Acacia nilotica sawdust activated carbon (ASAC) as an adsorbent for the adsorption treatment of toxic Indigo Carmine Dye (ICD). The effect on the adsorption characteristics of ASAC of the influent ICD concentration, flow rate, and column bed depth has been investigated. According to the column study, the highest efficiency of ICD removal was approximately 79.01% at a preliminary concentration of 100 mg/L with a flow rate of 250 mL/h at a bed depth of 30 cm and adsorption power of 24.67 mg/g. The experimental work confirmed the dependency of break-through curves on dye concentration and flow rate for a given bed depth. Kinetic models were implemented by Thomas, Yoon–Nelson, and Bed-depth-service-time analysis along with error analysis to interpret experimental data for bed depth of 15 cm and 30 cm, ICD concentration of 100 mg/L and 200 mg/L and flow rate of 250 mL/h, and 500 mL/h. The analysis predicted the breakthrough curves using a regression basin. It indicated that all three models were comparable for the entire break-through curve depiction. The characteristic parameters determined by process design and error analysis revealed that the Thomas model was better followed by the BDST and Yoon–Nelson models in relating the procedure of ICD adsorption onto ASAC. B-E-T surface area and B-E-T pore volume of ASAC were 737.76 m2/g and 0.2583 cm3/g, respectively. S-E-M and X-R-D analysis reveal the micro-porous and amorphous nature of ASAC. F-T-I-R spectroscope indicate distinctive functional assemblies like -OH group, C–H bond, C–C bond, C–OH, and C–O groups on ASAC. It could be computed that the ASAC can be used efficiently as an alternative option for industrial wastewater treatmentA continuous mode fixed-bed up-flow column adsorption analysis was conducted utilizing  About 10–15% of textile industry dyes are discharged in streams, making the effluents aesthetically unpleasant. Discharge of such colored effluents is dangerous from an environmental and ecological point of view. Color obstructs sunlight dispersion, hinders photosynthesis action, and constrains the growth and metabolism of aquatic biota. The eradication of color from the effluent-carrying dye is a crucial challenge owing to difficulties in handling conventional and fixed treatment methods to manage such wastewaters. Consequently, such techniques are ineffective but cannot be utilized to handle the large variety of organic pigment discharge efficiently.Colorants and dyestuffs are commonly used in manufacturing and commercial industries, including clothing, rubber, pharmacy, leather, printing press, fruit, cosmetics, carpet, and paper. The textile industry ingests more than 80% of the entire production of dyestuff, creating it the principal consumer 2. Industrial activated carbon (AC) is a well-known adsorbent used for admirable adsorption capabilities. However, it is expensive, and its rejuvenation makes it pricier in some world regions . Hence it is desirable to search for low-cost alternatives such as natural ingredients, agricultural by-products, or industrial waste as an adsorbent material. These products do not need any additional or expensive pre-treatment and should be regarded as possible adsorbents to eliminate dye-containing wastewater. These low-cost products provide acceptable output for diagnosing-colored effluents in laboratory measurements3.Surface assimilation, or the deposit of impurities on the surface of a solid, is an attractive alternative treatment. Suitable for its convenience, simplicity of use, handling, sludge-free facility, and rejuvenation potential, it has become trendy and appealing, demonstrating an appropriate process for extracting non-biodegradable chemicals  from wastewater4, orange peels activated carbon5, chicken feathers6, pongamia pinnata seed shell activated carbon7, palm wood cellulose activated carbon8,banana peels activated carbon9, acacia glauca sawdust activated carbon10, babul sawdust activated carbon11, etc. have been considered to adsorb dyestuffs, heavy metals, and other impurities from solution as unconventional adsorbents. Mall et al. 13 provided a critical analysis of such minimum cost adsorbents to diagnose different wastewaters to remove several toxins carrying wastewaters. Sorption of different adsorbates using other adsorbent materials like boron by sepiolite 14, azo dye by jute fibers15, phenolic compounds16, methylene blue dye by zeolite 17, etc. in column mode is also described by some researchers. But dye adsorption  by activated carbon of Acacia nilotica sawdust in column mode is hardly reported. Indigo Carmine is one of the dark blues colored poisonous and toxic, crystalline type of powdered dye having a chemical composition of C16H8Na2O8S2N2, molecular weight is 466.367 g/mol, and distinctive wavelength of 610 nm. It is very commonly used as a colorant and an indicator of pH in various activities. It has some drug allergies due to which it can damage the life of man 18, affects bones and chromosomes, and can cause dangerous hemodynamic effects on living beings 19. This study investigates the efficacy of Acacia nilotica sawdust activated carbon (ASAC) foradsorptive elimination of poisonous IndigoCarmine Dye (ICD) in a constant stable bed up-flow column.Several papers reported that manylow-cost materials such as natural, agricultural, or industrial wastes like Acacia nilotica sawdust activated carbonAcacia nilotica sawdust activated carbon (ASAC), was packed with glass beads from the top and bottom. The adsorbate, Indigo Carmine Dye solution (ICD), was pumped at the appropriate flow rates using a pump with a known initial concentration at natural pH. The final samples were taken at daily interims at the column's output, along with the concentrations were determined using a spectrophotometer.The current study used a perspex column for continuous fixed-bed up-flow column analysis. The adsorbent of known weight for a given bed depth, i.e., Acacia nilotica sawdust activated carbon (ASAC) as an adsorbent for the adsorptive treatment of toxic Indigo Carmine Dye (ICD) bearing wastewater. For this purpose, continuous mode fixed-bed up-flow column adsorption analysis is conducted. The effect of the adsorption characteristics of ASAC on the influent ICD concentration, flow rate, and column bed depth has been investigated.The objective of the present research work is to utilize 20 to get a uniform size in the range of 250–500 μ. It is then rinsed with doubly distillate water, naturally dehydrated, and incubated in an oven at 105 °C for around 2 h. After this, char is obtained by mixing 25 mL of ortho-phosphoric acid, i.e., H3PO4, in 50 g dehydrated sawdust in a 0.5:1 volume to weight ratio. To complete the activation and carbonization, the char was placed in a muffle furnace for around 1 h at 450 °C. The carbon was then rinsed with doubly distillate water for 2 h, dried at 378 K, and used in the new adsorption column analysis as Acacia nilotica sawdust activated carbon (ASAC)4.For continuous fixed-bed up-flow column study, the adsorbent material,i.e., carbon activated babool sawdust for the removal of ICD, was prepared by chemically activating the material with ortho-phosphoric acid. The plant chosen for the study is the Acacia nilotica tree. Acacia nilotica is a scientific name for the evergreen Babool tree. It is native to Africa, the Middle East, the Indian subcontinent and across Asia. It is locally available and found abundantly. This plant is not directly utilized as an adsorbent in the present research work. The sawdust of babool tree which is a waste material from sawmills and hence of marginal cost has been considered here to prepare the adsorbent. Sawmill and timber industries are commonly available sources of sawdust or wood waste. Secondly the plant is locally available and found abundantly across Asia. In addition to this, Acacia nilotica tree/sawdust/wood waste is not listed as vulnerable/rare/endangered/indeterminate. The cost-effective low cost raw sawdust material, obtained from the local saw mill  in a quantity of 0.5 kg, was crushed and sieved according to the protocol outlined in Part 4 of Bureau of Indian Standards IS-2720B-E-T) surface area and (B-E-T) pore volume analysis, scanning electron microscope (S-E-M) analysis, Fourier transform infrared (F-T-I-R), spectroscopy, and X-ray diffraction (X-R-D) technique. Adsorption is a surface process. It robustly depends on the adsorbent’s surface characteristics. The area-volume, morphology, chemistry and constitution of the ASAC surface were premeditated by B-E-T, S-E-M, F-T-I-R, and X-R-D analysis.Characterization of adsorbent ASAC includes Brunauer, Emmett and Teller  was acquired from a scientific store-Upper India, Nagpur. Standard 1000 mg/L stock solution was obtained by dissolving 1 g powdered ICD in 1000 mL doubly distillate water. Dilution of a standard stock of 1000 mg/L concentration yielded desired solutions of 100 mg/L and 200 mg/L concentrations. A double-beam Shimadzu ultraviolet–visible spectrophotometer was used to calculate the wavelength at an absorbance of 610 nm. (Model No. 2450).21. The schematic diagram showing the set-up of the column study is shown in Fig. Continuous flow analysis fully explores the concentration differential, which is believed to be a prime factor for adsorption, resulting in more optimal use of the adsorbent potential and improved effluent performance The fixed bed adsorption analysis was performed using perspex glass columns and consists of internal diameters of 2 cm and lengths of 100 cm. The experiments were conducted using doubly distillate water at natural flow and continued till the bed reached the exhaustion point of