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Sports injury prevention and management in university physical education: a qualitative study of lecturers and students in Sichuan Province, China
Treatment reliability of solar septic tank systems under comparative field conditions
Adaptive self-supervised knowledge transfer for computationally efficient thoracic disease screening from chest X-ray images
Vitamin a modulates neurogenesis-associated pathways and cholinergic signaling in Alzheimer’s disease: potential role of reactive astrocytes via NGN2/SOX-11 and SIRT-1
Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder lacking effective disease-modifying therapies. A promising regenerative approach involves enhancing endogenous neurogenic capacity within the injured brain. Reactive astrocytes—stellate-like cells in the AD brain—may contribute to a pro-neurogenic environment through transcription factors (TFs) such as neurogenin 2 (NGN2) and SOX-11. This process is tightly regulated by epigenetic mechanisms, particularly SIRT-1, a neuroprotective histone deacetylase that modulates TF activity and neuronal fate. Vitamin A (V A ), a key regulator of differentiation and epigenetic remodeling via its active metabolite retinoic acid, is stored in astrocytes and hepatic stellate cells (HSCs). We hypothesized that AD-related astrocyte activation depletes cerebral V A , mobilizes hepatic stores, contributes to liver fibrosis, and that V A supplementation may restore astrocytic function, activate endogenous TFs via SIRT-1, and drive cholinergic neuron regeneration. In a scopolamine (SCO)-induced AD rat model, V A biodistribution was traced using confocal microscopy. Brain and liver V A deficiency were confirmed via retinol-binding protein (RBP) and ALDH1A1 expressions. Rats received V A (1500, 3000, or 4500 IU/kg/day) or donepezil. Outcomes included neurogenesis (DCX), NGN2/SOX-11 expression, SIRT-1 activation, cholinergic regeneration, amyloid-β deposition, and serum tau. Liver fibrosis was assessed via TGF-β, hydroxyproline and histopathologically. AD induced systemic V A depletion and liver fibrosis. Medium-dose V A (VAMD) significantly enhanced neurogenesis, TF expression, SIRT-1 activation, cholinergic regeneration, and reversed liver fibrosis. VAMD demonstrated neuroregenerative and antifibrotic effects, indicating a possible therapeutic role in AD.
A hybrid forecasting and fuzzy comprehensive evaluation approach for graded early warning of experimental task frequency
Abstract Accurate prediction and graded warning of experimental task frequencies are critical for resource optimization and operational safety in large-scale scientific facilities. To address this, we propose a novel graded early-warning framework that seamlessly integrates deep sequential forecasting with fuzzy comprehensive evaluation. A hybrid Transformer-LSTM neural network is developed to capture both long-range dependencies and local temporal patterns in multivariate task frequency sequences. To address the sensitivity of deep models to hyperparameters, we employ the bioinspired Black-winged Kite Algorithm (BKA) to jointly optimize the learning rate, hidden layer units, and regularization coefficients. BKA is selected for its Cauchy mutation and adaptive leadership mechanisms, which effectively avoid local optima and balance exploration-exploitation. Preliminary tests confirm that BKA outperforms classical methods such as PSO and GA. Subsequently, a fuzzy comprehensive evaluation model, incorporating entropy-based weights and membership functions derived from prediction residuals, maps the forecasting uncertainties onto a four-level early warning scale (Safe, Mild, Moderate, Severe). Digital simulation verification demonstrates that the proposed method achieves high prediction accuracy, with a root mean square error (RMSE) of 2.18 counts $$/\textrm{h}$$ and a coefficient of determination ( $$R^2$$ ) of 0.774 on the test set, outperforming baseline LSTM and PSO-LSTM models. Furthermore, the fuzzy warning system attains a precision of 0.91 and a recall of 0.93 for the Severe level, with an overall area under the ROC curve (AUC) of 0.94, confirming its reliable graded warning capability. The integration of data-driven prediction and fuzzy logic provides a powerful framework for active anomaly management in complex dynamic systems.
