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Weight loss-independent changes in human growth hormone during water-only fasting: a secondary evaluation of a randomized controlled trial.
Frontiers in endocrinology
Feb 7, 2025
INTRODUCTION: Water-only fasting for one day or more may provide health benefits independent of weight loss. Human growth hormone (HGH) may play a key role in multiple fasting-triggered mechanisms. Whether HGH changes during fasting are independent o...
Optimizing MR-based attenuation correction in hybrid PET/MR using deep learning: validation with a flatbed insert and consistent patient positioning.
European journal of nuclear medicine and molecular imaging
Feb 6, 2025
PURPOSE: To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve preci...
Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Feb 6, 2025
Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatocellular carcinoma (HCC). In this study, attempts were made to investigate the excited-state kinetics of intrinsic fluorophores, tryptophan and nicotin...
Machine learning-driven Heckmatt grading in facioscapulohumeral muscular dystrophy: A novel pathway for musculoskeletal ultrasound analysis.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Feb 6, 2025
OBJECTIVE: This study introduces a machine learning approach to automate muscle ultrasound analysis, aiming to improve objectivity and efficiency in segmentation, classification, and Heckmatt grading.
Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports.
European journal of radiology
Feb 6, 2025
BACKGROUND: Large language models (LLMs) often struggle to fully capture the nuanced preferences and clinical judgement of radiologists in medical report summarization even when fine-tuned on massive medical reports. This could lead to the generated ...
Effects of Gait Rehabilitation Robot Combined with Electrical Stimulation on Spinal Cord Injury Patients' Blood Pressure.
Sensors (Basel, Switzerland)
Feb 6, 2025
BACKGROUND: Orthostatic hypotension can occur during acute spinal cord injury (SCI) and subsequently persist. We investigated whether a gait rehabilitation robot combined with functional electrical stimulation (FES) stabilizes hemodynamics during ort...
WDRIV-Net: a weighted ensemble transfer learning to improve automatic type stratification of lumbar intervertebral disc bulge, prolapse, and herniation.
Biomedical engineering online
Feb 6, 2025
The degeneration of the intervertebral discs in the lumbar spine is the common cause of neurological and physical dysfunctions and chronic disability of patients, which can be stratified into single-(e.g., disc herniation, prolapse, or bulge) and com...
Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.
BMC gastroenterology
Feb 6, 2025
BACKGROUND: Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis.
Integrating manual annotation with deep transfer learning and radiomics for vertebral fracture analysis.
BMC medical imaging
Feb 6, 2025
BACKGROUND: Vertebral compression fractures (VCFs) are prevalent in the elderly, often caused by osteoporosis or trauma. Differentiating acute from chronic VCFs is vital for treatment planning, but MRI, the gold standard, is inaccessible for some. Ho...
Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.
Scientific reports
Feb 6, 2025
Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML) offers a promising avenue for addressing these challenges by leveraging demographic and clinical da...
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