all the pH of respective solutions. Table The predetermined flow rates of 250–500 mL/h, the predetermined ICD concentration of 100–200 mg/L and the predetermined column bed depth of 15–30 cm were adopted in the present research work in order to examine the effect on the adsorption performance and to establish optimum conditions for the adsorption of ICD in a column by ASAC.22. The breakthrough curve is drawn based on lapse time by plotting output concentration against initial concentration, flow rate, bed width, and column diameter. In a fixed bed study, the adsorbent closest to the contaminated water saturates first, where maximum adsorption occurs initially. As time passes, these adsorption areas advance until they enter the bed exit 23. As the adsorption areas migrate through the column, the adsorbate concentration equals the feed concentration at the exit.Continuous flow analysis is a successful periodic desorption mechanism. The breakthrough curve is the efficiency of a continual adsorption sample or a fixed-bed column. A break-through curve happens if the outflow agglomeration from a column bed is (3–5) % of the inflow agglomerationb) effect on initial concentration on ICD adsorption on the ASAC's fixed-bed column was investigated, and measurements were conceded out at the preliminary stage of 100–200 mg/L concentration with a maintained flow discharge of 250 mL/h and 500 mL/h with column bed depths of 15 cm and 30 cm.The obtained results are depicted in Table Break-through time , Ct is the concentration of effluent ICD (mg/L), kT is the Thomas rate constant (L/mg/min), q0 is the equilibrium ICD uptake per gram of ASAC (mg/g), Q is the flow rate (mL/h), m is the ASAC volume (g), and t is time (min). A linear plot of ln [(C0/Ct) − 1] against time t was used to measure kT and q0 values from the intersection and slope. Table kT), i.e., Thomas rate constant and (q0) maximum adsorption capacity. The relative parameters and regression coefficient (R2) were determined using a correlation basin. The results of relative parameters and values of error analysis by the least-square of errors method (S.S) (that is less than or up to 0.004) are also listed in Table t and Ct/C0.The investigational column statistics were applied to the Thomas model equation to obtain  amplified from 15 to 30 cm, while the rate of kT decreased. Thus, lesser influent ICD concentration, lesser flow rate, and greater or higher depth will improve the efficiency of the column for ICD adsorption on ASAC. Thomas rate constant and contrast of adsorption capacity values acquired from experimental data and calculations showed that they were significantly close for given situations that specify the Thomas model's applicability.Table 28 was worn to learn the break-through action of ICD adsorption on ASAC. It takes into account the hypothesis that a decrease in the sorption probability rate is relative to adsorbate sorption and adsorbate break-through probability28.The Yoon–Nelson plotThe Yoon–Nelson kinetic plot for the column is given as Eq. :3\documeThe linear plotof one component structure is presented asEq. :4\documeC0 is the influent ICD concentration (mg/L), Ct is the effluent ICD concentration (mg/L), kYN is the Yoon–Nelson rate constant (L/min), is the time needed for 50% adsorbate break-through (min), along with t is the sampling time (min).As ln [Ct/(C0 − Ct)] against sample time (t) was worn to computekYN along with the slope and intercept. In Table A linearized design of τ and kYN, the Yoon–Nelson rate constant kYN improved along with 50% adsorbate break-through time (τ) falls with the grows in influent ICD concentration and flow rate for a given column bed depth. With the bed volume, i.e., flow rate increasing from 250 to 500 mL/h, the value has expanded, while the standard of kYN has reduced. Table 0) increases from 100 to 200 mg/L. In all cases, the regression correlation coefficient R2 was found to be nearly close to unity.When a basic hypothetical Yoon–Nelson model was applied to ASAC Table  to undertotal) decreases as the influent ICD concentration and flow rate increase. Though all the parameters obtained in a model satisfy the Yoon–Nelson hypothesis, an increase in kYN values with a decrease in τ values and slightly higher S.S values compared to other models may slightly deviate (negligible) internal significance of this model to a marginal extent.The total quantity of adsorbate (q29. BDST is a common researchplot for forecasting the connection between depth and time concerning concentrations and various sorption characteristics. The BDST plot is indicated as (Eq. The hypothesis of the BDST plot depicts that the pace of sorption is synchronized by the reaction between adsorbate (ICD) and adsorbent (ASAC)d as Eq. :5\documeThe adsorption capacity by the BDST model is expressed as Eq. :6\documeC0 is the inflow ICD concentration (mg/L), Ct is the outflow ICD concentration (mg/L), kais the BDST model rate constant (L/mg/min), N0 is the saturation concentration (mg/L), z is the bed depth (centimetre), and u is the influent linear velocity (centimetre/min), where tis the sampling time (min) q0 represents the equilibrium ICD uptake per gram of adsorbent (mg/g), Q means the flow rate (mL/h).Ct/C0 is plotted against sampling time (t) and column bed width, a straight line is obtained (z). The BDST model measures the adsorption power, saturation concentration, and BDST rate constant (ka). The model parameters, total adsorbed quantity  and the correlation coefficients are presented in Table Also, m represents the volume of adsorbent (ASAC) in the column (g). As ka and N0 values increase with the same. The BDST model variables will continue to advance the methods for different flow rates and other influential dye concentrations without additional laboratory experiments. To measure the column output at new feasible flow rates and influent dye concentrations, the BDST equation was used at either flow rate or influent ICD concentration. The error analysis values obtained by the least square method (S.S) were smaller than those obtained by the Yoon–Nelson, and robust predictions were found for adjusting feed concentration and flow rate. In every one ofthe cases, the regression coefficient R2 was approximately equal to one, indicating the significance andrationality of the BDST model for the current system.The result Table ; Fig. 7 Columns with a wide range of possible flow rates and concentrations can be generated using the model and measured constants.These outcomes show that the optimized conditions can be utilized to estimate adsorption efficiency on ASAC under desirable operating conditions for the ICD adsorption process.2) values, these values may significantly influence the accuracy throughout the linear regression examination; subsequently, the nonlinear regression investigation can be a superior alternative in evading such errors. As a result, the parameters of various kinetic models were determined using nonlinear correlation coefficients examination and the least square of errors procedure 17. Error analysis was conducted to validate which model gives better results. The relative formula for error analysis by least-square of errors method (S.S) is provided by the following equation (Eq. t/C0)c is the ratio of effluent to influent ICD concentrations calculated using Thomas, Yoon–Nelson and BDST plots, and (Ct/C0)e is the ratio of effluent to influent ICD concentrations calculated using experimentation conditions31. N is the number of experimental data points. It is important to evaluate the data using S.S according to the coefficients (R2) criteria to validate the most suitable and best-fitting kinetic model.As diverse equations are considered to calculate linear regression correlation coefficients (R0 = 100 mg/L and 200 mg/L).Figure 0 = 100 mg/L and 200 mg/L at (a) Q = 250 mL/h (b) Q = 500 mL/h). It was clear from the figures  for all the three models, it could be considered that BDST best followed the Thomas model along with the Yoon–Nelson models in relating the development of ICD adsorption onto ASAC.Figure res Fig.  that theB–E–T surface area and B–E–T pore volume of raw sawdust were 543.28 m2/g and 0.1925 cm3/g respectively. At the same time, B–E–T surface area and B–E–T pore volume of ASAC were obtained to be 737.76 m2/g and 0.2583 cm3/g, respectively. This rise in B–E–T surface area and B–E–T pore volume is due to the physico-chemical activation of raw sawdust into activated carbon (ASAC).B–E–T surface area and B–E–T pore volume of ASAC were analyzed using Brunauer, Emmett and Teller (B–E–T) method by ASTMD-3663-03. The standard test was conducted using ASAP 2020  surface area and porosity analyzer. The 17.S-E-M analysis explains the surface morphology and porosity of ASAC. ASAC laden with ICD was tested at 15 kV, 500× magnifications using a scanning electron microscope. From the picture of raw and laden ASAC Fig. , it was 17. F-T-I-R spectroscopes of raw ASAC and ICD-laden ASAC are depicted in Fig. −1 because of the –OH assembly, which vaguely shifts to 3682.03 cm−1 after ICD sorption onto ASAC. The group stretched in raw ASAC at 2933 cm−1 depicts well-built C–H links that swing faintly in ICD-laden ASAC to 2898 cm−1. The summit stretched in raw ASAC at 2160 cm−1 describes the occurrence of a weak C–C linkage thatdoes not swing after ICD adsorption. Similarly, the summit at 1551 cm−1 for raw ASAC swings to 1563 cm−1 for ICD laded ASAC33. This depicts the strong occurrence of C–OH and C–O groups. This swinging of summits validates the sorption of ICD onto ASAC.The F-T-I-R spectroscopes are usually worn-out to recognize the distinguishing functional assemblies with