural placement models [41] NR Yes Employment Rural practice in second year post-graduation 0–20 cumulative days of placement—not significantly different from zero rural CP, 21–40 days—double likelihood of rural practice than zero CP, more than 40 days—associated with 4.5 times the likelihood of rural CP with the rural background indicator returning similarly high odds. Pharmacy students’ intention to practise in a rural setting: measuring the impact of a rural curriculum, rural campus and rural placement on a predominantly metropolitan student cohort [42] NR Yes Intention Intention to practice rurally Rural CP is positively associated with rural practice intention but is only approaching significance. Rural background is the only significant factor found. Rural pharmacy workforce: influence of curriculum and clinical placement on pharmacists’ choice of rural practice [43] NR Yes Employment Choice to practice rurally, how placement influenced this choice Pharmacists reported their rural CP experiences as “predominately positively influencing their choice of rural career” (p. 134), with the opportunity to experience a rural lifestyle indicated as a significant influence by most. Up close and real: living and learning in a remote community builds students’ cultural capabilities and understanding of health disparities [44] Interprofessional learning, experiential and situated learning Partially Intention and employment Graduate feedback on CP impact, rural employment proportion Placement ‘reaffirmed’ students’ existing interest in practicing rurally. Six of eight employed recent graduates were working rurally. One student interested in working remotely in the long-term. Learning from follow-up of student placements in a remote community: a small qualitative study highlights personal and workforce benefits and opportunities [45] Experiential and situated learning frameworks, place-based social processes Yes Intention Graduates’ retrospective assessment of CP influence on their rural practice intentions Rural CP experience was perceived to provide substantial professional development for students which positively influenced or reinforced existing positive attitudes. Rural placements during undergraduate training promote future rural work by nurses, midwives, and allied health professionals [46] NR Yes Employment Hours worked in rural practice within the last week (1–14 years after graduation) Health professionals who reported the highest quintile of rural CP weeks during their studies reported working significantly longer hours in rural practice than those who undertook no placement. Immersive placement experiences promote rural intent in allied health students of urban and rural origin [47] NR Yes Intention Cross-sectional before and after CP rural work intention ratings Intention rated higher for both rural background and non-rural background students, but only significantly higher among non-rural students. Significantly higher ratings were found for all CP lengths and disciplines, except for medical radiation which had existing high ratings. NR: not reported. ^ classified as medium rural town to very remote; excludes larger rural towns and regional centers. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. WHO Increasing Access to Health Workers in Remote and Rural Areas through Improved Retention: Global Policy Recommendations Available online: https://apps.who.int/iris/handle/10665/44369 (accessed on 20 February 2022) 2. Lyle D. Greenhill J. 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Situated learning theory in health professions education research: A scoping review Adv. Health Sci. Educ. 2020 25 483 509 10.1007/s10459-019-09900-w 12. Malatzky C. Bourke L. Re-producing rural health: Challenging dominant discourses and the manifestation of power J. Rural Stud. 2016 45 157 164 10.1016/j.jrurstud.2016.03.005 13. Roberts C. Daly M. Held F. Lyle D. Social learning in a longitudinal integrated clinical placement Adv. Health Sci. Educ. 2017 22 1011 1029 10.1007/s10459-016-9740-3 14. Roberts P. Cosgrave C. Gillespie J. Malatzky C. Hyde S. Hu W.C. Bailey J. Yassine T. Downes N. ‘Re-placing’ professional practice Aust. J. Rural. Health 2021 29 301 305 10.1111/ajr.12717 33792996 15. Page M.J. McKenzie J.E. Bossuyt P.M. Boutron I. Hoffmann T.C. Mulrow C.D. Shamseer L. Tetzlaff J.M. Akl E.A. Brennan S.E. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews BMJ 2021 372 105906 16. Beks H. Walsh S. Alston L. Jones M. Smith T. Maybery D. Sutton K. Versace V.L. Approaches Used to Describe, Measure, and Analyze Place of Practice in Dentistry, Medical, Nursing, and Allied Health Rural Graduate Workforce Research in Australia: A Systematic Scoping Review Int. J. Environ. Res. Public Health 2022 19 1438 10.3390/ijerph19031438 35162455 17. Australian Bureau of Statistics (ABS) 1270.0.55.005-Australian Statistical Geography Standard (ASGS): Volume 5—Remoteness Structure, July 2016 2018 Available online: https://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/1270.0.55.005Main+Features1July%202016?OpenDocument (accessed on 16 February 2022) 18. Hong Q.N. Fàbregues S. Bartlett G. Boardman F. Cargo M. Dagenais P. Gagnon M.-P. Griffiths F. Nicolau B. O’Cathain A. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers Educ. Inf. 2018 34 285 291 10.3233/EFI-180221 19. Amorin-Woods L.G. Losco B.E. Leach M.J. 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==== Front Polymers (Basel) Polymers (Basel) polymers Polymers 2073-4360 MDPI 10.3390/polym14091666 polymers-14-01666 Article Mechanical Behavior and Energy Dissipation of Woven and Warp-Knitted Pvc Membrane Materials under Multistage Cyclic Loading Guo Shanshan 12 Wang Linlin 12 Shao Guangwei 12 https://orcid.org/0000-0002-6971-3768 Shao Huiqi 23 https://orcid.org/0000-0001-8848-0990 Jiang Jinhua 12* Chen Nanliang 12* Iván Béla Academic Editor 1 Shanghai Collaborative Innovation Center of High Performance Fibers and Composites, College of Textiles, Donghua University, Shanghai 201620, China; guoss@dhu.edu.cn (S.G.); 15821989252@163.com (L.W.); shaogw@dhu.edu.cn (G.S.) 2 Engineering Research Center of Technical Textiles, Ministry of Education, Donghua University, Shanghai 201620, China; hqshao@dhu.edu.cn 3 Innovation Center for Textile Science and Technology, Donghua University, Shanghai 201620, China * Correspondence: jiangjinhua@dhu.edu.cn (J.J.); nlch@dhu.edu.cn (N.C.) 20 4 2022 5 2022 14 9 166622 3 2022 19 4 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). In order to study the mechanical behavior and energy dissipation of architectural membrane materials under multistage cyclic loading, the deformation behavior, energy dissipation, and damage characteristics of four kinds of warp-knitted and woven polyvinyl chloride (PVC) membrane materials were analyzed using multistage cyclic loading experiments. The results show that, compared with the uniaxial tensile strength, the peak values of the cyclic loading and unloading of the four material samples are lower in the warp direction but higher in the fill (weft) direction. Under multistage cyclic loading, the loading and unloading moduli of the warp knitting membrane increase with the increase in fabric density. At the same fabric density, the loading modulus and the unloading modulus are smaller than those of the warp knitting material. The total absorbed strain energy, elastic strain energy, and dissipation energy of the fill samples are higher than those of the warp samples at a low load level but lower than those at a high load level. PVC membrane materials’ use strength should be controlled below a 15% stress level under long-term external force loading. In the cyclic loading process, the four PVC membrane materials are viscoelastic–plastic, so it is reasonable to define the damage variable based on the accumulation of plastic deformation. woven and warp-knitted PVC membrane materials multistage cyclic loading energy dissipation damage variable ==== Body pmc1. Introduction Flexible composites are generally made of two or more components with different thermal or mechanical properties and show great variations and complexity in tensile behavior compared to conventional material systems. Composite fabric reinforcements have a myriad of applications, including in the aerospace, automotive, and transportation areas [1,2]. Warp-knitted biaxial and woven fabric-reinforced membrane materials are two of the most common types of flexible composites [3]. Their applications include rotor blades, wind turbines, yachts, and bridge deck slabs [4,5]. Due to the complexity of the microstructure of the internally reinforced fabric, the nonlinearity of the material and geometry, and the difference in the control of the warp and fill (weft) tension in the process of weaving and coating, membrane materials exhibit different macroscopic mechanical behaviors under different applied loads [6,7,8,9]. In the process of use, the material is subjected to repeated effects, such as gusts, rain, and snow, and repeated adjustments of external load, and the membrane material is usually in a state of repeated loading and unloading [10,11]. In addition, the deformation, strength characteristics, and fracture damage mechanical properties of membrane materials are closely related to the stress state and loading history. At the same time, the failure of membrane materials under different stress conditions is actually the macroscopic manifestation of the fracture and expansion of internal micro-cracks and defects under loading conditions. Therefore, in order to prevent the mechanical failure of membrane structure materials under repeated loading and unloading, it is necessary to analyze their mechanical behavior under multistage cyclic loading. Studies have found that the elastic modulus and residual deformation of polytetrafluoroethylene membrane materials, vinylidene fluoride/polyethersulfone membrane materials, and woven fabric-reinforced flexible composites are closely related to the number of repeated loading and unloading cycles [12,13]. It was found that there is a coupling relationship between the warp and fill stress ratio and the elastic modulus, and the influence law of the warp and fill stress ratio and the coupling elastic modulus was also analyzed [14,15]. The uniaxial and biaxial tensile behaviors of PTFE membrane materials under cyclic loading are mainly affected by the stress amplitude, temperature, and reinforced skeleton fabric structure, and the loading history has an obvious influence on the mechanical behavior [16,17,18]. Compared with a composite material with a woven structure, the warp-knitted biaxial structure has been used more and more as a reinforcement for flexible membrane composites due to the biaxial characteristics of the fiber arrangement direction [19,20,21]. Moreover, the most common problem of polyester industrial yarns, such as lightbox fabrics, in their process of use [22] is mechanical failure. It was found that, while the coated biaxial warp-knitted fabric demonstrates anisotropic properties under monoaxial tensile conditions, the same fabric behaves more isotropically under multi-axial tensile loads [23]. A three-dimensional model of woven composites considering the viscoelastic nature of a matrix was tested to gain a better understanding of fabric composites [24,25]. Thus, it is important to question which one has the best mechanical behavior under multistage cyclic loading. It is necessary to study and compare the mechanical differences between warp-knitted PVC membrane materials and woven fabric-reinforced PVC membrane materials systematically. However, the above studies mainly focus on the change in the elastic modulus and linearity of the tensile curve. The energy dissipation and evolution in multistage cyclic loading processes are aspects that have been less studied. The aim of this work is to investigate the mechanical properties of woven and warp-knitted PVC membrane materials under multistage cyclic loading. This paper focuses on the peak strength of the cyclic loading and deformation characteristics, the mechanics of elasticity, and fracture damage mechanic performance characteristics in order to compare the performances of the woven and warp-knitted PVC membrane materials. The advantages and disadvantages of the linear fitting and direct equivalent simulation are compared to calculate the loading and unloading moduli. Additionally, the macroscopic mechanical behavior, energy dissipation, and damage evolution were analyzed in order to further comprehend the behavior of the damage mechanics of the woven and warp-knitted PVC membrane materials. The knowledge obtained from these tests provides a better understanding of the performance and application of dynamically loaded flexible composites. It also provides a reference for their engineering application. 2. Materials and Methods 2.1. Materials Two flexible composites were selected as samples, and they were enhanced using polyester biaxial warp-knitted and woven fabrics (Shanghai Shenda Kobond New Material Co., Ltd., China). The warp and fill lining/yarns were 1000D/192F (111.11Tex) high-strength polyester industrial yarns. The specification of the binding yarn in the biaxial warp-knitted fabric was 75D/25F(83.33Tex) DTY polyethylene terephthalate (PET). Two types of fabric were bonded with a PVC membrane on both sides by the coating. The first type was a biaxial warp-knitted fabric with a tricot binding stitch, and the second type was a woven fabric with a plain weave construction. All samples were laminated with the same pure PVC membrane, and the coating thickness of each sample was the same. The 3 warp/weft laying yarn densities of the biaxial warp-knitted fabrics were 9 wales/inch and 9 courses/inch, 12 wales/inch and 12 courses/inch, and 18 wales/inch and 18 courses/inch. However, 1 fabric count (18 × 18 picks/inch) was selected to prepare the woven fabrics as controls. The specifications of the samples are listed in Table 1. 2.2. Sample Preparation and Experimental Methods The width of the samples was (50 ± 0.5) mm, the length was (300 ± 0.5) mm, and the effective clamping distance was (200 ± 0.5) mm. In the multistage cyclic loading experiment, the automatic loading and unloading rate was 5 mm/min. There were 5 cyclic peak stress levels (100 times per level), which were 5%, 10%, 15%, 20%, and 25% of the samples’ fracture strength. The samples were named K1, K2, K3, and W1. In order to ensure that the samples were always in a state of tension during the experiment, the lower limit of unloading stress was the pre-tension level before the first cycle. In order to compare the strength characteristics of the PVC membrane materials under multistage cyclic loading, a uniaxial direct tensile fracture test was conducted on the warp and fill directions of samples; the loading speed was set at 10 mm/min, and these samples were named K1′, K2′, K3′, and W1′. All the experiments were carried out on the WDW-20 Hualong strength tester model at room temperature. 