Endocrinology

Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma.

The objective of the study is to develop and validate a multiparametric MRI (mpMRI)-based model that integrated with habitat-based radiomics, deep transfer learning (DTL), and quantitative parameters for the preoperative prediction of extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC). This retrospective study collected clinical, pathological, and mpMRI data from patients with con...

Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.

BACKGROUND: Optimizing insulin dosing and predicting future glucose levels for people with type 1 diabetes is challenging due to the dynamic nature of glucose metabolism. Traditional static insulin regimens fail to adapt to individual variability in diet, physical activity, stress, and metabolic fluctuations, leading to suboptimal glycemic control. Reinforcement learning (RL) offers a promising al...

Diabetes Management Through Glucose Dynamics Analysis Network: A Novel Approach for Accurate Blood Glucose Level Forecasting.

BACKGROUND: Accurate real-time prediction of blood glucose (BG) levels is essential for improving insulin-dosing decision support systems, including c...

Multimodal Ultrasound Integration Pathways and Paradigm Innovations in Precision Diagnosis and Treatment of Thyroid Cancer.

RATIONALE AND OBJECTIVES: Thyroid cancer, the fastest-growing endocrine malignancy, is shifting from morphological evaluation to molecular-functional ...

Proteomic signatures of early retinal neurodegeneration in type 2 diabetes mellitus.

BACKGROUND: Retinal neurodegeneration is an early and independent feature of diabetic retinal disease and has been proposed as a window into the syste...

Integrating network toxicology, machine learning, and single-cell sequencing to reveal the FASN-mediated role of phenolic endocrine disruptors in water in promoting prostate cancer.

BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) like nonylphenol (NP) and octylphenol (OP) are widespread water pollutants. Their estrogen-...

Plasticizer-responsive molecular axes in heart failure: a subtype-aware toxicogenomic framework relevant to environmental health.

Environmental exposure to synthetic chemicals has been associated with cardiovascular risk, yet mechanistic connections between real-world chemical mi...

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the...

Development and temporal external validation of a high-specificity XGBoost rule-in model for diabetes in middle-aged and older Korean adults.

Early identification of diabetes in older adults is essential for preventing complications, yet many high‑risk individuals remain undetected in commun...

[A nomogram combining ultrasound radiomics and clinical features for predicting pathological invasiveness of papillary thyroid carcinoma].

OBJECTIVES: To develop a nomogram model combining ultrasound radiomics and clinical features and to evaluate its predictive value for pathological inv...

The role of infrared thermography in thyroid nodule detection and classification: a scoping review.

PURPOSE: To conduct a scoping review to assess the extent and type of evidence on the use of IRT as a diagnostic tool for thyroid nodule detection and...

The Validity and Reliability of Artificial Intelligence Chatbots in the Self-management of Diabetes.

Artificial intelligence (AI) chatbots are increasingly used to support diabetes self-management, yet their validity and reliability require systematic...

Consensus Pituitary Atlas, a scalable resource for annotation, novel marker discovery, and analyses in mouse pituitary gland research.

Previous single-cell profiling studies of the pituitary gland have yielded minimally reproducible insights due to their low statistical power and meth...

Exploring the potential targets and mechanisms of artemisinin in the treatment of diabetic kidney disease using network pharmacology and molecular docking.

Diabetic kidney disease (DKD) is a secondary glomerular disease caused by diabetes, and its incidence is increasing annually. Artemisinin is an organi...

Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.

This review examines the convergence of wearable biosensors and artificial intelligence (AI) in personalized diabetes care. It addresses the limitatio...

Development of autoencoder-guided attention-LSTM models for predicting nocturnal hypoglycemia risk in Type 1 Diabetes.

OBJECTIVE: Nocturnal hypoglycemia (NH) is a major, often undetected risk for individuals with Type 1 Diabetes (T1DM). Current prediction models lack s...

Human and Robot Assistance for Cognitive Load in Younger and Older Adults: Multimodal Within-Subject Experimental Study.

BACKGROUND: Maintaining cognitive efficiency and independence is a central goal of healthy aging. Socially assistive robots (SARs) are increasingly pr...

Environmental Levels of Sodium p-Perfluorous Nonenoxybenzene Sulfonate Induce Nephritis in Renal Proximal Tubular Epithelial Cells via Modulation of Estrogen Signaling Pathways.

Sodium p-perfluorous nonenoxybenzene sulfonate (OBS), widely used as an alternative to per- and polyfluoroalkyl substances, has been implicated in tox...

Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study.

This study presents a comprehensive machine learning approach for predicting the percent displacement of ANSA from human transthyretin (TTR) at fixed ...

Differentiating Mummified Thyroid Nodules From Papillary Thyroid Carcinoma: A Machine Learning Approach Using Multi-Modal Ultrasound Radiomics.

OBJECTIVES: This study aimed to establish a machine-learning model that integrates contrast-enhanced ultrasound (CEUS) radiomics, conventional ultraso...