excellent sorption capability. F-T-I-R spectroscopy monitors the chemistry of the ASAC surface and ICD-ASAC surface 36. From Fig. X-R-D technique is a tool to analyze the crystalline or amorphous constitution of the adsorbents. The adsorption process may lead to changes in the adsorbent's constitution. Hence, understanding the molecular constitution and crystalline/amorphous constitution of the ASAC would provide valuable information regarding adsorption. Figure B–E–T surface area and B–E–T pore volume of raw sawdust were 543.28 m2/g and 0.1925 cm3/g respectively. Whereas B–E–T surface area and B–E–T pore volume of ASAC were found to be 737.76 m2/g and 0.2583 cm3/g, respectively. This rise in B–E–T surface area and B–E–T pore volume is due to physico-chemical activation of raw sawdust into activated carbon (ASAC). S-E-M analysis and X-R-D analysis reveal the micro-porous and amorphous nature of ASAC. F-T-I-R spectroscopes indicate distinctive functional assemblies like –OH group, C–H bond, C–C bond, C–OH, and C–O groups on ASAC.It could be computed that the Acacia nilotica sawdust activated column can be considered as an alternative treatment option for industrial  wastewater.The present study shows the utilization of ASAC as an effective solution for removing ICD from wastewater. A continuous study of the fixed bed adsorption column on ASAC for the treatment of ICD discovered that ASAC could be used as an adsorbent material in industrial wastewater treatment to remove dyes from the solution. The adsorption of ICD for a given column bed depth was influenced by the flow rate and the ICD concentration. It was found that break-through and exhaust time occurred faster for shallower bed depths (15 cm) and increased gradually as column depth increased (30 cm). It was also observed that with an increase in initial dye solution concentration (from 100 to 200 mg/L) and flow rate (from 250 to 500 mL/h), break-through time and exhaust time decreased. The percentage removal efficiency and adsorption capacity of ASAC increase for lower initial ICD concentration and flow rate. The column study revealed that the maximum removal efficiency of ICD and adsorption capacity of ASAC was found to be about 47.35% and 21.99 mg/g, respectively, at lower column depth (15 cm), which increased respectively to 79.01% and 24.67 mg/g at higher column depth (30 cm), with the initial dye concentration reduced from 200 to 100 mg/L, allowing flow rate to reduce from 500 to 250 mL/h. The column kinetic study and error analysis also depicted that the experimental break-through plots compared satisfactorily with the break-through profiles calculated by Thomas, Yoon–Nelson and BDST models. Through comparing the values of variables, constants, and error analysis for all three models in relating the mechanism of ICD adsorption onto ASAC, it was also discovered that Thomas was better approached over BDST and Yoon–Nelson models. The comparison of correlation coefficients shows that all three models match the experimental results well and are equal in fixed-bed adsorption systems. The


Fertility is a significant concern among adolescent and young adult (AYA) cancer survivors and their caregivers, especially after their completion of cancer treatment programs. Concerns about fertility affect not only cancer patients' psychological well‐being, but also all aspects of their medical treatments, including treatment protocol, decision‐making, and treatment adherence. In this scoping review, the PubMed, CINAHL, Web of Science, Embase, CNKI, and Wanfang electronic databases were searched according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis Extension for Scoping Reviews. The searches identified 669 articles, 54 of which met the inclusion criteria. Reviewers extracted the data on the study characteristics, measurements, positive factors, negative factors, and additional themes. This scoping review included studies from 10 countries. Most studies were quantitative using a cross‐sectional design. The prevalence of reproductive concerns among AYA cancer survivors ranged from 44% to 86%, and 28% to 44% of the survivors experienced moderate to severe concerns. The specific implementation of fertility consultation, including timing, consult frequency, and content, deserves ongoing exploration. Current research situations about reproductive concerns in adolescent and young adult cancer survivors. This scoping review overviewed the current research situations about reproductive concerns among adolescent and young adult (AYA) cancer survivors and showed the prevalence of reproductive concerns among AYA cancer survivors ranged from 44% to 86%, and 28% to 44% of the survivors experienced moderate to severe concerns. There was few research focusing on interventions to alleviate reproductive concerns. Therefore, theoretical frameworks for this problem should be explored, and appropriate psychotherapy should be designed to alleviate their concerns.The prevalence of reproductive concerns among AYA cancer survivors ranged from 44% to 86%, and 28% to 44% of the survivors experienced moderate to severe concerns. There lacks an appropriate interventions to alleviate reproductive concerns among AYA cancer survivors.1Fertility plays an important role in the continuation of human life, and young adulthood is the prime time to have own biological children. However, among cancer survivors, reproductive function is often impaired or interrupted due to the destructive nature of the cancer itself or due to the reproductive toxicity of the cancer treatment. Thus, there are concerns about the fertility potential of patients diagnosed with cancer, especially among adolescent and young adult (AYA) (aged 15–39 years) cancer survivors.With the steadily increasing 5‐year survival rate of cancer patients,To provide directions for improving the quality of life, it is significant to learn about the level, influencing factors, and clinical interventions of reproductive concerns in AYA cancer survivorship. The objective of this scoping review was to evaluate the literature on reproductive concerns among AYA cancer survivors after the completion of cancer treatment, to identify research gaps in the current literature and to describe future research directions to alleviate reproductive concerns.2We performed our scoping review guided by the six steps illustrated by Arskey and O′Malley2.1How have reproductive concerns among AYA cancer survivors been assessed?What have assessment on reproductive concerns among AYA cancer survivors shown?What factors may associate with the level of reproductive concerns among AYA cancer survivors?Are there interventions to alleviate reproductive concerns among AYA cancer survivors?To accomplish the aims of the study, the following research questions were identified:2.2We conducted a comprehensive search of the PubMed, CINAHL, Web of Science, Embase, CNKI, and Wanfang electronic databases in August 2021. We searched the above databases using a combination of the following keywords: child, OR young adults, OR adolescents, OR childbearing age, AND cancer survivorship, OR childhood cancer survivors, OR cancer, OR malignancy, AND fertility concern*, OR infertility concern*, OR reproductive concern*, OR fertility worries, OR fertility worry.2.3Inclusive criteria were met if studies (1) were published in peer‐reviewed journals in English or Chinese; (2) applied quantitative, qualitative, or mixed methods; (3) answered the review question; and (4) included study sample groups composed at least 50% by participants aged 15–39 years old who had completed cancer‐related treatment. Exclusion criteria were met if studies (1) were focused mainly on fertility, fertility preservation, or fertility‐related  distress; (2) reported overall outcomes of AYAs and older adults, such as the results of AYAs that could not be extracted and described; (3) included study sample groups composed over 50% by survivors older than 39 years; or (4) were case studies, review articles, correspondence, gray literature, or conference abstracts.2.4After removing duplicates, titles were filtered using the conservative method described by Higgins and Green2.5Two researchers (Sun Q and Xiao P) extracted data from eligible studies using electronic forms to ensure accuracy. The following information was extracted: study authors, publication year, nationality of the author, study design, type of cancer and age, measurements, and main outcomes. The results were depicted with statistics and thematic analysis. Although the PRISMA: Extension for Scoping Reviews does not ask for bias analysis across studies, we acknowledge the risk of selectively reporting themes, particularly in qualitative studies.3n = 4); focus on reproductive concerns after cancer scale (n = 9); focus on wrong age range (n = 7); French language (n = 1); and inability to access the full text (n = 1). The flowchart of the study selection process is shown in Figure n = 33) or observational cohort (n = 4) design. Only one study used a mixed method design, and four studies reported experimental data.Database searching yielded 669 articles, from which 374 repeated articles were removed. In the remaining 295 articles, 219 articles were excluded after screening the abstract and title. After screening the full text of 76 articles, 22 articles did not satisfy the inclusion criteria and were excluded for the following reasons: focus on fertility‐related information (3.1n = 18), 33.3% in China (n = 18), and 5.6% in the United Kingdom (n = 3). In this review, AYA‐aged survivors included childhood and AYA cancer survivors; 37 studies included only survivors who were diagnosed in adolescence