Endocrinology

A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.

Halide perovskite memdiodes have coupled ionic-electronic dynamics and are promising candidates for artificial synapses in neuromorphic computing. We provide an in silico neuromorphic circuit that includes a comprehensive perovskite memdiode model and confirm its synaptic plasticity repertoire through simulation-based validation. The proposed model recapitulates analog long-term potentiation/depre...

Multimodal Transformer Fusion of Clinical Information, Medical Text, and Pituitary MRI 2.5D Deep Learning Features for Differentiating Growth Hormone Deficiency and Idiopathic Short Stature.

RATIONALE AND OBJECTIVES: To develop a non-invasive, efficient and accurate auxiliary tool for the precise differential diagnosis between pediatric growth hormone deficiency (GHD) and idiopathic short stature (ISS). MATERIALS AND METHODS: This retrospective two-center study enrolled 618 children as the internal training cohort and 164 children as the independent external test cohort. We constructe...

Exploring and validating hub genes associated with atopic dermatitis through weighted gene co-expression network analysis and machine learning.

This study aimed to identify novel biomarkers of atopic dermatitis (AD) and investigate their pathogenic mechanisms. We analyzed 7 AD-related datasets...

Integrative systems biology identifies PSMB2 as a core oxidative stress-associated target in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) represents one of the most aggressive and therapeutically challenging subtypes of breast cancer, characterized by...

A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.

OBJECTIVE: This study sought to quantify, through a multi-reader study, whether AI assistance improves diagnostic accuracy across experience levels, r...

Artificial intelligence in pituitary medicine: what should an endocrinologist know?

Pituitary disorders often include complex radiology imaging, rare clinical presentations, and need for multidisciplinary decision-making and prolonged...

Emerging Research Trends in the Application of Artificial Intelligence, LLMs, Machine Learning, and Deep Learning in Ophthalmic Diseases: A Bibliometric and Visual Analysis.

BACKGROUND: To examine global research activity in the application of artificial intelligence, large language models, machine learning, and deep learn...

Multimodal Deep Learning for Diabetic Retinopathy: A Survey.

Diabetic retinopathy (DR) remains a leading cause of blindness globally, driving the rapid development of automated diagnostic systems leveraging deep...

The Eye as a Window to Cardio-Kidney-Metabolic Disease.

The Cardiovascular-Kidney-Metabolic (CKM) syndrome reframes cardiovascular, kidney, and metabolic disease as an integrated continuum, yet its manageme...

Linking bisphenol a exposure to pancreatic cancer: Evidence from population epidemiology, multi-omics screening, and cellular validation.

Bisphenol A (BPA) is a ubiquitous environmental endocrine disruptor; its association with pancreatic cancer and its potential mechanisms of action rem...

Hydrogen sulfide ameliorates cognitive dysfunction in diabetic encephalopathy by activating PI3K/AKT/GSK-3β signaling to attenuate tau hyperphosphorylation and necroptosis.

Diabetic encephalopathy (DE) is a serious complication of diabetes mellitus characterized by progressive cognitive dysfunction; but its underlying mec...

Artificial Intelligence Analysis of Standard 12-Lead Electrocardiograms for Screening of Symptomatic Diabetic Peripheral Neuropathy.

AIM: To investigate whether artificial intelligence (AI) models trained on standard 12-lead electrocardiograms (ECG) can identify symptom-defined diab...

Artificial Intelligence in Ultrasound Imaging: Opportunities for Improving Diagnostic Accuracy in Gulf Health Care.

Ultrasound imaging is widely used across cardiology, hepatology, obstetrics, breast and thyroid imaging, and emergency care because it is real-time, n...

Artificial intelligence-enhanced management system for secondary prevention of coronary heart disease: a randomized clinical trial.

BACKGROUND: Secondary prevention of coronary heart disease (CHD) remains suboptimal due to fragmented care and therapeutic inertia. While digital heal...

Laboratory Medicine in Immune Checkpoint Inhibitor-Induced Autoimmune Disorders: Baseline Assessment, Diagnostic Algorithms, Monitoring, and Predictive Biomarkers.

Immune checkpoint inhibitors (ICIs) have substantially improved clinical outcomes across multiple malignancies, but they can disrupt self-tolerance an...

Prospective Detection of Hypoglycemia and Near-Hypoglycemia Inducing Pressure-Induced Sensor Attenuation Onset in Continuous Glucose Monitoring Time Series.

BACKGROUND: Continuous glucose monitoring (CGM) sensors are vulnerable to pressure-induced sensor attenuations (PISAs). Pressure-induced sensor attenu...

Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration.

Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynami...

Subtyping and Prediction of the Glucose Based on the Hybrid Modeling of Deep Learning and Dietary Stimulated Glucose Metabolism Dynamical Model.

Diabetes represents a significant global health challenge, underscoring the need for enhanced methodologies in glycemic monitoring and risk assessment...

Thyroid Year-in-Review 2025-2026: Precision Medicine and De-escalation in Contemporary Thyroidology.

The 'Thyroid: Year in Review', presented at the AACE 2026 annual meeting, synthesized practice-influencing peer-reviewed clinical research published b...

Linking Metabolic Subgroups and Adverse Pregnancy Outcomes in GDM: A Complementary Analytic Approach of Logistic Regressions and Machine Learning.

AIMS: Associations between metabolic heterogeneity in women with gestational diabetes mellitus (GDM) and adverse pregnancy outcomes have often been ex...