bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proteomic analysis of serum extracellular vesicles from biliary tract infection patients to identify novel biomarkers
Authors: Chang Chih-Jung , Huang Yung-Ning , Lu Yang-Bor , Zhang Yi , Wu Ping-Hua , Huang Jian-Shan , Yang Wei , Chiang Tung-Ying , Hsieh Hui-Shan , Chung Wen-Hung , Weng Yu-Chieh 
Published: March 2024

AbstractBiliary tract infection (BTI), a commonly occurring abdominal disease, despite being extensively studied for its initiation and underlying mechanisms, continues to pose a challenge in the quest for identifying specific diagnostic biomarkers. Extracellular vesicles (EVs), which emanate from diverse cell types, serve as minute biological entities that mirror unique physiological or pathological conditions. Despite their potential, there has been a relatively restricted exploration of EV-oriented methodologies for diagnosing BTI. To uncover potent protein biomarkers for BTI patients, we applied a label-free quantitative proteomic method known for its unbiased and high-throughput nature. Furthermore, 192 differentially expressed proteins surfaced within EVs isolated from individuals afflicted with BTI. Subsequent GO and KEGG analyses pinpointed Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) and Crumbs homolog 3 (CRB3) as noteworthy biomarkers. Validation via data analysis of plasma-derived EV samples confirmed their specificity to BTI. Our study leveraged an unbiased proteomic tool to unveil CEACAM1 and CRB3 as promising protein biomarkers in serum EVs, presenting potential avenues for the advancement of diagnostic systems for BTI detection.
Laser-induced graphene electrochemical sensor for quantitative detection of phytotoxic aluminum ions (Al3+) in soils extracts
Authors: Reyes-Loaiza Vanessa , De La Roche Jhonattan , Hernandez-Renjifo Erick , Idárraga Orlando , Da Silva Mayesse , Valencia Drochss P. , Ghneim-Herrera Thaura , Jaramillo-Botero Andres 
Published: March 2024

AbstractAluminum in its Al3+ form is a metal that inhibits plant growth, especially in acidic soils (pH < 5.5). Rapid and accurate quantitative detection of Al3+ in agricultural soils is critical for the timely implementation of remediation strategies. However, detecting metal ions requires time-consuming preparation of samples, using expensive instrumentation and non-portable spectroscopic techniques. As an alternative, electrochemical sensors offer a cost-effective and minimally invasive approach for in situ quantification of metal ions. Here, we developed and validated an electrochemical sensor based on bismuth-modified laser-induced graphene (LIG) electrodes for Al3+ quantitative detection in a range relevant to agriculture (1–300 ppm). Our results show a linear Al3+ detection range of 1.07–300 ppm with a variation coefficient of 5.3%, even in the presence of other metal ions (Pb2+, Cd2+, and Cu2+). The sensor offers a limit of detection (LOD) of 0.34 ppm and a limit of quantification (LOQ) of 1.07 ppm. We compared its accuracy for soil samples with pH < 4.8 to within 89–98% of spectroscopic methods (ICP-OES) and potentiometric titration. This technology's portability, easy to use, and cost-effectiveness make it a promising candidate for in situ quantification and remediation of Al3+ in agricultural soils and other complex matrices.
Novel nano-fertilizers derived from drinking water industry waste for sustained release of macronutrients: performance, kinetics and sorption mechanisms
Authors: Elsabagh Samira S. , Elkhatib Elsayed A. , Rashad Mohamed 
Published: March 2024