and/or young adulthood , while eight studies included participants diagnosed across childhood and young adulthood , and one study focused on survivors diagnosed in patients 15 years of age or younger. Forty studies included only females, three included only males, and 11 included both male and female cancer survivors. Most studies focused on survivors of various cancers (n = 19) or on breast cancer survivors (n = 20). Three themes emerged as important concepts related to reproductive concerns: (1) reproductive concerns, encompassing the prevalence of reproductive concerns, and other aspects of reproductive concerns; (2) influencing factors ; and (3) interventions for reproductive concerns.As shown in Table 3.2Various measures were used to examine reproductive concerns, which were reported as a primary outcome in 40 articles (Supplement File). Measurements of reproductive concerns involved the Reproductive Concerns Scale (RCS),3.330, and no group differences in RCS mean scores were found between cancer survivors and infertile women without cancer.Due to the diversity of measurement tools used in the different studies, it is hard to determine the overall prevalence of AYA cancer survivors with reproductive concerns. In studies using the RCAC, 58% to 61% of AYA cancer survivors reported high concerns on at least one dimension, and 28% to 44% of the survivors reported moderate to high overall reproductive concerns, with the total scores for reproductive concerns ranging from 56.45 ± 8.18 to 65.73 ± 12.36.3.4Additional concepts about reproductive concerns were also reported in these studies. Three themes about reproductive concerns were extracted: worry and remorse, desire for communication and support, and demand for reproductive knowledge.3.5131 therapy, being nulliparous at diagnosis, and reporting treatment‐related ovarian damage.Factors associated with fertility concerns were identified in these studies and can be divided into three categories. As shown in Table 3.6Wang et al.4We sought to explore the available literature on reproductive concerns in AYA cancer survivors who ranked fertility among their top three life goals. This scoping review found that reproductive concerns universally occurred in areas of China, America, and some European nations, and concerns related psychological interventions were scarce. However, for some economically less developed regions, such as India and areas of Africa, which have high cancer rates and high reproductive rates,Our findings showed that the prevalence of reproductive concerns in AYA cancer survivors ranged from 44% to 86% and that 28% to 44% of survivors experienced moderate to severe concerns. Although cancer patients do have a risk for impaired fertility, Bartolo et al.Child health, personal health, and fertility potential were the top three of the six dimensions of fertility concerns identified by AYA cancer patients in both quantitative and qualitative studies. In comparison, dimensions such as acceptance and achieving pregnancy/becoming pregnant received less attention from both male and female patients. The results of this research suggest that AYA cancer patients have a high perception of the risk of infertility and overemphasize the health problems that childbirth after cancer may bring to themselves and their children. In contrast, they tend to ignore their own emotional responses to infertility risks and appropriate remedies. These two aspects keep patients trapped in a vicious cycle of constant negative emotions and coping behaviors that ultimately make their concerns circular and compounding. In fact, reproductive concerns, just as fears of cancer recurrence, are, in essence, a normal concern that arises after cancer diagnosis and treatment, with high levels of concern associated with impaired quality of life.Most published work on the reproductive concerns of AYA cancer survivors consists of cross‐sectional studies. These studies provide further information on the prevalence of and factors associated with reproductive concerns in different areas of the world; in addition, these studies serve as a basis for population‐oriented interventions on reproductive concerns such as those with marriage, low education levels, desire to have biological children, breast cancer, poor family function, etc. However, there have only been four clinical intervention studies on this topic, which were all in China,Although fertility concerns have previously been partly reviewed within a systematic review of fertility‐related psychological distress,Above all, the reproductive concerns of AYA cancer survivors are a psychological burden that urgently needs the attention of reproductive specialists, oncologists, psychologists, and nurses. But the limited number of studies to date have had small samples and were not designed to explore psychological processes involved or to construct a theoretical framework of reproductive concerns. Nonetheless, research with further clinical intervention trials needs to be conducted on cancer survivorship, centering on alleviating survivors' psychological distress or improving treatment outcomes. Further research into fertility consultations with AYA cancer patients in real clinical settings is also warranted.None.Sun and Xiao devised this study; Sun, Xie, Duan, Cheng, Luo, Zhou, and Liu searched the literature; Sun and Xie wrote the first draft of the paper; Xiao and Andy revised the paper. All authors reviewed the final paper.Neither informed consent to participate nor ethical approval is required.Table S1Click here for additional data file.


As the worldwide spread of coronavirus disease 2019 (COVID-19) continues for a long time, early prediction of the maximum severity is required for effective treatment of each patient.This study aimed to develop predictive models for the maximum severity of hospitalized COVID-19 patients using artificial intelligence (AI)/machine learning (ML) algorithms.The medical records of 2,263 COVID-19 patients admitted to 10 hospitals in Daegu, Korea, from February 18, 2020, to May 19, 2020, were comprehensively reviewed. The maximum severity during hospitalization was divided into four groups according to the severity level: mild, moderate, severe, and critical. The patient's initial hospitalization records were used as predictors. The total dataset was randomly split into a training set and a testing set in a 2:1 ratio, taking into account the four maximum severity groups. Predictive models were developed using the training set and were evaluated using the testing set. Two approaches were performed: using four groups based on original severity levels groups  and using two groups after regrouping the four severity level into two . Three variable selection methods including randomForestSRC were performed. As AI/ML algorithms for 4-group classification, GUIDE and proportional odds model were used. For binary classification, we used five AI/ML algorithms, including deep neural network and GUIDE.http://statgen.snu.ac.kr/software/nomogramDaeguCovid/).Of the four maximum severity groups, the moderate group had the highest percentage . As factors contributing to exacerbation of maximum severity, there were 25 statistically significant predictors through simple analysis of linear trends. As a result of model development, the following three models based on binary classification showed high predictive performance: (1) Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical. The performance of these three binary models was evaluated using AUC values 0.883, 0.879, and, 0.887, respectively. Based on results for each of the three predictive models, we developed web-based nomograms for clinical use (We successfully developed web-based nomograms predicting the maximum severity. These nomograms are expected to help plan an effective treatment for each patient in the clinical field. The coronavirus disease 2019 (COVID-19) pandemic is a rapidly evolving global emergency that continues to strain healthcare systems /ML algorithms such as GUIDE  and deepThis is a multicenter retrospective cohort study of polymerase chain reaction-confirmed COVID-19 patients admitted to 10 hospitals in Daegu, Korea . The cohA total of 46 variables were used in this study. Excluding an outcome variable , 45 variables were used as predictors. Records for 45 predictors with an average missing rate of 16% (IQR: 6–19%) were collected from each patient on the first day of admission. In this study, the original data was used as it is. This study was approved by the institutional review board of Kyungpook National University Hospital (KNUH 2020-03-044).Excluding those who died on the first day of admission, 2,254 of 2,263 patients were used in this study. To define the outcome, a disease severity variable was used. The disease severity was divided into four groups: mild, moderate, severe, and critical . The disp-value < 0.05 was considered significant.Of the 45 predictors, 2 demographic variables, 4 vital signs, and 12 laboratory findings were continuous variables. Note that the body temperature belonging to the four vital sign predictors is the body temperature measured only on the first day of admission. Based on the opinions of clinicians for practical use in the clinical field, an optimal cutoff was selected for dichotomizing each continuous predictor. To this end, the maximally selected rank statistics  were useThe overall workflow is shown in For the binary classification, we combined the four maximum severity groups into two groups: (1) Mild vs. Above Moderate (2) Below Moderate vs. Above Severe (3) Below Severe vs. Critical. Above Moderate refers to a group that combines