Transcutaneous electrical nerve stimulation for fibromyalgia-like syndrome in patients with Long-COVID: a pilot randomized clinical trial.

This study investigated the effect of Transcutaneous Electrical Nerve Stimulation (TENS) for fibromyalgia-like symptoms including chronic widespread musculoskeletal pain, fatigue, and/or gait impairment in twenty-five individuals with long-COVID. Participants were randomized to a high dose (intervention group, IG) or low dose (placebo group, PG) TENS device. Both groups received daily 3-5 h of TENS therapy for 4-weeks.

Dietary fatty acid pattern and its association with metabolic profile among overweight and obese adults.

Background: Numerous studies have revealed the role of dietary fatty acids in human health. However, few studies have evaluated dietary fatty acid patterns and their association with metabolic parameters. The current study aimed to explore the association between dietary fatty acid patterns and risk factors for metabolic syndrome (MetS) among overweight and obese adults.

Objective assessment of postural ergonomics in neurosurgery: integrating wearable technology in the operating room.

Objective: Physical stress associated with the static posture of neurosurgeons over prolonged periods can result in fatigue and musculoskeletal disorders. Objective assessment of surgical ergonomics may contribute to postural awareness and prevent further complications. This pilot study examined the feasibility of using wearable technology as a biofeedback tool to address this gap.

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults.

Introduction: Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity.

Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia.

Chronic limb-threatening ischemia (CLTI) is the most advanced form of peripheral artery disease. CLTI has an extremely poor prognosis and is associated with considerable risk of major amputation, cardiac morbidity, mortality, and poor quality of life. Early diagnosis and targeted treatment of CLTI is critical for improving patient's prognosis.

Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring.

Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected.