Endocrinology

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as a critical microvascular complication associated with high mortality rates. Despite the development of diverse therapeutic strategies targeting metabolic improvement, hemodynamic regulation, and fibrosis mitigation, the pre...

A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.

BACKGROUND: Individuals with diabetic kidney disease (DKD) often suffer cardiac and kidney events. We sought to develop an accurate means by which to stratify risk in DKD.

Effects of neonicotinoid pesticide exposure in the first trimester on gestational diabetes mellitus based on interpretable machine learning.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and seriously threatens the health of mothers and of...

A stretchable, adhesive, and wearable hydrogel-based patches based on a bilayer PVA composite for online monitoring of sweat by artificial intelligence-assisted smartphones.

Real-time monitoring of sweat using wearable devices faces challenges such as limited adhesion, mechanical flexibility, and accurate detection. In thi...

[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].

Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision a...

Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2.

The absence of effective public databases greatly limits high-throughput prediction of hormonal effects mediated by nuclear receptors in aquatic organ...

Optoelectronic-Coupled-Driven Microrobot for Biological Cargo Transport in Conductive Isosmotic Glucose Solution.

Electric field-driven micro/nanorobots, as micro/nanodevices with autonomous motion capability, have emerged as promising candidates for targeted carg...

Multimodal Fusion of Glucose Monitoring and Food Imagery for Caloric Content Prediction

Effective dietary monitoring is critical for managing Type 2 diabetes, yet accurately estimating caloric intake remains a major challenge. While con...

Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data

Modeling the continuous--time dynamics of probability distributions from time--dependent data samples is a fundamental problem in many fields, inclu...

Integrating Natural Language Processing and Exercise Monitoring for Early Diagnosis of Metabolic Syndrome: A Deep Learning Approach

Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It incr...

Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models

Advancements in artificial intelligence (AI) are transforming pathology by integrat-ing large language models (LLMs) with retrieval-augmented genera...

Predicting dementia in people with Parkinson's disease.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...

Application of Hyperspectral Imaging and Machine Learning for Differential Diagnosis of Hashimoto's Thyroiditis and Papillary Thyroid Carcinoma.

BACKGROUND: Hashimoto's thyroiditis (HT) and papillary thyroid carcinoma (PTC) often share similar features, leading to frequent misdiagnoses. Hypersp...

Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis.

Breast cancer (BC) is among the most prevalent malignancies and remains the leading cause of cancer-related mortality in women worldwide. While prior ...

Improving AI models for rare thyroid cancer subtype by text guided diffusion models.

Artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommo...

Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction.

BACKGROUND: Differentiated thyroid cancer (DTC) is a common endocrine malignancy with rising incidence and frequent recurrence, despite a generally fa...

A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction

Accurate blood glucose prediction can enable novel interventions for type 1 diabetes treatment, including personalized insulin and dietary adjustmen...

Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)

The effective diagnosis of acute and hard-to-heal wounds is crucial for wound care practitioners to provide effective patient care. Poor clinical ou...

4TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients

Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various interna...

Next-generation Approaches in Targeting Polycystic Ovarian Syndrome: Innovative Strategies.

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder that affects millions of women worldwide and is characterized by ovarian dysfunction,...