Menopause

A predictive 14-gene signature and FLI1 inhibition overcome tumor-infiltrating lymphocyte dysfunction in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking effective targeted therapies. Although immune checkpoint inhibitors such as pembrolizumab have improved clinical outcomes in a subset of patients, limited response rates and durability highlight the need for alternative strategies. Tumor-infiltrating lymphocyte (TIL)-based adoptive cell therapy represents a promising approach; h...

Self-Identified Age Cohorts and Personal Experience Sharing in Pseudonymous Online Spaces: Natural Language Processing Analysis of Reddit.

BACKGROUND: While older adults' social media use has been widely studied for its instrumental benefits, such as accessing health information or maintaining family ties, research has largely focused on identity-based platforms that mirror offline social networks, leaving pseudonymous, interest-driven environments such as Reddit underexplored. Although older adults actively participate in these spac...

Artificial Intelligence in Physical, Occupational and Neuro-Rehabilitation: Clinical Effectiveness, Prognostic Performance, and Pre-Implementation Feasibility - A Systematic Review.

BACKGROUND: Global rehabilitation needs far exceed capacity, and artificial intelligence (AI) is proposed to extend access, personalise therapy, and s...

Estradiol loss, the "estrobolome," and midlife symptoms: what the gut microbiome adds to menopause care.

IMPORTANCE AND OBJECTIVE: Menopause is characterized by sustained estradiol decline affecting vasomotor, metabolic, skeletal, and neurobehavioral syst...

MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.

Osteoporosis is underdiagnosed because dual-energy X-ray absorptiometry (DXA) is costly and scarce. We present MultiScaleKANNet, a hybrid deep-learnin...

Machine learning prediction for menopause women with low bone mass: a multicenter and retrospective study.

Early diagnosis of postmenopausal osteoporosis provides an opportunity to detect and prevent fractures. This study uses machine learning (ML) techniqu...

Compressive strength of mortar incorporating recycled nonmetallic printed circuit board waste: experimental and interpretable machine learning analysis.

When disposed of in landfills, printed circuit boards (PCBs) release hazardous substances. Furthermore, the global sand crisis has gained considerable...

Development of AI-assisted 3D-printed degradable hydrogel microneedles for transdermal delivery of progesterone-loaded solid lipid nanoparticles: a novel approach to slowing Alzheimer's disease progression.

Progesterone (PG) is used to slow the progression of neurodegenerative diseases, particularly Alzheimer's disease (AD) in postmenopausal women. Howeve...

The geographic digital divide among dementia caregivers in the U.S.: a cross-sectional study.

BACKGROUND: Digital health tools, particularly patient portals, can support caregiving, but there is limited understanding of how sociodemographic and...

Opportunistic screening for osteoporosis using chest X-rays and deep learning: A systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic performance of deep learning (DL) algorithms applied to chest radiographs (CXR) for detecting osteoporosis and ass...

Can a Large Language Model Grounded in Text-Based Agency-Specific Prehospital Protocols Provide Accurate Care Recommendations?

OBJECTIVES: Large language models (LLMs) using a retrieval-augmented generation (RAG) approach have the ability to respond to user queries with answer...

Improving predicted risk of recurrence using molecular profiling in papillary thyroid cancer.

Molecular testing can refine the prediction of cancer recurrence. We sought to compare patterns of gene expression in patients with and without recurr...

An intelligent auxiliary diagnostic system for early osteoporosis screening using stacking ensemble learning.

Expert Evaluation of the Perceived Accuracy, Relevance, and Safety of Large Language Model-Generated Patient Information in Geriatrics: Cross-Condition Study.

BACKGROUND: Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must ...

Increased Antidepressant Use after Intensive Care for Aneurysmal Subarachnoid Haemorrhage: A Machine-Learning-Enhanced Propensity-Matched Nationwide Cohort Study.

BACKGROUND: Mood disorders after aneurysmal subarachnoid haemorrhage (aSAH) are common. Meanwhile, mood disorders are also common after intensive care...

Development and evaluation of an ontology for non-invasive respiratory support in acute care.

Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often prev...

Identification of potential biomarkers for osteoporosis and chronic kidney disease through bioinformatics and machine learning algorithm.

OBJECTIVE: The purpose of this study is to identify hub genes associated with both osteoporosis (OP) and chronic kidney disease (CKD) through bioinfor...

The Use of Statistical Shape and Appearance Modelling for Predicting Osteoporotic Fracture Risk.

PURPOSE OF REVIEW: Osteoporotic fractures remain a major cause of morbidity and mortality worldwide. Current clinical assessment metrics (e.g., bone m...

Evaluation of the accuracy and readability of large language model responses on menopause and hormone therapy.

OBJECTIVE: Generative artificial intelligence is rapidly evolving and is now being explored in health care to support patient and clinician education....

Deciphering the predictors of endometrial nonbenign lesions in asymptomatic postmenopausal women via explainable machine learning.

OBJECTIVES: Timely identification of endometrial nonbenign lesions led to improved outcomes, but there was a lack of effective predictive models for a...