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A simple and low-cost electrode based on Nafion-stabilized silver nanoparticles supported on FTO for the electrochemical determination of Pb (II) and Cu (II)
Increasing awareness of the environmental risks posed by heavy metal accumulation in the environment—due to their toxicity and persistence in biological systems—has driven the development of more efficient and accessible detection methods. Conventional techniques, despite their accuracy, are often expensive, time-consuming, and reliant on non-portable specialized equipment. This study presents a novel, low-cost electrochemical sensor using a fluorine-doped tin oxide (FTO) electrode modified with Nafion-stabilized silver nanoparticles (AgNPs) for the rapid and accurate detection of Pb (II) and Cu (II) in water samples. The electrode preparation involved the ultrasonic cleaning of the FTO, followed by its surface modification with Nafion and the electrodeposition of AgNPs. Electrochemical and structural characterization confirmed the advantages of this approach, showing a significant improvement in conductivity and in the active surface area of the electrode, which allowed for the sensitive detection of the target metals. The optimization of analytical parameters, including accumulation time, deposition potential, and pH, facilitated the effective determination of the analytes by differential pulse anodic stripping voltammetry (DPV). The results demonstrated low detection limits of 8.87 ppb for Pb (II) and 3.26 ppb for Cu (II), suitable for in-situ applications in environmental monitoring according to environmental quality standards. The sensor’s portability, coupled with its low cost and rapid analysis capability, addresses critical challenges in current monitoring practices and opens new avenues for widespread environmental surveillance in remote areas such as the Andean regions, where heavy metal contamination is a significant concern.
Sex-specific Mendelian randomization phenome-wide association study of basal metabolic rate
Determinants of pelvic organ prolapse among adult gynecologic patients in Mekelle University Ayder Comprehensive Specialized Hospital, Tigray, Ethiopia: Case control study design
Background Pelvic organ prolapse is a major cause of morbidity among women in both high-income and low-income countries. Despite the severity of the problem, the risk factors associated with pelvic organ prolapse has been poorly understood in Ethiopia mainly in the study area. Hence, the purpose of this study was to identify determinants of pelvic organ prolapse among adult gynecologic patients at Mekelle University Ayder Comprehensive Specialized Hospital. Objective To identify determinants of pelvic organ prolapse among adult gynecologic patients in Mekelle University Ayder Comprehensive Specialized Hospital, Tigray, Ethiopia, 2024. Methods Hospital-based case-control study design was conducted from March 01, 2024 to May 30, 2024. All cases diagnosed with pelvic organ prolapse were enrolled in the study. Then, 4:1 control-to-cases ratio was applied. Data were entered into Epi Data version 4.6 and analyzed using SPSS version 21. Figures and tables were used for descriptive statistics. Variables with P-value less than 0.2 during binary logistic regression were labeled as candidates for multivariable logistic regression to identify independent predictors of pelvic organ prolapse at p-value < 0.05. The overall model fitness was checked by Hosmer Lemeshow at a P-value > 0.05. Finally, variables with P-value less than 0.05 and a 95% confidence interval of adjusted odds ratio were considered significant factors for the determinants of pelvic organ prolapse. Results A total of 478 participants were recruited with a 100% response rate for both cases and controls. Low income (AOR=3.3; 95% CI: 1.1–9.7), vaginal tear (AOR=6.6; 95% CI: 2.5–17.6), menopausal status (AOR=9.2, 95% CI:2.3–37.4), body mass index <18 kg/m2 (AOR=6.3, 95% CI: 2.7–14.4), body mass index ≥25 (AOR= 5.6, 95% CI: 1.5–21.2) and chronic constipation (AOR=6.4, 95% CI: 2.9–13.9) were identified as determinants of pelvic organ prolapse. Conclusions In this study, income of the participant, vaginal tear, menopausal status, body mass index (both underweight and overweight), and chronic constipation were factors found to be significantly associated with pelvic organ prolapse. Therefore, creating awareness about risk factors of pelvic organ prolapse, screening and early intervention, weight management program, and hormonal support is recommended.
Development and testing of an open source mobile application for audiometry test result analysis and diagnosis support
Core collection construction and genetic diversity analysis of tea plant (Camellia sinensis [L:] O. Kuntze) accessions in Huangshan city using SSR markers
Assessing genetic diversity and building a core collection is essential to advancing tea plant breeding. In this study, ten SSR markers exhibiting robust amplification and polymorphism were employed to genotype 292 tea accessions sourced from various regions in Huangshan city. The results revealed significant genetic variation, encompassing 180 alleles. Genetic structure was evaluated using neighbor-joining clustering, principal coordinate analysis, and Structure analyses, which categorized the tea accessions into two primary clusters. The genetic diversity within these clusters demonstrated high similarity, likely due to their close geographical proximity. A core collection was established utilizing Core Hunter software, resulting in the selection of 35% of the accessions to effectively represent the genetic diversity of the entire collection. This core collection comprises 102 tea accessions, preserving a high percentage of allele richness and genetic diversity. This research offers valuable insights into genomics research and the sustainable management of tea plant genetic resources in Huangshan city.
