Sports Medicine

Meconium metabolomic profiling dysregulation and neonatal brain injury in selective fetal growth restriction.

BACKGROUND: Meconium serves as a valuable biological matrix for characterizing fetal metabolic signatures throughout gestation. Selective fetal growth restriction (sFGR) is associated with adverse neurological outcomes, potentially mediated by underlying metabolic perturbations. However, the specific relationship between dysregulated meconium metabolome and brain injury in sFGR remains poorly unde...

De novo biosynthesis of eriocitrin in Saccharomyces cerevisiae through deep learning-guided enzyme screening and systematic metabolic engineering.

Eriocitrin, a flavanone-7-O-disaccharide known for its antioxidant and anti-inflammatory properties, holds considerable promise for use in functional foods and pharmaceuticals. However, its large-scale production is constrained by the limitations of conventional plant extraction, including low abundance of active compounds and seasonal variability. Here, we established a de novo biosynthetic pathw...

Recent advances in wearable biophysical sensors for continuous muscle monitoring.

Continuous muscle monitoring is paramount for revealing the dynamics of neuromuscular behaviors including fatigue kinetics, muscle recruitment, coordi...

A scoping review of portable ultra-low-field MRI studies in patients with acquired brain injury: Past, present, and future.

Acquired Brain Injury (ABI) refers to any post-birth damage to the brain, commonly resulting from traumatic events (traumatic brain injury) or non-tra...

Effects of Robot-Assisted Gait Therapy on Urodynamic Changes in the Subacute Phase After Spinal Cord Injury: A Prospective Study.

BACKGROUND Approximately 70% to 84% of patients with spinal cord injury (SCI) have varying degrees of bladder dysfunction, which can lead to upper uri...

Morphological classification of Schizochytrium and mutagenic selection of high-oil-producing strains based on deep learning.

As a natural producer of omega-3 fatty acids, Schizochytrium demonstrates exceptional cell density and docosahexaenoic acid (DHA) production efficienc...

Association of echocardiographic findings with mortality: human assessment vs. automated deep learning analysis.

AIMS: Artificial intelligence (AI) has emerged as a promising tool for echocardiographic image analysis, potentially improving efficiency and reducing...

Machine Learning-based Radiomic Model for Early Diagnosis of Male Urethral Injury in Pelvic Fracture Patients.

BACKGROUND AND OBJECTIVE: Pelvic fracture urethral injury (PFUI) is serious and requires prompt diagnosis. Traditional diagnostic methods, which rely ...

A novel deep-learning model to convert DAS strain to geophone particle velocity: application to PoroTomo data from the Brady geothermal field.

Distributed Acoustic Sensing (DAS) has emerged as a promising observational tool for a variety of geophysical monitoring applications. Its cost-effect...

Perspectives on biomechanical data: a cross-disciplinary survey of current practices, challenges, and future needs.

OBJECTIVES: The objective of this study was to assess the current usage, challenges, and specific needs regarding biomechanical data, with a focus on ...

Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.

Non-contact Lower limb sports injuries represent some of the most prevalent and impactful conditions within athletic populations, prompting increasing...

AI-Driven Smart Sportswear for Real-Time Fitness Monitoring Using Textile Strain Sensors.

Wearable biosensors have revolutionized human performance monitoring by enabling real-time assessment of physiological and biomechanical parameters. H...

Design, Modeling and Evaluation of Lattice Structure Pneumatic Actuators (LSPAs) for Soft Wrist Rehabilitation Robot.

Soft robots have gained recognition as promising solutions in rehabilitation robotics due to their intrinsic flexibility and safety features. However,...

Knowledge-Augmented Patient Network Embedding-Based Dynamic Model Selection for Predictive Analysis of Pediatric Drug-Induced Liver Injury.

OBJECTIVE: To address the challenges of developing machine learning frameworks for Electronic Health Records (EHRs)-based predictive tasks, such as th...

FedEMG: Achieving Generalization, Personalization, and Resource Efficiency in EMG-Based Upper-Limb Rehabilitation Through Federated Prototype Learning.

Upper extremity amputation, often necessitated by traumatic injuries, significantly impacts an individual's well-being. This paper addresses the criti...

A Shape and Size-Scaled Deep Learning Brain Injury Model for Near Real-Time Dynamic Impact Simulation.

Efficient deep learning brain injury models enable large-scale, strain-based investigations of traumatic brain injury (TBI). Here, we extend a previou...

Explainable machine learning model for predicting traumatic brain injury-induced coagulopathy in elderly patients: A multicenter cohort study.

BACKGROUND: Traumatic brain injury-induced coagulopathy (TBI-IC) in the elderly is a severe complication of traumatic brain injury (TBI) that leads to...

Machine learning for predicting outcomes, complications and resource utilisation after hip arthroscopy: A systematic review.

PURPOSE: Machine learning (ML) algorithms are increasingly used to predict outcomes in orthopaedic surgery, but their utility in hip arthroscopy remai...

Outcome Prediction in Pediatric Traumatic Brain Injury Utilizing Social Determinants of Health and Machine Learning Methods.

BACKGROUND AND PURPOSE: Considerable socioeconomic disparities exist among pediatric patients with traumatic brain injury (TBI). This study aims to an...

Characterizing the Immune Cell Infiltration in Renal Interstitium and Therapeutic Targets of Drugs in Diabetic Nephropathy by Multiomics Study.

BACKGROUND: Immune cell infiltration in the renal interstitium contributes to the progression of diabetic nephropathy (DN), yet the precise mechanisms...