Diet & Nutrition

Seasonal variation in BMI outcomes at 6 months: secondary analyses of a multidisciplinary healthy lifestyle programme for children and adolescents with obesity.

BACKGROUND: While international evidence suggests seasonal variations may influence outcomes of interventions for pediatric obesity, data for Aotearoa New Zealand are limited. We examined seasonal variations in changes in body mass index standard deviation score (BMI SDS) in young people with obesity enrolled in an intervention programme. METHODS: We studied 397 children and adolescents (median = ...

Development of an explainable machine learning model for predicting depression in adults with type 2 diabetes mellitus: A cross-sectional SHAP-based analysis of NHANES 2009-2023.

Depression (DEP) is a common yet underdiagnosed comorbidity in adults with type 2 diabetes mellitus (T2DM), worsening glycemic control and increasing complication risk. Practical, interpretable risk tools using routine patient data are limited. We conducted a cross-sectional analysis using data from adults with T2DM enrolled in the National Health and Nutrition Examination Survey between 2009 and ...

Multidisciplinary prediction of running-related injuries using machine learning.

The causes of endurance running-related injury (RRI) are multifactorial, yet little research has been conducted which utilizes multidisciplinary risk ...

Comparison of the predictive power of novel insulin resistance, lipid metabolism and obesity indices for the risk of developing dyslipidaemia in adults.

Assessment of iodine nutrition status in individual adults using machine learning: a cross-sectional study integrating multidimensional features.

Machine learning models for identifying urinary incontinence in women with a history of hysterectomy using basic demographic and clinical characteristics: A cross-sectional study.

BACKGROUND: Urinary incontinence (UI) in women with a history of hysterectomy represents a significant global health concern. It is crucial to clarify...

Developing an Interpretable Machine Learning Framework to Predict and Analyse Early Childhood Caries in Children Aged 2 to 6 Years: A Single-centre Observational Study.

BACKGROUND: Early childhood caries (ECC) is a prevalent global oral health condition, exerting profound adverse effects on children's oral health, gen...

Rapid quality assessment of organic fertilizers using fused FTIR and LIBS spectroscopy with machine learning.

Organic fertilizers are vital for sustainable agriculture, but conventional quality assessment methods are often time-consuming and costly. This study...

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...

Dual elastography ultrasound for classifying metabolic dysfunction-associated steatotic liver disease: a cross-sectional study within a prospective cohort.

BACKGROUND: The binary diagnostic approach does not reflect the entire spectrum of metabolic dysfunction associated steatotic liver disease (MASLD. We...

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 ...

Closing the paper mines.

Scientific fakery is a centuries old problem. Twinned with the long history of hard-working scientists earning fame for genuine discoveries, runs a ta...

Patients' Attitudes and Beliefs Toward Artificial Intelligence Use in Cancer Care: Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) is being rapidly integrated into oncologic care, yet little is known about how patients perceive these applic...

Association between red cell distribution width-albumin ratio and osteoarthritis in middle-aged and older adults: Analysis of NHANES data (1999-2018).

OBJECTIVE: This study aimed to investigate the association between the Red blood cell distribution width-to-albumin ratio (RAR) and osteoarthritis (OA...

Autoencoders reveal polyunsaturated fatty acids (PUFA)-Related metabolic signature linked to cancer risk.

BACKGROUND: Metabolomics is a valuable tool for characterising biological mechanisms involved in cancer development, but produces complex datasets wit...

Spine age derived from DXA vertebral fracture assessment images predicts incident fractures and mortality: the Manitoba Bone Mineral Density Registry.

Biological age may better predict health outcomes than chronological age by capturing individual heterogeneity in aging. We investigated whether accel...

Integrating body composition analysis and machine learning for non-invasive identification of metabolic dysfunction-associated fatty liver disease: a large-scale health examination-based study.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a highly prevalent liver condition closely linked to obesity, insulin resistance, and ...

Tracing the geographical origin of tiger nut (Cyperus esculentus L) in China based on stable isotopes and mineral elements combined with multi-modal recognition.

The stable isotopes (δ13C, δ15N, δ2H, and δ18O) and elemental compositions, combined with chemometrics, were used to discriminate tiger nut from diffe...

Identification of patients at risk for adverse events and poor symptom improvement after transcatheter aortic valve implantation.

BACKGROUND: Transcatheter aortic valve implantation (TAVI) aims to improve symptoms and prognosis, while minimising adverse outcomes. Available predic...

Large Language Model-Assisted Research Question Development in Public Health: A Case Study in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).

OBJECTIVE: To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nut...