A novel variant of telomerase reverse transcriptase (TERT) associated with risk of glioma in a Korean population
Determinants of successful driving rehabilitation training in licensed individuals with disabilities
Previous studies have provided that self-driving can enhance the mobility of people with disabilities and their quality of life. The National Rehabilitation Center has been providing driving rehabilitation education for people with disabilities since 1994, as part of a welfare service project aimed at guaranteeing their right to free movement. However, there is no analysis of the status and results of driving rehabilitation education and evaluations in South Korea, and research on these programs is lacking. This study aims to analyze the on-road driving rehabilitation education and evaluation results conducted by the National Rehabilitation Center from 2019 to 2021. It seeks to identify the characteristics of the prior license holders with disabilities and the factors influencing the need for additional driving rehabilitation education. Out of a total of 676 prior license holders, 532 were included in the analysis regarding the need for additional driving rehabilitation education. The results of this study indicate that women were 2.07 times more likely than men to require additional driving rehabilitation education. Conversely, the likelihood of requiring additional driving rehabilitation education was lower for those with better driving senses (0.17 times), less tension (0.46 times), and less impact from their disability (0.45 times). For prior license holders, it was found that demographic characteristics (excluding gender) or the nature of their disabilities had less significant impacts compared to the driver’s response level, the type of driving license held, and the number of assistive devices used. These findings can be used for developing effective driving education programs for people with disabilities and designing strategies to enhance license acquisition rates, thereby improving their mobility rights.
A multifunctional nursing pad for lactating mothers
Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research
Background Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches. Methods We choosed datasets from the Gene Expression Omnibus (GEO) database, including samples from both IPF patients and healthy controls. For the training set, we combined two gene array datasets (GSE24206 and GSE10667) and utilized GSE32537 as the test set. We identified differentially expressed genes (DEGs) between IPF and normal tissues and determined IPF-related modules using Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, we employed two machine learning strategies to screen potential diagnostic biomarkers. Candidate biomarkers were quantitatively evaluated using Receiver Operating Characteristic (ROC) curves to identify key diagnostic genes, followed by the construction of a nomogram. Further validation of the expression of these genes through transcriptomic sequencing data from IPF and normal group animal models. Next, we conducted immune infiltration analysis, single-gene Gene Set Enrichment Analysis (GSEA), and targeted drug prediction. Finally, we created an artificial neural network model specifically for IPF. Results We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90. These genes were validated by RT-qPCR. Immune infiltration analysis revealed that specific immune cell types are closely related to IPF, suggesting that these immune cells may play a significant role in the pathogenesis of IPF. Conclusion ASPN, COMP, and GPX8 have been identified as potential diagnostic genes for IPF, and the most relevant immune cell types have been determined. Our research results propose potential biomarkers for diagnosing IPF and present new pathways for investigating its pathogenesis and devising novel therapeutic approaches.
Carbon-supported ZnO materials for sulfur capturing in supercritical water
Abstract Sulfur (S) capturing materials working at supercritical water (SCW) conditions need to be designed and developed to overcome issues related with catalyst poisoning during the hydrothermal gasification of wet biomass, an efficient and sustainable technology for alternative fuels production. Sorbent materials of zinc oxide (ZnO) deposited on porous carbon (C) support were prepared by an innovative continuous flow SCW impregnation method. Their S-adsorption performance was tested under the same supercritical conditions in the presence of sodium hydrosulfide (NaHS), as model inorganic sulfur compound. During sulfidation experiments, ZnS replaced ZnO indicating an efficient chemisorption of S with the formation of the sulfide particles by a pseudomorphic replacement mechanism. The S adsorption capacity of the ZnO/C composites reaches 1.55 mol S /mol Zn at relatively low temperature, which is much higher than those of other reported S capturing materials employed in SCW processes. The results reported here confirm that S sorbents can be both generated and used under the continuous flow SCW conditions relevant for technological applications towards the production of hydrogen and methane from biomass wastes and residues.
