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Investigating Symptom Duration Using Current Status Data: A Case Study of Postacute COVID-19 Syndrome.

Epidemiology (Cambridge, Mass.)
BACKGROUND: For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For examp...

Enhancing patient-centered care with AI: a study of responses to neuroendocrine neoplasms queries.

Endocrine
INTRODUCTION: Large Language Models (LLMs) are increasingly used in oncology, but their application in neuroendocrine neoplasms (NENs) is still unexplored.

Preliminary analysis of AI-based thyroid nodule evaluation in a non-subspecialist endocrinology setting.

Endocrine
PURPOSE: Thyroid nodules are commonly evaluated using ultrasound-based risk stratification systems, which rely on subjective descriptors. Artificial intelligence (AI) may improve assessment, but its effectiveness in non-subspecialist settings is uncl...

Cerebral aneurysm surgical training in the neuroendovascular era and its impact on the production of comfortable aneurysm surgeons.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: The increasing use of endovascular techniques has greatly decreased the number of intracranial aneurysms treated with open surgery. The objective of this study is to quantify chief residents' experience and comfort level with clipping of ...

Race-Performance Parameters Differentiating World-Best From National-Level Swimmers: A Race Video Analysis and Machine-Learning Approach.

International journal of sports physiology and performance
BACKGROUND: Elite swimming performance is determined by a complex interplay of anthropometric, physiological, biomechanical, and technical factors. Previous research highlights how the 100-m freestyle demands explosive power, technical proficiency, a...

Neuronal dynamics of slow and fast-motion motor imagery.

Neuroscience
Motor imagery (MI) is a cognitive process requiring mental simulation of physical actions, engaging neural networks that overlap with those activated during actual execution. This study investigated the neural correlates of slow and fast MI in ten he...

Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches.

Ecotoxicology and environmental safety
BACKGROUND: Sarcopenia, a condition marked by the decline of skeletal muscle mass and function, is prevalent in the elderly and closely linked to abnormal glucose metabolism, particularly type 2 diabetes. Hyperglycemia can increase the formation of a...

Effects of the lumbar support function of wearable robot (Bot Fit) on sitting position.

Biomedical engineering online
BACKGROUND: Sedentary lifestyles can lead to musculoskeletal disorders, but proper sitting posture, particularly maintaining a slight anterior pelvic tilt, helps prevent issues like lower back pain and spinal misalignment. Samsung Electronics wearabl...

Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases.

BMC endocrine disorders
The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-control study was conducted to evaluate the predictive effect of GSK3B genetic polymorphisms on the susc...

Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.

BMC medical informatics and decision making
BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.