Obesity

From Ageing Clocks to Organ Networks: Biological Age-Driven Organ Asynchrony and Inter-organ Interactions Shaping Mortality Risk.

Ageing is a multidimensional and heterogeneous process that progresses asynchronously across organ systems. Advances in multi-omics technologies have led to the development of diverse ageing clocks, including epigenetic, proteomic, metabolomic, and imaging-based models, which extend beyond estimating crude biological age to more precisely capture organ-specific ageing trajectories and predict age-...

Predicting recurrent cardiac arrest within one year after surviving In-Hospital Cardiac Arrest using a machine learning model.

BACKGROUND: Patients discharged alive after in-hospital cardiac arrest (IHCA) have an increased mortality up to a year after hospital discharge. Improving our ability to identify patients at high risk of death one year after IHCA may improve survival through secondary prevention. We aimed to evaluate the possibility to predict recurrent cardiac arrest or death among in-hospital cardiac arrest surv...

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...

AI-FLEET: Phase I-Multimodal Deep Learning Model for Phyllodes Tumor Classification.

BACKGROUND: Fibroepithelial breast lesions, including fibroadenomas and phyllodes tumors (PTs), can be difficult to classify on needle biopsy. Misclas...

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 ...

Predictors of Glycemic Response to Sulfonylurea Therapy in Type 2 Diabetes Over 12 Months: Comparative Analysis of Linear Regression and Machine Learning Models.

BACKGROUND: Sulfonylureas are commonly prescribed for managing type 2 diabetes, yet treatment responses vary significantly among individuals. Although...

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

The future of PCOS management: Disease modification through regenerative, metabolic, and digital therapeutics.

Polycystic ovary syndrome (PCOS) represents the leading endocrine problem affecting women of reproductive age, leading to serious complications relate...

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...

Personal reflection on future of psychiatry.

History of Psychiatry can be of help, not only not to repeat the mistakes of the past, but also to find good heuristic sources in old books. A constan...

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...

Risk assessment in cardiac surgery: Exploring machine learning and laboratory indices as adjunctive tools.

BACKGROUND: Post-operative outcomes of cardiovascular surgery vary greatly among patients for a variety of reasons. While the specific reasons are oft...

HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression.

INTRODUCTION: Mobile health (mHealth) technologies have become increasingly popular for monitoring mental health symptoms and lifestyle behaviours, an...

Identification of common genes and biomarkers between Dermatomyositis and rheumatoid arthritis through integrated bioinformatics.

BACKGROUND: Dermatomyositis (DM) and rheumatoid arthritis (RA) share immuno-inflammatory features, yet mechanisms underlying their comorbidity remain ...

DisSubFormer: A subgraph transformer model for disease subgraph representation and comorbidity prediction.

Disease comorbidity-the co-occurrence of multiple diseases in the same individual-is increasingly prevalent and poses major clinical and biological ch...

A hybrid swin transformer-BiLSTM framework and ensemble learning for multimodal brain stroke detection and risk prediction.

Stroke is one of the leading causes of mortality and long-term disability worldwide, primarily resulting from the sudden disruption of cerebral blood ...

Integrated Prediction System for Individualized Ovarian Stimulation and Ovarian Hyperstimulation Syndrome Prevention: Algorithm Development and Validation.

BACKGROUND: Accurately predicting ovarian response and determining the optimal starting dose of follicle-stimulating hormone (FSH) remain critical yet...

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 ...

Brain-cancer interactions outside the CNS.

Emerging research highlights the key role of the central nervous system in regulating peripheral tumor progression via neural, neuroendocrine, and imm...

[Efficacy prediction of acupuncture and moxibustion combined with conventional treatment for PCOS-IR based on real-world data: machine learning and interpretability analysis].

OBJECTIVE: To construct an efficacy prediction model for polycystic ovary syndrome with insulin resistance (PCOS-IR) treated with acupuncture and moxi...