moderate, severe, and critical. Below Moderate refers to a group that combines mild and moderate. Above Severe refers to a group that combines severe and critical. Below Severe refers to a group that combines mild, moderate, and severe. For each outcome with a binary group, multiple predictive markers were selected using the area under the receiver operating characteristic curve (AUC)-based stepwise selection , the leaFor the 4-group classification, the proportional odds model  and GUIDY is the outcome with four ordinal categories , αj is an intercept corresponding to the jth category and β is a vector of coefficients. In the case of the proportional odds model, the proportionality assumption was confirmed through the likelihood ratio test, which compares the proportional odds model and the cumulative logit model , precision, and F1-score were used as evaluation measures. A parsimonious model, a simple model with a high predictive ability for each outcome, was considered the final predictive model.For the binary classification, we developed three predictive models: (1) Mild vs. Above Moderate (2) Below Moderate vs. Above Severe (3) Below Severe vs. Critical. During the marker selection process, a 5-fold cross-validation (CV) was performed. We considered five AI/ ML algorithms: LR, RF, SVM, DNN, and GUIDE. For RF, SVM, and DNN, we tuned hyperparameters n = 548; 24.3%), moderate , severe , and critical . p-value = 2.8E-98; CA test). In terms of sex, the maximum severity was more severe for men than women . Body mass index (BMI) and two vital signs  were statistically significant predictors showing a linear trend with the maximum severity. Among the other predictors, three initial clinical findings were fatigue, shortness of breath, altered consciousness, 8 comorbidities , chest X-ray infiltration, and 8 laboratory findings  showed the linear trend with the maximum severity . Thus, we developed a predictive model using the proportional odds model and evaluated its performance on the testing data. In the case of the proportional odds model when evaluating the performance, the probability of being a specific category j was calculated by using the difference between the cumulative probability corresponding to j and j−1 . Each sample of the testing data is classified into the group with the highest probability. The evaluation results of the proportional odds model are shown in First, we developed predictive models using the four ordinal groups which represent triage COVID-19 patients more informatively. To select multiple markers associated with the maximum severity, we used the proportional odds model and GUIDE model. As a result of AIC-based stepwise selection, eight predictors were selected including age, SBP, cough, sore throat, shortness of breath, hypertension, ALT, and lymphocyte. Based on these eight predictors, the proportional odds assumption was held  Mild vs. Above Moderate, (2) Below Moderate vs. Above Severe, and (3) Below Severe vs. Critical]. For each of the three binary outcomes, the variable selection was performed using AUC-based stepwise, LASSO, and randomForestSRC methods. For each outcome, predictive models were developed using selected variables based on five AI/ML algorithms: LR, RF, SVM, DNN, and GUIDE. For (1) Mild vs. Above Moderate model, three predictors including chest X-ray infiltration, body temperature, and age were finally selected for the final model. The final model showed good performance with an AUC of 0.882, balanced accuracy of 0.811, and F1-score of 0.874 for GUIDE. In particular, the predictive performance of the GUIDE for binary classification is much better than a model for 4-group classification, when converting the 4 × 4 confusion matrix of 4-group classification to a 2 × 2 version . Figure For (2) Below Moderate vs. Above Severe model, 5 predictors were finally selected as the final model: age, shortness of breath, chest X-ray infiltration, CRP, and AST. Based on LR with the highest performance, all predictors showed positive effects on the Above Severe group . When rahttp://statgen.snu.ac.kr/software/nomogramDaeguCovid/ and is expected to help plan effective treatment for each patient in a clinical setting.For (3) Below Severe vs. Critical model, 6 predictors were finally selected as the final model: CRP, respiration rate, chronic kidney disease, AST, age, and diabetes. As with the Below Moderate vs. Above Severe model, based on LR with the highest AUC value, all predictors showed positive effects on the Critical group . When raAs the COVID-19 pandemic continues for a long time, the importance of proper preparation and distribution of medical resources at an early stage is growing. Early prediction of the high-risk group for severe COVID-19 pneumonia is important because it can reduce mortality by providing timely treatment to critically ill patients such as the elderly , 41. ForMost of the predictors used to develop the nomogram were found to be consistent with previously reported results in the literature. Age, the common predictor of the three models used in the nomogram, is known to be a major risk factor for clinical severity . Chest XOur study showed similar results in a large cohort retrospective study conducted in the United States. The previous study used 64 input variables, including vital signs, various laboratory findings, and comorbidities. As the previous study found that age, male sex, and liver disease were associated with higher clinical severity, the Below Severe vs. Critical model in this study had a high predictive performance with clinical parameters including age, male sex, and elevated AST relating to liver diseases. In the previous study, ferritin and d-dimer were used as input variables, but in our study, cytokine storm syndrome-related these blood tests occurring in severe COVID-19 were not included. However, a high predictive model was presented without using these laboratory findings, and through this, convenience in predicting the disease severity in clinical situations can be expected. Our study demonstrated that the predictive model has the potential to predict the maximum disease severity of patients with COVID-19 with high accuracy and to help healthcare systems in planning for surge medical capacity for COVID-19, especially in a situation where medical resources are limited.Compared to our previous study , the maiMost of the existing methods have focused on classifying two groups, such as mild and severe patients . HoweverHowever, this study has some limitations. Firstly, we could not evaluate the impact of COVID-19 treatment, new COVID-19 variants, and vaccination status on the clinical severity course because this study was conducted in early COVID-19 pandemic patient groups. Secondly, full therapeutic options were not available such as remdesivir, tocilizumab , baricitinib (janus kinase inhibitor), and anti-SARS-CoV-2 monoclonal antibody. Thirdly, laboratory findings related to cytokine storm syndrome occurring in severe COVID-19 such as ferritin, interleukin 6 (IL-6), and d-dimer were not included. Ferritin (macrophage activation indicator) and IL-6 (T lymphocyte activation) are known to suspect cytokine storm syndrome in severe COVID-19 exacerbation . If laboIn conclusion, three predictive models were developed to predict the maximum severity during hospitalization based on the initial hospitalization records. The five AI/ML algorithms including DNN and GUIDE were used for model development. Each of the three predictive models showed excellent predictive performance using a few predictors. Representatively, the Mild vs. Above Moderate model showed the predictive performance of 0.882 for AUC using three clinicopathologic predictors. Based on these three predictive models, we successfully developed web-based nomograms useful in the clinical field. These nomograms are expected to help plan effective and timely treatment for each patient.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by the Institutional Review Board of Kyungpook National University Hospital (KNUH 2020-03-044). The patients/participants provided their written informed consent to participate in this study.SH and TP led the overall study and conceived the model. YK and S-WK contributed to the data collection. SH and CL contributed to the data analysis. CL developed the nomogram. SH, SL, BO, MM, S-WK, and TP contributed to data interpretation. SH and YK wrote the manuscript. TP and S-WK supervised the project. All authors read, edited, and approved the final manuscript.This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2021M3E5E3081425).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Research is limited on the use of technology to help individuals who have a mismatch between physiological fall risk (Body) and perceived fall risk (Mind) and are unable to access traditional fall interventions. We examined the feasibility and acceptability of a technology-based body-mind intervention in low-income older adults during the COVID-19 pandemic and explored barriers to access and adopting the technology. Data were collected using a survey, balance test, accelerometer-based physical activity (PA), and semi-structured interviews with twenty participants who engaged in an 8-week intervention at a low-income setting in Florida. We found that: 1) the technology-based intervention is feasible, 2) participants tend to accept technology to alter their perceptions of fall risk and balance capacity, 3) tailored activities to each component are not a one-size-fits-all approach. There were no statistically significant changes in sedentary time, light PA, and moderate to vigorous PA between pre and post-intervention.


