Endocrinology

Preliminary machine-learning model with clinical, US, and CEUS features for the diagnosis of thyroid follicular-patterned lesions.

BACKGROUND: Though follicular thyroid carcinoma can be confirmed postoperatively by the histological findings of capsular or vascular invasion, preoperative diagnosis of follicular-patterned lesions has long been a diagnostic challenge. This study seeks to establish a machine-learning (ML) model based on clinical, US, and CEUS features for the differential diagnosis of thyroid follicular-patterned...

Nanostructured High-Entropy Yolk-Shell Oxides Platform for Efficient Metabolic Profiling of Diabetic Retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged and elderly populations worldwide, and its diagnosis remains challenging due to the reliance on subjective interpretation of retinal images by experienced ophthalmologists. Here, we report a nanoenabled metabolomics strategy based on laser desorption/ionization mass spectrometry (LDI MS) for accurate DR discrimination. High-e...

A clinically interpretable deep learning pipeline for diabetic retinopathy classification using EfficientNet, advanced data augmentation and GradCAM.

BACKGROUND: Early and reliable grading of diabetic retinopathy is important for preventing avoidable vision loss. Although deep learning methods have ...

Biological and Social Risk Factors for Predicting Type 2 Diabetes in Youth with Prediabetes: Review of Existing Prediction Models.

With the rising prevalence of type 2 diabetes (T2D) among children and adolescents, the ability to predict the progression of prediabetes to T2D in yo...

Composite small vessel disease scores predict hemorrhagic transformation after thrombectomy: a machine learning study.

BACKGROUND: Hemorrhagic transformation (HT) is a major complication of acute ischemic stroke (AIS), especially after mechanical thrombectomy (MT) and ...

Randomised Controlled Multicentre Trial to Investigate the Effectiveness of a Mobile Artificial Intelligence Solution for Diabetes Adapted Care: The MELISSA Trial Protocol.

AIMS: Achieving optimal glycaemic control remains a burden for many people with diabetes on intensive insulin treatment. The MELISSA trial aims to cli...

MANet: a multimodal attention convolutional neural network for brain tumor classification.

Classification of brain tumors is a difficult problem in medical imaging analysis. Over the past few years, various deep learning-based techniques hav...

Predicting incident type 2 diabetes using wearable activity and polygenic risk: A survival-modeling study in All of Us.

BACKGROUND: Evidence is limited on whether daily step counts are uniform across individuals or whether combining them with genetic risk improves predi...

Predicting metabolic syndrome in Iranian adults using artificial neural networks: a comparative analysis with logistic regression based on demographic, lifestyle, and clinical indicators.

OBJECTIVE: Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases and type 2 diabetes, imposing a substantial economic and publi...

Network pharmacology and bioinformatics study of Shuangjiang decoction and labetalol hydrochloride in the treatment of preeclampsia in pregnancy.

Preeclampsia (PE) remains a leading cause of maternal and perinatal mortality worldwide, and the combined use of Shuangjiang Decoction with Labetalol ...

Evaluating the Accuracy, Quality, and Reproducibility of AI Chatbot-Generated Diabetes Self-Management Education and Support Plans Aligned With ADCES7.

BACKGROUND: The rapid development of artificial intelligence, particularly large language models (LLMs) such as ChatGPT, Gemini, and Claude, offers ne...

Supervised machine learning identifies impaired mitochondrial quality control in β-cells with development of type 2 diabetes.

Defining molecular pathways driving β-cell failure in type 2 diabetes (T2D) is challenging given donor heterogeneity. We developed an interpretable ma...

Metabolomic profiling of exhaled breath condensate identifies a novel biomarker panel for detecting thyroid and breast cancer.

BACKGROUND: The non-invasive detection of cancer via exhaled breath condensate (EBC) represents a promising frontier in oncology. This study explores ...

Few-shot learning-driven discovery of Lutein suppresses Th1-mediated inflammation via glucose metabolism.

The discovery of selective immunosuppressants for T cell-mediated diseases like Ulcerative Colitis (UC) is a significant challenge. While traditional ...

Network toxicology and bioinformatics analysis predict potential molecular targets and mechanisms by which sevoflurane and propofol influence type 2 diabetes mellitus.

BACKGROUND: Sevoflurane and propofol are commonly used anesthetics that may exert pronounced effects on glucose metabolism and cardiovascular function...

Development and validation of a logistic regression model for predicting visual impairment in middle-aged and older adults with diabetes: results from the China Health and Retirement Longitudinal Study.

AIM: To develop and validate a clinician-friendly logistic regression prediction model for self-reported visual impairment (VI) in middle-aged and old...

Comparative prognostic value of TyG index and TyG-BMI for 90-day all-cause mortality in critically ill older adults with ASCVD: a machine learning analysis of the MIMIC-IV database.

BACKGROUND: The triglyceride-glucose (TyG) index and triglyceride-glucose-body mass index (TyG-BMI) are emerging surrogate markers of insulin resistan...

Predicting cardiovascular death in overweight/obese people with prediabetes using machine learning-a proof-of-concept study.

BACKGROUND: Individuals with prediabetes face an increased risk of cardiovascular (CV) complications, which can ultimately lead to premature mortality...

RDE-DR: robust deep ensemble CNNs for automated diabetic retinopathy detection from fundus images.

Diabetic retinopathy (DR) is a leading cause of preventable blindness, motivating the development of reliable automated screening systems. This work p...

Microaneurysm segmentation under out-of-domain generalization: from diabetic retinopathy to leukemic retinopathy.

PURPOSE: To propose inter-disease out-of-domain generalization (OODG) across retinal diseases for microaneurysm (MA) segmentation using a deep-learnin...