Genetic diversity and verification of plant material compliance of Cocoa (Theobroma cacao L.) in the Barombi-Kang Regional variety trial
Cocoa (Theobroma cacao L.) is a pivotal agricultural commodity in Cameroon, which ranks as one of the top five global cocoa producers. This study focused on evaluating the genetic diversity and verifying the plant material compliance of cocoa genotypes in the Barombi-Kang Regional variety trial, employing 12 highly polymorphic SSR markers. A comprehensive analysis of 318 hybrid families and 15 parental genotypes was conducted, which revealed extensive genetic variability. The study found an average polymorphic information content (PIC) of 0.72 for hybrids and 0.68 for parents, alongside observed heterozygosity rates of 0.54 and 0.42, respectively, indicating a rich genetic reservoir. Significantly, an 18.55% labeling error rate was identified, underscoring prevalent issues in germplasm management that could impact the efficacy of breeding programs. These errors highlight the critical need for enhanced genetic verification protocols to ensure the accuracy and reliability of plant materials used in breeding. The genetic analysis also demonstrated substantial allelic richness with the hybrids showing an average of 72 alleles per locus, suggesting a high capacity for selection within the breeding pool. The data from this study not only reinforce the potential for genetic improvement of cocoa in Cameroon but also provide crucial insights into the genetic structure and population dynamics within the trial. Addressing the genetic and management challenges identified could lead to the development of superior cocoa varieties, enhancing yield, disease resistance, and environmental stress tolerance, thereby contributing to the sustainable advancement of the cocoa industry in Cameroon and beyond.
Digital economy and entrepreneurial vitality: unveiling the impact and mechanisms through the lens of smart cities
RETRACTED: N-Beats architecture for explainable forecasting of multi-dimensional poultry data
The agricultural economy heavily relies on poultry production, making accurate forecasting of poultry data crucial for optimizing revenue, streamlining resource utilization, and maximizing productivity. This research introduces a novel application of the N-BEATS architecture for multi-dimensional poultry data forecasting with enhanced interpretability through an integrated Explainable AI (XAI) framework . Leveraging its advanced capabilities in time series modeling, N-BEATS is applied to predict multiple facets of poultry disease diagnostics using a multivariate dataset comprising key environmental parameters. The methodology empowers decision-making in poultry farm management by providing transparent and interpretable forecasts. Experimental results demonstrate that N-BEATS outperforms conventional deep learning models, including LSTM, GRU, RNN, and CNN, across various error metrics, achieving MAE of 0.172, RMSE of 0.313, MSLE of 0.042, R-squared of 0.034, and RMSLE of 0.204. The positive R-squared value indicates the model’s robustness against underfitting and overfitting, surpassing the performance of other models with negative R-squared values. This study establishes N-BEATS as a superior and interpretable solution for complex, multi-dimensional forecasting challenges in poultry production, with significant implications for enhancing predictive analytics in agriculture.
A unified probability distribution of second order difference of global streamflow
Development of a size-separation technique to isolate Caenorhabditis elegans embryos using mesh filters
The free-living nematode Caenorhabditis elegans has been routinely used to study gene functions, genetic interactions, and conserved signaling pathways. Most experiments require that the animals are synchronized to be at the same specific developmental stage. Bleach synchronization is traditionally used to obtain a population of staged embryos, but the method can have harmful effects on the embryos. The physical separation of differently sized animals is preferred but often difficult to perform because some developmental stages are the same sizes as others. Microfluidic device filters have been used as alternatives, but they are expensive and require customization to scale up the preparation of staged animals. Here, we present a protocol for the synchronization of embryos using mesh filters. Using filtration, we obtained a higher yield of embryos per plate than using the standard bleach synchronization protocol and at a scale beyond microfluidic devices. Importantly, filtration has no deleterious effects on downstream larval development assays. In conclusion, we have exploited the differences in the sizes of C. elegans developmental stages to isolate embryo cultures suitable for use in high-throughput assays.
Exploring the relationship between AI literacy, AI trust, AI dependency, and 21st century skills in preservice mathematics teachers
Imaging-based assessment of muscles and malnutrition predict prognosis in patients with primary hepatocellular carcinoma
Background The significance of imaging-based assessment of muscles and malnutrition in patients with primary hepatocellular carcinoma (HCC) remains unclear. This study aimed to elucidate the prognostic role of the combination of Low Muscle Volume and Value (LMVV) and malnutrition. Methods A total of 714 Child-Pugh grade A/ B patients with first-diagnosed HCC were enrolled, and analyzed factors associated with overall survival. LMVV was defined using psoas muscle mass index and computed tomography values of multifidus muscle at the level of the third lumbar vertebra. We used hypoalbuminemia, Child-Pugh grade B, Subjective Global Assessment (SGA) grade B/C, and Royal Free Hospital Nutrition Prioritizing Tool (RFH-NPT) score > 2 as malnutrition factors in this study. Results At baseline, 29% showed LMVV, and 59% met one or more of the malnutrition criteria. No items meeting the criteria of LMVV and malnutrition was observed in 41%, 1 of them was found in 29%, and both were found in 29%. The number of items meeting criteria was an independent factor for a shorter survival. The frequency of liver-related deaths did not differ by presence of LMVV alone, while it was associated with malnutrition. In contrast, the incidence of other types of deaths was influenced by LMVV and malnutrition. Conclusions The combination of LMVV and malnutrition is a prognostic factor in patients with primary HCC.