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HybridViT for robust wheat leaf disease detection using CLAHE and attention-based feature fusion
EEG activities and connectivities associated with surtitle presentation during live opera viewing: an exploratory analysis of cognitive absorption
Impact of preoperative shoulder osteoarthritis severity score on outcomes after rotator cuff repair: A correlation study
Abstract Arthroscopic rotator cuff repair (ARCR) generally improves clinical outcomes, but the impact of pre-existing glenohumeral osteoarthritis (GHOA) severity on these outcomes remains unclear. This study aims to evaluate the correlation between preoperative GHOA severity, assessed via the Shoulder Osteoarthritis Severity (SOAS) score, and postoperative patient-reported outcomes (PROs) following ARCR with a minimum of two years follow-up. In this retrospective cohort study, 150 patients who underwent ARCR between 2018 and 2023 were included. Preoperative GHOA severity was assessed independently by two reviewers using magnetic resonance imaging (MRI) and graded using the SOAS score. PROs included the American Shoulder and Elbow Surgeons (ASES) score, University of California-Los Angeles (UCLA) score, and Western Ontario Rotator Cuff (WORC) index. Correlation and multivariate regression analyses were used to identify predictors of PROs. Receiver operating characteristic (ROC) curve was performed to assess the SOAS score’s predictive value. Of 150 eligible patients, the cohort had a mean age of 66.6 ± 8.8 years and a mean follow-up of 42.3 ± 14.5 months. Inter-reader reliability of total SOAS was excellent (ICC = 0.942). Higher total SOAS scores were significantly associated with lower ASES ( r =–0.21, p = 0.012) and UCLA ( r =–0.22, p = 0.006) scores. Subdomain analysis revealed that labral-bicipital complex pathology negatively correlated with all three PROs (ASES: r =-0.21, p = 0.010; UCLA: r =-0.20, p = 0.013; WORC: r =-0.18, p = 0.026), while cartilage degeneration correlated with lower UCLA score ( r =-0.18, p = 0.033). On multivariate analysis, long head of biceps pathology was an independent predictor of a lower ASES score (β=–2.14, p = 0.029) and WORC index (β=–0.1, p = 0.032). An optimal SOAS cut-point of 17 for predicting failure showed poor discriminative ability (AUC = 0.596). The SOAS score demonstrated a significant correlation with Kellgren-Lawrence grading system ( r = 0.71, p < 0.001). Although continuous GHOA severity correlates with lower postoperative scores, the overall SOAS score alone is a poor prognostic discriminator for clinical failure. Therefore, GHOA should not be considered an absolute contraindication for ARCR. A holistic preoperative assessment integrating these critical factors is essential for optimizing clinical decision making.
Phytochemical profiling and integrated in vitro and in silico assessment of the antioxidant, thrombolytic, and anti-inflammatory properties of Urena lobata L. leaf extracts
A comparative study on buzzy device vs digital distraction technique for mitigating pain during venipuncture among hospitalised children: randomised controlled trial
Experimental simulation investigation on fracture behavior and bolt anchoring mechanism in a circular tunnel under support in layered rock mass
An oral nutritional supplement with TRPM8 agonists as a cooling flavor improved swallowing function in post-stroke patients with oropharyngeal dysphagia
Exploring physical activity, perceived barriers, and social media influence in Turkish physiotherapy students
TinyDark-YOLO for adaptive and lightweight object detection in low-light conditions
Extending the tendency involvement experience model in street food experience
Effect of targeted wall shape thickness on flow response and recovery for engineering design
Application of the QUAL2K model for assessing water quality in the anthropogenically impacted Dil Stream, Turkey
Detecting entanglement in high-spin quantum systems via a stacking ensemble of machine learning models
An interpretable attention-based TabTransformer framework with feature fusion for green architecture classification
Eye tracking reveals expert gaze patterns during intracytoplasmic sperm injection
Abstract Intracytoplasmic sperm injection (ICSI) is a highly complex procedure that involves injecting a single sperm into an oocyte, requiring extensive training and advanced technical expertise, making it a task performed by specialists. Acquiring specialized microinjection skills alone often requires several years of training. ICSI performance has been primarily evaluated based on developmental outcomes, with little detailed assessment of operators’ intrinsic skills. We analyzed expertise during microinjection using eye-tracking technology, which was recently employed in surgical and sports domains to evaluate expert performance. Eye tracking was used to compare the fixation patterns and eye-gaze behaviors of experts and novices during microinjection. Our results showed that the experts had shorter and more consistent procedural times than the novices did. In contrast, the novices initially took longer times; although their time gradually decreased, they remained unstable. This difference was particularly noticeable during oocyte rotation. Similar patterns were observed for fixation duration and the number of saccades. The heat maps and gaze plots revealed interesting distinctions between experts and novices. The experts exhibited efficient and highly consistent eye gaze patterns. Their eye-gaze data may contribute to developing AI-driven automated ICSI skill evaluation systems and AI- and robotics-based ICSI technical support and automation methods.
MultiGSA: wind power prediction based on a multi-scale cross-graph network combined with an enhanced simple attention mechanism
Regenerative repair is connected to early and specific structural, immune, and metabolic MSC signatures in adult mammals
Abstract Adult mammals exhibit a limited capacity for tissue regeneration following injury and typically heal through scar formation. Mesenchymal stem/stroma cells (MSCs), which are phenotypically plastic and ubiquitous across tissues, play a critical role in maintaining tissue architecture during repair. We hypothesized that early events in regenerative and non-regenerative repair involve changes in MSC heterogeneity, which in turn determine repair outcomes. To test this hypothesis, we performed extensive single-cell RNA sequencing (scRNA-seq) in a mouse model of tissue injury. This model standardizes the comparison of non-regenerative and regenerative repair in adults with identical developmental stages and genetic backgrounds. Our analysis of MSCs during the early phases of tissue repair in adult mammals enabled the identification of distinct regenerative and non-regenerative MSC clusters, suggesting that specific MSC states may actively drive tissue repair outcomes. Furthermore, unsupervised approaches allowed us to revisit the functional signatures of MSCs centering on their impact on tissue structure (S), inflammation/immunity (I) and metabolism (M). By integrating these S, I and M functions, the SIM framework provides a conceptual model to interpret MSC behavior as a coordinated tissue-level response rather than a collection of isolated pleiotropic activities. This work positions MSCs at the center of the “SIM” triad, underscoring their pivotal role in tissue repair.
Machine learning model for predicting rebleeding risk after endoscopic variceal ligation in esophageal variceal bleeding
Abstract Rebleeding is a severe complication following recovery from esophageal variceal bleeding (EVB), yet robust predictive tools for assessing post-treatment risk after endoscopic variceal ligation (EVL) therapy remain scarce. This study developed and independently validated a machine learning (ML) model using multidimensional clinical data to predict 1-year rebleeding risk. Two independent cohorts were included: a retrospective cohort ( n = 373) for model development and a prospective cohort ( n = 119) for validation, with a one-year rebleeding endpoint. Predictors were identified using Recursive Feature Elimination (RFE), and eight ML algorithms were evaluated. Each algorithm was optimized via 5-fold cross-validation. The model with optimal performance was chosen to develop an online computational platform. RFE identified eight key predictors. The XGBoost model demonstrated superior predictive performance in both the training and validation cohorts, achieving AUCs of 0.883 and 0.887, respectively. This model was subsequently implemented in an online clinical platform for individualized 1-year rebleeding risk assessment. Our findings establish XGBoost as an effective tool for predicting EVB rebleeding risk, providing an evidence-based decision aid for post-EVL management.