AbstractNanotechnology has emerged as a promising approach for the controlled release of nutrients, particularly phosphorus and potassium. These essential plant nutrients are often applied in excess, leading to environmental pollution and loss of efficiency in crop production. Innovative economic and highly efficient fertilizers are urgently needed to achieve the targeted crop production worldwide in the presence of limited land and water resources. Therefore, in this study, novel, eco-friendly, cost-effective and enhanced efficiency nano-enabled fertilizers, NEF (nWTF1and nWTF2) were synthesized by impregnation of nanostructured water treatment residuals (nWTR) with (KH2PO4 + MgO) at 1:1 and 3:1 (w/w) ratios respectively using a planetary ball mill. The nWTR, nWTF1 and nWTF2 were extensively characterized. The water retention behavior and the sustained release of nutrients from the fabricated nano-enabled fertilizers (nWTF1 and nWTF2) in distilled water and sandy soil were investigated and monitored over time. The water retention capacity of the soil treated with nWTF2 after 26 days was 9.3 times higher than that of soil treated with conventional fertilizer. In addition, the nWTF2 exhibited lower release rates of P, K and Mg nutrients for longer release periods in comparison with the conventional fertilizers. This is a significant advantage over traditional fertilizers, which release nutrients quickly and can lead to leaching and nutrient loss. The main interaction mechanisms of PO4–K–Mg ions with nWTR surface were suggested. The results of the kinetics study revealed that power function was the best suitable model to describe the kinetics of P, K and Mg release data from NEF in water and soil. The produced NEF were applied to Zea maize plants and compared to commercial chemical fertilizer control plants. The obtained results revealed that the nano-enabled fertilizers (nWTF1 and nWTF2) significantly promoted growth, and P content compared with the commercial chemical fertilizer treated plants. The present work demonstrated the power of nano enabled fertilizers as efficient and sustained release nano-fertilizers for sustainable agriculture and pollution free environment.
Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer
Authors: Kumar Yogesh , Garg Pertik , Moudgil Manu Raj , Singh Rupinder , Woźniak Marcin , Shafi Jana , Ijaz Muhammad Fazal 
Published: March 2024

AbstractParasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.
Optimization of the plasmonic properties of titanium nitride films sputtered at room temperature through microstructure and thickness control
Authors: Nieborek Mateusz , Jastrzębski Cezariusz , Płociński Tomasz , Wróbel Piotr , Seweryn Aleksandra , Judek Jarosław 
Published: March 2024

AbstractA current approach to depositing highly plasmonic titanium nitride films using the magnetron sputtering technique assumes that the process is performed at temperatures high enough to ensure the atoms have sufficient diffusivities to form dense and highly crystalline films. In this work, we demonstrate that the plasmonic properties of TiN films can be efficiently tuned even without intentional substrate heating by influencing the details of the deposition process and entailed films’ stoichiometry and microstructure. We also discuss the dependence of the deposition time/films’ thickness on the optical properties, which is another degree of freedom in controlling the optical response of the refractory metal nitride films. The proposed strategy allows for robust and cost-effective production of large-scale substrates with good plasmonic properties in a CMOS technology-compatible process that can be further processed, e.g., structurized. All reported films are characterized by the maximal values of the plasmonic Figure of Merit (FoM = − ε1/ε2) ranging from 0.8 to 2.6, and the sample with the best plasmonic properties is characterized by FoM at 700 nm and 1550 nm that is equal 2.1 in both cases. These are outstanding results, considering the films’ polycrystallinity and deposition at room temperature onto a non-matched substrate.
Incidence, risk and prognosis of second primary malignancy of patients with gastric adenocarcinoma
Authors: Jin Liyan , Su Xinyue , Li Wenjing , Wu Jie , Zhang Hua 
Published: March 2024

AbstractDue to the long-term low survival rates of gastric adenocarcinoma (GAC) patients, the occurrence and prognosis of second primary malignancies (SPMs) are often underreported and overlooked as a significant concern.To date, only a few studies have addressed this issue in the context of GAC. These studies, however, are limited by their small patient cohorts and lack of substantial, meaningful findings. Our study aims to fill this gap by investigating the incidence, risk factors, and prognostic significance of SPMs among GAC survivors. Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we analysed data from patients diagnosed with GAC between 2000 and 2020. The study employs the standardized incidence ratio (SIR) to assess the relative risk of SPMs, competing risk regression to identify risk factors for SPM development after GAC, and Kaplan-Meier and COX regression analyses for survival outcomes. Out of 44,041 GAC patients analyzed, 2,032 (4.3%) developed SPMs, with a median latency period of 36 months. The incidence of SPMs was significantly higher in GAC patients (SIR 1.36, 95% CI 1.32-1.4, EAR 53.57) compared to the general population. Key factors including older age, sex, tumor grade, summary stage, and history of surgical and radiation therapy were related to the higher risk of developing SPMs following GAC. Interestingly, GAC patients without SPMs exhibited poorer overall survival compared to those with SPMs. Age, summary stage, and surgical history were identified as independent prognostic factors for GAC patients with SPMs. This comprehensive analysis underscores the necessity of vigilant monitoring and tailored follow-up for SPMs in GAC survivors, highlighting the study's contribution to enhancing GAC survivors care strategies.
Pharmacological evaluation and phytochemical profiling of butanol extract of L. edodes with in- silico virtual screening
Authors: Ejaz Umer , Afzal Muhammad , Naveed Muhammad , Amin Zeemal Seemab , Atta Asia , Aziz Tariq , Kainat Gul , Mehmood Noshaba , Alharbi Metab , Alasmari Abdullah F. 
Published: March 2024

