Browse Articles

Longitudinal trends in modifiable cancer risk factors in the Generations Study cohort in the United Kingdom

Abstract Understanding how modifiable cancer risk factors change across the life course is crucial for evidence-based prevention. However, surveillance of lifestyle factors often relies on cross-sectional studies, unable to capture within-person patterns across the life course. Longitudinal studies assessing long-term exposure patterns are scarce. Within the Generations Study, a prospective cohort of > 113,700 UK women (median recruitment age: 48.4 years; range: 16–102) recruited between 2003 and 2009, we explored life course trajectories of modifiable cancer risk factors, leveraging up to six self-reported exposure measurements collected retrospectively and prospectively. Linear mixed-effects models with natural cubic splines for age were fitted for alcohol consumption, smoking, physical activity and body mass index (BMI). Mean velocity curves were derived from differentiated spline curves. Interactions between age and birth cohort assessed generational differences. Each risk factor showed distinct, nonlinear patterns in the level and rate of change across the life course. Alcohol consumption, smoking and physical activity peaked in late adolescence, whilst BMI increased with age, peaking in the late 60s. Younger cohorts (born 1960–2003) exhibited progressively higher BMI trajectories and earlier peaks in alcohol consumption and smoking intensity compared to older cohorts (born 1908–1959). Modifiable cancer risk factor exposure varied throughout life, highlighting the importance of repeated exposure measurements to capture these patterns. Our findings revealed key life stages for behavioural transitions. This insight can guide targeted prevention and intervention efforts.

Research on the mechanical properties and modification mechanism of loess treated by consolid system

Effective removal of methyl violet dye by montmorillonite-incorporated carboxymethyl cellulose-g-poly(methacrylic acid-co-acrylamide) nanocomposite hydrogel

Quality and reliability of videos about pneumoconiosis on TikTok and Bilibili in China

The integrating analytical framework of the GxE interactions in tomato: a comprehensive multi-analytical approach toward across-elevations

Clinico-demographic and molecular characteristics of familial Mediterranean fever in Egypt: a multicenter study

Abstract Familial Mediterranean Fever (FMF) is an inherited autoinflammatory disease characterized by recurrent episodes of fever and serositis caused by mutations in the MEFV gene. FMF primarily affects individuals of Mediterranean ancestry. Typical manifestations include short-lasting attacks of abdominal pain, chest pain, and arthritis. Long-term complications, such as amyloidosis, might be prevented with colchicine treatment. This study aimed to assess the clinical, demographic and molecular features of FMF in Egyptian patients. This multicenter prospective study included 280 clinically suspected FMF patients referred to the outpatient clinics of participating centers. Patients were enrolled based on recurrent episodes of fever and abdominal pain suggestive of FMF. The diagnosis of FMF was established based on the Eurofever/PRINTO criteria, in addition to molecular genetic confirmation of MEFV mutations. Patients who did not meet diagnostic criteria were excluded. The study included 173 female patients, and 107 male patients with age range from 2 up to 60 years. All patients were descending from different families. Parental consanguinity was found in 24.6% while positive family history was seen in 33.2%. The duration of the attack in almost two third of the patients was less than 48 h, only 6% of the patients suffered attacks longer than 72 h. About half the patients achieved a marked reduction or complete cessation of FMF attacks, along with normalization or significant reduction of inflammatory markers (e.g., serum amyloid A), following colchicine therapy at a daily dose of 1.5–3 mg., only 6% needed higher doses. The initial serum amyloid A was normal in 47.5% in the patients and elevated in the remaining patients. Fever was documented in only 19.6% of patients at presentation; abdominal pain was among the most common presentation, seen in 62.1%. The most frequently observed MEFV allele was E148Q (143, 39.5%) followed by M694I ( n  = 59, 16.3%), A744S and V726A ( n  = 44, 12.2% for each), then M680I ( n  = 35, 9.7%). In our multicenter study focused on Egyptians, 93.6% of the enrolled FMF patients carried at least one MEFV variant. The E148Q allele was the most frequently observed followed by M694I. The study also revealed that Egyptian patients exhibited a mild form of the disease with a female predominance. This mild presentation might be attributed to the high E148Q prevalence and low rate of amyloidosis in our cohort.

A Transformer-Based Deformable Convolution UNet for Adaptive Arbitrary Style Transfer

Abstract In recent years, image style transfer has matured significantly in the field of computer vision. However, current methods for image style transfer still face the challenge of balancing between content edge contours and style texture strokes, as it is difficult to control the degree of stylization. To address this issue, we propose a UNet based on Transformer feature fusion, named Trans-DCUNet. The network adaptively integrates content and style features by taking advantage of the self-learning characteristics of the cross-attention mechanism in the Transformer. The network combines Transformer and CNN, which takes advantage of the global context capture ability of Transformers and the local modeling ability of convolutional neural networks to fully learn image features at multiple levels, thereby generating stylized images. To enhance texture generation, we introduce a deformable convolutional residual module, which allows the convolution kernel to adapt to varying image features, capturing fine texture details more effectively. Additionally, we augment the traditional perception loss with edge detection loss and frequency perception loss, aiming to better preserve the edge contours of the content image and learn the texture strokes of the style image. Our experiments were conducted on the Microsoft COCO and WikiArt datasets. Experimental results show that our method achieves a content retention SSIM of up to 0.8655 and a style similarity LPIPS of 0.5655, outperforming most competing methods, while generating more artistic stylized images with significantly improved visual effects.

Improved accuracy of PCG signal classification for myocardial infarction biomarker using automatic feature selection and boosting process

Abstract Myocardial infarction (MI) is a leading global health concern, typically diagnosed using ECG, biomarkers, or imaging, which costly or unavailable in low-resource settings. This study presents a non-invasive, machine learning-based approach using phonocardiogram (PCG) signals for classifying normal, ST-elevation MI (STEMI), and non-ST-elevation MI (NSTEMI). The proposed approach leverages the acoustic signatures of cardiac mechanical activity, capturing subtle variations in heart sound morphology and timing associated with ischemic myocardial dysfunction. The processing pipeline included PCG acquisition via electronic stethoscope, band-pass filtering, envelope-based segmentation, feature extraction, and selection using Mutual Information and K-best ranking. We evaluated the method using a diverse dataset of 104 subjects from Indonesia and Japan to ensure generalizability across ethnic and physiological variations. Eighteen key features with mutual information values up to 0.82 bits were used to train AdaBoost and Gradient Boosting models. Without parameter tuning, these models achieved 88.30% and 93.00% accuracy, respectively. After optimization, AdaBoost reached 94.00% accuracy, and Gradient Boosting achieved 98.30% accuracy and a 95.10% F1-score. These results outperform previous bagging-based methods of 86.00% accuracy, demonstrating improved accuracy and robustness. This work highlights the potential of PCG-based MI detection as a low-cost, non-invasive diagnostic alternative, particularly valuable for early screening and triage in resource-limited healthcare settings.

Propofol maintains more stable cochlear amplifier responses than isoflurane anesthesia does in a porcine model

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.