3. Results and Discussion 3.1. Analysis of Relaxation Characteristics The stress–strain curves of PVC membrane materials under direct stretching and after multistage cyclic loading are shown in Figure 1 and Figure 2. It is shown in Figure 1 that the stress drops suddenly after the peak strength in the direct tensile curves of both the warp and fill direction samples of membrane materials. While under multistage cyclic loading, the stress–strain curves change periodically. Figure 2 shows that the areas of hysteresis loop formed by the loading and unloading curves increase with the increase in the number of cycles. The results show that the direct warp tensile fracture strengths are 29.51 N/mm2 (K1′), 40.03 N/mm2 (K2′), 46.73 N/mm2 (K3′), and 46.88 N/mm2 (W1′), and the average peak strengths of the multistage cyclic fracture are 22.39 N/mm2 (K1), 38.68 N/mm2 (K2), 45.19 N/mm2 (K3), and 40.99 N/mm2 (W1), which are 3.27–24.12% lower than the direct tensile fracture strength. The fill uniaxial tensile fracture strengths are 24.27 N/mm2 (K1′), 37.42 N/mm2 (K2′), 41.00 N/mm2 (K3′), and 42.35 N/mm2 (W1′), and the average peak tensile strengths of the multistage cycle are 24.97 N/mm2 (K1), 38.05 N/mm2 (K2), 47.29 N/mm2 (K3), and 49.55 N/mm2 (W1), which are 1.7–17% higher than the original tensile fracture strength. The fabric structure is greatly affected by the weaving and coating process; thus, in order to ensure a clear shed during the weaving process, the warp yarns were subjected to a higher tension and better elongation. In the process of coating, the warp yarns were also subjected to a higher tension, so they were further straightened and the degree of buckling was reduced. Under the action of the applied load, the warp strength utilization rate was higher, resulting in a higher direct tensile strength of the membrane materials [6]. In the process of weaving and coating, the warp yarns were subjected to high tension and repeated tensile, bending, friction, and impact stresses, and the macromolecules in the fibers were fully straightened. During the multistage cyclic loading process, slips occur between macromolecules, resulting in a decrease in the intermolecular forces. Macroscopically, the peak strength of multistage cyclic loading is lower than the direct tensile fracture strength [26]. However, the fill yarns were subjected to a low level of tension and the tension fluctuation was small in the weaving and coating process; cyclic loading is beneficial as it further straightens the macromolecules in the fiber and enhances the effect of bearing the applied load, which is macroscopically manifested since the multistage cyclic peak strength is higher than the direct tensile fracture strength. It can be seen from Figure 2 that, under five different stress gradients, the strain produced by materials with a high density is lower than that produced by materials with a low density in both the warp and fill samples, which shows K3 < K2 < K1, while the strain produced by warp knitting and woven materials with the same density is W1< K3 in comparison. The reason for this is that, according to the structural characteristics of the membrane material, the PVC resin coating on the surface of the membrane material is bonded to the surface of the base fabric by an adhesive, and the resin coatings on both sides are bonded to each other in the gap between the warp and the fill yarns. The PVC resin on both sides of the yarn interweaving point is bonded to the yarn in the base fabric. Due to the different adhesion of the interface, the void ratio between the high-density K3 yarns is lower. Therefore, the viscoelastic deformation caused by the PVC resin on the surface of the K1 and K2 materials is more involved, resulting in a larger shape variation under the same stress level. Similarly, compared with the binding yarn gap caused by the warp knitting structure of K3, the yarns are more closely interwoven in the woven structure of W1, so the shape variable of W1 is smaller. 3.2. Analysis of the Cyclic Elastic Modulus In the cyclic loading process, two main methods are used for calculating the elastic modulus of the loading and unloading: the linear fitting method and the approximate imitation method. In the linear fitting method, based on the initial stage of the loading and unloading stress–strain curves, the elastic modulus of loading and unloading can be obtained by linear fitting. This method can reflect the characteristics of the elastic modulus of materials. In the approximate imitation method, based on the slope of the connecting line of the initial loading point A and loading terminal B used to calculate the loading elastic modulus, and based on the slope of the connecting line of the initial unloading point B (same with loading terminal) and unloading terminal C used to calculate the unloading elastic modulus, the key step is to approximate the load and unload curves to a straight line. The slope of the straight line is the loading or unloading modulus of elasticity, which is shown in Figure 3. The multistage cyclic loading and unloading curves of both the fill and warp samples show obvious nonlinear characteristics. When calculating the elastic modulus of the loading and unloading, the slope of the line between points A and B is higher than that of the initial section of the curve AB, and the slope of the line between points B and C is also higher than that of the initial section of the curve BC. Compared with the linear fitting method, the elastic modulus of the loading and unloading calculated based on the approximate imitation method is higher and cannot objectively reflect the mechanical behavior of the materials. Therefore, in this paper, the elastic moduli of the loading and unloading were calculated based on the linear fitting method, and their curves are shown in Figure 4. It can be seen that the loading and unloading elasticity of the PVC membrane materials decreases with an increase in the number of cycles, while the loading and unloading elasticities of the K1 and K2 fill samples increase slightly with an increase in the number of cycles and those of the K3 and W1 decrease slightly. In the same cycle, the unloading modulus is higher than the loading modulus, and the difference between them first increases and then decreases with the increase in the number of cycles. By comparing the factors that affect density, the values of the loading and unloading moduli increase with the increase in density, the order of which is K3 > K2 > K1. At the same density, the loading and unloading moduli of the woven material are lower than those of the warp knitting material (W1 < K3). Under cyclic loading, the macromolecular chain of the reinforcing fiber and coating materials with a low adhesion slips first and provides a new combination with each other. As the macromolecular chain becomes straighter, the stresses improve, and the fiber deformation is mainly caused by the extension and retraction of the macromolecular chain. The aspect of macro performance evaluated is the modulus of elasticity increased. 3.3. The Characteristics of Energy Dissipation It is shown in Figure 3 that the area of ABFEA under curve AB is the total absorbed strain energy, which represents the work done by external force on the sample during a loading and unloading cycle. The CBFDC area under the curve BC is the elastic strain energy of the cycle. The area of ABCDEA is dissipated energy, which is used for sample damage and plastic deformation. The relationship between the total absorbed strain energy, elastic strain energy, and dissipated energy can be expressed as follows:(1) Ud=U−Ue where U is the total strain energy, mJ/mm3; Ue is the elastic strain energy, mJ/mm3; and Ud is the dissipated energy, mJ/mm3. The loading and unloading curves show hysteresis rather than coincidence loops, which is mainly due to the fact that PVC membranes are typical viscoelastic materials. During multistage cyclic loading, the material produces plastic deformation, so the hysteresis ring is not closed. The variation trends of the total absorbed strain energy, elastic strain energy, and dissipated energy of samples are shown in Figure 5. It can be seen that the energy variation curves of the warp and fill samples are similar and show a nonlinear growth trend, indicating that the energy absorption, storage, and dissipation mechanisms of the warp and fill samples are consistent under multistage cyclic loading. The total absorbed strain energy, elastic strain energy, and dissipated energy of the weft samples are lower when the cyclic load is the same. Taking the warp and fill samples of K1 for the second loading cycle as an example (Figure 6), due to the high loading and unloading moduli of the warp sample, the initial loading and unloading strains of the warp sample are small when they reach the same cyclic load peak, so the area under the loading and unloading curve is smaller. That is, the total absorbed strain energy and elastic strain energy are smaller. Meanwhile, in the same circular process, the dissipation energy of the warp sample is lower under the double effects of the difference between loading and unloading moduli and plastic deformation because the plastic deformation of this sample is smaller [27]. Figure 5 indicates that, under the experimental conditions in this paper (5%, 10%, 15%, 20%, and 25% of the samples’ fracture strength), when the cyclic peak load level is low, the total absorbed strain energy, elastic strain energy, and dissipated energy of the weft samples are relatively higher. It can be seen from Figure 5a,b that the total absorption strain energy and elastic strain energy of the K1, K2, K3, and W1 samples have the same variation rule; they all increase with the increase in density, and the fill direction value is larger than the warp direction. The comparison of woven and warp-knitted samples of the same density shows K3 > W1. Figure 5c,d indicates that the overall trend of the dissipated energy of the K1, K2, K3, and W1 samples is relatively consistent, increasing with the increase in density, and, furthermore, the fill direction value is larger than the warp direction. The comparison of woven and warp-knitted samples of the same density shows K3 < W1. When the cyclic load peak level is low, the energy absorption and storage are mainly realized by the change of fabric structure and macromolecular conformation of the yarn and coating materials. The energy dissipation mainly comes from the slip of the macromolecular chains in the reinforcement fiber and coating material, the extension of the original damage and the destruction of the binding point between the coating and the reinforcement fiber. Due to the difference in strain in the manufacturing process of weaving and coating, relative to the warp yarns, the fill yarns’ macromolecular chain segments are in lower degrees, and the yarns’ extended degrees are insufficient. Under the applied load, the elongation of the fill yarns is mainly caused by a decrease in buckling and the macromolecular chain segments slipping; the fill elongations result in a lower modulus, so the fill samples may show a higher total absorption. In the recovery stage, the stretched macromolecular chain segments return to their initial state, the fill yarns have a degree of buckling, and their capacity for energy storage is stronger. In the cyclic loading process, the original weak areas are destroyed, and due to the great change in the fills’ buckling structure, the probability of failure of the bonding points between the reinforcement fibers and the coating materials is higher and is macroscopically expressed as the increase in dissipated energy. When the cyclic load peak level is high, the absorption, storage, and dissipation of energy are mainly dependent on the stretching, recovery, and slip of the macromolecular chains in the reinforced fiber and coating materials. The higher the load ratio is, the higher the degree of elongation of the macromolecular chain segment and the greater the degree of slip will be, while the recovery capacity will also be improved. 3.4. The Rule of Rapid Elastic Recovery Rate The ratio of the elastic strain energy Ue to the total absorbed strain energy U is defined as the energy rapid elastic recovery rate u, as follows:(2) u=UeU×100% Figure 7 shows the curves of the energy rapid elastic recovery rate. It can be seen that, as the peak of the multistage cyclic load increases, the energy rapid elastic recovery rates of the fill and warp samples show a nonlinear change. The warp samples’ u value of K1, K2, K3, and W1 tend towards 100% during the first 300 instances of multistage cyclic loading, and the fill samples’ u value of K1, K2, K3, and W1 tend towards 100% during the first 200 instances of multistage cyclic loading. When the warp samples are loaded at a 20% (400 cycles) stress level of the fracture strength of the sample, the u value decreases significantly, to 88% (K1), 91% (K2), 92.5% (K3), and 93.2% (W1). There was an overall trend of increase with the increase in density. When the fill samples of K2 and K3 are loaded at a 15% stress level (300 cycles), the u value decreases significantly to 85.6% (K2) and 82.5% (K3), while the u value of the fill samples of K1 and W1 decreases significantly when the samples are loaded at 20% stress level (400 cycles) and the rapid elastic recovery rate decreases to 91.5% (K1) and 90.8% (W1), respectively. The rapid elastic recovery rate of the four samples is higher in the fill direction than in the weft direction. The resilience of W1 is better than that of K3. To explore the causes of this phenomenon, at the beginning of the multistage cyclic load, the proportion of dissipated energy Ud is low, and most of the absorbed energy turns into elastic strain energy Ue. This shows that the energy consumption, which is caused by the expansion of the internal defects of the slip of the materials, fibers, and coating materials in the debonding of the macromolecular chain, fibers, and coating materials, is relatively small. It is mainly turned into the recoverable elastic strain energy Ue. With the increase in the multistage cyclic load peak, the dissipated energy Ud increases, while the energy dissipation rate increases and reaches the maximum value when the cyclic load is in the range of a 15–20% stress level of the fracture strength. When the peak value of the cyclic load exceeds this critical value, although the dissipated energy continues to increase (Figure 5), most of the energy is converted into elastic strain energy Ue, and the energy dissipation rate presents a trend of gradual decrease. From the perspective of the energy dissipation rate, PVC membrane materials’ use strength should be controlled below a 15% stress level under long-term external force loading. It can also be seen from Figure 7 that the rapid elastic recovery rate of the fill sample is relatively low in the first stage of the cyclic loading and unloading process, which is still related to the difference in the tension state of the warp and fill direction during weaving and coating processing. 