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Eco-friendly fabrication of Fe₃O₄/α-MnO₂ nanocomposites using Pentas schimperiana leaf extract for enhanced biomedicinal and environmental applications
The efficacy and safety of 5% lidocaine patch for postoperative pain in unilateral inguinal herniorrhaphy: prospective randomized controlled pilot study
Identification of key factors for early detection of rheumatoid arthritis in primary care using machine learning
Abstract Rheumatoid arthritis (RA) is a chronic disease that causes irreversible joint damage. Early detection, especially in primary care settings, is crucial for effective disease management. This study aimed to identify the factors that help screen individuals at risk of RA to reduce delays in referral to rheumatologists. This analytical and applied research used a questionnaire to gather data from 377 patients at a rheumatology diagnostic center in Ahvaz, Iran, between August and November 2024. Study variables included patients’ articular and extra-articular symptoms at disease onset, demographic data, and initial laboratory markers. After performing statistical correlation analysis, the dataset was split into training (80%) and testing (20%) subsets. Five machine learning models were developed, and the SHAP method was applied to the best-performing model to identify influential features. The results were obtained via 5-fold nested cross-validation, which identified the CatBoost model as the top performer, with AUC-ROC = 0.966, Accuracy = 0.947, and F1-Score = 0.951. SHAP (with a threshold of 0.01) highlighted the following significant features: Anti-CCP, tender joint count, swollen joint count, gastrointestinal issues, fatigue, age, RF (Rheumatoid Factor), and hearing problems. Due to the importance of early RA diagnosis and the challenges encountered in primary care, three main screening factors stand out: Anti-CCP, tender joint count, and swollen joint count. These, along with fatigue, age, and positive RF, markedly increase the likelihood of RA and justify referring a patient to a specialist.
Helioseismic evidence that the solar dynamo originates near the tachocline
Correction: Elimination of certain honeybee venom activities by adipokinetic hormone
Evaluation of the coupled coordination of digital village, green agriculture and farmers’ well-being in China
Evaluating the sports performance of badminton players based on grip strength of the real hitting scenario
Reinforcement learning-driven dynamic optimization strategy for parametric design of 3D models
Abstract The concept of parametric design is changing the way 3D modeling works, allowing precise manipulation of complex forms in the areas of architecture, digital fabrication, and product design. However, exploring and optimizing large coupled spaces of parameters remains a significant computational challenge. We present a new, Hierarchical Reinforcement Learning based Dynamic Optimization Strategy (HRL-DOS), which decomposes the parametrized design process into a series of multi-level subproblems. The high-level policy determines the global direction of the design while the low-level policy adapts individual parameters, responding to changes from multiple performance criteria (structural stability, geometric efficiency, and fabrication constraints). The hierarchical approach provides greater efficiency in learning and computational scaling in a complex design environment. Experimental tests on benchmark 3D modeling tasks revealed a 27% improvement in convergence and 18% improvements in quality of the model, relative to simple heuristic or gradient-based optimizations. In addition, HRL-DO permits adaptability in real-time, and the approach can potentially translate to various domains, including automated form-finding for architectural structures, generative design of products, or intelligent computer-aided design (CAD) systems. Through the use of HRL, we have developed a new and adaptive approach for the additional automation of parametric design tasks in the future.
Delineation of palaeochannels using DEM and spectral indices in the Gundar basin of Kadaladi region
Pravastatin combined with fibrin sealant-embedded BMSCs enhances recovery in steroid-induced avascular necrosis of the femoral head
Hydro-mechanical damage modeling of water-bearing sandstone using an energy dissipation approach under triaxial stress
Spontaneous somatic Pten loss contributes to functional heterogeneity of T cells
Abstract We found that healthy mice harbor T cells with heritable low Pten expression and that monoallelic Pten loss in CD4 T cells causes a bias in their differentiation toward T follicular helper cells during acute viral infection. These results suggest that somatically induced mono- or biallelic loss of expression of signaling-related genes in T cells can impact the quality of population-level T cell responses—without conspicuous pathological sequelae such as autoimmune and inflammatory manifestations or lymphomagenesis.