AbstractL. edodes (L. edodes) is the most consumed mushroom in the world and has been well known for its therapeutic potential as an edible and medicinal candidate, it contains dietary fibers, vitamins, proteins, minerals, and carbohydrates. In the current study butanolic extract of mushroom was used to form semisolid butanol extract. The current study aimed to explore biometabolites that might have biological activities in n-butanol extract of L. edodes using FT-IR and GC–MS and LC–MS. The synergistic properties of bioactive compounds were futher assessed by performing different biological assays such as antioxidant, anti-inflammatory and antidiabetic. FTIR spectra showed different functional groups including amide N–H group, Alkane (C-H stretching), and (C = C stretching) groups at different spectrum peaks in the range of 500 cm−1 to 5000 cm−1 respectively. GC–MS profiling of n-butanol extract depicted 34 potent biomolecules among those dimethyl; Morphine, 2TMS derivative; Benzoic acid, methyl ester 1-(2-methoxy-1-methylethoxy)-2-propanol were spotted at highest range. Results indicate that L. edodes n-butanol extract showed a maximum anti-inflammatory potential 91.4% at 300 mg/mL. Antioxidant activity was observed by measuring free radical scavenging activity which is 64.6% at optimized concentration along with good antidiabetic activity. In-silico study executed the biopotential of active ingredient morphine which proved the best docking score (− 7.0 kJ/mol) against aldose reductase. The in-silico drug design analysis was performed on biometabolites detected through GC–MS that might be a potential target for sulfatase-2 to treat ruminated arthritis. Morphine binds more strongly (− 7.9 kJ/mol) than other bioactive constituents indicated. QSAR and ADMET analysis shown that morphine is a good candidates against ruminated arthritis. The current study showed that L. edodes might be used as potent drug molecules to cure multiple ailments. As mushrooms have high bioactivity, they can be used against different diseases and to develop antibacterial drugs based on the current situation in the world in which drug resistance is going to increase due to misuse of antibiotics so new and noval biological active compounds are needed to overcome the situation.
Comparison of autoclaving and γ-radiation impact on four spices aroma profiles and microbial load using HS-SPME GC–MS and chemometric tools
Authors: Baky Mostafa H. , Elkenawy Nora M. , El-Nashar Heba A. S. , Abib Bishoy , Farag Mohamed A. 
Published: March 2024

AbstractHerbal spices are widely consumed as food additives owing to their distinct aroma and taste as well as a myriad of economic and health value. The aroma profile of four major spices including bay leaf, black pepper, capsicum, and fennel was tested using HS-SPME/GC–MS and in response to the most widely used spices´ processing methods including autoclaving and γ-radiation at low and high doses. Additionally, the impact of processing on microbial contamination of spices was tested using total aerobic count. GC–MS analysis led to the identification of 22 volatiles in bay leaf, 34 in black pepper, 23 in capsicum, and 24 in fennel. All the identified volatiles belonged to oxides/phenols/ethers, esters, ketones, alcohols, sesquiterpene and monoterpene hydrocarbons. Oxides/phenol/ethers were detected at high levels in all tested spices at ca. 44, 28.2, 48.8, 61.1%, in bay leaves, black pepper, capsicum, and fennel, respectively of the total blend and signifying their typical use as spices. Total oxides/phenol/ethers showed an increase in bay leaf upon exposure to γ-radiation from 44 to 47.5%, while monoterpene hydrocarbons were enriched in black pepper upon autoclaving from 11.4 in control to reach 65.9 and 82.6% for high dose and low dose of autoclaving, respectively. Cineole was detected in bay leaf at 17.9% and upon exposure to autoclaving at high dose and γ-radiation (both doses) its level increased by 29–31%. Both autoclaving and γ-radiation distinctly affected aroma profiles in examined spices. Further, volatile variations in response to processing were assessed using multivariate data analysis (MVA) revealing distinct separation between autoclaved and γ-radiated samples compared to control. Both autoclaving at 115 °C for 15 min and radiation at 10 kGy eliminated detected bioburden in all tested spices i.e., reduced the microbial counts below the detection limit (< 10 cfu/g).
Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach
Authors: Ali Rohaid , Connolly Ian D. , Tang Oliver Y. , Mirza Fatima N. , Johnston Benjamin , Abdulrazeq Hael F. , Lim Rachel K. , Galamaga Paul F. , Libby Tiffany J. , Sodha Neel R. , Groff Michael W. , Gokaslan Ziya L. , Telfeian Albert E. , Shin John H. , Asaad Wael F. , Zou James , Doberstein Curtis E. 
Published: March 2024