3.5. Definition and Analysis of Damage Variables Lemaitre et al. [28] defined the damage variable from the perspective of the change of elastic modulus, that is, the elastic modulus is taken as the damage factor, and the ratio of the elastic modulus of the damaged sample to that of the non-destructive sample can be used to define the damage variable DE:(3) DE=1−E′/E where E and E’ are the elastic moduli of undamaged material and damaged materials, respectively, in N/mm2. Damage is reflected in the generation and expansion of material defects, during which the elastic modulus gradually decreases. When the material is completely damaged, the material is also completely destroyed, and at this time, the elastic modulus E’ is 0. The maximum damage variable of the material is 1.0. Figure 5 shows that, during the multistage cyclic loading, the elastic modulus of the PVC membrane material presents an increasing trend, so it inevitably obtains the negative damage variable based on the elastic modulus change, and the material is thus characterized as “negative damage”. The damage variable’s value and the actual damage evolution characteristics are contradictory, so in multistage cyclic loading conditions, it is unreasonable to use the elastic modulus as the damage factor. There are two types of failure limits that may occur during cyclic loading. (1) When the material is under the action of cyclic loading, although there is no plastic deformation, actually the internal defects are produced. When the damage accumulation reaches a certain level, the damage in the material structure accumulates, and there is a failure in the material structural fatigue. (2) When the material is in an elastic–plastic working state, cyclic loading causes plastic deformation and also causes damage to the material. When the accumulated plastic deformation exceeds a certain limit, the service performance of the material is weakened until the failure limit appears. From the viewpoint of energy conversion, the accumulation of damage represents the energy dissipation, which can be defined as the damage variables from the perspective of energy dissipation. So, the division of the dissipation of energy, which is accumulated in the process of each cyclic loading to the final loading cycle dissipation, with the total strain energy results in the damage variable Du, during cyclic loading to the level i. The damage variable Du (i) can be represented as:(4) DU(i)=∑k=1iUdkU(t) where U(t) is the total strain energy of the last stage cycle, in mJ/mm3. The relationship between the calculated damage variables and cyclic progression based on Equation (4) is shown in Figure 8. It can be seen that, when the number of cyclic loading instances exceeds about 400, the damage variable values of both the warp and fill samples are greater than 1.0, which is inconsistent with the damage limit of 1.0. Therefore, Equation (4) cannot be used to calculate the damage variable. From Figure 2, Figure 3 and Figure 6, it can be observed that the PVC membrane materials’ loading and unloading curves do not overlap and the hysteresis loops are not closed, which shows that, in the process of multistage cyclic loading, the PVC membrane materials are in an elastoplastic state, and cyclic loading will cause the accumulation of plastic deformation, resulting in the degradation of the properties of the material. Based on this, the damage variable can be defined as the ratio of plastic strain accumulated in each loading cycle to the fracture strain of the non-destructive sample (the original sample). So, the damage variable Dε(i) can be expressed as:(5) Dε(i)=∑k=1iεper(k)εb=∑k=1i[εunloading(k)-εloading(k)]εb where εb is the fracture strain of the specimen under unidirectional tension, in mm/mm, and εper is the plastic deformation produced by each cycle, in mm/mm. The variation relationship between the damage variable Dε, which is calculated based on Equation (5), and the number of cyclic loading instances is shown in Figure 9. It can be seen from Figure 9 that the Dε value of the damage variable, calculated based on the plastic deformation accumulation, is between 0 and 1; the four samples K1, K2, K3, and W1 satisfy this rule. So, within the range of the damage limit, it can be considered that it is reasonable to define the damage variable based on the plastic deformation accumulation [29]. At the same time, it was found that, during multistage cyclic loading, the damage of both the fill and warp samples showed a similar nonlinear growth trend, indicating that the damage mechanism of the PVC membrane material is similar in the fill and warp directions, and the damage level of the fill samples is higher than that of the warp samples under the same cyclic load peak conditions. In addition, the damage level of K3 is higher than that of W1 from the difference in the fabric structure between the woven and warp-knitted fabrics. 4. Conclusions In this paper, four kinds of PVC membrane materials were tested and analyzed under multistage circulation. The deformation behavior and mechanism of warp-knitted and woven PVC membrane materials under multistage cyclic loading were studied and analyzed, especially in terms of the multistage damage characteristics under cyclic loading. The results of the experimental analysis are as follows. (1) For the four PVC membrane materials selected in this paper, the multistage cyclic fracture peak strengths of the warp-direction samples are lower than the original tensile strengths, while those for the fill-direction samples are higher than the direct tensile strengths, which is mainly due to the tension difference between the warp and weft yarns during weaving and coating processing. The overall trend of the dissipated energy of the K1, K2, K3, and W1 samples is consistent, increasing with the increase in fabric density. Similarly, the fill values are larger than the warp values. According to the analysis of the energy dissipation rate, PVC membrane materials’ use strength should be controlled below a 15% stress level under long-term external force loading. (2) The loading and unloading curves of the PVC membrane materials are obviously nonlinear, so it is more reasonable to use the linear fitting method to obtain the loading and unloading moduli compared with the approximate equivalent calculation method. For warp knitting materials, the loading and unloading moduli increase with the increase in density, and the values are K3 > K2 > K1. At the same density, the loading and the unloading moduli of the woven material are lower than those of the warp knitting material (W1 < K3). (3) Due to the peak strength of multistage cyclic loading being different from that of cyclic loading and unloading, the deformation processes of the warp and fill samples of the four kinds of PVC membrane materials are at different stages at the same cyclic loading times. However, the variation trends of energy and stress show the same law, that is, the total absorbed strain energy, elastic strain energy, and dissipation energy of the warp samples are higher than those of the fill samples under a low load level but lower under a high load level. (4) Under multistage cyclic loading, the four PVC membrane materials are in the elastic–plastic deformation stage. It is more reasonable to define the damage variable based on the accumulation of plastic deformation than the change in elastic modulus and dissipation energy. Under the same cyclic load peak conditions, the damage levels of the fill samples are higher than those of the warp samples. The damage level of K3 is higher than that of W1 for the same density of woven and warp-knitted membrane materials. Acknowledgments The authors gratefully acknowledge the Shanghai Natural Science Foundation of Shanghai Municipal Science and Technology Commission (20ZR1400600), the Fundamental Research Funds for the Central Universities (grant number 2232021G-06, 2232020A4-09, 2232020A-05), the funding provided by National Key R&D Program of China (grant number 2016YFB0303300), and financial support by the Shanghai Collaborative Innovation Center of High-Performance Fibers and Composites. Author Contributions All authors listed in this paper have contributed to this study. Methodology, investigation, writing—original draft, S.G.; formal analysis, L.W.; investigation, G.S. and H.S.; writing—review and editing, funding acquisition, J.J. and N.C. All authors have read and agreed to the published version of the manuscript. Funding The Shanghai Natural Science Foundation of Shanghai Municipal Science and Technology Commission (20ZR1400600), the Fundamental Research Funds for the Central Universities (grant number 2232021G-06, 2232020A4-09, 2232020A-05), the funding provided by National Key R&D Program of China (grant number 2016YFB0303300). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement The raw data presented in this study are available upon request from the corresponding author. Conflicts of Interest All authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Figure 1 Fracture stress–strain curves of the tested specimens. (a) Warp direction; (b) fill direction. Figure 2 Stress–strain curves of the tested specimens under multistage cyclic loading and unloading. (a–d) Warp direction of K1, K2, K3, and W1; (e–h) fill direction of K1, K2, K3, and W1. Figure 3 Level 2 stress–strain curves of the K1 warp specimen. Figure 4 Elastic modulus curves: (a) warp and fill specimens of K1; (b) warp and fill specimens of K2; (c) warp and fill specimens of K3; and (d) warp and fill specimens of W1. Figure 5 Energy variation curves with stress. (a–d) Total absorption strain energy and elastic strain energy of the warp direction of K1, K2, K3, and W1. (e–h) Total absorption strain energy and elastic strain energy of the fill direction of K1, K2, K3, and W1. (i–l) Dissipated energy of the warp direction of K1, K2, K3, and W1. (m–p) Dissipated energy of the fill direction of K1, K2, K3, and W1. Figure 6 Second and third level stress–strain curves of the tested K1 samples. Figure 7 Variation curves of the rapid elastic recovery rate with stress. (a–d) Rapid elastic recovery rate of the warp direction of K1, K2, K3, and W1. (e–h) Rapid elastic recovery rate of the fill direction of K1, K2, K3, and W1. Figure 8 Curves of the damage variable Du and cycle number. Figure 9 Curves of the damage variable Dε and cycle number. polymers-14-01666-t001_Table 1 Table 1 The specifications of samples. Samples Warp and Fill Lining Fine- Ness/(Tex) Warp and Fill Yarn Fine- Ness/(Tex) Fabric Density/(Yarns/Inch2) Thickness/(mm) Area Density/(g/m2) K1′ 111.11 / 9 × 9 0.41 365.10 K2′ 111.11 / 12 × 12 0.46 436.53 K3′ 111.11 / 18 × 18 0.51 651.16 W1′ / 111.11 18 × 18 0.50 635.73 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Jafaripour M. Taheri-Behrooz F. Creep Behavior Modeling of Polymeric Composites Using Schapery Model Based on Micro-Macromechanical Approaches Eur. J. Mech. A/Solids 2020 81 103963 10.1016/j.euromechsol.2020.103963 2. Miller S.A. Natural Fiber Textile Reinforced Bio-Based Composites: Mechanical Properties, Creep, and Environmental Impacts J. Clean. 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==== Front Molecules Molecules molecules Molecules 1420-3049 MDPI 10.3390/molecules27093000 molecules-27-03000 Article Phytochemical Compositions and Antioxidant Activities of Essential Oils Extracted from the Flowers of Paeonia delavayi Using Supercritical Carbon Dioxide Fluid https://orcid.org/0000-0002-0269-6270 Yu Xiao 1 Zhang Huaibi 2 Wang Juan 3* Wang Junming 4 https://orcid.org/0000-0002-6110-9216 Wang Zhenxing 5 Li Jinbo 6 Gentili Alessandra Academic Editor 1 Faculty of Landscape Architecture and Horticulture Sciences, Southwest Forestry University, Kunming 650224, China; yuxiao19920215@163.com 2 New Zealand Institute for Plant & Food Research Limited, Private Bag, Palmerston North 11600, New Zealand; huaibi.zhang@plantandfood.co.nz 3 Eco-Development Academy, Southwest Forestry University, Kunming 650224, China 4 Faculty of Forestry, Southwest Forestry University, Kunming 650224, China; magina@swfu.edu.cn 5 Faculty of Life Science, Southwest Forestry University, Kunming 650224, China; wangzhenxingfood@163.com 6 Dianxiangguose Agricultural Technology Company Limited of Yunnan Province, Kunming 652501, China; 19908718177@163.com * Correspondence: schima@swfu.edu.cn 07 5 2022 5 2022 27 9 300007 4 2022 03 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Essential oils were extracted from dark-purple, red and yellow petals of Paeonia delavayi using Supercritical Carbon Dioxide method. The compositions of essential oils were analyzed using gas chromatography-mass spectrometry (GC-MS). Antioxidant activity assays were carried out using DPPH, ABTS- and FRAP methods. Total polyphenols and total flavonoids were measured to evaluate the in vitro antioxidant activity in addition to the volatile compounds contained in the essential oils extracted from the flower petals of P. delavayi with the three flower colors. A total of 194 compounds were detected from essential oils of P. delavayi flowers, including 83 in dark-purple petals, 90 in red petals and 80 in yellow petals. These compounds mainly include alcohols, aldehydes, ketones, alkenes, alkanes, esters and polyphenols. The results showed that the volatile compounds accumulated differentially among the essential oils from the different colors of flower petals. Principal component analysis (PCA) indicated that essential oils derived from dark-purple and red petals were more closely clustered while the yellow petal essential oil was very different with both the purple-red and red. Antioxidant assays suggested that the radical scavenging activity and the iron reduction antioxidant activity in the essential oils were highly correlated with the flower petal colors. These results suggest P. delavayi flower petals are potentially good resources for high quality essential oils and natural antioxidants. Paeonia delavayi flower colors essential oil antioxidant activity Yun Development and Reform Commission [2018]212 Major Basic Special Biological Resources Digital Development and Application Project202002AA10007 National Natural Science Foundation of China32060089 This work was supported by “10000 Talent Plan” of Yunnan Province (Yun Development and Reform Commission [2018] No. 212); Major Basic Special Biological Resources Digital Development and Application Project in Yunnan Province (grant number 202002AA10007); Thousand Talents Project of Yunnan Province (2019); National Natural Science Foundation of China (grant number 32060089). ==== Body pmc1. Introduction