Endocrinology

Using artificial intelligence-assisted retinal imaging devices operated by non-specialist workers in detecting diabetic retinopathy in Sri Lanka.

BACKGROUND: Diabetic retinopathy (DR), a microvascular complication of diabetes, is an important cause of preventable blindness and can cause a significant reduction in the quality of life of working-age adults. Sri Lanka has one of the highest prevalences of diabetes globally, and like many low- and middle-income countries, faces significant barriers to DR screening. Easy-to-use, relatively cheap...

Elucidating the molecular mechanisms linking bisphenol A to breast cancer: an integrated study of bioinformatics, machine learning, and molecular docking.

Bisphenol A (BPA) is a prevalent environmental endocrine disruptor linked to breast cancer. However, the precise molecular mechanisms and core therapeutic targets remain to be fully elucidated. This study employed an integrative multi-omics approach to explore the potential mechanism of BPA-associated breast cancer. We integrated multiple transcriptomic datasets from the Gene Expression Omnibus (G...

Gut microbiome composition and functional potential associate with incident type 2 diabetes in 4,685 adults from a Swedish prospective cohort.

Cross-sectional studies link gut microbiome alterations to type 2 diabetes (T2D), but prospective evidence remains limited. We aim to identify taxonom...

Artificial intelligence analysis of temporalis muscle thickness for monitoring sarcopenia and clinical outcomes in individuals with paediatric brain tumours: a retrospective cohort study.

BACKGROUND: People with and who have survived paediatric brain tumour (PBT) have a poor quality of life due to physiological frailty, a primary compon...

Integrated cell-free DNA and omics genetic scores for early detection of gestational diabetes: evidence from a nationwide multicenter study.

BACKGROUND: Gestational diabetes mellitus (GDM) affects 15.6% of pregnancies globally, with Vietnam exhibiting one of the highest prevalences at 21%. ...

Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation.

BACKGROUND: YouTube is increasingly used for healthcasting, the sharing of evidence-based dietary and lifestyle interventions by domain experts. In th...

Stress hyperglycemia ratio improves 28-day mortality prediction beyond GRACE score in critically ill patients with acute coronary syndrome: a retrospective cohort study from MIMIC-IV.

BACKGROUND: The stress hyperglycemia ratio (SHR) is increasingly recognized as a reliable biomarker for adverse outcomes. However, its prognostic valu...

[Emerging classifications of cardiometabolic risk in obesity].

Obesity, historically defined by body mass index and waist circumference, is a major risk factor for cardiometabolic complications; however, these ind...

Metabolic Health is Highly Predictive of Chronic Pain Among Non-Obese Adults: Evidence from the NHANES and CHARLS Studies.

OBJECTIVE: To investigate the predictive value of the estimated glucose disposal rate (eGDR) for chronic pain (CP) and to evaluate its consistency as ...

AI-driven personalization of food systems: From precision nutrition to climate-resilient supply chains.

Traditional large-scale production and standardized interventions cannot address the risks due to individual metabolic diversity, climate change, and ...

Development and evaluation of AI model with deep learning for segmentation of extraocular muscles in thyroid eye disease.

PURPOSE: To develop and evaluate an AI model for the segmentation of extraocular muscles (EOMs) using Magnetic Resonance Imaging (MRI). DESIGN: Single...

Microplastics and the Endocrine-Metabolic Interface: Novel Diagnostic Tools Targeting Thyroid-Adipose Axis.

Microplastics (MPs) have been identified as major environmental contaminants that can affect organisms directly and act as carriers of particles and c...

Enhancing Early Prediction of Gestational Diabetes Mellitus Through Data Augmentation and Feature Guidance: Model Development and Validation Study.

BACKGROUND: Early prediction of gestational diabetes mellitus (GDM) is critical for improving maternal health outcomes. However, predictive models are...

Prediction and Clinical Application of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma Based on Multi-modal Ultrasound Feature Fusion: A Multi-center Study.

OBJECTIVE: To develop and validate a deep learning model integrating multi-modal ultrasound information from B-mode ultrasound (BMUS) and strain elast...

A U-Net-based multimodal deep learning model for high-precision blood glucose prediction using non-invasive physiological data.

This study proposes a novel U-Net-based convolutional neural network for blood glucose prediction by integrating historical continuous glucose monitor...

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer.

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression...

Unveiling the role of air pollution in diabetic kidney disease: an integrated study combining network toxicology, machine learning, and Mendelian randomization.

Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for me...

Identifying critical windows of apparent temperature exposure and male serum reproductive hormone levels.

Although previous studies have suggested that environmental temperature may influence male reproductive hormone levels, evidence regarding the associa...

Targeting dysregulated glycolysis in type 2 diabetic osteoporosis: Identification of a diagnostic gene signature and therapeutic validation of calcifediol.

Type 2 diabetic osteoporosis (T2DOP) is a significant complication of type 2 diabetes, characterized by an increased risk of fractures. While dysregul...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered suppo...