Primary Care

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.

BACKGROUND: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence-enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD wo...

Reliable discrimination of type 1 and type 2 diabetes by flow-through leukocyte-endothelium interactions exploiting advanced hydrodynamic detection parameters.

Type 1 (T1D) and type 2 diabetes (T2D) are both associated with chronic inflammation and endothelial dysfunction, yet their discrimination based on conventional inflammatory biomarkers remains challenging. In this context, a proof-of-concept study exploring whether advanced hydrodynamic descriptors of leukocyte-endothelium interactions under flow conditions might disclose invaluable information to...

Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.

Maternal postpartum depression (PPD) is a significant mental health issue that affects over 15% of mothers, with adverse effects on their children. Et...

Research Progress in Machine Learning Techniques for Metal-Organic Framework Screening.

Metal-organic frameworks (MOFs) are prime candidate materials for gas adsorption and separation owing to their exceptional porosity and structural tun...

Proactive haemodynamic management using the hypotension prediction index during caesarean section: a randomised controlled study.

INTRODUCTION: Intra-operative hypotension is common during caesarean section and may result in adverse maternal effects, such as nausea and vomiting. ...

Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.

Gastrointestinal (GI) cancers, particularly colorectal cancer, continue to be a major contributor to global cancer-related morbidity and mortality. De...

Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.

BACKGROUND: Chronic atrophic gastritis (CAG) is a significant precancerous condition of gastric cancer (GC). CAG often lacks typical symptoms in its e...

Corrigendum to "Predicting postpartum glucose intolerance in women with gestational diabetes mellitus in primary care: A machine learning approach using XGBoost and SHAP values" [Diabetes Res. Clin. Pract. 232 (2026) 113098].

KMGTMDA: KAN-driven multiscale graph neural network and context-enhanced prediction for human microbe-disease associations.

OBJECTIVE: Microorganisms play an irreplaceable role in critical physiological processes such as human metabolic regulation, cancer inhibition, and pa...

Essence of toxicology research to Saudi Arabia Vision 2030 and beyond: Current state and future perspectives.

The Kingdom of Saudi Arabia has been making significant progress as part of its Vision 2030 to diversify its economy and reduce dependence on fossil f...

Expanding the horizon of oculomics: A narrative review on the emerging roles of the anterior segment and mobile health.

Oculomics uses the eye to gain insights into systemic health. Although initially focused on retinal imaging, oculomics now studies various ocular sign...

Klebsiella oxytoca: an opportunistic bacterial pathogen that poses challenges for treatment and vaccine development.

Klebsiella oxytoca is a clinically significant opportunistic bacterium that contributes to global morbidity and mortality. Despite its clinical releva...

TropMol: a cloud-based web tool for virtual screening and early-stage prediction of acetylcholinesterase inhibitors using machine learning.

Alzheimer's disease (AD) is the most common type of dementia, accounting for at least two-thirds of dementia cases in people aged 65 and older. Numero...

Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.

Diabetic nephropathy (DN) represents the predominant microvascular complication associated with diabetes mellitus; however, existing diagnostic techni...

Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial.

OBJECTIVES: Non-contrast MRI (bi-parametric MRI-bpMRI) has been investigated as a potential tool to be integrated in clinically significant prostate c...

Patients Are Generally Supportive of Artificial Intelligence in Breast Imaging: A Multisite Survey of Breast Imaging Patients.

OBJECTIVE: To understand the perspective of patients undergoing breast imaging on the use of artificial intelligence (AI) in breast cancer screening. ...

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.

Type 1 diabetes (T1D) is a T cell-mediated disease with a strong immunogenetic human leukocyte antigen (HLA) dependence. HLA allelic influence on the ...

Artificial Intelligence-Assisted Point-of-Care Ultrasound for Evaluating Left Ventricular Ejection Fraction: A Systematic Review of Prospective Observational Studies.

Artificial intelligence (AI) embedded in point-of-care ultrasound (POCUS) could reduce operator dependence in left ventricular ejection fraction (LVEF...

Toward practical screening of mortality risk: Insights from interpretable machine learning in NHANES.

BACKGROUND: Efficient community-based screening for individuals at high risk of mortality is a major public health challenge. While many predictors ha...

Empowering Alzheimer's and Related Dementia Prevention Through Primary Care Using Responsible AI: A Pilot Walk-in Memory Sync Booth.

Alzheimer's disease and related dementias (ADRD) remain underdiagnosed early due to reliance on costly, invasive, and time-intensive assessments, prom...