Diet & Nutrition

Construction of interpretable machine-learning diagnostic models for erectile dysfunction based on routine blood and biochemical detection data.

Despite its high incidence, the diagnosis of erectile dysfunction (ED) is impeded by current diagnostic constraints and patient hesitancy in seeking medical intervention. The integration of machine learning with standard blood and biochemical markers has demonstrated diagnostic efficacy across a spectrum of diseases, yet its application in ED remains unexplored. Based on data from the 2001-2004 Na...

Leveraging machine learning algorithm to predict minimum dietary diversity among children aged 6-23 months in Ethiopia.

Lack of nutrient-rich food consumption is considered an important underlying factor affecting the healthy development of children, and can lead to developmental delays and various disorders. There is limited evidence on the predicators of dietary diversity. We aimed to train and test eight machine learning algorithms in the Ethiopian demographic and health survey (EDHS) from 2005-2019. We used sec...

Cardiovascular Science, Medicine, and Society: The Brave New World: The ACC Braunwald Lecture 2025.

Recent scientific and technological advances have dramatically expanded the possibilities in cardiovascular medicine. Landmark clinical trials have in...

Deep Learning-Based Prediction of Trabecular Bone Mineral Density From Lumbar CT: A Superior Alternative to DEXA in Hyperostosis.

Study DesignRetrospective Cohort Study.ObjectivesThis study explores using deep learning to predict lumbar spine bone mineral density (BMD) from CT im...

Exploring the Impact of Physical Activity Metrics on Calorie Consumption: A Machine Learning Approach Combined with SHAP Analysis.

Precision nutrition and food biomanufacturing for space missions: Toward intelligent and bioregenerative life-support systems.

Long-duration spaceflight presents unique physiological and nutritional challenges due to factors such as microgravity, radiation, confinement, and ci...

Integrative metabolomics and machine learning reveal the toxic mechanisms of elaidic acid in polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is characterized by reproductive dysfunction and metabolic disturbances, including obesity, insulin resis...

Integrative network toxicology and molecular docking preliminarily explore the potential role of polystyrene microplastics in childhood obesity.

Childhood obesity is a severe global epidemic, and emerging evidence suggests environmental pollutants like polystyrene microplastics (PS-MPs) may dis...

Non-pharmacological treatment of AECOPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPDs) are acute events characterized by rapid worsening of dyspnea, cough, and sputum...

Predicting low oxygen in patients with acute COVID-19 infection isolating at home: a clinical prediction model.

RATIONALE: Identifying patients who could be safely managed at home versus those needing hospitalisation was a particular concern during early COVID-1...

Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection.

Metabolic-Associated Fatty Liver Disease (MAFLD) is common among Type 2 Diabetes (T2DM) patients. The coexistence of these conditions increases the ri...

Operationalizing the malnutrition-based chronic disease model in adults.

The malnutrition-based chronic disease (MBCD) model for use in adults to optimize nutrition care is a framework based on epidemiological classifiers, ...

Familial colorectal cancer: risk factors, screening strategies and personalized medicine.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with approximately 25-30 % of cases exhibiting a familial compo...

Lean mass loss in the intensive care unit and its restoration: A narrative review.

The preservation of lean mass (LM) and its restoration following catabolic loss represents a primary challenge for clinical nutrition in critically il...

Environmental toxicant exposure and diabetes risk: An exposome-wide association study integrating mixture effects and molecular mechanisms.

Environmental toxicant exposure has emerged as a potential contributor to diabetes, yet systematic investigations integrating multiple chemicals and b...

Evaluation of the impact of NOAC underdosing and exploration of bleeding risk factors in elderly patients with atrial fibrillation: artificial intelligence-based approach.

OBJECTIVE: Atrial fibrillation in elderly patients increases the risk of thromboembolism, necessitating long-term anticoagulation. While non-vitamin K...

Clay Edges Are Dynamic Proton-Conducting Networks Modulated by Structure and pH.

Montmorillonite, a ubiquitous clay mineral, plays a vital role in geochemical and environmental processes due to its chemically complex edge surfaces....

Multi-scale geographical origin discrimination of geographical indication rice based on multi-source data fusion combined with machine learning: A case of Panjin rice.

Growing fraud incidences have made it more difficult to authenticate the provenance of geographical indication (GI) rice. Taking Panjin rice (a GI pro...

Antioxidant vitamin index and risk of age-related macular degeneration: multicenter validation and clinical translation.

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults, with oxidative stress as a central driver. We proposed the Ant...

Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.

AIMS/HYPOTHESIS: Data-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose an...