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The effect of angiotensin receptor blockers on peripheral artery disease progression: A retrospective cohort study in Jordan

Background Peripheral artery disease (PAD) is a common atherosclerotic vascular condition associated with many complications. The specific effect of angiotensin receptor blockers (ARBs) on the short-term complications in PAD patients has not been explored before. Objectives This study aimed to evaluate the association between ARBs use and the disease severity, progression, the frequency of surgical interventions, and arterial occlusion rates among users. Methods A retrospective cohort study was conducted at King Abdullah Ⅱ Hospital, including 133 patients followed between 2019 and 2024. The cohort was divided into two groups based on their exposure to ARBs: the ARBs group and the control group. Data related to patient demographics, clinical characteristics, medical history, laboratory results, vital signs, in addition to clinical outcomes were extracted from electronic medical records. Statistical analysis was performed using SPSS version 27.0. Results Among the participants, 24.8% experienced amputation, with 21.1% in the control group compared to 3.8% in the ARBs group. ARBs group has 79.2% lower Limb amputation (P-value 0.011; OR= 0.208, 95% CI 0.062–0.700) and 71% lower likelihood of having advanced PAD stage (stages III and IV) (P-value = 0.034, OR= 0.294, 95% CI = 0.095–0.911) compared to the control group. Linear regression analysis showed that the ARBs group has a decreased frequency of revascularization procedures (P-value 0.025;95% CI −1.004 – - 0.068). Conclusion ARBs may serve as a promising protective treatment option for PAD patients, potentially benefiting disease progression by lowering the risk of amputation and reducing the need for revascularization procedures.

Life scientists: educate others to help strengthen biosecurity

AI-assisted discovery of potent FGFR1 inhibitors via virtual screening and in silico analysis

Fibroblast growth factor receptor 1 (FGFR1) is recognized as an oncogene that fosters tumor development, playing a vital role in cancer progression. This has established it as a promising target for cancer drug development. However, existing FGFR1 inhibitors are often limited by drug resistance and lack of specificity, emphasizing the need for more selective and potent alternatives. To address this challenge, the present study employed an AI-driven virtual screening approach, integrating molecular docking (MD) and molecular dynamics simulations (MDS) to discover novel FGFR1 inhibitors. A voting classifier integrating three machine learning classifiers was utilized to screen 10 million compounds from the eMolecules database, leading to 44 promising candidates with a prediction probability exceeding 80%. MD identified compound with PubChem Compound Identifier (CID) 165426608 (−10.8 kcal/mol) as the highest-scoring ligand, while compounds with CID 145940129 (−9.8 kcal/mol), CID 131910163 (−9.4 kcal/mol), CID 155915988 (−9.2 kcal/mol), and CID 132423733 (−9.1 kcal/mol), exhibited binding affinities comparable to or slightly lower than that of the native ligand (−10.4 kcal/mol). MDS further revealed that all these compounds, except CID 131910163, maintained structural stability with time. Thermodynamic stability assessment confirmed the spontaneity and feasibility of their complex formation reactions with negative ΔGBFE values ranging from −21.87 to −12.76 kcal/mol. Decomposition of binding free energy change further provided key stabilizing residues. The heatmaps and histograms of the interaction over the full 200 ns simulation period highlighted the prominent interaction profiles. Structural similarity analysis of the four MDS-stable compounds displayed the dice similarity scores of 0.200000 to 0.452830 with known FGFR1 inhibitors. Additionally, the pIC50 prediction using a voting regressor indicated promising pIC50 values (7.07 to 7.47), highlighting their potential as hit candidates for further structural optimization and therapeutic development. Further, this study underscores the efficiency of machine learning-based virtual screening and in silico analysis as a cost-effective and reliable strategy for accelerating hit drug discovery from large datasets, even with limited resources and time.