Paeonia delavayi belongs to sect. Moutan of Paeonia, which is listed as a rare and endangered perennial woody plant in China [1]. It is mainly distributed in the central and northwestern of Yunnan province, southwestern Sichuan province and southeastern Tibet province [2]. P. delavayi plants produce a range of flower colors. Particularly, the gene resource for yellow flower color provides the basis for a very precious flower trait in peony cultivars. Therefore, P. delavayi plants have become a very valuable breeding material in the creation of new peony varieties [1]. Meanwhile, peony petals of various colors contain a wide range of polyphenolic compounds and flavonoids [3], including anthocyanins, which provide the power of scavenging superoxide anion free radicals. Peony flowers have been proposed as a potential resource for the development of new drugs or functional foods due to their high healthy and nutritional values [3]. At present, most studies on P. delavayi mainly focus on breeding new varieties through hybridization, cultivation, seed production, taxonomy and genetic diversity. These more conventional studies have laid a solid foundation for further research and development into molecular and phytochemistry levels [4,5,6]. Traditionally, growers and ornamental industries have economic benefit largely from the flowers, but flowers of many species also produce nectary and fragrances, In P. delavayi, flowers also provide most ornamental value, with blooming period being between April and June each year. The flower petals of P. delavayi are colorful, showing white, yellow, light red, dark red and dark-purple colors. Yellow, red and dark-purple are the most common colors found in the wild [7,8]. In addition, P. delavayi flowers emit refreshing and pleasant fragrances. However, there are few reports on the phytochemical constituents of P. delavayi flowers with different flower colors [9,10,11]. In this study, essential oils were extracted from P. delavayi flower petals of different flower colors, and their phytochemical compositions were detected by gas chromatography-mass spectrometry (GC-MS). The essential oil components and antioxidant activities of different petal colors were compared and analyzed, to provide a fundamental basis for comprehensive utilization and further development of essential oils using this species. 2. Results 2.1. Physical and Chemical Properties of Essential Oils Extracted from Petals of Different Colored Flowers The extraction rates, physical and chemical indexes of the essential oils extracted from the petals of the three different flower colors were not meaningfully different (Table S1). The highest extraction rate was 0.78% for dark-purple petals, followed by 0.64% for red petals and 0.58% for yellow petals. Although the essential oils extracted from petals with different colors were cleared, the obtained oil liquid retained the characteristic aroma of the fresh peony flowers. The colors of essential oils from dark-purple and red flower petals manifested milky white while yellow petals was light yellow. The aroma of the essential oil obtained from the yellow petals was more refreshing and elegant than that of dark-purple and red petals. There was no significant difference in the relative density, refractive index and optical rotation of essential oils among the essential oils derived from the three colors. 2.2. Comparison of Volatile Profiles among Essential Oils from Different Colors by Total Ion Chromatograms The total ion chromatograms of volatile components in P. delavayi essential oils from different flower colors were examined with GC-MS analysis using 1.0 μL injection volume of essential oils (Figure S1, Supplementary Materials). While the fractionation time of essential oils from dark-purple, red and yellow petals was concentrated in 15–60 min, the predominant peaks of components in dark-purple and red petals were more concentrated between 15–30 min, and the peaks accounted for 38.46% and 30.19% of the total peaks, respectively. The fractionation peaks from yellow petals were relatively more dispersed, accounting for only 28.23%. Significant differences of chemical components and their relative contents in the three essential oils were revealed by the GC-MS (Table S2). A total of 194 compounds were obtained from the essential oil extractions of three colors petals of P. delavayi. Among them, 83 were found in dark-purple petals, 90 in red petals, and 80 in yellow petals. The types of compounds with relatively large contents in the essential oil extracts of the three petals are different. Among them, the main compositions with relatively large content in the essential oil of dark-purple petal were identified as 2-(5-Ethenyl-5-methyloxolan-2-yl)propan-2-ol (20.88%), 2H-Pyran-3-ol, 6-ethenyltetrahydro-2,2,6-trimethyl-, (3R,6S)-rel- (11.90%), benzenepropanal(8.46%), cinnamaldehyde, (E)- (4.92%), and phytol (4.34%). In the red petals, essential oil contained 2-(5-Ethenyl-5-methyloxolan-2-yl) propan-2-ol(21.81%), (E)-linalool oxide (pyranoid) (10.85%), β-methylenephenethyl alcohol(5.60%), cyclooctyl isopropylphosphonofluoridate(5.16%), and trans-cinnamaldehyde(4.31%). In the yellow petals, essential oil had tricycle [4.4.0.02,7]decane, 1-methyl-3-methylene-8-(1-methylethyl)-, (1R,2S,6S,7S,8S)-rel-(20.34%), 2-naphthalenemethanol, decahydro-8-hydroxy-α,α,4a,8-tetramethyl-(7.9%), dl-1-Phenethylalcohol (5.97%), 2-(4-methylidenecyclohexyl)prop-2-en-1-ol (5.35%), and N-Hexadecane (4.36%). 2.3. Comparative Analysis of Volatile Compounds in Essential Oil of P. delavayi with Different Flower Colors 2.3.1. Analysis of Common VOLATILE Compounds in Essential Oils of P. delavayi Petals with Three Colors The essential oils extracted from the flower petals with three different colors of P. delavayi had 11 compositions detected in common (Table 1). The percentage of common compositions in total accounted for 50.46% for dark-purple petals, 49.47% for red petals and 22.9% for yellow petals. In addition to the 11 common compounds, the dark-purple petals and the red petals also shared 18 compounds, namely 3-phenyl-1-propanol, (βR,2S,5S)-β,5-dimethyl-5β-vinyltetrahydrofuran-2α-ethanol, epoxy-linalooloxide, cinnamyl alcohol, 2,6-dimethyloct-7-ene-2,6-diol, 4,4-dimethyladamantan-2-ol, 3-methylbut-2-enoic acid, 3-fluorophenyl ester, palmitic acid ethyl ester, 1-bromo-1,2-dichlorocyclopropane, 2,6,10-trimethyldodecane, 4-methoxy-3-hydroxyacetophenone, 1-nonanal, 6,6-dimethylbicyclo[3.1.1]heptane-2-carbaldehyde, phenylpropyl aldehyde, (1E,6E,8S)-1-methyl-5-methylene-8-isopropyl-1,6-cyclodecadiene, 2,7-octadiene-1,6-diol,2,6-dimethyl-, bicyclo[5.2.0]nonane, 4-ethenyl-4,8,8-trimethyl-2-methylene-, and 2(5H)-Furanone,5-(1-methylethyl)-. Dark-purple petals and yellow petals also shared another nine common compounds, such as trans-3-hexen-1-ol, 3-isopropyl-6,7-dimethyltricyclo[4.4.0.0(2,8)]decane-9,10-diol, 1-cyclopropylpropane, 8,8-diheptylpentadecane, 4-chloro-4′-hydroxybutyrophenone, 2-methylpent-4-enal, trans-2-hexenal, 2,7-octadiene-1,6-diol, 2,6-dimethyl-, (2Z)-, and 1-benzothiophene-3-carboxylic acid. Red and yellow petals shared 11 compounds, benzyl alcohol, dl-1-phenethylalcohol, linalool, phenethyl alcohol, acetic acid, 2,2,2-trifluoro-, 5-methyl-2-(1-methylethenyl)-4-hexen-1-yl ester, pentanoic acid,5-hydroxy-,2,4-bis(1,1-dimethylethyl)phenyl ester, 3,3-dimethylhexane, n-hexadecane, carissone, cis-muurola-4(14),5-diene, 2-naphthalenemethanol, and decahydro-8-hydroxy-α,α,4a,8-tetramethyl. The analysis of these common compounds not only shows the composition differences between different sources of P. delavayi essential oils with different colors, but also can be used as a data support for P. delavayi classification. 2.3.2. Differences in Chemical Classes of the Essential Oils from the Three Colors of Flower Petals in P. delavayi According to chemical classification, the compounds detected in the essential oils of the three different colors belonged to 8 types of compounds, namely alcohols, esters, alkanes, ketones, aldehydes, alkenes, phenols and others. Figure 1A,B, respectively, compare and analyze different classes of compounds. According to the chemical properties and analysis of the identified chemical constituents, 17 alcohols (16.72%), 14 esters (29.99%), 8 aldehydes (14.42%), 6 ketones (5.45%), 5 alkanes (2.99%), 4 alkenes (2.64%) and 2 phenols (1%) were identified in the essential oil of dark-purple P. delavayi flowers. Red P. delavayi petal essential oil had 24 alcohols (29.18%), 16 esters (29.76%), 13 alkanes (7.59%), 5 alkenes (2.12%), 6 ketones (3.64%), 7 aldehydes (5.60%), 16 alcohols (31.88%), and 15 esters (7.99%). In the yellow color, 12 alkanes (8.85%), 11 alkenes (10.58%), 6 ketones (3.44%) and 5 aldehydes (3.55%) were detected in the essential oil. The special fragrance of peony flower may be related to its abundant alcohols, esters and alkanes. The relative percentage of alcohol substances in the essential oil of yellow P. delavayi is the highest. The alcohol content of the essential oil in the yellow and red petals displayed about 2% difference, which was significant (p < 0.05). The essential oil derived from the dark-purple P. delavayi had much lower alcohol than that in the red or yellow (p < 0.05) (Figure 1B). Alcohols usually provide soft aromatic and plant aroma. The percentage content of alkanes in essential oils from yellow petals was also significantly higher than that from red petals and dark-purple petals (p < 0.05). In terms of aldehydes, the essential oils of dark-purple petals were significantly higher than those of red petals and yellow petals (p < 0.05). In terms of esters, the percentages of P. delavayi dark-purple flowers and red flowers were similar (p > 0.05) and significantly higher (p < 0.05) than those of yellow flower essential oils. The content of ketones in dark-purple P. delavayi petal essential oil was the highest, while the content of ketones in red and yellow P. delavayi petal essential oil was similar, with non-significant differences (p > 0.05). Additionally, there was no significant difference in the contents of alkenes among the three P. delavayi essential oils (p > 0.05). Phenolic compounds only exist in the essential oil of P. delavayi dark-purple petals. 2.3.3. Principal Component Analysis Principal component analysis (PCA) uses a mathematical dimensionality reduction method to replace many original variables with several comprehensive variables. Therefore, these comprehensive variables can represent the information of all the original variables without causing collinearity problems [12]. The principal component analysis of 11 common compounds was carried out by SPSS17.0 to explore the main components of P. delavayi essential oils with different colors. The eigenvalues and contribution rates of 11 common compounds are shown in Table S3 and the results of component load matrix analysis are shown in Table S4. According to the principle of eigenvalue greater than one, two principal components were analyzed, and the contribution rates were 67.528% and 32.472%, respectively. The cumulative contribution rate reached 100%, indicating that the two principal components can reflect most of the information of the original variable. The analysis results reduced the 11 common compounds in the original P. delavayi flowers to two irrelevant principal components, achieving the purpose of dimension reduction. According to the analysis in Table S4, the positive influence compounds with higher load in the first principal component (PC1) were mainly benzaldehyde, trans-Cinnamaldehyde, 2-(5-Ethenyl-5-methyloxolan-2-yl)propan-2-ol, (E)-linalool oxide (pyranoid), and the negative influence compounds with higher load were phenethyl alcohol, α-cadinol, (S,3E,7E)-α,α,4,8-Tetramethyl-3,7-cyclodecadiene-1-methanol. The positive influence compounds with higher load in the second principal component (PC2) were mainly benzyl alcohol, phytol, bicyclo[3.2.2]nona-6,8-dien-3-one, and the negative influence compounds with higher load were phenethyl alcohol and fitone. The principal component analysis of three kinds of flower essential oils was carried out by SPSS17.0 software. According to the principle of eliminating the variable of the maximum eigenvector corresponding to the principal component of the minimum eigenvalue, one variable was eliminated each time, and then the principal component analysis was carried out on the remaining variables. The sample score diagram (Figure 2) and the principal component load diagram (Figure 3) were obtained, respectively. Analysis of the essential oil scores of different flower colors (Figure 2) found that the highest score of PC1 was red flowers, and the highest score of PC2 was yellow flowers. The P. delavayi essential oils with dark-purple and red petals are distributed in the same quadrant, while the yellow petals are distributed in other quadrants, indicating that the essential oils of yellow petals are quite different from those of dark-purple and red petals. It can be seen from the principal component load diagram (Figure 3) that 11 compounds are scattered in each quadrant. According to the analysis of sample score diagram and principal component load diagram, (E)-linalool oxide (pyranoid), trans-cinnamaldehyde and 2-(5-Ethenyl-5-methyloxolan-2-yl)propan-2-ol may be the main components of dark-purple and red petal essential oils. Phytol, while benzaldehyde and bicyclo[3.2.2]nona-6,8-dien-3-one may be the main components of yellow petal essential oil [13,14,15]. 2.4. Antioxidant Properties 2.4.1. DPPH Radical Scavenging Activity DPPH is a stable nitrogen-centered proton free radical in organic solvents, and its scavenging capacity is significantly positively correlated with its antioxidant capacity. From Figure 4, it can be seen that essential oils from P. delavayi flowers with different colors have obvious scavenging effects on DPPH free radicals. In the mass concentration range of 10–100 μg/mL, the DPPH free radical scavenging activity increases with the increase of concentration. In the test concentration range, the DPPH scavenging activities of essential oils from different flower colors were lower than those of the positive control ascorbic acid (VC) and butylated hydroxytoluene (BHT). When the concentration of essential oil reached 100 μg/mL, the DPPH free radical scavenging activity of essential oils with different flower colors could reach 70% of that of the positive control at the same concentration. The scavenging activity of essential oil from red petals (89.19% ± 1.35) was significantly higher than that from dark-purple petals (84.47% ± 0.98) and yellow petals (80.4% ± 1.01). The IC50 values of essential oils from dark-purple, red and yellow P. delavayi flowers for DPPH radical scavenging activity were 30.49 μg/mL, 33.61 μg/mL and 41.07 μg/mL, respectively. 