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Prediction and mechanistic insights into drug-induced reproductive toxicity through integrated machine learning, FAERS-based signal comparison, and network toxicology analyses.

Drug-induced reproductive toxicity is a critical concern in drug safety evaluation, whereas conventional assessment methods are often constrained by high costs and long experimental cycles. In this study, a machine learning-based predictive model for reproductive toxicity was developed and integrated with data from the FDA Adverse Event Reporting System (FAERS), network toxicology analysis, molecu...

Unsupervised machine learning derived bone phenotypes exhibit differential biomarker responses following acute ballistic loaded exercise.

Resistance exercise can stimulate new bone formation and result in changes to circulating markers of bone metabolism, but the relationship between the bone metabolic response to resistance exercise and bone morphological phenotypes is unknown. This study compared circulating bone biomarker responses to acute ballistic resistance exercise between groups characterized by bone phenotypes. Fuzzy c-mea...

How WRKY transcription factors fine-tune specificity in plant stress responses: from W-box to regulatory code.

WRKY transcription factors are among the largest plant-specific transcription factor families and play central roles in coordinating gene expression d...

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study.

BACKGROUND: Large symptomatic brain metastases require initial surgical resection. However, local control (LC) after Gamma Knife radiosurgery (GKRS) t...

Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis.

Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatm...

Machine learning-based prediction of 3-6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is common and disabling, but identifying patients at risk early remains difficult. We developed an...

Prognostic value of preoperative CT-derived fractional flow reserve after transcatheter or surgical aortic valve replacement in patients with severe aortic stenosis.

BACKGROUND: Patients with aortic stenosis (AS) often have concomitant coronary artery disease (CAD), and coronary CTA (CCTA) is performed for anatomic...

Trigger tool methodologies in hospital settings: reappraising their role in pharmacovigilance and digital safety surveillance.

INTRODUCTION: Trigger tool methodologies have become important approaches for detecting adverse events in hospital care because they identify more har...

Transformer-Based Deep Learning Model for Predicting Hemoglobin Response to Mircera® in Hemodialysis Patients.

BACKGROUND: Anemia management in hemodialysis (HD) depends on individualized erythropoiesis-stimulating agent (ESA) dosing to achieve and maintain tar...

The rise of medical autonomous care, a paradigmatic turning point for military and civilian delivery of health care.

RATIONALE: Resource-limited or austere environments represent a direct threat to the likelihood of survival of patients in need of emergent care. Medi...

Machine learning-driven correction of handgrip strength: a novel biomarker for neurological and health outcomes in the UK Biobank.

Handgrip strength (HGS) is a significant biomarker for overall health, offering a simple, cost-effective method for assessing muscle function. Lower H...

Large language models as versatile predictive engines for notifiable infectious diseases.

Accurate forecasting of infectious disease cases and deaths is crucial for public health decision-making. Traditional statistical and machine learning...

Risk prediction for lung cancer screening: a systematic review and meta-regression.

BACKGROUND: Lung cancer (LC) remains the deadliest cancer, often diagnosed at advanced stages. Screening reduces mortality in high-risk individuals. E...

xHD-Vox, an Automated Speech Model for Estimating Motor and Cognitive Scores in Huntington Disease: Development and Longitudinal Validation.

BACKGROUND: Huntington disease (HD) is a rare genetic neurodegenerative disease that causes progressive motor, cognitive, and psychiatric symptoms ove...

Multi-method statistical signal aggregation with machine learning for severity classification of neonatal adverse drug reactions.

Neonates represent one of the most pharmacologically vulnerable patient populations, yet they remain systematically underrepresented in clinical drug ...

Quantitative Cerebrovascular Analysis for Improved Prediction of Post-Stroke Complications.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experi...

Energy-efficient logarithmic floating-point multipliers for neural network and image processing applications.

Many existing Floating-Point (FP) multiplier designs often tend to have high hardware overhead due to additional blocks like lookup tables, correction...

A multimodal deep learning classifier for prediction of HER2-low expression in triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is an aggressive molecular subtype. With the new definition of HER2-low status and the availability o...

Automated detection of lumbar disc herniation at L4-L5 and L5-S1 levels on sagittal MRI using a YOLO-based deep learning model.

OBJECTIVE: To conduct a preliminary single-center feasibility study of a YOLO-based deep-learning model for automated detection of lumbar disc herniat...

Zero-shot burned area mapping with the Segment Anything Model (SAM): a label-free framework for post-fire environmental assessment.

Accurate and rapid mapping of burned areas is critical for understanding the impacts of forest fires on ecosystems, the carbon cycle, and post-fire re...