Endocrinology

Machine learning evaluation of TyG-based metrics for arteriosclerosis progression.

Insulin resistance (IR) is a significant risk factor for arteriosclerosis. The triglyceride-glucose (TyG) index and its obesity-related derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable markers of IR. While diabetes, a consequence of IR, is a primary risk factor for arteriosclerosis, it is important to note that arteriosclerosis may develop prior to the onset of diabetes. Theref...

Life's Essential 8-Defined Cardiovascular Health and Mortality Risk in Cardiovascular-Kidney-Metabolic Health Stages 0-4: A Cohort Study.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome represents a primary contributor to global morbidity and mortality. Despite the Life's Essential 8 framework offering a comprehensive assessment of cardiovascular health (CVH), its prognostic utility in CKM syndrome remains unclear. This study aimed to investigate the association between Life's Essential 8-defined CVH and mortality risk in...

Gender disparities in random blood glucose levels among Pakistani adults with type 2 diabetes: a cross-sectional analysis.

This study investigated gender disparities in random blood glucose (RBS) levels among Pakistani adults with Type 2 Diabetes (T2D), examining biologica...

Integrated metabolic and inflammatory biomarkers for early association and prediction of adverse pregnancy outcomes: a multicenter validation study.

BACKGROUND: Adverse pregnancy outcomes (APOs) affect long-term maternal and offspring health. Conventional metabolic markers, including the triglyceri...

Development and validation of an MRI-based deep learning system for triple-class ER expression classification in breast cancer: a large-scale multicenter study.

BACKGROUND: Estrogen receptor (ER) expression is a key prognostic and predictive marker in breast cancer. The 2020 ASCO/CAP guidelines classify tumors...

Association between oxidative balance score and cardiometabolic multimorbidity: differential mortality, mediation mechanisms, and machine learning insights.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is a major global health burden associated with increased morbidity and mortality. The oxidative bala...

Assessing Extracellular Vesicle Proteins as Predictive Biomarkers for Developing Type 1 Diabetes.

Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and ...

Precise Staging of Diabetic Retinopathy Through Machine Learning Analysis of Leakage Source Characteristics: A Radiomics-Driven Approach Integrating OCT-A and FFA.

INTRODUCTION: Evaluating retinal fundus image for diabetic retinopathy (DR) assessment is used to reduce the risk of blindness among diabetic patients...

A multi-omics atlas of multisystem complications in type 2 diabetes reveals molecular signatures and improves risk prediction.

BACKGROUND: Type 2 diabetes (T2D) causes multisystem complications, but an integrated multi-omics framework for cross-system, multi-outcome analysis i...

Screening for Periodontitis Using Blood Biomarkers and Demographic Data: A Machine Learning Study.

PURPOSE: Periodontitis is a common chronic disease associated with systemic conditions such as diabetes and cardiovascular disease. Diagnosis typicall...

MRI radiomics-based approach to predict pituitary neuroendocrine tumor invasiveness.

OBJECTIVES: To assess the diagnostic potential of magnetic resonance imaging (MRI) radiomics and machine learning models using T2-weighted and contras...

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-la...

Alignment Between Cardiologists and AI-Driven Diagnostic Systems: Mixed Methods Study.

BACKGROUND: The clinical value of artificial intelligence (AI)-based diagnostic systems depends not only on their accuracy but also on how well their ...

Predicting Treatment Failure With Sodium-Glucose Cotransporter-2 Inhibitors in People With Type 2 Diabetes: Novel Artificial Intelligence and Machine Learning Approach.

BACKGROUND: The rate of treatment failure with sodium-glucose cotransporter-2 inhibitors (SGLT2i) is high among individuals with type 2 diabetes (T2D)...

Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration.

BACKGROUND: Explainable artificial intelligence (xAI) is increasingly used in medical imaging to enhance transparency, clinical interpretability, and ...

Cascade-amplified 2D ultrathin hexagonal Cu-doped LDO nanozyme for multiplexed wearable monitoring of sweat metabolites.

Sweat-based metabolic monitoring offers a non-invasive alternative to blood tests, but its clinical utility is limited by the limited catalytic effici...

Patient preferences and willingness-to-pay for AI-enabled blended type 2 diabetes care by digital experience and socioeconomic status: a discrete-choice experiment in China.

OBJECTIVES: To elicit stated preferences and willingness-to-pay (WTP) for artificial intelligence (AI)-enabled blended care in type 2 diabetes mellitu...

Thyroid disease detection using enhanced extreme learning machine based on drop-connect method.

The long-term physiologic effects of thyroid problems make them one of the most important endocrine disorders. Even if a lot of machine learning and d...

National trends and comparative outcomes of insulin versus non-insulin therapy in acute ischemic stroke patients treated with mechanical thrombectomy: a retrospective cohort study using the national inpatient sample.

Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...

Development of colorimetric and machine learning based accurate glucose detection platform for point of care applications.

Improving overall health and preventing complications is crucial for timely and effective treatment of diabetes patients. In this direction, accurate ...