Endocrinology

Integrative computational and experimental analysis identifies MEK-mediated carcinogenic effects of bisphenol A and diethyl phthalate in head and neck cancer.

We investigated the carcinogenic effects of four endocrine-disrupting chemicals-bisphenol A (BPA), diethyl phthalate (DEP), dimethyl phthalate (DMP), and dioctyl phthalate (DOP)-in nasopharyngeal carcinoma (NPC) and thyroid carcinoma (THCA) using an integrated toxicogenomic-machine learning-docking-experimental pipeline. Intersection analysis identified 31 NPC-related overlapping genes and 39 THCA...

Role of lactic acid-mediated ALKBH5 in depression induced by blue light exposure at night.

Artificial light at night (ALAN) has been classified as a significant environmental endocrine disruptor. Excessive exposure to ALAN has associated with an increased risk of depression. With the extensive use of high-density blue light LED screens, the risk of blue light exposure at night has increased. Animal studies have suggested a close relationship between blue light and depression, but the re...

Environmental toxicant exposure and diabetes risk: An exposome-wide association study integrating mixture effects and molecular mechanisms.

Environmental toxicant exposure has emerged as a potential contributor to diabetes, yet systematic investigations integrating multiple chemicals and b...

Machine Learning Integration of MRI Intratumoral and Peritumoral Radiomics Features for Predicting PNSTs Postoperative Complications.

RATIONALE AND OBJECTIVES: Postoperative complications (15-76%) substantially affect patients with peripheral nerve sheath tumors (PNSTs), yet objectiv...

Integrative multi-omics refines the molecular subtypes of thyroid cancers and enhances cancer-progression prediction: a retrospective cohort study.

BACKGROUND: Current molecular classification model for thyroid cancer (TC), which relies on BRAF-RAS score genes has limited efficacy in differentiati...

Cardiorespiratory Markers of Type 2 Diabetes: Machine Learning-Based Analysis.

BACKGROUND: The global prevalence of type 2 diabetes mellitus (T2DM) poses significant challenges due to its association with increased cardiovascular...

Expression of Concern: Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices.

Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection and segmentation.

Early diagnosis of brain tumors is important for successful treatment and better patient consequences in industrial information systems. This research...

A transformer and 3D CNN-based feature fusion network with interpretable ability for Raman spectra analysis: improving the diagnosis of thyroid cancer.

Accurate differentiation of benign and malignant thyroid lesions continues to pose a significant clinical challenge. Raman spectroscopy offers label-f...

Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.

AIMS/HYPOTHESIS: Data-driven subtyping of type 2 diabetes has not been translated into clinical practice due to the lack of routine fasting glucose an...

Machine learning-based prediction of persistent diabetic macular edema using OCT-derived structural biomarkers and clinical features.

BACKGROUND: Persistent diabetic macular edema (DME) remains a leading cause of vision loss in diabetic retinopathy, even with anti-VEGF therapy. About...

Immunotherapy in Triple-Negative Breast Cancer: From Molecular Mechanisms to Precision Medicine-Overcoming Resistance and Optimizing Clinical Outcomes.

Triple-negative breast cancer (TNBC) is the most violent type of breast cancer, in which estrogen receptors (ER), progesterone receptors (PR), and hum...

Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort.

BACKGROUND: Hepatitis E virus (HEV) infection remains a major cause of liver failure with high short-term mortality, yet predictive models incorporati...

Identification of a Potential Biomarker PDGFRA for Endometriosis Based on Comprehensive Analysis of Multiple Omics Data.

Endometriosis (EMs) is a common chronic inflammatory gynecological disorder. But the exact pathogenetic mechanism of the disease is not clear, with so...

Interpretable machine learning for predicting in-hospital bleeding after PCI in patients with diabetes and acute coronary syndrome: a retrospective cohort study.

Multimodal ultrasound and artificial intelligence for characterization of thyroid nodules in Hashimoto's thyroiditis: current challenges and future perspectives.

Patients with Hashimoto's thyroiditis (HT) frequently present with concurrent nodular lesions such as nodular goiter and thyroid cancer (especially pa...

Machine Learning Prediction of Recurrence in Pediatric Thyroid Cancer: Malignant Endocrine Tumors Cohort Analysis Using XGBoost and SHAP.

CONTEXT: Pediatric differentiated thyroid carcinoma (DTC) often presents with advanced disease but generally has excellent long-term survival. However...

Skin deep: dermatologic challenges in PCOS through the female lifespan.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a lifelong endocrine-metabolic condition with prominent dermatologic manifestations such as hirsutis...

Multi-level evidence reveals KCTD15 and FAM19A2 as key targets in PFOA/PFOS-mediated PCOS pathogenesis.

BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine disorder, has been linked to environmental factors such as exposure to per- and polyf...

Smart Kiosk for Nutritional Management of People With Diabetes in Underserved Communities: Development and Technical Evaluation.

BACKGROUND: Diabetes is a chronic disease with a high global prevalence, increasing from 200 million people in 1990 to 830 million in 2022, with a hig...