Endocrinology

Exploring the feasibility of near-infrared spectroscopy and machine learning for detecting cardiovascular diseases and diabetes mellitus in fingernails.

Cardiovascular diseases (CVDs) and diabetes mellitus (DM) are significant conditions that impact lives around the globe. Frequently employed methods for detecting CVDs and/or DM such as blood work and cardiac catheterisation are often invasive, intrusive and can cause the patient additional physical and psychological harm. Vibrational spectroscopic methods including near-infrared (NIR) spectroscop...

Deep-Q-Network in Multiview Ensemble Learning to Predict Anti-diabetic Peptide and Diabetic Types.

Diabetes mellitus disease has become a significant human health concern globally, causing cardiovascular complications, kidney disease, cerebrovascular accidents, etc. In humans, bioactive molecules, specifically Antidiabetic peptides (ADPs), target $\beta$ or $T$-cells to regulate insulin production, making them potential alternatives for diabetes therapy. Recently, several computational ensemble...

AI-integrated smartphone platform enables POC dual-channel glucose monitoring with an indicator-free nanozyme gel kit.

We developed an indicator-free, dual-mode glucose monitoring platform by synthesizing glucose oxidase (GOX)-templated MnO2 nanosheets via a microwave-...

Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer.

Insulin resistance is suggested to be a risk factor for cancer; however, large-scale epidemiological evidence linking insulin resistance to cancer rem...

Risk Prediction of Chronic Kidney Disease Progression in Type 2 Diabetes Mellitus Across Diverse Populations.

Chronic kidney disease (CKD) is a common complication of type 2 diabetes mellitus (T2DM), with limited predictive tools for individualized prognosis, ...

Brain tumor classification using optimized ResNet50 with dynamic precision optimization for enhanced speed and diagnostic accuracy.

The detection and classification of brain tumors remain a major challenge in the medical field due to their morphological complexity. In this study, b...

Reliable discrimination of type 1 and type 2 diabetes by flow-through leukocyte-endothelium interactions exploiting advanced hydrodynamic detection parameters.

Type 1 (T1D) and type 2 diabetes (T2D) are both associated with chronic inflammation and endothelial dysfunction, yet their discrimination based on co...

The Prediction Paradox: AI, Genetics, and Occult Metastasis in Thyroid Cancer.

Corrigendum to "Predicting postpartum glucose intolerance in women with gestational diabetes mellitus in primary care: A machine learning approach using XGBoost and SHAP values" [Diabetes Res. Clin. Pract. 232 (2026) 113098].

Multimodal Cross-Attentive Graph-Based Framework for Predicting In Vivo Endocrine Disruptors.

Endocrine hazard assessment needs models that are accurate and mechanistically transparent. We present a multimodal cross-attentive graph framework th...

Real-time intraoperative depth estimation in transsphenoidal surgery using deep learning: A feasibility study.

PURPOSE: Endoscopic endonasal and transcranial approaches are used for the resection of various pathological lesions in neurosurgery, especially pitui...

An allostatic load domain-specific metabolic profile in young adults: The African-PREDICT study.

Allostatic load scores (ALSs) quantify the cumulative physiological burden of sustained stress across neuro-endocrine, metabolic, cardiovascular and i...

An interpretable machine learning model for preoperative prediction of aldosterone secretion and CYP11B2 status of adrenal gland.

BACKGROUND: To investigate whether a machine learning (ML) model integrating CT-based radiomics and clinical features can noninvasively evaluate the a...

Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches.

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dys...

Neuromodulation using Bioelectronics-enabled therapies to combat insulin resistance and its comorbidities.

INTRODUCTION: Insulin resistance is a complex metabolic disorder that involves multiple molecular pathways to disrupt insulin signaling and is associa...

Surface-based cortical thickness and gyrification mapping with data-driven prediction of cognitive impairment in prediabetes.

BACKGROUND: Prediabetes is a serious health condition characterized by blood glucose levels that are higher than normal but not high enough for a diag...

Late distant recurrence prediction model in premenopausal women with ER-positive/HER 2-negative breast cancer: A multicenter retrospective study.

BACKGROUND: Late distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (...

Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.

Diabetic nephropathy (DN) represents the predominant microvascular complication associated with diabetes mellitus; however, existing diagnostic techni...

Identification of VEGFB associated with NKT cells in diabetic foot ulcers: Single-cell analysis and machine learning.

The management options for diabetic foot are restricted, and the outlook is unfavorable. Immune cells have been implicated in diabetic foot ulcer (DFU...

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.

Type 1 diabetes (T1D) is a T cell-mediated disease with a strong immunogenetic human leukocyte antigen (HLA) dependence. HLA allelic influence on the ...