Optimizing clinical prediction model for new-onset atrial fibrillation in critically ill patient: Based on machine learning

Background New-onset atrial fibrillation (NOAF) increases the risk of embolism and sudden death in critically ill patients; however, limited data exist attempting to identify modifiable risk factors and predict the incidence of NOAF. We aimed to investigate the risk factors for NOAF and develop an optimized clinical prediction model based on machine learning algorithms. Materials and methods Data from patients admitted to the intensive care unit (ICU) of the Affiliated Hospital of Nanjing University of Chinese Medicine from August 2019 to January 2022 were retrospectively analyzed. LASSO regression and Random Forest (RF) algorithms were used to screen predictive variables. Logistic Regression, RF, Gradient Boosting and Support Vector Machine models were constructed to evaluate the recognition ability of different machine learning algorithms. The confusion matrix and calibration curve were used to assess the degree of accuracy of the four models. Decision curve analysis (DCA) was conducted to evaluate the utility of the model in decision-making. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were also calculated to evaluate the performance of the models. The learning curves of the four models were plotted to evaluate the precision of different models. The SHapley Additive exPlanations (SHAP) was used to explain the supreme-performing model. Results In total, 417 patients were enrolled in the study, and 333 patients were allocated to the training group and 84 to the validation group. The baseline characteristic distributions were similar between the two groups. Age, heart rate, mean arterial pressure, activated partial thromboplastin time, and brain natriuretic peptide were revealed as independent predictors of NOAF by LASSO regression and the RF algorithm. The RF model had the best performance, with the area under the receiver operator characteristic curve (AUROC) of 0.758, the area under the precision-recall curve (AUPRC) of 0.524, and accuracy of 0.735 in the training set, paralleled by AUROC of 0.796, AUPRC of 0.686, and accuracy of 0.702 in the validation set. The confusion matrix and calibration curves showed that RF had the best performance. DCAs also showed that the RF model provided the highest net benefit in the clinical setting. The NRI results showed that the RF significantly improved reclassification ability compared to the baseline model (NRI = 0.38). The IDI results further demonstrated a moderate improvement in discrimination ability for the RF (IDI = 0.033) compared to the baseline. The learning curves revealed that RF also showed superior performance. SHAP could be used visualized individual NOAF risk predicted by the model. Conclusions The RF model exhibited the best performance in predicting NOAF in critically ill patients and has the potential to help clinicians identify high-risk patients and guide clinical decision making.

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Method validation and measurement uncertainty estimation of pesticide residues in Okra by GC/HPLC

Reliability and accuracy of an analytical method is ensured by method validation technique. The present study was aimed to optimize and validate a rapid, reliable and accurate method for quantitatively determining pesticide residues of a diverse group in okra matrix. All method performance characteristics pertaining to method validation was tested. Three different pesticides viz. Thiamethoxam, Ethion, and lambda Cyhalothrin of diverse chemical classes which are applied on okra cultivation and have high MRLs as per FSSAI, were selected. Okra available in local market is often laced with these pesticides. The higher concentrations of pesticide residues in okra can be severely toxic to consumers. Thus validation of method that is simple and cost effective and can give accurate results is desirable for monitoring of these pesticides in okra. Hence a method was validated for analysis of Thiamethoxam, Ethion, and lambda Cyhalothrinby HPLC/GC. Pesticide residues fromokra samples were extracted using modifiedQuEChERs method, followed by injection into GC/HPLC. The validated method demonstrated suitable specificity, linearity, recovery etc.The calibration curves were linear for all the threepesticides with a regression coefficient, r2 > 0.99. Matrix effect observed for all three pesticides in okra, fell within the range of ±20%. All pesticides were quantified successfully at a concentration of 0.30 mg/kg with an average recovery of more than 70% and a relative standard deviation (RSD) of less than 20%. The procedure was simple, rapid, cost effective and depicted high accuracy. The greenness of the method evaluated on Agro Eco Scale was satisfactory. Theestimation of uncertainties based on the validation data, werefound to be below the default limit of 50%. The quality control (QC) charts based on the basis of intra-laboratory performance were prepared at LOQ of pesticides to ensure the validity and accuracy of laboratory test results.

AI chatbots are already biasing research — we must establish guidelines for their use now

Effect of preoperative virtual reality cartoon viewing on postoperative pain and anxiety in children undergoing tonsillectomy and adenoidectomy: A randomized controlled trial