2.4.2. ABTS Radical Scavenging Activity ABTS radical scavenging activity is a widely used method for determining the total antioxidant capacity of biological samples. It is easy to operate and rapid. It can be seen from the results (Figure 5) that P. delavayi essential oils with different colors both have better ability to scavenge ABTS, and the scavenging effect is lower than that of the positive controls ascorbic acid (VC) and BHT. ABTS scavenging activity increased with the increase of essential oil concentration. The scavenging activity increased slowly before the concentration of 40 μg/mL, indicating that there was not only a dose-dependent relationship between the sample volume and the scavenging effect, but also a significant dose-dependent effect. When the essential oil concentration reached 100 μg/mL, the scavenging activity of dark-purple petal essential oil (75.55% ± 0.84) was significantly higher than that of red (72.11% ± 1.01) and yellow (64.25% ± 0.58) petals. The IC50 values of ABTS scavenging capacity of essential oils from dark-purple, red and yellow flowers of P. delavayi were 49.231 μg/mL, 53.565 μg/mL and 84.010 μg/mL, respectively. 2.4.3. Ferric Reducing Antioxidant Power Through the FeSO4 standard solution, the linear equation of the total reducing ability standard curve was y = 0.0114x + 0.022, R2 = 0.9994. The ferric reducing antioxidant activity of the tested samples increased with the increase of volume (Figure 6). When the substrate concentration changed from 10 to 100 μg, the FRAP value showed a good linear relationship with the volume of the sample. This indicated that essential oils from different flower colors of P. delavayi had high ferric reduction antioxidant activity, but the antioxidant activity was lower than that of the positive control ascorbic acid (VC) and BHT. When the concentration of essential oil reached 100 μg/mL, the ferric reducing antioxidant activity of dark purple petal essential oil (762.03 ± 4.84 μg/mg) was significantly higher than that of red (684.53 ± 3.01 μg/mg) and yellow (577.94 ± 5.58 μg/mg) petals. 2.5. Total Phenolic and Flavonoid Content Use the absorbance value to make a standard curve for the concentration of gallic acid standard solution: y = 0.0687x + 0.0443, R2 = 0.9946. The standard curve was made by absorbance value to the concentration of rutin standard solution: y = 0.9489x − 0.0053, R2 = 0.9996. As shown in Table 2, the contents of total phenols and flavonoids in dark purple petals were the highest, which were 28.71 ± 0.42 mg/g and 10.8 ± 0.06 mg/g, respectively, followed by red petals and yellow petals. 3. Discussion 3.1. Chemical Compositions in the Essential Oils Are Differentially Accumulated in Relation to the Flower Colors in P. delavayi Peony is a traditional and well-known ornamental plant in China. It is widely used as a garden and medicinal plant as well as providing ecological values, and it is suitable for developing functional foods [16]. The wide distribution of P. delavayi in Southwestern China led to the diversity in phenotypic traits such as leaf lobe number, leaf lobe width, flower colors, carpel and bract number [17]. Some studies have analyzed the metabolome of flavonoids of P. delavayi, and differential accumulation of the metabolites between dark-purple flowers, red flowers and yellow flowers was reported [3]. While the difference between dark-purple flowers and red flowers is small, the differences between the yellow color and other colors are large. This reflects similarity of the metabolic pathways between the red and dark-purple flowers and a divergence of the yellow flowers. These findings are consistent with the results of this study. According to the composition analysis on the essential oils by GC-MS and SPSS, P. lutea and P. rockii contain more terpenoids than other essential oil compositions, mainly oxidized linalool, isophytic alcohol, and farnesol and phytic alcohol [18]. Additionally, the compositions of P. suffruticosa essential oil extracted by hydro distillation extraction, ultrasonic assisted hydro distillation extraction and ultrahigh pressure extraction were compared [19]. The common compositions in the essential oils extracted by the three methods contained a large number of aroma compounds, such as linalool and its oxides, cumin alcohol, nerol, geraniol, lauryl alcohol, etc. [19]. These results are in line with our results in this study, i.e., aroma alcohols have the largest representation in the essential oils extracted from the three flower colors of P. delavayi (Figure 1). The percentage content of alkanes in P. delavayi essential oils from yellow petals was significantly higher than that from red petals and dark-purple petals (p < 0.05). This is represented by β-Copaene (20.34%), a sesquiterpene hydrocarbon. Although alkane compounds generally have a higher threshold value and have little contribution to the overall odor [20], β-Copaene presents in many essential oils and could become an influential factor of essential oils and other applications [21,22]. The higher percentage content of hydrocarbons may be due to the protective wax layer on the petal surface, and the main component of the wax layer was alkane compounds [23]. In terms of aldehydes, the essential oils of dark-purple petals showed significantly higher content than those in the red petals and yellow petals (p < 0.05). Aldehydes can provide stronger volatile and fat aroma [20]. Many intrinsic factors affect the color of flowers, such as regulations of metabolic pathways for pigments (e.g., anthocyanins, carotenoids, flavonoids), influenced also by ecological factors. These factors comprehensively influence the biosynthesis, accumulation and stability of anthocyanins that lead to different colors [24,25]. These factors also affect the compositions of essential oil extracted from petals, resulting in a differential accumulation in different colors of flowers in P. delavayi. Among the detected common ingredients, benzyl alcohol, phenethyl alcohol, α-cadinol, benzaldehyde and trans-cinnamaldehyde are all good spice substances. Phenethyl alcohol has which has sweet rose-like fragrance, and it is an edible spice permitted in USA. It has been widely used in the production of food processing industry [26]. Trans-cinnamaldehyde, as a hydroxy acid fragrance-containing compound, has a good fragrance holding effect. It is used as a raw material for flavoring to make the aroma of the main spices clearer. At the same time, it can be sterilized, disinfected and antiseptic in medical applications, especially for fungi. It has anti-ulcer effect, strengthening stomach and intestinal movement effect. It has been widely used in floral flavors for daily use [27]. Therefore, P. delavayi essential oils with different colors have a great applicable prospect in food, fragrance, medicine and other industries. These three kinds of essential oils extracted from flowers with different colors contain a variety of valuable chemical components, and can serve as the basis for further development and utilization. Particularly the high level of copaene in the yellow flower holds a promising potential in the future research and development. 3.2. Essential Oils of P. delavayi Possess Significant Antioxidant Activities Influenced by Polyphenolic and Flavonoids Free radicals can lead to the aging of human cells and many diseases, such as cancer and heart disease [28,29,30,31,32]. Since phenols and flavonoids extracted from plants can effectively mitigate free radicals in the body, they can help prevent and treat related diseases caused by free radicals [33,34,35,36]. A large number of literature review shows that flowers contain certain antioxidant activity, and there are significant differences in antioxidant activity among different flowers. The antioxidant activity of peony is relatively strong [37], which is related to its rich active substances. In this study, three antioxidant capacity methods (DPPH, ABTS and FRAP) were used to analyze and measure the extracts of purple, red and yellow flower petals of P. delavayi. Our results have shown the essential oils from all three colors of flowers provide good level of antioxidant activity, with the dark-purple flowers displaying the strongest. A previous study had also shown that purple flowers produced highest antioxidant activity [38], in agreement with our result. However, their measurements showed a higher antioxidant activity in the yellow than the red flowers they used [38]. The antioxidant activities of essential oils of the 14 peony samples measured by DPPH and ABTS assays showed that P. lutea had the strongest activity in the DPPH and ABTS, 306.42 TE µmol/100 g (DW) and 755.83 TE µmol/100 g (DW), respectively [18]. Previous studies revealed that scavenging activities of DPPH and ABTS free radicals from essential oil of P. lactiflora flower increased from morning-picked to afternoon-picked flowers [39]. Compared with this study, the scavenging effect of DPPH of P. delavayi is higher than that of VC and P. lactiflora,, but the scavenging effect of ABTS was weaker than that of VC and P. lactiflora. The study on the total phenol content of 23 edible flowers showed that the total polyphenols content of 22 flowers ranged from 4.83 to 68.19 mg·g−1 (FW) [40], while that of P. lactiflora was 222.01 mg·g−1 (FW), showing that the total phenol content in the petals of P. lactiflora is higher than that of other edible flowers. The contents of total flavonoids in the petals of different varieties of P. lactiflora were 4.90–11.00 mg·g−1 (FW) [41]. In this study, the content of total flavonoids in different flower colors of P. delavayi was 5.45–10.8 mg of RE (rutin equivalent)/g. Compared with different flower colors of P. delavayi, the content of total polyphenols was lower, while the content of total flavonoids was close to that in P. lactiflora. Total flavonoids were also expressed at a high level in some plant essential oils. The total flavonoid content of essential oils using water distillation extracted from Citrus reticulata peel [42], untan pumelo (Citrus grandis Osbeck) peel [43], aerial parts of Teucrium alyssifolium [44], and Laurocerasus phaeosticta [45] leaves were 14.63 ± 0.95 mg CE (catechin equivalents)/g, 134 mg QE(quercetin equivalents)/g, 16.82 mg of Ru(rutin equivalent)/g, and 37.53 mg of RE(rutin equivalent)/g, respectively. The total flavonoid content of Lycium europaeum fruit essential oil extracted with supercritical carbon dioxide can also reach 6.8 mg QE(quercetin equivalents)/g [46]. Studies have shown that P. delavayi is rich in flavonoids, including isolirin, kaempferol, quercetin, isorhamnetin, land eugenol and apigenin glucoside [3]. Through the transcriptome sequencing analysis of yellow and purple black flowers of P. delavayi, the color of yellow and purple red petals of P. delavayi is closely related to flavonoids. Using KEGG database, many single genes correspond to the pathways involved in the biosynthesis of secondary metabolites and anthocyanin deposition, including ‘flavonoid biosynthesis’ (179,0.74%), ‘flavonoids and flavonol biosynthesis’ (100,0.41%), ‘anthocyanin biosynthesis’ (10,0.04%) and ‘isoflavone Biosynthesis’ (65,0.27%). Overall, 3969 of 18,784 DEGs were mapped to 127 pathways. It was pointed out that 59, 39 and 5 DEGs were involved in ‘flavonoid biosynthesis’ (ko00941), ‘flavonoid and flavonol biosynthesis’ (ko00944) and ‘anthocyanin biosynthesis’ (ko00942), respectively, and all belonged to the flavonoid biosynthesis pathway. The research shows that the accumulation of flavonoids plays an important role in the formation and development of flower color of P. delavayi [1]. Additionally, different colors have different biochemical components. Studies have shown that flavonoid and polyphenolic accumulation in yellow and purple Passiflora caerulea fruit are significantly different [47]. The antioxidant capacity of P. delavayi petals with different flower colors, the total polyphenolic content and total flavonoids content can be used as a selection criterion for functional food [48]. In this study, we have clearly shown the positive correlation between the polyphenolic, flavonoids content in the essential oils of three different colored flowers (Table 2) and their antioxidant capacities (Figure 4, Figure 5 and Figure 6). These results provide an insight into future applications of peony essential oils in food, medicine and/or skin care fields. 4. Materials and Methods 4.1. Materials and Reagents The dark-purple, red, and yellow fresh petals of P. delavayi (Figure S2) were collected from the collection garden of P. delavayi germplasm resources located in Liangwang mountain, Chengjiang county, Yuxi city, Yunnan province, China (coordinates:102°53′55″ E, 24°45′38″ N, attitude: 2733 m). The samples were refrigerated at 4 °C and shipped to the laboratory for sampling and analysis. All chemical reagents including anhydrous ethanol, potassium persulfate (K2S2O8), ferric chloride (FeCl3), acetic acid (CH3COOH), sodium acetate (CH3COONa), sodium nitrite (NaNO2), sodium hydroxide (NaOH), aluminum nitrate (Al(NO3)3) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). 1,1-diphenyl-2-trinitrophenylhydrazine (DPPH), 2,2′-diazo-bis-3-ethylbenzothiazoline -6-sulfonic acid (ABTS), 2,4,6-tripyridyltriazine (TPTZ), and other analytical grade chemicals were purchased from Aladdin (Shanghai, China). Rutinum and gallic acid standards were purchased from Yuanye Bio-technology (Shanghai, China). 4.2. Main Instruments and Equipment SFE2-supercritical carbon dioxide fluid extractor (Applied Separations (ASI), Los Angeles, CA, USA); GC-MS (gas chromatograph, Agilent company, San Francisco, CA, USA; 7890A mass spectrometer, Waters company, Milford, CT, USA);N-1200B Rotary Evaporator (Shanghai Quanjie Instrument Co., Ltd., Shanghai, China). 4.3. Essential Oil Extraction First, 120 g of fresh petals of different colors were placed ut into the extraction tank. The carbon dioxide is pressurized to the extraction tank by a pressurizing pump. The extraction temperature is 40 °C, the extraction pressure is 25 MPa, the carbon dioxide flow rate is 15 L/h, and the entrainer is anhydrous ethanol. When the pressure reached the set pressure, the cycle extraction was started. The extraction time was set as 3 h, and the extracted essential oil was collected every 1 h for three times [49,50,51]. After the extraction, the translucent oily extracts were combined and mixed and collected, then concentrated by rotary evaporation with a rotary evaporator, after which they were weighed and the extraction yield was calculated. Extraction rate (%) = quality of essential oil/quality of petals (dry weight) × 100%. 