Endocrinology

Leveraging AI to explore structural contexts of post-translational modifications in drug binding.

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can si...

Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.

OBJECTIVE: Pituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke's cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. This study aims to develop and evaluate machine learning models using MRI-based radiomics features to differentiate these lesions.

Multimodal GPT model for assisting thyroid nodule diagnosis and management.

Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI mod...

Development of an activity-based ratiometric electrochemical substrate for measuring circulating dipeptidyl peptidase-IV/CD26 in whole blood samples.

Dipeptidyl peptidase-IV (DPP-IV) is a circulating blood biomarker that diagnose pancreatic and thyroid cancers, as well as type 2 diabetes. Although c...

Comprehensive analysis of Syzygium cumini L. pomace extract as an α-amylase inhibitor: In vitro inhibition, kinetics, and computational studies.

Type 2 diabetes mellitus (T2DM) is a widespread metabolic disorder characterized by impaired regulation of blood glucose levels. Jamun (Syzygium cumin...

Agreement Between AI and Nephrologists in Addressing Common Patient Questions About Diabetic Nephropathy: Cross-Sectional Study.

This research letter presents a cross-sectional analysis comparing the agreement between artificial intelligence models and nephrologists in respondin...

Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.

Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive underst...

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images.

BACKGROUND: The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This ...

A cascade nanoreactor based on metal azolate framework integrated natural enzyme for α-glucosidase activity assay and inhibitor screening.

Enzyme cascades have attracted widespread attention owing to the exceptional specificity and efficient signals transduction, however, constrained by t...

Advancement in early diagnosis of polycystic ovary syndrome: biomarker-driven innovative diagnostic sensor.

Polycystic ovary syndrome (PCOS) is a heterogeneous multifactorial endocrine disorder that affects one in five women around the globe. The pathology s...

GBE1 alleviates MPTP-induced PD symptoms in mice by enhancing glycolysis and oxidative phosphorylation.

In Parkinson's disease (PD), the disturbance of energy metabolism due to glucose metabolic reprogramming may be a critical factor contributing to neur...

Insulin resistance in type 1 diabetes is a key modulator of platelet hyperreactivity.

AIMS/HYPOTHESIS: Individuals with type 1 diabetes are at increased cardiovascular risk, particularly in the presence of insulin resistance. A prothrom...

From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.

Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effect...

A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images.

Recent advancements in deep learning have significantly impacted medical image processing domain, enabling sophisticated and accurate diagnostic tools...

M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.

Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds pro...

ConsisTNet: a spatio-temporal approach for consistent anatomical localization in endoscopic pituitary surgery.

PURPOSE: Automated localization of critical anatomical structures in endoscopic pituitary surgery is crucial for enhancing patient safety and surgical...

Brain tumor detection empowered with ensemble deep learning approaches from MRI scan images.

Brain tumor detection is essential for early diagnosis and successful treatment, both of which can significantly enhance patient outcomes. To evaluate...

Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes.

BACKGROUND AND AIMS: Low muscle mass (LMM) is a critical complication in patients with obesity and diabetes, exacerbating metabolic and cardiovascular...

Retinal Ischemic Perivascular Lesions (RIPLs) as Potential Biomarkers for Systemic Vascular Diseases: A Narrative Review of the Literature.

Retinal ischemic perivascular lesions (RIPLs) are characteristic focal thinning of the inner nuclear layer, with an upward expansion of the outer nucl...

Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics Dataset.

The use of machine learning to integrate and analyse multimodal information has broad prospects for enhancing the precision of tumour diagnosis. Our s...