AbstractDespite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American’s reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.
Memory reactivation in slow wave sleep enhances relational learning in humans
Authors: Santamaria Lorena , Kashif Ibad , McGinley Niall , Lewis Penelope A. 
Published: March 2024

AbstractSleep boosts the integration of memories, and can thus facilitate relational learning. This benefit may be due to memory reactivation during non-REM sleep. We set out to test this by explicitly cueing reactivation using a technique called targeted memory reactivation (TMR), in which sounds are paired with learned material in wake and then softly played during subsequent sleep, triggering reactivation of the associated memories. We specifically tested whether TMR in slow wave sleep leads to enhancements in inferential thinking in a transitive inference task. Because the Up-phase of the slow oscillation is more responsive to cues than the Down-phase, we also asked whether Up-phase stimulation is more beneficial for such integration. Our data show that TMR during the Up-Phase boosts the ability to make inferences, but only for the most distant inferential leaps. Up-phase stimulation was also associated with detectable memory reinstatement, whereas Down-phase stimulation led to below-chance performance the next morning. Detection of memory reinstatement after Up-state stimulation was negatively correlated with performance on the most difficult inferences the next morning. These findings demonstrate that cueing memory reactivation at specific time points in sleep can benefit difficult relational learning problems.
Human cytomegalovirus infection triggers a paracrine senescence loop in renal epithelial cells
Authors: Raviola Stefano , Griffante Gloria , Iannucci Andrea , Chandel Shikha , Lo Cigno Irene , Lacarbonara Davide , Caneparo Valeria , Pasquero Selina , Favero Francesco , Corà Davide , Trisolini Elena , Boldorini Renzo , Cantaluppi Vincenzo , Landolfo Santo , Gariglio Marisa , De Andrea Marco 
Published: March 2024

AbstractHuman cytomegalovirus (HCMV) is an opportunistic pathogen causing severe diseases in immunosuppressed individuals. To replicate its double-stranded DNA genome, HCMV induces profound changes in cellular homeostasis that may resemble senescence. However, it remains to be determined whether HCMV-induced senescence contributes to organ-specific pathogenesis. Here, we show a direct cytopathic effect of HCMV on primary renal proximal tubular epithelial cells (RPTECs), a natural setting of HCMV disease. We find that RPTECs are fully permissive for HCMV replication, which endows them with an inflammatory gene signature resembling the senescence-associated secretory phenotype (SASP), as confirmed by the presence of the recently established SenMayo gene set, which is not observed in retina-derived epithelial (ARPE-19) cells. Although HCMV-induced senescence is not cell-type specific, as it can be observed in both RPTECs and human fibroblasts (HFFs), only infected RPTECs show downregulation of LAMINB1 and KI67 mRNAs, and enhanced secretion of IL-6 and IL-8, which are well-established hallmarks of senescence. Finally, HCMV-infected RPTECs have the ability to trigger a senescence/inflammatory loop in an IL-6-dependent manner, leading to the development of a similar senescence/inflammatory phenotype in neighboring uninfected cells. Overall, our findings raise the intriguing possibility that this unique inflammatory loop contributes to HCMV-related pathogenesis in the kidney.
Validity of Markovian modeling for transient memory-dependent epidemic dynamics
Authors: Feng Mi , Tian Liang , Lai Ying-Cheng , Zhou Changsong 
Published: March 2024

AbstractThe initial transient phase of an emerging epidemic is of critical importance for data-driven model building, model-based prediction of the epidemic trend, and articulation of control/prevention strategies. Quantitative models for real-world epidemics need to be memory-dependent or non-Markovian, but this presents difficulties for data collection, parameter estimation, computation, and analyses. In contrast, such difficulties do not arise in the traditional Markovian models. To uncover the conditions under which Markovian and non-Markovian models are equivalent, we develop a comprehensive computational and analytic framework. We show that the transient-state equivalence holds when the average generation time matches the average removal time, resulting in minimal Markovian estimation errors in the basic reproduction number, epidemic forecasting, and evaluation of control strategy. The errors depend primarily on the generation-to-removal time ratio, while rarely on the specific values and distributions of these times. Overall, our study provides a general criterion for modeling memory-dependent processes using Markovian frameworks.