Diet & Nutrition

Machine learning-assisted paper-based chemiluminescence biosensor for choline quantification in infant milk: toward portable nutritional quality monitoring.

Choline is a critical micronutrient that is involved in neurotransmission, lipid metabolism, and neurodevelopment of infants. Poor consumption of choline in the initial stages of growth might lead to severe cognitive and developmental defects. Hence, there is a need to constantly monitor choline level in infant food. To meet this requirement, we present a low-cost paper-based luminol cobalt chemil...

Source characterization of trace elements and impacts of salinization using geochemical modelling with machine learning approach and Monte-Carlo based health risk assessment in coastal aquifer zones.

This work investigated the trace element contamination, spatial distribution and their source of origin along with health risk assessment in the coastal region of eastern India. Geochemical modelling, Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and self-organizing map (SOM) has been employed to understand the effect of salinization and the prevailing Eh-pH condition on the tr...

Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.

This study aims to assess the predictive value of dietary antioxidants in diabetes-cancer comorbidity using interpretable machine learning (ML) models...

Dominant factors governing benzene adsorption in soils: thermodynamic analysis and predictive modeling.

Understanding the processes controlling benzene adsorption in soils is critical for predicting their environmental fate and associated risks. However,...

Predicting abdominal obesity in children and adolescents via machine learning: a longitudinal cohort study.

OBJECTIVES: This study aimed to construct and verify machine learning (ML) models to predict long-term abdominal obesity (AO) risk in children and ado...

Artificial Intelligence Applications across the Spectrum of Malnutrition: From Undernutrition to Obesity.

BACKGROUND: Malnutrition is a significant global public health challenge, with rising prevalence and vital consequences. Recent advances in artificial...

Biomarker-anchored screening of dietary microplastics via a gut microbiota-informed machine learning model.

Microplastics (MPs) are increasingly recognized as emerging contaminants in the human diet, yet the absence of unified biomarker-anchored screening th...

Optimizing Ternary Preparation Parameters of Thin-Film Nanocomposite Membranes for Tailored Selectivity via Interpretable Machine Learning.

Novel thin-film nanocomposite (TFN) nanofiltration membranes have attracted wide attention for their generally boosted water permeability. However, th...

Association of the neutrophil percentage-to-albumin ratio with sarcopenia in U.S. adults: Evidence from NHANES 2011-2018 and machine learning-based analyses.

BACKGROUND: Sarcopenia is an age-related muscle disorder driven by complex interactions between chronic inflammation and nutritional imbalance. The ne...

Leveraging routine clinical data for dementia risk prediction using machine learning.

BackgroundEarly diagnosis of dementia is essential for enabling timely interventions that may slow disease progression, improve patient outcomes, and ...

Precision nutrition in breast cancer: Towards patient- and tumour-informed dietary strategies.

Dietary strategies are increasingly recognized as important modulators of breast cancer outcomes, acting through effects on metabolic regulation, weig...

Comparative evaluation of obesity-related indices for predicting incident hypertension: evidence from Chinese and UK longitudinal cohorts with machine learning interpretation.

Comparative study on the predictive value of PNI and GNRI for all-cause mortality rates of elderly patients in cardiac care unit and cardiovascular intensive care unit.

BACKGROUND: The prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) are reliable alternative biomarkers of nutrition. Howev...

Forecasting under-five stunting in Ethiopia using classical and machine learning time series models.

BACKGROUND: The prevalence of under-five stunting in Ethiopia remains above 30%, which, according to World Health Organization (WHO), constitutes a ma...

Gut microbiome and metabolic health: mechanisms and precision interventions.

The gut microbiome is increasingly recognized as a fundamental regulator of metabolic health, shaping energy balance, insulin sensitivity, inflammator...

Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD.

This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidn...

The Effect of Age, Sex, and Body Mass Index on Spinopelvic Balance Throughout Childhood and Adolescence: An Automated 3D-Computed Tomography Study of 9,721 Pediatric Patients.

BACKGROUND: Spinopelvic assessment is critical for surgical planning. It is unknown if age, sex, and body mass index (BMI) influence pelvic orientatio...

Online surveys, a breakthrough in research, especially in nutrition and food sciences.

BACKGROUND: Technological advancements and widespread internet connectivity have fundamentally transformed data collection across academic disciplines...

Interpretable machine learning models based on CMR radiomics for predicting left ventricular diastolic dysfunction in patients with metabolic-associated steatotic liver disease and type 2 diabetes mellitus.

BACKGROUND: Obesity-induced left ventricular diastolic dysfunction (LVDD), associated with ectopic fat and dysfunctional epicardial adipose tissue (EA...

AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions.

The convergence of artificial intelligence and chemometrics has revolutionized multi-omics data integration, enabling unprecedented insights into comp...