According to guidelines from the European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases (AASLD), abdominal ultrasound (US) is recommended for surveillance of hepatocellular carcinoma (HCC) in high-risk patients. However, US is limited as a surveillance modality for various reasons. Magnetic resonance imaging (MRI) is generally considered a better modality for detection of early HCC, but too elaborate in a surveillance setting. Consequently, abbreviated MRI (AMRI) protocols are investigated for surveillance purposes. The aim of our study was to evaluate the potential of non-contrast-enhanced AMRI (NC-AMRI) for surveillance of HCC, using multiple readers to investigate inter-observer agreement and the added value of double reading. We found that NC-AMRI presents a valuable screening tool for HCC and that double reading improves the sensitivity and specificity of HCC detection.Purpose: To evaluate NC-AMRI for the detection of HCC in high-risk patients. Methods: Patients who underwent yearly contrast-enhanced MRI  of the liver were included retrospectively. For all patients, the sequences that constitute the NC-AMRI protocol, namely diffusion-weighted imaging (DWI), T2-weighted (T2W) imaging with fat saturation, and T1-weighted (T1W) in-phase and opposed-phase imaging, were extracted, anonymized, and uploaded to a separate research server and reviewed independently by three radiologists with different levels of experience. Reader I and III held a mutual training session. Levels of suspicion of HCC per patient were compared and the sensitivity, specificity, and area under the curve (AUC) using the Mann–Whitney U test were calculated. The reference standard was a final diagnosis based on full liver MRI and clinical follow-up information. Results: Two-hundred-and-fifteen patients were included, 36 (16.7%) had HCC and 179 (83.3%) did not. The level of agreement between readers was reasonable to good and concordant with the level of expertise and participation in a mutual training session. Receiver operating characteristics (ROC) analysis showed relatively high AUC values (range 0.89–0.94). Double reading showed increased sensitivity of 97.2% and specificity of 87.2% compared with individual results . Only one HCC (2.8%) was missed by all readers. Conclusion: NC-AMRI presents a good potential surveillance imaging tool for the detection of HCC in high-risk patients. The best results are achieved with two observers after a mutual training session. HCC is the most frequent primary tumor of the liver and the third most common cause of cancer-related deaths annually worldwide . The majCurrent surveillance guidelines of both the EASL and AASLD recommend bi-annual US for surveillance ,5. AdvanMRI and computed tomography (CT) are considered to be superior over US for detection and diagnosis of HCC, particularly in patients clinically considered to be unsuited for US owing to obesity, liver steatosis, fibrosis, or cirrhosis ,9. AlthoRecently, different AMRI protocols have been proposed for surveillance purposes with promising results ,14. In gEfforts to further improve screening results that are nowadays common practice in breast cancer screening, for example, have to the best of our knowledge not been explored in HCC screening. Potential improvements  are supplemental training to readers and double-reading ,17. The purpose of this retrospective study was to evaluate NC-AMRI for HCC detection in high-risk patients who underwent yearly full MRI for surveillance of HCC. The medical ethical committee of our institution granted permission for this retrospective study and informed consent was waived, as the study was performed with anonymized data and in accordance with the Central Committee on Research involving Human Subjects. Consecutive patients who underwent MRI of the liver for screening of HCC in a surveillance program between January 2010 and January 2019 were reviewed. Patients received yearly full MRI protocol, owing to failed surveillance with abdominal US, mostly because of fatty infiltration of the liver, advanced liver cirrhosis, or obesity. For inclusion, patients received at least two full MRI examinations. In patients without HCC , the second-to-last MRI was included for further analysis. The reason for this was that the last MRI was considered necessary as a reference to ensure that the patient was truly HCC naïve. In the event of HCC, the MRI examination with first detection of HCC was included for analysis.Patients’ demographics were retrieved from medical records. For each patient, gender, age, and presence of liver disease with underlying cause were registered. The number of patients with and without HCC was recorded. The Li-RADS classification of focal liver lesions per patient was documented. The Child–Pugh classification and clinical stage of patients with HCC according to the Barcelona Clinic Liver Cancer system (BCLC) and the treatment were noted.The MRI examination for all patients was performed on a 1.5 Tesla system  with use of a dedicated 8–16-channel abdominal coil. This protocol included the following sequences: T2W fast spin echo, in axial and coronal plane; axial T1W gradient-echo sequences in-phase and opposed-phase; axial DWI with at least three b-values; axial T2W imaging with fat saturation; and T1W dynamic CE series, with arterial phase acquired using bolus triggering, repeated at least four times after injection of 7.5 cc Gadobuterol 1 mmol/mL  at a rate of 2 mL/sec and saline flush of 30 mL, followed by delayed coronal 3D T1W images. Total imaging time, including localizer and calibration, was around 27 min. Details of these sequences are shown in Our NC-AMRI protocol consists of the above-mentioned axial T1W in-phase and opposed-phase, DWI, and T2W with fat-saturation sequences, with a total acquisition time of 12.5 min . FurtherOnly NC-AMRI sequences per MRI examination of the study population were extracted, anonymized, and uploaded onto a separate research server. An open-source imaging informatics platform   was usedThree readers with different levels of experience evaluated the NC-AMRI sequences of each patient separately and were blinded for the remaining sequences of the full MRI examination, including all previous imaging studies and clinical information. Readers I and II were abdominal radiologists with fifteen and twelve years, respectively, of professional experience in liver imaging at a tertiary center for hepatobiliary diseases. Reader III was a radiologist with six years of professional experience in general and abdominal radiology at a middle-sized primary hospital. Experienced reader I and less-experienced reader III held a joint training session prior to scoring. Experienced reader II purposefully did not participate in this joint training session. The joint training session was conducted by reviewing ten patients with liver cirrhosis from a teaching file collected by experienced reader I, including five cases with HCC as well as five cases with benign and challenging non-HCC cases such as confluent fibrosis. None of these teaching cases were included in this study. T2W imaging with fat saturation and DWI (restricted diffusion) typically depict HCC as a lesion with a high signal intensity (SI), while T1W in-phase and opposed-phase imaging may be used to confirm the presence of the lesion and to detect intralesional fatty deposits, which are oftentimes seen in early HCC or well-differentiated HCC ,20.The readers considered image quality, presence of focal lesions, size, segment location, visibility on the different sequences, and conclusion of a benign observation or possible HCC. Data were registered, digitally secured, and stored using a standardized anonymized and coded clinical reporting form in the online clinical software program OpenClinica  . OpenCliThe reference standard for HCC was based on the corresponding full MRI protocol with confirmation by the multidisciplinary liver tumor board (MDTB). The institutional MDTB comprises specialized radiologists, hepatologists, surgeons, radiotherapists, and oncologists. The reference standard for benign lesions, including technical artifacts, was based on follow-up full MRI reports. Final diagnosis of all focal lesions (benign and HCC) was registered with the Li-RADS V. 2018  classifip-values of <0.05 were considered statistically significant. Descriptive statistics were used to describe the study population. The primary analysis was patient-based. The difference between categorical variables was presented by numbers and percentages and tested with the Fisher exact test. The conclusiveness of the findings from each reader was presented as numbers and percentages. For further analysis of readers’ scoring data, the qualification of confident, probable, or possible HCC was considered positive for HCC. Sensitivity, specificity, ROC/AUC value, positive predictive value (PPV), negative predictive value (NPV), and accuracy of NC-AMRI were calculated using SPSS software  for each reader separately and for all readers together using the majority of votes. AUC of 1.0 indicated a perfect model, whereas 0.5 indicated a very poor model. Generally, AUC > 0.7 indicates a good model . Inter oAfter completion of data analyses, the original imaging data and clinical information were made available for all three readers, for selecting illustrative cases and figures for this publication. A total of 240 consecutive patients were eligible for inclusion. Twenty-five patients were excluded because of substandard quality MRI. These patients lacked one sequence of our NC-AMRI protocol (4 patients) or had too disturbing motion artefacts of the NC-AMRI sequences (21 patients). Minor and moderate motion artefacts were accepted. The remaining 215 patients, 149 (69.3%) male and 66 (30.7%) female with a mean age of 56 years (range 19–81 years), were included for final analysis. The study population consists of 179 (83.3%) HCC-naïve patients and 36 (16.7%) patients with HCC. Most patients in the HCC-naïve subgroup had cirrhosis  followed by non-cirrhotic chronic hepatitis B  and non-cirrhotic hepatitis C . In the HCC subgroup, most patients  had cirrhosis and only one patient (2.8%) had chronic hepatitis B. Twenty-eight patients