4.4. GC-MS Composition Detection Gas Chromatography (GC) analysis: The essential oil was dissolved in anhydrous ethanol to prepare a solution with a concentration of 5.0 mg/mL for GC/MS testing. The essential oil was analyzed by using an Agilent Technologies HP 6890 Plus Gas chromatograph. The analysis was carried out on an HP-5MS column (Agilent Technologies, Santa Clara, CA, USA) measuring 30 m × 0.25 mm, film thickness 0.25μm. The carrier gas as hydrogen was at a flow rate of 1 mL/min. The injector temperatures were maintained at 250 °C. The column temperature was programmed from 40 °C with a 2 min hold to 220 °C with 10 min at 4 °C/min. A volume of 1.0 mL of each oil sample was injected into the GC by splitting method with the split ratio of 10:1. The inlet pressure was maintained at 6.1 kPa. The relative amounts of each component were calculated based on the GC peak area percentage that was using the GC HP-Chemstation software (Agilent Technologies, Santa Clara, CA, USA). Gas Chromatography-Mass Spectrometry (GC-MS) analysis: The electron energy of electron bombardment (EI) ion source is70 eV. The voltage of the electronic multiplier is 1.00 kV. The scanning range of mass charge ratio is 30~550. The temperature of ion source and interface is 250 °C. Identification of components: The acquired mass spectra were searched using NIST11 and NIST11s standard mass spectral libraries, and combined with the retention index for comprehensive characterization. The percentage content of each compound was calculated by the normalization method of chromatographic peak area. 4.5. Determination and Analysis of Physical and Chemical Indicators of Essential Oils Refer to ISO212:1973 and ISO279:1998 for the nomenclature principle of essential oils and the determination of relative density; refer to ISO280:1998 and ISO592:1998 for the determination of the refractive index and optical rotation of essential oils. 4.6. Determination of Antioxidant Capacity of Different Flower Colors 4.6.1. Determination of the Ability to DPPH Free Radical Scavenging At the condition of room temperature, 100 μL of the sample that was diluted to the appropriate concentration was fully mixed with 100 uL of DPPH (1, l-diphenyl-2-picrylhydrazyl) solution in a 96-well microplate reader. After dark reaction for 30 min, the absorbance (A) was measured at 517 nm. The reaction with 70% ethanol solution of equal volume instead of the sample was taken as blank (A0). The reaction of equal volume of 70% ethanol solution instead of DPPH solution was used as the sample blank (A1), and 10 μg/mL, 20 μg/mL, 40 μg/mL, 60 μg/mL, 80 μg/mL and 100 μg/mL ascorbic acid (Vc) and quercetin (BHT) were used as the positive control. The inhibition percentage of DPPH free radicals was calculated as inhibition percentage(%) = [A0 − (A − A1)]/A0 ∗ 100. Origin 2018 was used to perform nonlinear curve fitting between the clearance rate and the sample concentration (μg/mL) to calculate the IC50 value (the sample concentration when the DPPH free radical clearance rate was 50%). The DPPH free radical scavenging ability of the samples and the positive control was expressed by the IC50 value [52]. 4.6.2. Determination of the Ability to ABTS Free Radical Scavenging At room temperature, add 50 μL of sample and 200 μL of ABTS (7 mM) solution to a 96-well microtiter plate and mix well, and react in the dark for 6 min. After that, the absorbance value (A) was measured at 734 nm, the reaction of replacing the sample with 70% ethanol solution was the blank (A0), and the reaction of replacing the ABTS solution with 70% methanol solution was the sample blank (A1), which was repeated 3 times. The generally used formula of DPPH assay was used for the calculation of the percentage of inhibition of ABTS radicals. Free radical scavenging activity is expressed as IC50 value, the essential oil concentration (μg/mL) at which 50% of ABTS free radicals are inhibited. The IC50 value was calculated from the linear regression curve (concentration vs. effect) [52]. 4.6.3. Ferric Reducing Antioxidant Power The FRAP solution was prepared by mixing 1 mL TPTZ (10 mM/L), 1 mL FeCl3 (20 mmM/L) and 10 mL sodium acetate buffer (pH 3.6), which was stored at 37 °C. The 50 μL sample diluted to the appropriate concentration and 250 μL FRAP working solution were mixed in 96-well plate, and the absorbance was measured at 593 nm after 10 min of dark reaction at 37 °C. FRAP standard curve was drawn with FeSO4 standard solution concentration as ordinate and absorbance as abscissa. Ascorbic acid (VC) and quercetin (BHT) was used as antioxidant standard. The FRAP value was expressed as μg of FeSO4 equivalents/mg dry extract (DE) [53]. 4.7. Determination of Bioactive Compounds 4.7.1. Total Phenolic Content The Folin–Ciocalteu method was used to determine the total phenolic content in the extract. At the condition of room temperature, add 50 μL of standard and samples diluted to appropriate concentrations to a 96-well microtiter plate. Add 125 μL of Folin–Ciocalteu solution diluted to 0.5 mol/L. Add 100 μL of 7.5% (w/v) Na2CO3. The same volume of distilled water was added to the control group. The absorbance was measured at 765 nm after reacting in the dark for 30 min. Under the same conditions, measure the absorbance of gallic acid at 10 μg/mL, 20 μg/mL, 40 μg/mL, 60 μg/mL, 80 μg/mL and 100 μg/mL. Take the concentration of gallic acid as the abscissa, and take the absorbance value as the on the ordinate, draw a standard curve, calculate the total phenolic content. The result is expressed as mg gallic acid equivalent (GAE)/g fresh extract (FE), repeated 3 times [54]. 4.7.2. Total Flavonoids Content At the condition of room temperature, 20 μL NaNO3 (3%, w/v) was mixed with 40 μL appropriately diluted samples in a 96-well microtiter plate. After 6 min, 20 μL Al (NO3) 3 (6%, w/v) was added to react for 6 min. Then, 140 μL NaOH (4%, w/v) and 70% methanol (containing 1% formic acid) were added, the reaction was continued for 15 min. The absorbance of the mixture was measured at 510 nm. Draw the standard curve with the absorbance of rutin (100 μg/mL, 200 μg/mL, 400 μg/mL, 600 μg/mL, 800 μg/mL and 1000 μg/mL). Take the rutin concentration as the abscissa, the absorbance is the ordinate, the content of total flavonoids was calculated. Results were expressed as mg rutin equivalents (RE)/g fresh extract (FE), repeated 3 times [54]. 4.8. Data Processing and Analysis All experiments were repeated 3 times. Execel statistical test data were used. Data are presented as mean ± standard deviation (SD). SPSS v20.0 statistical software was used to test the significant differences of the experimental data (p < 0.05), and Origin Pro v9.0 was used for graphing. 5. Conclusions In this study, the phytochemical composition of the essential oils from P. delavayi dark-purple, red and yellow flowers were determined. Essential oil extraction yields varied in the different colors of flower petals. Compositions of essential oils extracted from P. delavayi flowers showed differential accumulations corresponding to different colors, with 2-(5-Ethenyl-5-methyloxolan-2-yl)propan-2-ol being the predominant phytochemical in dark-purple flowers and copaene being dominant and unique in the yellow flower. Essential oils from different flower colors showed high free radical scavenging activity and strong antioxidant activity, indicating that they may serve as promising sources of natural antioxidants. To the best of our knowledge, this is the first comparative study of the composition and biological activity of essential oils of peony plants with different flower colors, in order to provide data to support for the development of P. delavayi flower products and industry. Future studies should also focus on the phytochemicals and their biological activities of the essential oils of P. delavayi. Acknowledgments The authors thank the raw material support of the Dianxiangguose agricultural. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules27093000/s1, Table S1: Physical and chemical properties of essential oil from P. delavayi flowers with different colors; Table S2: Essential oil composition of P. delavayi flower with different colors; Table S3: Eigenvalues and contribution rates of principal components; Table S4: Principal component load matrix; Figure S1: GC-MS total ion chromatogram of essential oil from P. delavayi flowers with different colors (A: Dark-purple petals B: Red petals C: Yellow petals); Figure S2: Test materials of dark-purple, red and yellow flowers of P. delavayi. Click here for additional data file. Author Contributions X.Y. and J.W.(Juan Wang) planned and designed the research. X.Y. collected samples, extracted essential oil, antioxidant activity, analysis data, preparation of literature and manuscripts. J.W.(Junming Wang) operates the GC/GC-MS system. Z.W. guides the antioxidant activity experiment. J.L. provided experimental raw materials. X.Y. performed the experiments and analyzed data. X.Y. and J.W.(Juan Wang) wrote the manuscript. H.Z. revised and proofread the final manuscript. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement Not applicable. Conflicts of Interest No potential conflict of interest was reported by the authors. Sample Availability Samples of the compounds are available from the authors. Figure 1 Comparison of number and content of different types of compounds in P. delavayi essential oil with different flower colors. (A): Number of compounds. (B): Content percentage of compound. Different lowercase letters in the same class of compounds in the figure indicate significant differences (p < 0.05). Figure 2 Sample score chart. Figure 3 Load diagram of 11 components on the principal component. Figure 4 Comparison of the DPPH radical scavenging activities of the essential oil for different colors of P. delavayi flowers. Figure 5 Comparison of the ABTS radical scavenging activity of essential oil for different colors of P. delavayi flowers. Figure 6 Ferric reducing antioxidant power of essential oil for different colors of P. delavayi flowers. molecules-27-03000-t001_Table 1 Table 1 Common compounds of essential oil for different colors of P. delavayi flowers. Number Compounds Relative Content/% Dark-Purple Red Yellow 1 Benzyl alcohol 2.24 a ± 0.97 3.24 a ± 1.00 3.44 a ± 0.86 2 Phenethyl alcohol 1.76 ab ± 0.51 0.80 a ± 0.15 2.06 bc ± 0.92 3 (S,3E,7E)-α,α,4,8-Tetramethyl-3,7-cyclodecadiene-1-methanol 0.42 ab ± 0.13 0.38 ab ± 0.17 2.50 c ± 0.35 4 α-cadinol 1.11 a ± 0.38 0.94 ab ± 0.26 4.30 c ± 0.85 5 Phytol 2.15 a ± 0.58 3.48 ac ± 0.73 2.51 ab ± 0.62 6 2-(5-Ethenyl-5-methyloxolan-2-yl)propan-2-ol 20.88 ab ± 1.64 21.81 ab ± 1.04 4.06 c ± 0.82 7 Bicyclo[3.2.2]nona-6,8-dien-3-one 0.60 a ± 0.17 0.79 a ± 0.08 0.60 a ± 0.13 8 Fitone 4.34 c ± 0.52 2.20 ab ± 0.37 2.02 ab ± 0.28 9 Benzaldehyde 0.15 a ± 0.04 0.17 a ± 0.07 0.10 a ± 0.02 10 Trans-Cinnamaldehyde 4.92 a ± 0.64 4.31 a ± 0.81 0.33 b ± 0.06 11 (E)-linalool oxide (pyranoid) 11.90 a ± 1.45 10.85 ac ± 1.26 0.97 b ± 0.14 Note: Different letters lowercase in the same line indicate significant differences (p < 0.05). molecules-27-03000-t002_Table 2 Table 2 Comparison of total phenolic compounds and flavonoids in the essential oils for different colors of P. delavayi flowers. 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==== Front Materials (Basel) Materials (Basel) materials Materials 1996-1944 MDPI 10.3390/ma15093376 materials-15-03376 Article Bone Regeneration of Critical-Size Calvarial Defects in Rats Using Highly Pressed Nano-Apatite/Collagen Composites Hatakeyama Wataru 1 Taira Masayuki 2* https://orcid.org/0000-0002-4937-7111 Sawada Tomofumi 2 Hoshi Miki 1 Hachinohe Yuki 1 Sato Hirotaka 3 Takafuji Kyoko 1 Kihara Hidemichi 1 https://orcid.org/0000-0002-4231-3262 Takemoto Shinji 2 Kondo Hisatomo 1 Schierano Gianmario Academic Editor Muzio Giuliana Academic Editor 1 Department of Prosthodontics and Oral Implantology, School of Dentistry, Iwate Medical University, 19-1 Uchimaru, Morioka 020-8505, Iwate, Japan; whatake@iwate-med.ac.jp (W.H.); hoshmiki@iwate-med.ac.jp (M.H.); ykhcnh@iwate-med.ac.jp (Y.H.); takafuji@iwate-med.ac.jp (K.T.); hkihara@iwate-med.ac.jp (H.K.); hkondo@iwate-med.ac.jp (H.K.) 2 Department of Biomedical Engineering, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun 028-3694, Iwate, Japan; sawada@iwate-med.ac.jp (T.S.); takemoto@iwate-med.ac.jp (S.T.) 3 Division of Anatomical and Cellular Pathology, Department of Pathology, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun 028-3694, Iwate, Japan; staisei@iwate-med.ac.jp * Correspondence: mtaira@iwate-med.ac.jp; Tel.: +81-19-651-5110 08 5 2022 5 2022 15 9 337620 4 2022 04 5 2022 © 2022 by the authors. 2022 https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Osteo-conductive bone substitute materials are required in dentistry. In this study, highly pressed nano-hydroxyapatite/collagen (P-nHAP/COL) composites were formed by a hydraulic press. Critical-size bone defects (Φ = 6 mm) were made in the cranial bones of 10-week-old Wistar rats, in which P-nHAP/COL and pressed collagen (P-COL) specimens were implanted. Defect-only samples (DEF) were also prepared. After the rats had been nourished for 3 days, 4 weeks, or 8 weeks, ossification of the cranial defects of the rats was evaluated by micro-computed tomography (micro-CT) (n = 6 each). Animals were sacrificed at 8 weeks, followed by histological examination. On micro-CT, the opacity of the defect significantly increased with time after P-nHAP/COL implantation (between 3 days and 8 weeks, p < 0.05) due to active bone regeneration. In contrast, with P-COL and DEF, the opacity increased only slightly with time after implantation, indicating sluggish bone regeneration. Histological inspections of the defect zone implanted with P-nHAP/COL indicated the adherence of multinucleated giant cells (osteoclasts) to the implant with phagocytosis and fragmentation of P-nHAP/COL, whereas active bone formation occurred nearby. Fluorescent double staining indicated dynamic bone-formation activities. P-nHAP/COL is strongly osteo-conductive and could serve as a useful novel bone substitute material for future dental implant treatments. nano-hydroxyapatite collagen composite hydraulic press bone regeneration osteo-conduction micro-computed tomography bone remodeling JSPS KAKENHI21K17070 20K10015 20K10101 21K09984 This study was supported in part JSPS KAKENHI Grant Numbers 21K17070, 20K10015, 20K10101 and 21K09984. ==== Body pmc1. Introduction To restore highly atrophic alveolar ridges, alloplasts (bone substitute material) are frequently applied in dental implant therapy [1]. Although autogenous bone grafts are considered the gold standard [2], alloplasts are regarded as safer and more patient-friendly (i.e., less invasive) materials [3]. Further development of alloplasts has the potential to remarkably improve the current success rate of implant treatments (e.g., by enhancing vertical bone augmentation [3]). Calcium phosphates, such as hydroxyapatite (HAP) and tricalcium phosphate, have been used as bone substitute materials in various configurations, including particulates, plates, and blocks [4,5]. These large-sized materials show high biocompatibility and slow osteo-conductivity, although HAP