HRT

Advancements in ex vivo bone biomechanics: multimodal technologies and their integration with artificial intelligence.

Bone biomechanics is essential for understanding how bone structure, composition, and mechanical function interact in healthy and diseased states. Conventional approaches such as mechanical testing, micro computed tomography, and histology provide valuable information but cannot fully capture the multiscale heterogeneity that governs bone strength and fragility. This review summarizes recent progr...

Exploring estrogen synthesis and metabolism related markers and immune dynamics in osteoporosis-insights from single-cell RNA sequencing and Mendelian randomization.

BACKGROUND: Osteoporosis (OP) is characterized by decreased bone mineral density and an increased fracture risk, often due to declining sex hormone levels that weaken bone strength. This study aims to explore estrogen related markers in OP to improve diagnosis and treatment. METHODS: Differentially expressed estrogen synthesis and metabolism-related genes (DE-ERGs) were identified through differen...

Multimodal Cross-Attentive Graph-Based Framework for Predicting In Vivo Endocrine Disruptors.

Endocrine hazard assessment needs models that are accurate and mechanistically transparent. We present a multimodal cross-attentive graph framework th...

Late distant recurrence prediction model in premenopausal women with ER-positive/HER 2-negative breast cancer: A multicenter retrospective study.

BACKGROUND: Late distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (...

Computational discovery and prioritization of ribose-5-phosphate isomerase A as a mitochondrial target for reversing senescence in mesenchymal stem cells.

Age-related osteoporosis is increasingly recognized as a disease of stem cell dysfunction, hallmarked by the skewed lineage commitment of mesenchymal ...

Spatiotemporal Remodeling of Bone as a Reversibly Adaptive Biological Material in Djungarian Hamsters under Regulated Photoperiod Conditions.

Bone loss in humans is typically progressive and difficult to reverse, posing challenges for both pharmacological therapy and the long-term performanc...

Objective Assessment in Clinical Psychological Science: Progress in Wearable Alcohol Biosensors.

Clinical psychology is a discipline reliant on self-reports but uniquely susceptible to specific biases associated therewith. Here we provide a protot...

Multimodal MRI reveals hypothalamic structural-functional alterations associated with bone mineral density loss in postmenopausal women.

Emerging evidence implicates central nervous system dysregulation in the pathogenesis of osteoporosis. However, the role of hypothalamic subregions - ...

Clinical impact of adopting an AI-driven opportunistic bone health screening software using routine X-ray.

OBJECTIVES: To evaluate the clinical impact of an artificial intelligence device, Rho, that opportunistically screens X-rays for low bone mineral dens...

Artificial intelligence on opportunistic computed tomography for predicting vertebral fracture risk in women undergoing estrogen deprivation therapy for breast cancer.

PURPOSE: Fracture risk prediction in women exposed to hormone deprivation therapies (HDTs) for breast cancer is challenging, since bone mineral densit...

Artificial Intelligence-based analysis of pre-eclampsia gene expression profiles for identification of novel potential pre-eclampsia diagnostic biomarkers.

Preeclampsia (PE) is a multifactorial and heterogeneous hypertensive disorder of pregnancy that poses significant diagnostic and therapeutic challenge...

Opportunistic osteoporosis screening using CT-based Hounsfield units: a machine learning approach for preoperative spine surgery planning.

Open-Source Machine Learning Computed Tomography Scan Segmentation for Spine Osteoporosis Diagnostics.

BACKGROUND AND OBJECTIVES: Dual-energy x-ray absorptiometry (DXA) is the standard for assessing bone mineral density (BMD); however, its accuracy is l...

Association of urinary heavy metals with osteoporosis in US adults using interpretable machine learning.

BACKGROUND: Exposure to heavy metals in the environment has always been the focus of public concern. More and more evidence suggests that heavy metal ...

Machine learning-enabled meta-analysis reveals the effect of microplastics on nitrogen removal performance in constructed wetlands and its potential mechanisms.

Constructed wetlands (CWs) are an effective wastewater treatment system and an important sink for microplastics (MPs). However, MPs impacts on nitroge...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are o...

Osteoporosis prediction using lumbar CT Hounsfield units: comparative performance and clinical implications of seven machine learning models.

PURPOSE: This study aimed to evaluate the utility of L1-L4 average Hounsfield Unit (HU) values from lumbar spine computed tomography (CT) in predictin...

Lipid monitoring using non-invasive measurement technologies and machine learning: a systematic review.

BACKGROUND: Cardiovascular diseases (CVD) are the leading cause of death among women, with risk increasing after menopause. Lipid levels are key bioma...

Childhood dyslexia risk elevated by heavy metal mixtures from e-waste: A machine learning-driven mixture modeling study.

Environmental heavy metal mixtures from informal e-waste recycling are potential neurotoxicants, but their link to developmental dyslexia remains uncl...

Novel mechanisms of atrazine endocrine disruption: an integrated approach reveals progesterone and glucocorticoid receptor targeting.