with HCC had Li-RADS 5 lesions (77.8%) and 8 patients had Li-RADS 4 lesion (22.2%). HCC-naïve patients had no focal lesions in 55 patients (30.7%); Li-RADS 1 and 2 lesions in 95 patients (53.1%), and Li-RADS 3 lesions in 29 patients (16.2%) . Of the The mean HCC lesion size was 31 mm, including seven patients with more than one HCC lesion. Readers I and III, who held a training session prior to scoring, had the highest sensitivity per patient of 97.2% (35/36) and 91.7% (33/36), respectively, as compared with reader II, with 80.1% (29/36) sensitivity. Conversely, reader II had a much greater specificity of 91.1% (163/179) than reader I with 82.1% (147/179) and reader III with 72.1% (129/179) . NonetheSimulations for double-reading showed a good interclass correlation coefficient of all readers . Using majority vote, sensitivity and specificity increase to 97.2% (35/36) and 87.2% (156/179), respectively, with NPV ranging from 95.9% to 99.4%. HCC was correctly detected and interpreted by all three readers in 61.1% of patients (22/36) . In the Area under the curve (AUC): reader I 0.94 (95% CI 0.90–0.99), reader II 0.88 (95% CI 0.81–0.96), and reader III 0.93 (95% CI 0.89–0.97). AUC of 1.0 indicated a perfect model, whereas 0.5 indicated a very poor model. Generally, AUC > 0.7 indicates a good model.In HCC-naïve cases, diagnosis was correct in 87.2% (156/179) using majority vote. All three readers correctly scored 65.4% (117/179) of the patients as negative for HCC. In about one-eighth of the patients , two readers qualified a benign lesion as positive  for HCC, and about one-fifth of the patients with benign lesions , while one reader scored a benign lesion as positive for HCC . ExampleOur retrospective study based on a simulated NC-MRI liver protocol in a surveillance population shows promising sensitivities (range 80–97%) and specificities (range 72–91%) for both highly experienced and less experienced abdominal radiologists. Although we did not perform a direct comparison with US in our study population, the results hold promise that NC-AMRI may be a substantial improvement over US (reported sensitivity of 47%) for the detection of lesions that resemble HCC and warrant further evaluation with full MRI for confirmative final diagnosis . Because of disappointing reports on US surveillance for HCC in high-risk patients, surveillance with AMRI gained much attention in recent years ,27,28,29In our study, the readers had no access to prior examinations, which would reflect a (most pessimistic) screening situation in which all patients are considered newly enrolled. For this reason, it is expected that the relatively high false-positive detection rate of 13% will likewise be lower in daily practice when the readers have access to prior studies for a fair comparison. Previously established benign lesions such as  hemangiomas as illustrated in It is possible that early HCC, which are only clearly seen with full MRI, may be missed using NC-AMRI. In our study, there was one such lesion that was consequently missed by all three readers , as illustrated in The further improved sensitivity (97%) and specificity (87%) of NC-AMRI based on majority vote suggests that double-reading may be preferable over single-reading in HCC imaging surveillance programs. These improvements appear in line with increases in sensitivity of more than 10% when double-reading was applied in an imaging surveillance program for breast cancer ,37. To tInter-observer agreement was substantial for the experienced readers and, in accordance with a recent systematic review and meta-analysis, inter-observer variability of 0.72 (95% CI 0.62–0.82) for NC-AMRI for the detection of HCC was determined . HoweverAnother point to consider is that all patients were imaged using a 1.5T MRI system, and no use was made of 3T systems that might provide even better lesion detection owing to a higher signal-to-noise ratio. In our opinion, 1.5T is more widely available and perhaps more robust than 3T, as it is less susceptible to motion artifacts that might obscure parts of the liver, especially of the sub-capsular regions and lateral left liver lobe . It is dOur study has some limitations. Foremost are the retrospective nature and simulated analysis of our NC-AMRI. However, our results are in line with other publications on NC-AMRI protocols for HCC detection in a diagnostic cohort . AdditioOur study demonstrates that NC-AMRI of the liver may present a valuable surveillance modality for HCC surveillance in high-risk patients. Diagnostic accuracy may further be improved with double-reading. In addition, implementing a mutual training session is promising, and the exact role of this needs further evaluation.


Dental implants (3.5 mm in diameter and 8 mm in length) with the experimental surfaces (n = 96) were inserted into the tibias of six sheep, which were left to heal for 3 and 7 weeks. Histologic, histomorphometric (bone–implant contact (BIC%)) and mechanical tests ) were performed. The boron-coated surface (BC group) was smoother (Rz: 4.51 μm ± 0.13) than the SLA (5.86 μm ± 0.80) and the SLA-B (5.75 μm ± 0.64) groups (p = 0.033). After 3 weeks, the highest mean RTV was found in the SLA group (37 N/cm ± 2.87), and the difference compared with the BC group (30 N/cm ± 2.60) was statistically significant (p = 0.004). After 7 weeks, the mean RTV was >80 N/cm in all groups; the highest was measured in the H3BO3-treated (BS) group (89 N/cm ± 1.53) (p < 0.0001). No statistically significant differences were found in the BIC%s during both healing periods between the groups. H3BO3 seems to be a promising medium for dental implant osseointegration.The aim of this study was to compare the topographical, chemical and osseointegration characteristics of sandblasting and acid-etching (SLA) surfaces and dental implants treated by boron compounds. Titanium (Ti) disks (n = 20) were modified using boron (B) and boric acid (H Following the discovery of osseointegration, numerous types of dental implant surface treatment and modification modalities have been introduced ,2,3, whi3BO3, BN, CaB, TiB and NaB) are used for treating recurrent or chronic infections [Boron (B) is a bioactive trace element widespread in nature . It playfections , alveolafections  and surffections . Human efections . Nonethefections ,11,12.3BO3)-treated Ti surfaces demonstrated improved proliferation and viability for human osteoblast cells and diminished the adherence of pathogen bacteria onto the corresponding substrates [In a previous in vitro investigation, B-  and partbstrates . This mabstrates .This study was performed to evaluate the topographical, chemical and osseointegration characteristics of Ti surfaces and dental implants treated with B compounds.Bone-to-implant contact (BIC%) and removal torque value (RTV) were designated as the primary outcomes, and relevant data were obtained from similar previous studies ,19. The 2O3) particles, followed by acid-etching with hydrochloric and sulfuric acid (HCl and H2SO4) (SLA surface group); (b) large grit (250 μm) sandblasted with Al2O3 particles and H3BO3 particles (1–5 μm) , followed by acid-etching with hydrochloric and sulfuric acid (HCl and H2SO4) (SLA-B surface group); (c) SLA-B surfaces coated with 99.5% amorphous boron powder (B) (<1 μm)  by heating at 900 °C  for 10 h (BC surface group); and (d) SLA surfaces submerged in H3BO3 saline solution (BS surface group). The BS group implants were taken out of the H3BO3 saline solution at the stage of surgical insertion (Twenty Ti disks (10 mm in diameter and 3 mm in height) and 96 Ti dental implants (3.5 mm in diameter and 8 mm in length) were machined by a commercial manufacturer  . Four dinsertion . Samples2). To determine the three-dimensional description of the surfaces  and texture aspect ratio (Str); scan size 2 × 2 μm2 with 40×; optical zoom 10×) a confocal laser scanning microscope  was used. Energy-dispersive X-ray spectroscopy  and X-ray photoelectron spectroscopy (XPS) analysis  were used to decide the chemical configuration of the surfaces.Surface morphology was examined using a scanning electron microscope , and the surface roughness was quantitatively evaluated using an atomic force microscope  with an allowance of six sheep for this experimental study. All experimental procedures were performed in compliance with the animal research guidelines of the Mehmet Akif Ersoy Experimental Research and Development Center. Six Anatolian-breed sheep  were used. All animals were fasted 24 h prior to surgical procedures. The tibia was selected as the experiment site to refrain from the risks of infection and early implant loss. A block randomization list was obtained via a designated software , accounting equal distribution of surface groups to each tibia and animal for 96 implants.All surgical procedures were performed with general anesthesia under sterile conditions. Xylazine ; Rompun, Bayer, Switzerland) and ketalar , Ketamin HCl, Vancouver, BC, Canada) were used for sedation. General anesthesia was accomplished using an i.v. injection of pentobarbital and maintained with 3–4% sevoflurane  and 100% oxygen.