Endocrinology

Virtual Hydrolysis-Based Screening of Wheat-Derived DPP-IV Inhibitory Peptides: A Mechanistic Analysis Integrating Cell Experiments and Molecular Dynamics Simulations.

Dipeptidyl peptidase-IV (DPP-IV) inhibitors play a critical role in the treatment of diabetes and metabolic diseases. This study combines computational simulations with experimental validation to identify peptides with potential DPP-IV inhibitory activity from wheat proteins. A peptide database was constructed through trypsin digestion simulation, and screening was performed using the ConPLex deep...

A comparative approach of machine learning models to predict attrition in a diabetes management program.

Approximately 11.6% of Americans have diabetes and South Carolina has one of the highest rates of adults with diabetes. Diabetes self-management programs have been observed to be effective in promoting weight loss and improving diabetes knowledge and self-care behaviors. The ability to keep vulnerable individuals in these programs is critical to helping the growing diabetic population. Utilizing m...

Identification and verification of feature biomarkers associated with CD14 monocytes in type 1 diabetes.

AIMS/INTRODUCTION: Monocytes contribute to the immune dysregulation of pancreatic islets in Type 1 diabetes mellitus (T1D). This study aims to identif...

Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.

BACKGROUND: Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further compl...

Diabetic retinopathy in rural communities: a review of barriers to access of care and potential solutions.

INTRODUCTION: Diabetic retinopathy (DR) is a leading cause of vision loss. With an estimated 38.4 million Americans diagnosed with DM, the disease exe...

Multi-modal convolutional neural network-based thyroid cytology classification and diagnosis.

BACKGROUND: The cytologic diagnosis of thyroid nodules' benign and malignant nature based on cytological smears obtained through ultrasound-guided fin...

Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework.

BACKGROUND: Effective diabetes management requires precise glycemic control to prevent both hypoglycemia and hyperglycemia, yet existing machine learn...

Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.

Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each year worldwide. The mortality rate is over 251,000 de...

Uncovering key markers and therapeutic targets for renal fibrosis in diabetic kidney disease through bulk and single-cell RNA sequencing.

BACKGROUND: Diabetic kidney disease (DKD) is the major cause of chronic kidney failure, with tubulointerstitial fibrosis playing a crucial role in dis...

Unraveling the oxidative stress landscape in diabetic foot ulcers: insights from bulk RNA and single-cell RNA sequencing data.

BACKGROUND: Oxidative stress plays a crucial role in the development of diabetic foot ulcers (DFU). However, its underlying mechanisms are not fully u...

Can Whole-Thyroid-Based CT Radiomics Model Achieve the Performance of Lesion-Based Model in Predicting the Thyroid Nodules Malignancy? - A Comparative Study.

Machine learning is now extensively implemented in medical imaging for preoperative risk stratification and post-therapeutic outcome assessment, enhan...

Integrating MobileNetV3 and SqueezeNet for Multi-class Brain Tumor Classification.

Brain tumors pose a critical health threat requiring timely and accurate classification for effective treatment. Traditional MRI analysis is labor-int...

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an...

Interpretable machine learning analysis of immunoinflammatory biomarkers for predicting CHD among NAFLD patients.

BACKGROUND: Coronary Heart Disease (CHD) and Non-Alcoholic Fatty Liver Disease (NAFLD) share overlapping pathogenic mechanisms including adipose tissu...

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒...

A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.

Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (...

Deep Learning Discovers New Morphological Features while Predicting Genetic Alterations from Histopathology of Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC) is the most common malignant tumor of the endocrine system. mutations occur in 40-60%, mutations in 10-15%, and d...

Development of a prediction model by combining tumor diameter and clinical parameters of adrenal incidentaloma.

When adrenal incidentalomas are detected, diagnostic procedures are complicated by the need for endocrine-stimulating tests and imaging using various ...

Artificial Intelligence-Assisted Conductive Hydrogel Dressings for Refractory Wounds Monitoring.

Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wo...

Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.

Community-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient o...