Endocrinology

Application of Artificial Intelligence-Based Transfer Learning Models to the Bethesda System for Thyroid Cytopathology.

BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) provides a standardised framework for thyroid fine needle aspiration cytology (FNAC). Deep learning approaches, particularly transfer learning, have shown potential for cytology but are rarely applied to TBSRTC categorisation. AIMS AND OBJECTIVES: To evaluate the performance of an ensemble soft voting transfer learning mo...

Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.

INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from three phase 2/3 imeglimin trials in Japan, this analysis applied machine learning to determine characteristics associated with HbA1c improvement. METHODS: Regression tree and random forest methods identified baseline characteristics predictive of Hb...

Major depressive disorder and Hashimoto's thyroiditis: Shared immunometabolic signatures revealed by integrative transcriptomics.

Major depressive disorder (MDD) and Hashimoto's thyroiditis (HT) frequently co-occur, yet their shared molecular underpinnings remain unclear. We perf...

Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review.

BACKGROUND: Inpatients with diabetes have higher early unplanned readmission (EUR) rates. Diabetes management team (DMT) review reduces EUR. While glu...

Unraveling the microbiome's role in breast cancer progression and treatment response.

The human microbiome, encompassing microbial communities in the gut and breast tissue, has emerged as a critical modulator of breast cancer (BC) initi...

Raman imaging uncovers Ebracteolatain A's subtype-selective metabolic reprogramming in luminal versus triple-negative breast cancer cells.

Ebracteolatain A (EA), a potential anti-cancer agent, has demonstrated efficacy against breast cancer through protein kinase D1 inhibition. However, i...

Joint Associations of Type D Personality and Allostatic Load With Coronary Artery Disease in Type 2 Diabetes: Evaluating the Role of Allostatic Load.

OBJECTIVE: This study aimed to (1) evaluate and compare the independent associations and predictive strength of type D personality and allostatic load...

Gut microbiome profiles and associated functional pathways are linked to Mediterranean diet adherence and blood glucose control in adults with type 1 diabetes mellitus.

BACKGROUND AND AIMS: The Mediterranean diet (MD) has been associated with better glycaemic control in children with type 1 diabetes mellitus (T1DM) an...

Prospective validation of deep-learning algorithms for diabetic retinopathy screening: A systematic review and meta-analysis.

Deep-learning (DL) algorithms are widely promoted for diabetic-retinopathy (DR) screening, yet their prospective diagnostic accuracy is not well defin...

Unveiling CDKN1A and RELA: Machine learning-driven aging biomarkers for precision diagnosis and therapy in knee osteoarthritis.

BACKGROUND: Knee Osteoarthritis (KOA) is a degenerative joint disease marked by progressive cartilage deterioration, closely tied to cellular senescen...

Incorporating information retrieval into AI chatbots for patient education on thyroid eye disease.

PURPOSE: To evaluate the performance of general-purpose, retrieval-augmented, and medicine-specific AI chatbots in answering common thyroid eye diseas...

Diabetes-linked metabolic dysfunction relates with distinct tau phosphorylation patterns, neuroinflammation and cognitive impairment in mouse models of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques and tau neurofibrillary tangles, with tau path...

Personalized adrenal gland volume reference ranges and development of a fully automated deep learning screening tool.

OBJECTIVE: This study aims to develop a low-dose CT-based, fully automated deep learning tool for screening adrenal gland volume abnormalities and est...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Integrated approaches to testing and assessment for the endocrine disrupting activity of tartrazine based on adverse outcome pathways and OECD frameworks.

Tartrazine (TTZ), a widely used synthetic azo dye in processed foods, beverages, and pharmaceuticals, raises various health concerns including hematot...

Bridging the variant-to-function gap in type 2 diabetes: advances and challenges.

Type 2 diabetes is a polygenic, heterogeneous disease affecting over 530 million individuals worldwide, a number projected to rise to 1.3 billion by 2...

C-peptide Index under Non-fasting Conditions Can Predict the Likelihood of Future Insulin Therapy in Outpatients with Type 2 Diabetes Mellitus Not Receiving Insulin at Baseline.

Objective This study aimed to develop a clinical model in which the C-peptide index (CPI) under non-fasting conditions can predict future insulin ther...

Review on electrochemical sensors for anticancer drug susceptibility testing.

Anticancer drug susceptibility tests play an essential role in areas such as drug development, pharmacokinetic research, and precision oncology. Acros...

Bridging tradition and innovation: Acupuncture for depression through clinical efficacy and neurobiological insights.

BACKGROUND: To elucidate the research progress on acupuncture therapy as a complementary and alternative medicine treatment for depression by reviewin...

Investigation of biochemical alterations in the brain of db/db diabetic mice using integrated FTIR and Raman spectroscopy combined with machine learning.

This study systematically analyzed the molecular metabolic alterations in the brain tissue of type 2 diabetic mice at different disease stages by inte...