®, Adeka, Samsun, Turkey). An incision of approximately 25 cm was made, skin and fascia were incised, respectively, and muscles were dissected. The implants were placed in the proximal tibia referring to the previously established block randomization order. A periodontal probe was used to maintain 10 mm distance between the implants. A total of 16 implants (8 implants for each tibia) were inserted into the right and left tibia of each animal ; Zentiva, İstanbul, Türkiye) and analgesics ; Nobel Drug, İstanbul, Turkey) were administered during postoperative care for 1 week. For the representation of the early and late-term healing, the six sheep were separated into two groups.Fluorescence labeling was used for evaluating the dynamic bone mineralization and deposition according to the healing schedule in accordance with the guidelines proposed by van Gaalen et al. . Three dUsing a high dosage of anesthesia, three animals were sacrificed after 3 weeks, and three were sacrificed after 7 weeks. The corresponding tibia region was exposed, and the implants were examined using X-rays . The RTV was measured immediately following the stabilization of the tibia in a dedicated bench clamp. A digital torque meter  was used for precise measurement. Reversal torque was enforced, and the maximum torque (N/Cm) was registered.After the animals were euthanized, the tibia of the sheep were removed at the specified time periods for histomorphometric analysis. Sections were prepared with nondecalcified histologic slicing system . The sections were analyzed using a light microscope  to measure the BIC%. All measurements were made using an image analysis software . The implant surfaces were analyzed in three adjoining microscopic images. The BIC% was measured at a magnification of 40×. The calculation was performed by dividing the length of the attached bone by the length of the complete implant surface . All measurements were made by an independent examiner on two separate days (M.S.T), and the mean values were recorded as final. A confocal scanning laser microscope (CLSM)  was used for fluorescence evaluation.p values. Group-by-time interaction was evaluated using the two-way ANOVA test. A p value < 0.05 indicated a statistically significant difference.Data were analyzed using Statistical Packages of Social Sciences (SPSS) 25.0 software. Descriptive statistics, including mean, standard deviation (SD), median, range of quartiles and 95% confidence interval were calculated. The distribution of data was evaluated using the Shapiro–Wilk normality test. Homogeneity of variances was evaluated using Levene’s test. Analysis of variance (ANOVA) was used to compare the measurements that fit the normal distribution between the groups and provide the assumption of homogeneity of variances. The Tukey test was used for post hoc comparison. The Kruskal–Wallis test was used to compare the measurements that did not fit the normal distribution between the groups. The Mann–Whitney U test was used for pairwise comparisons of the groups, and Bonferroni correction was applied to ARRIVE guidelines were referred to while preparing the manuscript.3BO3 particles were observed on the SLA-B surface. Nanowire-shaped dense crystallized B areas  were visible on the BC surface. H3BO3 particles were visible on the surfaces in both the BS and SLA-B groups, which were distributed homogenously in the SLA-B group but were rather disordered in the BS surface group (In the low-magnification SEM imaging (×2000), the SLA surface revealed the typically recognized topographical features . The remce group .p = 0.015). The differences in the Rz values between the groups were statistically significant : 10.53, p = 0.015)). The BC group had significantly lower Rz values (mean: 4.51 μm ± 0.13) than the SLA (mean: 5.86 μm ± 0.80) and the SLA-B (mean: 5.75 μm ± 0.64) groups ; ; .p > 0.05). The highest mean Str (0.36 ± 0.02) and Sdr% (76.32% ± 4.41) were measured in the SLA surface, and the lowest was in the BC surface (Str: 0.19 ± 0.01 and Sdr%: 62.84% ± 3.57). The differences in the Str and Sdr% values were statistically significant between the groups . The SLA surface revealed significantly higher Str and Sdr% values compared with the remaining groups . The BC group demonstrated a significantly lower Str value than the remaining groups  and a lower Sdr% value than the SLA group  and BS group   .The B, Al, C, T, N and O elements were observed on the SLA, SLA-B and BS surfaces in the EDS analysis. Ti was not detected on the BC surface due to a dense coating of B with a nanowire-shaped morphology. The atomic percentage of the B element was 7.6% on SLA-B and 18.71% on the BC surfaces.The O, C and Ti elements were detectable using XPS on all surfaces (except Ti on the BC surface). Cl was detected on the SLA surface only (2.6%). The B element was found only on the BC surface (14.96%). The highest amount of O was measured on the SLA-B surface (43.74%), while the lowest amount of C was measured on the SLA surface (35.55%) .Healing was uneventful in all animals, with no adverse reactions or inflammation or implant loss in any of them. Proper healing of all experimental sites was confirmed by the X-rays .p > 0.05). All implants achieved primary stability with an approximate mean ITV of 40 N/cm, and the differences in ITV were not statistically significant in or between any of the surface groups.The normal distribution of the mechanical test values was confirmed using the Shapiro–Wilk normality test (p < 0.0001), surface groups  and surface group x time interaction . After 3 weeks of healing, the highest mean RTV was found in the SLA group , and the differences compared with the BC group  were statistically significant .The RTV tests were successfully completed in the designated 48 implants. Two-way ANOVA revealed statistically significant differences in time . The BS surface demonstrated the highest mean RTV values (89.46 N/cm ± 1.53), and the differences compared with the remaining groups were statistically significant  . The lowest mean RTV values were measured in the SLA group (80.45 N/cm ± 2.46) with statistically significant differences with the remaining groups  .No signs of inflammatory response, foreign body reaction or necrosis were noted in any histologic slices. The active osteoid formation was visible around all groups and all implants during both healing periods. An increased fill of new bone in between the threads was visible in all groups, especially in the 7th week. The process of osseointegration was ongoing during the early healing period (3 weeks), while it was concluding in the late-term healing (7 weeks) sections .Compared with the BC groups, the intensity of the early term fluorochrome staining at the bone–implant interface and in the surrounding bone area appeared to be higher in the SLA, SLA-B and BS groups. Orange and light yellow staining indicating active late-term mineralization was especially discernable in the SLA-B, BC and BS groups.Highest fluorescence intensity in the late-term healing period was observable in the BS surface group .p > 0.05). The range of BIC% was 23.13–33.0% during the early healing period and 52.49–68.58% during the late-term healing period. The change in BIC% measurements from 3 to 7 weeks was statistically significant .The normality of the BIC% measurements was confirmed using the Shapiro–Wilk normality test  and SLA-B groups (mean: 68.58% ± 11.76), respectively. However, the differences in BIC% between the groups were statistically not significant during both healing periods .In this study, the surfaces with distinctive B-based modifications were analyzed and compared with SLA—the surface that has been used widely in modern implantology . Detaile3BO3. As for the BC surface, a dense B-film formation with nanowire-shaped morphology might have caused a significant drop in the Rz, Sdr% and Str values on the BC surface compared with others and particularly with the SLA.The height descriptive two-dimensional parameter Ra  is regarded as a reference when comparing the dental implant surfaces . In the The B amount detected using EDS and XPS was low most probably as a result of the poor binding energy of the B element, which complicated the detection of B. This incident was reported in previous studies that revealed peaks of low magnitude at a level around 187 eV corresponding to the bonding energy of B and B-oxides at 187–189 eV  and 188.Removal torque forces applied in the counterclockwise direction have been used as a tool for the objective quantification of the strength of osseointegration. Furthermore, in clinical implant dentistry, resistance to rotational forces was regarded as critical, especially in implants that were early or immediately loaded ,23. In tIt was apparent that the biologic effect of B on the osseointegration seemed to be initiated no earlier than the third week, thereby achieving significantly higher RTV than the SLA surface in the seventh week. This might be a result of the biologic effect of B, including increased osteoblastic activity , angiogeThe positive effect of B was also noted in the BIC% measurements. Although the differences were not significant, the highest BIC% during both healing periods were measured in the BS and SLA-B groups. The fluorescence microscopic observations were also indicative of a higher bone mineral deposition in BS group for late-term healing. In a study by Witek et. al., a lower amount of BIC% (16.44% ± 7.9) was reported from the boronized-machined  implants left to heal in sheep tibia for 3 weeks . It is nDespite an expected increase in the BIC% in accordance with the RTV values, BIC% and RTV did not reveal any correlation in this study. A similar outcome was reported by Sennerby et al. (1992) who used screw-type implants left to heal in rabbit tibiae for 6 weeks, 3 months and 6 months, which revealed no significant associations with the recorded BIC% at relevant healing intervals. The amount of compact bone surrounding the titanium fixture was shown to be related to the resistance of the reverse torques . It was Contrary to the positive outcomes of the B-treated surfaces for RTV in the late term, no significant differences were found in the BIC%, despite a slightly higher percentage of measured BIC% values in the B-modified groups after 7 weeks. Such incidents were merely reported by some studies , and thiIt should be emphasized that the implants on the sheep tibia may not appropriately represent the outcomes in the human jaw bone due to the biologic and topographic differences . Owing t3BO3, as employed in the BS group, seems to be a promising medium for dental implant osseointegration and warrants further investigation to optimize the dose and the method of application onto the blasted Ti surfaces.Within the limits of this study, it was concluded that the presently employed surface modifications via B yielded a smoother surface than the conventional SLA, which seemed to cause a reduced resistance to reverse rotational forces (RTV) in the early term healing (3 weeks). No adverse reactions were observed in the B-treated surfaces. Nevertheless, B treatment, especially the B coating, did not provide a significant advantage over the conventional SLA in the early term healing but provided a significant resistance to rotational removal forces in the late term (7 weeks). H