The journal of prevention of Alzheimer's disease
Jun 28, 2025
BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, and early diagnosis is crucial to enable effective interventions. Currently, Alzheimer's disease is diagnosed through cognitive assessments, brain imaging and fluid biomarkers ...
Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies a...
BACKGROUND: Depression is highly recurrent, and predicting relapses in a timely manner is critical. We applied machine learning to predict the worsening of depressive symptoms.
BACKGROUND: Rapid weight gain (RWG) during infancy, defined as an upward crossing of one centile line on a weight growth chart, is highly predictive of subsequent obesity risk. Identification of infant RWG could facilitate obesity risk assessment fro...
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...
Cephalalgia : an international journal of headache
Jun 12, 2025
BackgroundUnderstanding characteristics and reasons associated with using calcitonin gene-related peptide monoclonal antibodies (CGRP mAb) for migraine prevention may help clinicians individualize treatment plans and achieve better patient outcomes.M...
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contributor to the rise in ASD cases. This study investigated how the risk of ASD associated with prenatal...
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Jun 11, 2025
BACKGROUND: Adequate preoperative identification of patients at risk of significant healthcare utilization after surgery could help guide preoperative decision-making as well as postoperative patient management. While several studies have proposed me...
BACKGROUND: Implementing machine learning models to identify clinical deterioration in the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.
BACKGROUND: This study developed an explainable machine learning model for baseline internal mammary lymph node metastasis (IMNM) in breast cancer patients.
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