Endocrinology

Linsitinib inhibits proliferation and induces apoptosis of both IGF-1R and TSH-R expressing cells.

BACKGROUND: The insulin-like growth factor 1 receptor (IGF-1R) and the thyrotropin receptor (TSH-R) are expressed on orbital cells and thyrocytes. These receptors are targeted in autoimmune-induced thyroid eye disease (TED). Effective therapeutic treatment of TED inhibits activation of the IGF-1R/TSH-R complex.

Evaluating the efficacy of AI systems in diabetic retinopathy detection: A comparative analysis of Mona DR and IDx-DR.

PURPOSE: To compare two artificial intelligence (AI)-based Automated Diabetic Retinopathy Image Assessment (ARIA) softwares in terms of concordance with specialist human graders and referable diabetic retinopathy (DR) diagnostic capacity.

Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY.

PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration r...

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.

Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively ...

Subclinical/overt hypothyroidism may be associated with diminished ovarian reserve in infertile women independent of thyroid autoimmunity.

OBJECTIVE: To investigate the association between thyroid dysfunction or thyroid autoimmunity (TAI) and diminished ovarian reserve (DOR).

Revolutionary drug repositioning: the preventive and therapeutic potential of metformin and other antidiabetic drugs in age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness among the elderly worldwide. Anti-vascular endothelial growth factor (anti-VEGF...

Modeling the fasting blood glucose response to basal insulin adjustment in type 2 diabetes: An explainable machine learning approach on real-world data.

INTRODUCTION: Optimal basal insulin titration for people with type 2 diabetes is vital to effectively reducing the risk of complications. However, a s...

ECLStat: A robust machine learning based visual imaging tool for electrochemiluminescence biosensing.

Visual electrochemiluminescence (ECL) has emerged as a prominent diagnostic method for accurately quantifying various disease markers even at point of...

Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.

BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for clas...

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies.

The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previo...

Multiple instance learning method based on convolutional neural network and self-attention for early cancer detection.

Early cancer detection using T-cell receptor sequencing (TCR-seq) and multiple instances learning methods has shown significant effectiveness. We intr...

A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids.

We use three-dimensional culture systems of human pluripotent stem cells for differentiation into pituitary organoids. Three-dimensional culture is in...

Classification of glucose-level in deionized water using machine learning models and data pre-processing technique.

Accurate monitoring of glucose levels is essential in the field of diabetes detection and prevention to ensure appropriate treatment planning. Convent...

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity.

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understan...

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary com...

Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status.

Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently....

Potential impact of organophosphate esters on thyroid eye disease based on machine learning and molecular docking.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in daily commodities and building materials. Some OPEs, acting as a...

Exploring the risk factors and clustering patterns of periodontitis in patients with different subtypes of diabetes through machine learning and cluster analysis.

AIM: To analyse the risk factors contributing to the prevalence of periodontitis among clusters of patients with diabetes and to examine the clusterin...

The Effects of Perfluorooctanesulfonic acid (PFOS) on Human Umbilical Vein Endothelial Cells (HUVECs) Proliferation and Gene Expression and its Implications on Fetal Development.

Polyfluoro-alkyl substances (PFAS) are widely distributed environmental contaminants linked to human toxicity and developmental delays, especially low...

Comparative evaluation of ChatGPT-4, ChatGPT-3.5 and Google Gemini on PCOS assessment and management based on recommendations from the 2023 guideline.

CONTEXT: Artificial intelligence (AI) is increasingly utilized in healthcare, with models like ChatGPT and Google Gemini gaining global popularity. Po...