has a slower degradation rate than tricalcium phosphate in vivo [5]. Furthermore, nano-hydroxyapatite (nHAP) has recently received considerable attention in the biomaterials research community because of specific characteristics such as superior bio-absorbability, osteo-conduction, and osteo-induction compared with conventional macro- and micro-sized HAP [6,7,8]. On the one hand, a practical disadvantage of nHAP is the difficulty associated with its handling; generally, nHAP is powdery and easily phagocytized by macrophage in vivo [9]. To overcome this disadvantage, nHAP powder has been combined with collagen (COL) as a scaffold material (a binder), thereby yielding nHAP/COL composite [10]. In our previous in vivo study, nHAP/COL composite (soft, porous sponge without pressing) was prepared by mechanical mixing and freeze drying [11]. This scaffold material indicated osteo-conductive effects at 4 weeks in rat calvarial bone defects; however, new bone formation at 8 weeks was slightly decreased due to the rapid bio-absorption of the material. Thus, further improvement was needed for achieving a prolonged osteo-conductive effect of nHAP/COL composite. One possible method to address this requirement is the condensation of the composite using a hydraulic press [12]. This technique has already been employed to decellularize and sterilize animal skin and meat in the food industry [13,14]. Under pressing, the collagen structure (e.g., α-helix and β-sheet) is significantly altered, changing the functional and structural properties of collagen, dependent on the magnitude and period of pressure [13,14]. Pressed collagen might possess increased hydrophobicity, compressive strength, and in vivo longevity [13]. On the other hand, for preparation of HAP, cold isostatic pressing was used as one process for raw materials prior to sintering [15]. Although once sintered HAP might not be chemically altered by pressing, pressing might cause HAP particles to agglomerate, leading to different physical properties [16]. In addition, hot isostatic pressing has often been employed to produce dense and highly crystalline HAP [17]. In our previous studies, the pressing technique could embed HAP particles within COL with greater physical energy and the osteogenic differentiation of osteoblasts was accelerated on pressed nHAP/COL (P-nHAP/COL) composite compared with pressed COL(P-COL) in vitro [12,18]. However, the evidence of P-nHAP/COL is still insufficient for clinical use. Up to now, there have been little reports concerning the effect of high pressure on not only physical and chemical properties but also in vivo osteo-conduction of P-nHAP/COL composites. It is highly expected to unveil these properties. Currently, most bulk scaffold materials are porous with interconnected porosity [19]. P-nHAP/COL composites are, however, plain and dense without pore structure, and their usefulness remains unknown. The novelty of this research lies in their clarification. A rat-skull critical-size defect model has been employed in many previous studies to evaluate bone regeneration by osteo-conductive materials [20,21]. Three-dimensional (3D) micro-computed tomography (micro-CT) analysis is commonly used to observe microscopic bone structures and quantify bone formation within bony defects [22,23]. Histological observations often follow, using non-decalcified sliced specimens stained with Villanueva solution and fluorescent double labeling [24]. Therefore, in this study, P-nHAP/COL composites were prepared by mechanical mixing, freeze drying, and hydraulic press; these were then evaluated to determine their utility as osteo-conductive bone substitute materials in a rat-skull critical-size bone-defect model using micro-CT analyses and histological observations. 2. Materials and Methods 2.1. Preparation of P-nHAP/Col Composite Material Virus-free medical grade COL pellets (NMP collagen PS, Nippon Meat Packers Inc., Tokyo, Japan) (1 g) were dissolved in distilled water (28 mL) in 50-mL polystyrene conical tubes (Greiner Bio-one, Frickenhausen, Germany) at 4 °C. The resulting acidic solution was neutralized with a 0.1 N NaOH solution (6.5 mL) in a plastic dish (100 × 70 × 12 mm) to obtain a COL gel with an appropriate pH (7.5). A mixture of COL gel and nHAP powder in the solution (MHS-00405 type nano SHAp, Sofsera, Tokyo, Japan) (1.5 g) was prepared by manual mixing. This nHAP powder material was spherical in shape and exhibited a very small average particle size (40 nm) [25]. The mixture gel was frozen at −80 °C for 3 h and freeze dried (FD-5N, Eyela, Tokyo, Japan) overnight. The resulting sponge was successively cross-linked by dehydrothermal treatment at 140 °C for 24 h in a vacuum-dry oven (VO-300, AS ONE, Tokyo, Japan). A hydraulic press (NT-100H, Sansho Industry, Osaka, Japan) (Figure 1a) ensured that the material in the metal mold (9.95 mm in inner diameter and 20 mm in height) was consistently pressurized, while manual and oil-produced pressure (29.4 kN; 3000 kgf) were applied concurrently from the top and bottom (i.e., co-axial bidirectional pressing) for 2 min. The resulting sheet, prepared by high pressure (P-nHAP/COL) (Figure 1b), was punched out to obtain specimens approximately 6 mm in diameter and 1 mm in height. As controls, punched pressed collagen (P-COL) specimens without nHAP were used. These disks were sterilized with ethylene oxide gas for at least 24 h and kept in a vacuum desiccator. Scanning electron microscopy (SEM) observations of both COL and nHAP/COL before and after hydraulic press were performed by using a scanning electron microscope (SU8010, Hitachi High-Tech Corp., Tokyo, Japan) at 2 or 10 kV after plasma coating with OsO4 to understand morphological changes by hydraulic press. In addition, the pore sizes of COL and nHAP/COL sponges (pre-forms of P-COL and P-nHAP/COL) were calculated from different locations (n = 20) per one sample. 2.2. Animal Experiments Eighteen male Wistar rats weighing 340 ± 16 g (mean ± SD) were used. All rats were housed in separate cages (three rats per cage) with standard diet and water ad libitum. Under anesthesia with a mixture of isoflurane (3 vol.%) and oxygen (0.5 L/min) gas generated by a carburetor (IV-ANE, Olympus, Tokyo, Japan), the centers of the rat calvariae were shaved and sterilized with 10% povidone iodine, followed by local injection of anesthetic (0.2 mL, 2% lidocaine with 1:80,000 epinephrine). Then, full-thickness periosteum flaps were elevated and bone defects were created using a trephine bur (6 mm in diameter; Implant Re Drill System, GC, Tokyo, Japan). Six P-nHAP/COL and six P-COL specimens were implanted in rat calvarial bone defects, and six holes were left empty (DEF). Each flap was repositioned and closed with soft nylon (Softretch 4-0, GC). At 8 weeks after surgery, all rats were sacrificed by CO2 inhalation. Animal experiments were performed in accordance with the guidelines for the care and use of laboratory animals and approved by the Institutional Ethics Committee of Iwate Medical University on 9 September 2013 (approval number: #25-015). 2.3. Micro-CT Imaging New bone formation in the defect area of rat calvarial bone that contained P-nHAP/COL or P-COL was examined by using a 3D micro-CT system (eXplore Locus, GE Healthcare, Boston, MA, USA). All samples were scanned at 90 μm intervals at 80 kV and 450 μA, and Vextus Factor-compiled storage files (VFF data) were acquired. After scanning, transverse reformatted micro-CT images of the calvariae were reconstructed using 3D imaging analysis software (MicroView Version 2.2, GE Healthcare). The instrumental opacity threshold value was set to 8000 to minimize the interference from other bony tissues (e.g., maxillary bones and the cranial base). ImageJ 1.53k software (National Institutes of Health, Bethesda, MD, USA) was used to quantify the radiographic opacity of the defect area containing P-nHAP/COL or P-COL. DEF was also evaluated. Micro-CT measurements (i.e., X-ray opacity) were statistically assessed with respect to intra- and inter-individual differences via repeated-measures analysis of variance (ANOVA) followed by the least significant difference post-hoc test, using SPSS software (version 16.01; SPSS Inc., Chicago, IL, USA). Intra- and inter-individual differences were considered significant if p-values were less than 0.05 (p < 0.05), and highly significant if p-values were less than 0.01 (p < 0.01). Tukey multiple comparison tests were used to determine which pairs of groups were significantly different. Graphs were generated using Kaleida graph software (version 4.5.3, Hulinks Inc., Tokyo, Japan). 2.4. Histological Observations Fluorescent double staining was performed on one of six rats implanted with P-nHAP/COL at 8 weeks of age. Sequential labeling was performed to evaluate postoperative bone formation and remodeling. Rats underwent an intraperitoneal injection of tetracycline (TC) (2 mg/100 g body weight) dissolved in phosphate-buffered saline (PBS(–)) (40 mL) at 5 weeks after surgery, followed by calcein (CL) (1 mg/100 g body weight) in PBS(–) (40 mL) at 7 weeks and 7 weeks and 5 days (2 days before sacrifice) after surgery. The rat calvariae with P-nHAP/COL were then processed for non-decalcified histology. After a 1-week fixation in 70% ethanol at 4 °C, the samples were dehydrated in a graded series of ethanol (1 day at each concentration) and then placed in pure acetone for 24 h. Those samples were subsequently stained with Villanueva solution (222-01445, Wako, Osaka, Japan). Finally, the samples were embedded in methylmethacrylate for 4 days and chemically polymerized for 10 days. The non-decalcified resin blocks (~15 × 15 × 20 mm) were cut sagittally using a circular diamond cutter (MC-201 Microcutter, Maruto, Tokyo, Japan). Sections were attached to plastic slides, ground to a thickness of 20 μm using a precision lapping machine (ML-110N, Maruto, Tokyo, Japan), and then manually polished in accordance with the method of Frost [26]. As controls for comparison with P-nHAP/COL, ground sections of calvarial defects without material (DEF) were also prepared, employing identical staining and fluorescent labeling techniques. Histological observations were performed using fluorescence microscopy (All-in-one BZ-9000, Keyence, Osaka, Japan). 3. Results 3.1. Scanning Electron Microscopy (SEM) Observations Prior to pressing the COL and nHAP/COL sponges using hydraulic press, highly inter-connected pores were in both sponges (Figure 2). The pore sizes of COL were 215.5 ± 100.9 µm and 122.7 ± 44.4 µm in short and long diameters, respectively, whereas those of nHAP/COL were 240.8 ± 70.5 µm and 93.0 ± 19.9 µm. In the nHAP/COL sponge, some aggregates of nHAP particles were observed on the COL wall, whereas most nHAP existed in COL (Figure 2b). Characteristically, both P-COL and P-nHAP/COL were dense with plain surfaces without pores after pressing (Figure 3). In P-COL, multiple plane surfaces were observed under low magnification (Figure 3a); their micro-surfaces exhibited grooves under high magnification (Figure 3b), reflecting collagen fibers. In P-nHAP/COL, the surfaces were highly interleaved with nHAP particles under low magnification (Figure 3c), whereas agglomerated nHAP particles were found under high magnification (Figure 3d). Thus, hydraulic press apparently altered the sponge configurations of COL and nHAP/COL (Figure 2) to bulk plain structures of P-COL and P-nHAP/COL (Figure 3). It became evident for P-nHAP/COL, nHAP powder were highly condensed in the COL matrix, compacted as aggregate, and widely exposed outside (Figure 3b,d). 3.2. Micro-CT Figure 4 and Figure 5 show representative micro-CT image analysis results of the region of interest (ROI) of cranial defects and the overall X-ray opacity in the defects with and without implant materials at 3 days, 4 weeks, and 8 weeks after surgery, respectively. With P-COL and DEF, the defects at 3 days were relatively X-ray transparent (Figure 4a,b), as neither COL nor soft tissue is X-ray opaque. The opacity of the defects with P-COL and DEF increased slightly from 3 days to 4 weeks due to soft tissue infiltration; opacity plateaued from 4 to 8 weeks (Figure 5), implying no new active bone formation. In contrast, with P-nHAP/COL, both nHAP and newly formed bone were X-ray opaque (Figure 4c). With P-nHAP/COL, the defect was originally radiopaque at 3 days, due to the radiopacity of nHAP. Although P-nHAP/COL was gradually digested, leading to reduced radiopacity in the defect zone, the radiopacity increased overall due to the ossification of new bone formation and growth. The opacity of the defect area implanted with P-nHAP/COL was significantly greater at 8 weeks after surgery than at 3 days (p < 0.05) (Figure 5); this reflected considerable new bone formation, which overwhelmed the reduction in radiopacity associated with degradation of P-nHAP/COL. The results of two-way repeated-measures ANOVA (n = 6) of the X-ray opacity data (Figure 5) were as follows: factor of samples: p = 3.48 × 10−9 (<0.01), factor of periods: p = 2.33 × 10−7 (<0.01), and samples × periods interactions: p = 0.32. The defect zones implanted with P-nHAP/COL had mean opacity values of 96, 111, and 135 at 3 days, 4 weeks, and 8 weeks, respectively. P-nHAP/COL itself was moderately radiopaque in the defect at 3 days postoperatively. The opacity increased significantly from 3 days up to 8 weeks (between 3 days and 8 weeks, p < 0.05), indicative of active bone formation. The time-dependent bone formation was morphologically identified (Figure 4c). On the other hand, the defect zones with P-COL showed mean opacity values of 69, 84, and 90 at 3 days, 4 weeks, and 8 weeks, respectively (between 3 days and 4 weeks, p < 0.05; between 3 days and 8 weeks, p < 0.05). Those of DEF showed values of 61, 89, and 96, respectively (between 3 days and 4 weeks, p < 0.01; between 3 days and 8 weeks, p < 0.01). With both P-COL and DEF, bone formation in the defect area with time was fairly limited and sluggish (Figure 4a,b). At 3 days, 4 weeks, and 8 weeks, P-nHAP/COL always had higher X-ray opacity than that of P-COL and DEF (p < 0.05 or p < 0.01), while the opacity of the defect without implant material (DEF) was similar to that with P-COL at all time points (Figure 5). In summary, the 3D micro-CT image analyses suggested that P-nHAP/COL was osteo-conductive in rat critical-size cranial defects. 