Objective Surgery causes anxiety in children and negatively affects postoperative pain control. Various distraction methods, such as virtual reality (VR), have been shown to reduce anxiety levels and improve surgical outcomes. This study aimed to determine the effect of watching cartoons through a VR headset before surgery on systolic blood pressure, postoperative pain, and anxiety levels as primary, secondary, and tertiary outcomes, respectively, in children aged 7–12 years undergoing tonsillectomy and adenoidectomy. Methods This randomized controlled experimental study was conducted at a tertiary hospital between November 10, 2023, and June 1, 2024, among 102 children scheduled for tonsillectomy and adenoidectomy, who were randomly divided into an experimental group (n = 51; VR intervention) and a control group (n = 51; no intervention). The primary outcomes were anxiety levels measured using the Perioperative Multidimensional Anxiety Scale for Children and postoperative pain evaluated using the Visual Analog Scale. Sociodemographic characteristics and vital signs were also assessed. Results Systolic blood pressure values were significantly lower in the experimental group at than in the control group at all time points (p < 0.05). Postoperative pain values were lower in the experimental group (3.35 ± 1.43 vs. 6.53 ± 1.36, p < 0.05), with similar results observed 8 h post-surgery (1.29 ± 1.08 vs. 6.57 ± 1.17, p < 0.05). Anxiety values were also significantly lower in the experimental group (24.12 ± 11.17 vs. 69.41 ± 12.56, p < 0.05), with similar results observed 8 h post-surgery (12.35 ± 10.50 vs. 67.0 ± 11.37, p < 0.05). Conclusion VR technology, particularly through watching the Shrek cartoon, significantly reduced systolic blood pressure, pain, and anxiety levels in children undergoing tonsillectomy and adenoidectomy. Thus, VR could be an effective noninvasive tool for managing pain and anxiety in pediatric patients during the preoperative and postoperative periods. Trial registration ClinicalTrials.gov (NCT06763276).

Verifying the accuracy of Japanese version of the pediatric delirium assessment scale: SOS-PD and the high accuracy of family assessments of pediatric delirium

Background Detecting pediatric delirium in critically ill children is important. The Sophia Observation withdrawal Symptoms and Delirium scale (SOS-PD) is a tool for assessing both pediatric delirium and iatrogenic withdrawal symptoms and contains a question for parents to assist in detecting pediatric delirium. Objectives The aim was to translate the Japanese SOS-PD and to perform a cross-culture validation of the J-SOS-PD pediatric delirium dimension while confirming the accuracy of family assessments of pediatric delirium. Methods The translation was undertaken with the internationally established forward- backward translation method. Pediatric delirium was simultaneously evaluated and compared between psychiatric diagnoses based on assessment by a pediatric intensivist and the Japanese version of the SOS-PD as evaluated by PICU researchers. We evaluated the criterion validity (sensitivity and specificity), cut-off point using a receiver operating characteristic (ROC) curve, and reliability with Cohen’s κ coefficient and intraclass correlation coefficients (ICC). Results A total of 125 independent assessments were performed in 67 children with a median age of 15 (IQR 5, 54) months and with a pediatric delirium-positive rate of 30% based on psychiatric evaluation. Based on the ROC curve analysis, the cut-off point of 4 was the most appropriate within the original scale and the Japanese SOS-PD version showed high sensitivity (0.92, 95% CI 0.84–1.00) and specificity (0.97, 95% CI 0.94–1.00) at a cut-off point of 4 and high reliability within the researcher assessments (κ = 0.95). We also verified family assessments of pediatric delirium as showing high sensitivity (0.90) and specificity (0.89) over 36 assessments. Conclusions The Japanese version of the SOS-PD shows a high accuracy similar to the original. Moreover, we revealed high accuracy in family perception of pediatric delirium that could promote family presence in PICU settings.

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Does adoption of the rice–crayfish co-culture model improve farmers’ income?

This study empirically assesses the income effects of the rice–crayfish co-culture model using endogenous switching regression (ESR) and mediation models, based on survey data from 1,058 farm households in Hubei Province. Key findings reveal that adoption of the rice–crayfish co-culture model significantly boosts farmers’ total income: counterfactual analysis shows non-adopters would experience a 22.423% decline in average household income if they ceased adoption. The adoption of the rice–crayfish co-culture model differential impacts on the income of various farmer groups. This divergence primarily stems from the model’s significantly stronger positive effect on agricultural income compared to its minimal dampening effect on non-farm income. By examining the mechanism of its effect on farmers’ income, we find that adopting the rice–crayfish co-culture model mainly promotes farmers’ income by affecting the human capital of the family. Based on the above conclusions, the Chinese government should further promote the sustainable development of the rice–crayfish co-culture model and give full play to its role in increasing farmers’ income. Simultaneously, constructing a comprehensive industrial system for the rice–crayfish co-culture model and intensifying technical training are imperative. These efforts aim to enhance the human capital of farmers, which in turn will effectively promote the growth of their income.

