Primary Care

Network toxicology and bioinformatics analysis predict potential molecular targets and mechanisms by which sevoflurane and propofol influence type 2 diabetes mellitus.

BACKGROUND: Sevoflurane and propofol are commonly used anesthetics that may exert pronounced effects on glucose metabolism and cardiovascular function in patients with type 2 diabetes mellitus (T2DM). This study aimed to elucidate the molecular mechanisms underlying the association of sevoflurane and propofol with T2DM progression. METHODS: Targets associated with sevoflurane, propofol, and T2DM w...

Development and validation of a logistic regression model for predicting visual impairment in middle-aged and older adults with diabetes: results from the China Health and Retirement Longitudinal Study.

AIM: To develop and validate a clinician-friendly logistic regression prediction model for self-reported visual impairment (VI) in middle-aged and older adults (≥45y) with diabetes. METHODS: Leveraging data from the China Health and Retirement Longitudinal Study (CHARLS), a model for VI among adults aged ≥45y with diabetes were developed. Feature selection involved LASSO regression and subsequent ...

Predicting cardiovascular death in overweight/obese people with prediabetes using machine learning-a proof-of-concept study.

BACKGROUND: Individuals with prediabetes face an increased risk of cardiovascular (CV) complications, which can ultimately lead to premature mortality...

GATA Family Transcription Factor OsGATA1 Regulates Grain Size and Brassinosteroid Biosynthesis in Rice.

Grain size is a critical agronomic trait that influences grain yield in rice. However, the molecular mechanisms underlying grain size regulation in ri...

Explainable TabNet for gestational diabetes prediction with physician-in-the-loop and multi-site clinical validation.

BACKGROUND: Gestational diabetes mellitus (GDM) affects 15-25% of pregnancies worldwide and poses serious risks of macrosomia, preeclampsia, neonatal ...

Prediction Model for Delirium in Patients with Sepsis-Associated Liver Injury: An Interpretable Machine Learning Approach.

BackgroundPatients with sepsis-associated liver injury (SALI) are at marked risk of delirium, a severe complication strongly linked with poor neurolog...

Health Risk Assessment for Rare Earth Ions: From End Point Identification of Cytotoxicity to Mixed Toxicity Prediction.

Rare earth elements (REEs) are emerging contaminants with escalating environmental releases. However, REE health risk assessment faces critical challe...

Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.

UNLABELLED: Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due ...

Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.

BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) is seen as a potential solution to alleviate workforce demands arising from growing use of magn...

Predicting type II diabetes mellitus in young and middle-aged adults: A machine learning approach using the Utah population database.

AIMS: To develop a machine learning framework for predicting type 2 diabetes mellitus (T2DM) using administrative data and electronic health records (...

Advancing continuous in-situ quantification of microbial contamination in environmental waters using tryptophan-like fluorescence-Sensor design and validation.

Microbial contamination of recreational and source waters poses persistent public health risks, yet conventional monitoring based on laboratory cultur...

Crash root-cause identification via trace-rewarded causation chain reasoning large language model.

Road traffic crash is one of the top leading causes of death worldwide. To support the deployment of modern safety improvements such as Highly Automat...

Unveiling the gap in heart failure: a Brazilian Unified Health System study.

BACKGROUND: Heart failure (HF) decompensation is the leading cause of hospitalisations in developed countries and the third most common cause in Brazi...

Obesity, microRNA circulating microRNA signatures reveal core and reversible dysregulations in obesity via machine learning.

Obesity is a complex metabolic disease characterized by systemic metabolic and inflammatory dysregulation, yet the molecular signatures underlying the...

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

BACKGROUND: Complication risks in children and adolescents with type 1 diabetes (T1D) can lead to serious health outcomes if not detected early. Despi...

Using Digital Phenotyping for Depression Screening in Community-Dwelling Older Adults: Bayesian Multilevel Hurdle Model Machine Learning Approach.

BACKGROUND: With the rapidly aging population, mental health among older adults has received growing attention. Although the likelihood of experiencin...

Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysis.

Population-based diabetic retinopathy (DR) screening requires diagnostic strategies that optimize clinical utility by balancing missed disease against...

Leveraging artificial intelligence to predict non-adherence to pediatric immunization schedules: a systematic review.

UNLABELLED: Immunization is one of the most effective interventions to prevent infectious diseases. Identifying individuals at risk of non-adherence t...

Interpretable machine learning model integrating MRI-derived paraspinal muscle parameters for predicting new vertebral compression fractures after vertebral augmentation.

OBJECTIVES: To develop and validate interpretable machine learning (ML) models incorporating MRI-derived paraspinal muscle parameters to predict new v...

Development and validation of an explainable machine learning model using routine laboratory biomarkers for identifying prevalent MASLD: Evidence from two observational studies.

Although many predictive models for metabolic dysfunction-associated steatotic liver disease (MASLD) have been developed, their performance remains su...