3.3. Histological Observations Villanueva-stained histological images of a cranial bone defect (red rectangle) filled with P-nHAP/COL and without implant material (DEF) at 8 weeks after surgery are shown in Figure 6a,b, respectively. New bone appeared white, whereas older bone was light yellow to brown in color. When filled with P-nHAP/COL, most of the remaining fragmented P-nHAP/COL particles (indicated by *) were present in the upper half of the cranial defect. Island-like areas of new bone were detected in the lower half of the defect (indicated by NB in Figure 6a). In contrast, no active bone formation was noted in the cranial defect only (DEF), in which only connective tissues were detected (Figure 6b). Figure 7 shows the magnified image of one spot from the red rectangle cranial bone defect area in Figure 6a, clarifying phagocytosis of fragmented P-nHAP/COL particles (*) in a cranial bone defect filled with P-nHAP/COL at higher magnification at 8 weeks after surgery. Multinucleated giant cells (virtually, osteoclasts), (#) as well as macrophages, had direct contact with and actively digested the remaining P-nHAP/COL particles (*). The nucleus appeared blue. Multinucleated giant cells (osteoclasts) (#) appeared as blue aggregates, whereas macrophages appeared as single blue dots. Collagenous tissues appeared as tangled threads. The remaining P-nHAP/COL particles (*) were brown. Double-stained fluorescence images of a cranial bone defect filled with P-nHAP/COL and without implant material (DEF) 8 weeks after surgery are shown in Figure 8a,b, respectively. The defects filled with P-nHAP/COL showed island-like areas of new bone, localized in the lower half of the defect. Active bone formation was confirmed by double staining with yellow TC at 5 weeks after surgery and green CL at 7 weeks after surgery and at 2 days before sacrifice. Double staining clarified the dynamic bone formation activities in both inward and outward directions (Figure 8a). New bone formation occurred in one of two ways: extension of existing bone edges or island-like bone formation inside the defect zone. In contrast, no active bone formation was seen in the defect gap zone of the counterpart sample (DEF) (Figure 8b). Figure 9 shows the magnified images of one spot from cranial defect in Figure 8a, namely, Villanueva-stained and double fluorescently labeled slices of the new bone formation zone in the cranial defect filled with P-nHAP/COL at higher magnification. In the Villanueva-stained images (Figure 9a), new bone was covered with purple (uncalcified) osteoid produced by osteoblasts, which would soon be calcified, leading to growth of bone. The new bone had many spot-like osteocytes. In TC fluorescently labeled samples (Figure 9b), a strong yellow line was observed at injection (at 5 weeks after surgery), but the yellow color diffused over 5 weeks postoperatively. In CL fluorescently labeled samples (Figure 9c), marked localization at two injection times (at 7 weeks after surgery and at 2 days before sacrifice) was reflected in the color green. In overlaid TC + CL (Figure 9d), dynamic bone formation trends were well characterized. The top two bone islands were formed quickly after 7 weeks, whereas most of the bottom bone was formed before 7 weeks. 4. Discussion Our assumption discussed in the Introduction section, i.e., that the new biomaterial, P-nHAP/COL, would be osteo-conductive in rat cranial bone defects, was confirmed by both micro-CT and histological observations. P-nHAP/COL led to successful new bone formation (covering approximately 40–50% of the total area) in critical-size defects in rat skulls at 8 weeks after surgery (Figure 4c, Figure 6a and Figure 8a); this represented remarkable success. In this context, the following should be noted: bone regeneration was not achieved in the defect filled with P-COL that lacked nHAP (Figure 4b), a large single-body HAP disk sintered from nHAP particles was bio-inert and hindered bone regeneration in the defect zone in a previous study [27], the P-nHAP disk without COL in our preliminary experiments was also bio-inert and hindered bone regeneration (i.e., as a large HAP block), and the nHAP/COL porous sponge counterpart without hydraulic press was more hydrophilic with lower agglomeration energy and reduced density, compared with P-nHAP/COL, and tended to virtually disintegrate in the bone defect for up to 4 weeks after implantation [11]. Thus, effective osteo-conduction was solely facilitated by P-nHAP/COL in the rat cranial bone defect. The success of P-nHAP/COL with respect to osteo-conduction may be attributed to two key factors, namely the use of (1) hydraulic press and (2) nHAP. (1) Application of hydraulic press considerably changed the physical state of nHAP/COL from a soft porous sponge to a highly condensed hard disk. With intensified physical agglomeration energy (i.e., surface energy), the P-nHAP/COL composite disk became more hydrophobic and less bio-absorbable [12], allowing bone regeneration in rat critical-size cranial bone defects for the period up to 8 weeks. (2) HAP in HAP/COL composites showed different bone regeneration efficacy depending on size (i.e., macro-, micro-, or nano-size) [28]. The nano-size of nHAP in the composite seemed to play an important role in rapid bone regeneration [29]. Precursors of multinucleated giant cells (i.e., osteoclasts) attached to, differentiated, and matured on P-nHAP/COL (Figure 7). Low-level dissolution of calcium and phosphate ions from nHAP in P-nHAP/COL might be chemotactic to precursor cells [30], followed by differentiation and maturation of osteoclasts. COL is minimally osteo-conductive, partly because it serves as a scaffold for stem cells and osteoblasts [31], and is also an excellent physical stabilizer for P-nHAP. Indeed, natural bone consists largely of COL and nHAP [32], and its basic structure resembles that of our P-nHAP/COL sample [33]. The COL portion of the composite may assist in formation of the ruffled border of mature osteoclasts [34]. Proton pump H+-adenosine triphosphatase (ATPase) of mature osteoclasts could secrete H+ ions, which effectively dissolve nHAP, while the enzyme Cathepsin K from osteoclasts efficiently degrades highly condensed P-COL [35]. Dual use of hydraulic press and n-HAP is recommended in future biomaterial applications to prepare new HAP/COL-based biomaterials. In addition, growth factors could be coupled with n-HAP for accelerated bone regeneration [36]. Rapid bone regeneration at the rat cranial bone defect was apparently achieved by bone remodeling, in which the presence of multinucleated giant cells (osteoclasts) was important (Figure 7). Notably, new bone formation occurred in the zones beneath or near fragmented P-nHAP/COL particles, in contact with and digested by multinucleated giant cells (osteoclasts) (Figure 6a, Figure 7 and Figure 8a). This bone remodeling [37] may be essential for rapid bone regeneration within critical-size rat cranial defects [38]. As mentioned, disintegrating nHAP/COL particles may attract precursor cells, which then differentiate and mature into osteoclasts [39], leading to rapid bone remodeling. Osteoclasts also produce soluble factors (e.g., receptor activator of nuclear factor κB (RANK)), stimulating and activating nearby pre-osteoblasts and osteoblasts expressing RANK ligand (RANKL) [40]. As a result, a bone remodeling system mediated by osteoblasts and osteoclasts [37,40,41] may be established in rat cranial bone defects filled with P-nHAP/COL, thus facilitating active bone remodeling. P-nHAP/COL appeared to act as old natural bone to be replaced [42]. Dissolution of calcium and phosphate ions from nHAP may further cause angiogenesis in the defect zone [43], thus aiding bone formation. The P-nHAP/COL applied in implant surgery can be in disk, particulate, or film form. Before P-nHAP/COL is applied clinically in humans, successful treatment of cranial bone defects must be demonstrated in studies using larger animals, such as dogs and monkeys, because bone-forming capability in cranial areas varies among animal species; bone defects of rats are regenerated more quickly compared with larger animals and humans [44]. The overall findings of this study are as follows: P-nHAP/COL was highly osteo-conductive in critical-size calvarial defects in rats, promoting the formation of newly regenerated bone (covering up to 50% of the total area) for up to 8 weeks after implantation. Bio-degradation of P-nHAP/COL effectively facilitated osteo-conduction via bone-remodeling. P-nHAP/COL could further increase bone regeneration over 8 weeks (to >50% of the total cranial defect area) and might assist in bone regeneration in dental patients with an atrophic alveolar ridge in the near future. Referring to current alloplasts employed in clinical dentistry, one commercial sintered apatite bone substitute material (Spongious granules 0.25 to 1 mm) (Bio-Oss, Geistlich Pharma AG, Wolhusen, Switzerland) was preliminary tested in the rat cranial bone defect. This material is considered as deproteinized bovine bone mineral, and has been most utilized worldwide [45]. However, this material was very inert and did not exert effective osteo-conductive activities in rat cranial bone defects at 8 weeks after surgery (Figure S1). In clinical situation, a longer period exceeding 6 to 7 months was required to bio-absorb this material, followed by new bone formation [46]. Thus, P-nHAP/COL could be more suitable for enabling quick and large-volume bone formation in bone defect areas without use of growth factors [36]. 5. Conclusions Within the limitations of this study, the evaluation of P-nHAP/COL (pressed nano-apatite/collagen) composite can be summarized as follows:P-nHAP/COL composite was prepared by mechanical mixing, freeze drying, dehydrothermal cross-linking, and hydraulic press. The composite was punched into critical-size disks. P-nHAP/COL disks were implanted into rat critical-size cranial defects, and bone regeneration status was evaluated by micro-CT imaging and histological examination. After 8 weeks of observation, P-nHAP/COL was highly osteo-conductive in vivo. The defect zone implanted with P-nHAP/COL rapidly became X-ray opaque, indicating newly formed bone. Active bone remodeling composed of both osteoblasts and osteoclasts was histologically observed. P-nHAP/COL could be used as a new osteo-conductive bone substitute in dental implants, although further studies using larger animals (e.g., rabbits and dogs) are needed prior to clinical testing. P-nHAP/COL would be applied to bone-shallow area, followed by dental implant treatment (Figure 10). Acknowledgments An independent statistician, Fumie Aizawa, Center for Liberal Arts and Sciences, Iwate Medical University, reviewed the methodology, results, and conclusions of this paper. Supplementary Materials The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ma15093376/s1, Figure S1: Villanueva-stained histological images of the zone obtained from the rat cranial bone defect filled with one commercial alloplast (Bio-Oss) at 8 weeks after surgery at low magnification (a) and high magnification (b) with CL- fluorescent labeling (c); micro-CT sagittal section image (d). Click here for additional data file. Author Contributions Conceptualization, M.T., W.H. and T.S.: methodology, M.T. and W.H.; validation and investigation, W.H., M.H., Y.H., K.T., H.K. (Hidemichi Kihara), M.T., T.S.; S.T., H.S. and H.K. (Hisatomo Kondo); writing—original draft preparation, W.H., M.T. and T.S.; writing—review and editing, H.K. (Hidemichi Kihara) and S.T.; supervision and project administration, H.K. (Hisatomo Kondo). All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement The animal study protocol was approved by the Institutional Ethics Committee of Iwate Medical University on 9 September 2013 (approval number: #25-015). Informed Consent Statement Not applicable. Data Availability Statement All data are included in the manuscript. Conflicts of Interest The authors declare no conflict of interest. Abbreviations ANOVA Analysis of variance ATPase Adenosine triphosphatase CL Calcein COL Collagen DEF Defect only HAP Hydroxyapatite HAP/COL Hydroxyapatite/collagen micro-CT micro-computed tomography nHAP Nano-hydroxyapatite PBS Phosphate-buffered saline P-COL Pressed collagen P-nHAP/COL Pressed nano-hydroxyapatite/collagen RANK Receptor activator of nuclear factor κB RANKL Receptor activator of nuclear factor κB ligand ROI Region of interest SEM Scanning Electron Microscopy TC Tetracycline VFF Vextus Factor-compiled storage files 3D Three-Dimensional Figure 1 (a) A hydraulic press machine used in this study, (b) specimen on the die. Figure 2 Scanning Electron Microscopy (SEM) photographs of sponges ((a): collagen (COL) and (b): nano-hydroxyapatite/collagen (nHAP/COL)) prior to hydraulic press at low magnification (×100). Figure 3 SEM photographs of pressed collagen (P-COL) and pressed nano-hydroxyapatite/collagen (P-nHAP/COL) ((a): P-COL at low magnification (×500), (b): P-COL at high magnification (×20,000), (c): P-nHAP/COL at low magnification (×500), and (d): P-nHAP/COL at high magnification (×20,000)). Figure 4 Micro-computed tomography (CT) images of cranial defects with and without implant materials ((a): defect only (DEF), (b): P-COL, and (c): P-nHAP/COL)) at 3 days, 4 weeks, and 8 weeks after surgery. Figure 5 Graphs of X-ray opacity in the defects with and without implant materials (DEF, P-COL, and P-nHAP/COL) (n = 6). Note: Pairs indicated with the same letter were significantly different. Note: a and b, p < 0.01; c–e, p < 0.05. Figure 6 Villanueva-stained histological images of cranial bone defect filled with P-nHAP/COL ((a): red rectangle) and without implant material (DEF: (b)) at 8 weeks after surgery. Note: (a) Most fragmented P-nHAP/COL remnants (*) existed in the upper half of the cranial defect (red rectangle). NB = newly formed bone. (b) Connective tissues alone were seen in the defect gap. Figure 7 Magnified image of one spot from red rectangle cranial bone defect area in Figure 6a. Note: Nuclei were stained blue. Multinucleated giant cells (osteoclasts) (#) and single-nucleated macrophages were in contact with and digested fragmented remnant P-nHAP/COL particles (*). Figure 8 Double fluorescently stained images of cranial bone defect filled with P-nHAP/COL (a) and without implant material (DEF; (b)) at 8 weeks after surgery. Top = contrast; second = TC, fluorescently labeled; third = CL, fluorescently labeled; bottom = TC + CL overlay images. Figure 9 The magnified images one spot from cranial bone defect area in Figure 8a, namely, Villanueva-stained histological image of the new bone formation zone obtained from the cranial bone defect filled with P-nHAP/COL at 8 weeks after surgery at higher magnification (a) and double fluorescently labeled images such as TC fluorescent labeling (b). CL fluorescent labeling (c), TC + CL overlay (d). Note: In Villanueva-stained images (a), new bones (NB) with osteocytes (dots) were lined with purple-stained osteoid. Figure 10 Schematic illustration of the future use of P-nHAP/COL: (a) bone-shallow area on mandible, (b) material application, (c) bone augmentation, (d) drilling and tapping for dental implant, (e) implant insertion, bone healing, and osteo-integration, and (f) screwing super-structure to dental implant. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Kim D.M. Nevins M.L. 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