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Case-based reasoning for emergency response planning of coal mine gas explosion accidents

Gas explosions in coal mines pose a serious threat to miner safety and operational sustainability, often resulting in significant casualties and production losses. To address the deficiencies in emergency decision-making and preparedness, this study proposes a case-based reasoning (CBR) model for emergency response planning, using a representative gas explosion incident at Mine B as the target case. Historical accident cases were analyzed to extract and quantify key descriptive and decision-related attributes. A cloud model-based weighting method was employed to determine the relative importance of features, followed by improved K-nearest neighbor (KNN) retrieval for similar case matching. A multi-population genetic algorithm (MEA) was used to optimize the weights and thresholds of a backpropagation (BP) neural network for case adaptation and reuse. The cloud model was further introduced to evaluate the effectiveness of the proposed emergency plans. Simulation results demonstrate that the model yields reliable and practical emergency responses, with the evaluated plan rated between “fair” and “good.” Finally, this study outlines implementation and safeguard measures for emergency plan execution, offering a scientifically grounded reference for coal mine enterprises to enhance gas explosion preparedness and response efficiency.

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Correction: Knowledge and perception of mHealth medication adherence applications among pharmacists and pharmacy students in Jazan, Kingdom of Saudi Arabia

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The effects of concentrate to roughage ratio in the diet on growth performance, carcass traits, and meat quality of housed yaks

Automated classification of clinical diagnoses in electronic health records using transformer

The automated classification of clinical diagnoses in electronic health records (EHRs) is critical for enhancing clinical decision-making and enabling large-scale medical research, yet existing methods struggle with heterogeneous data structures and limited annotated datasets. Current approaches fail to adequately address the dual challenges of extracting contextual medical semantics from unstructured clinical narratives while maintaining generalizability across institutions with divergent documentation practices. This study proposes a novel framework integrating three core components: a Transformer-based architecture for hierarchical feature extraction from clinical text, a multi-task learning paradigm leveraging diagnostic interdependencies, and transfer learning initialization using pretrained medical language models. Evaluation on the MIMIC-III dataset demonstrates state-of-the-art performance with 89.2% accuracy and 87.6% F1-score, outperforming conventional CNN-RNN hybrids by 8.0% in recall and showing 4.9-6.2% improvements over ablated configurations in critical metrics. The results establish that synergistic integration of contextual attention mechanisms, cross-task knowledge sharing, and medical domain adaptation effectively addresses EHR heterogeneity while reducing reliance on institution-specific annotations, providing a robust foundation for clinical decision support systems that balance accuracy with real-world implementability across diverse healthcare environments.

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Low prevalence of helminth infection in Ugandan children hospitalized with severe malaria

Co-infection by intestinal helminths and Plasmodium spp. may be common in endemic communities. In 2003, Uganda instituted a national deworming program, with anti-helminth medication provided twice annually to children 6 months to 5 years of age, but few follow-up studies have been conducted. Several studies have identified a relationship between helminth infection, Plasmodium spp. infection and malaria severity. However, the relationship is not well defined, and results are inconclusive. We analyzed 177 stool samples from a cohort of children with severe malaria enrolled in two hospitals in Uganda from 2014–2017. All children were 6 months to 48 months of age and had a clinical presentation of and laboratory confirmation for severe malaria. We also analyzed 25 stool samples from community children who were negative for malaria via rapid diagnostic test and were enrolled from the same household or neighborhood and matched by age, sex, and time of enrollment. We investigated if intestinal helminth infection modified risk of severe malaria. We extracted nucleic acids from stool and tested them for six helminth species (Anyclostoma duodenale, Ascaris lumbricoides, Necator americanus, Strongyloides stercolaris, Trichuris trichiura, Shistosoma mansoni) using highly sensitive quantitative PCR. We found a low prevalence of infection by ≥1 intestinal helminth species in children with severe malaria (5.1%, n = 9/177) and community control children (4.0%, n = 1/25). Helminth infection did not increase or decrease the risk of severe malaria in this cohort (aRR = 1.0, 95% Confidence Interval = 0.82, 1.3, p = 0.78). In these areas of Uganda, the national deworming campaign has been highly successful, as stool-based helminth infection was rare even when using sensitive methods of detection and helminths were not associated with severe malaria in this study.