Socioeconomic and partner-related determinants of different forms of sexual violence against women in Türkiye
Default-threshold operating-point validation of a commercial chest radiograph AI system for selected CT-anchored thoracic findings: a bi-national multicenter retrospective study
Abstract This retrospective bi-national multicenter diagnostic accuracy study evaluated the locked default-threshold performance of a commercial chest radiograph AI system for selected thoracic findings using a CT-anchored, radiographic-detectability reference framework. Consecutive eligible adult patients who underwent frontal chest radiography and chest CT at four tertiary-care centers in Turkey and Bulgaria between June and December 2024 were included. Reference labels were assigned using temporally paired CT, with adjudication of whether CT-confirmed abnormalities had a corresponding radiographic manifestation on the paired chest radiograph. For potentially dynamic findings, the allowable CT–CXR interval was restricted to ≤ 2 days. The AI system was evaluated at the manufacturer’s default threshold of 0.50. Diagnostic performance was summarized using prevalence, sensitivity, specificity, positive predictive value, negative predictive value, and false-positive burden with 95% confidence intervals. The study included 940 patients with paired CXR-CT examinations. Reference-positive prevalence ranged from 2.2% for pneumothorax to 21.0% for pleural effusion. Sensitivity was highest for fracture (91%) and pneumothorax (90%) and lowest for atelectasis (64%); specificity ranged from 82% for consolidation/opacity to 98% for fracture and pneumothorax. PPV ranged from 43% to 63%, indicating a non-trivial false-positive burden at the evaluated operating point. Because continuous probability scores, human-reader comparison, and workflow outcomes were unavailable, these findings should be interpreted as default-threshold technical validation and support further evaluation of the system as radiologist-supervised decision support with local performance monitoring, rather than standalone diagnosis.
Gut acidification impairment links altered acidogenic microbiota to infantile eczema: a cross-sectional study
Agent-based modeling of low-emission fertilizer adoption for dairy farm decarbonisation using empirical farm data
A lightweight underwater biological object detector with enhanced cross-scale feature interaction
Abstract Underwater biological object detection is important for intelligent marine monitoring, yet its performance is often limited by severe image degradation, background clutter, and large variations in target scale. These factors can weaken feature representation during multi-scale fusion and reduce localization reliability in lightweight detectors. In this study, we propose UD-YOLO, a lightweight underwater object detector designed to improve cross-scale feature interaction and localization stability under degraded underwater conditions. The framework enhances contextual representation in the backbone, stabilizes bidirectional information propagation in the neck, and improves multi-scale localization with a lightweight shared detection head and scale-aware regression supervision. On the RUOD dataset, UD-YOLO improves mAP@0.5:0.95 by 2.1% points over YOLOv11n, while reducing parameter count by 0.1 M and computational cost by 0.2 GFLOPs. Additional evaluations on the URPC and DUO show consistent gains over YOLOv11n under different underwater benchmark settings. These results suggest that UD-YOLO provides an effective accuracy-efficiency trade-off for lightweight underwater biological object detection.
Community-based health insurance enrollment and service experiences in Ethiopia’s Somali Region: a sequential explanatory mixed-methods study
Abstract Community-based health insurance (CBHI) is a key strategy for improving financial protection and access to healthcare in Ethiopia; however, evidence from pastoralist settings remains limited. This sequential explanatory mixed-methods study assessed CBHI enrollment, determinants, and healthcare service experiences among 600 adult patients attending three public hospitals in Ethiopia’s Somali Region between July and August 2025. Quantitative data were analyzed using descriptive statistics and multivariable logistic regression, while qualitative interviews with 12 patients and four healthcare providers were analyzed thematically to contextualize the quantitative findings. Although awareness of CBHI was nearly universal (98.3%), only 65.0% of participants were enrolled, and functional knowledge of benefit packages and referral procedures remained limited. Higher educational attainment, greater trust in CBHI management, and stronger perceived benefits were positively associated with CBHI participation, whereas perceived barriers were negatively associated. Medication shortages were the most frequently reported barrier (72.7%). Interviews further revealed that administrative complexity, medicine shortages, long waiting times, and transportation difficulties continued to undermine healthcare experiences despite insurance coverage. Although CBHI enrollment was associated with perceived improvements in healthcare access and financial protection, service experiences remained constrained by persistent health-system limitations. Policymakers should prioritize community education, simplify enrollment procedures, ensure reliable medicine availability, and strengthen CBHI governance and transparency to improve implementation and sustainability in pastoralist settings.