Endocrinology

Thermo-Sensitive Polymeric Networks for Next-Generation Wound Management: A Review.

Wound healing is a dynamic and highly coordinated biological process involving hemostasis, inflammation, proliferation, and tissue remodeling. However, chronic wounds such as diabetic ulcers, burn injuries, and infected wounds often fail to progress through the normal healing cascade due to persistent inflammation, bacterial infection, excessive oxidative stress, and impaired angiogenesis. Convent...

Multi-site validation of an AI-based biomarker test for determining ER, PR, and HER2 status from H&E-stained breast cancer slides.

BACKGROUND: The assessment of estrogen/progesterone receptors (ER/PR) and human epidermal growth factor receptor 2 (HER2) is essential for managing breast cancer (BC) patients, as these biomarkers guide targeted therapies. Traditional methods such as immunohistochemistry (IHC) and in situ hybridisation (ISH) are widely used, but they are resource-intensive and time-consuming. To address these chal...

Identification and Validation of Transcriptomic Signatures in Inflammatory Bowel Disease with Metabolic Syndrome via Bioinformatics and Machine Learning.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic, nonspecific inflammatory disorder affecting the gastrointestinal tract. The condition's pat...

Deep Learning-Based Multimodal Fusion of Ultrasound, Cytology, and Clinical Features to Distinguish Follicular Thyroid Carcinoma from Adenoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: Preoperative differentiation between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challen...

Use of Continuous Glucose Monitoring With Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes.

The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily drive...

Differentiation of adrenal metastases and adenomas based on clinical characteristics, deep learning features, and radiomics features derived from ultrasound imaging.

Analysis of B-Scan Ultrasonography Using Neural Networks to Predict Risk of Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).

OBJECTIVE: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, underscoring the need for tools to st...

Accuracy of deep learning for risk prediction and screening of diabetic foot Ulcers: A systematic review and meta-analysis.

BACKGROUND: This research sought to systematically estimate the accuracy of deep learning (DL) in diagnosing diabetic foot ulcers (DFUs), thereby prov...

Unsupervised phenotyping of the periodontal architecture through high-dimensional clustering of electronic health records: A multicenter study.

BACKGROUND: To identify novel periodontal phenotypes using unsupervised machine learning on a large-scale, multicenter cohort, specifically characteri...

Exploring Metabolic Changes in Children with Congenital Hypothyroidism: A Serum Metabolomic Study Combined by Machine Learning.

Congenital hypothyroidism (CH) is a genetic endocrine disorder that can cause developmental delays if it is untreated. In this study, NMR-based metabo...

Diabetes Technology: An Update.

Diabetes is one of the most prevalent chronic diseases worldwide, with rates that continue to increase. Over 30 years ago, the Diabetes Control and Co...

An Explainable Artificial Intelligence Tool (FibroX) for Detecting Advanced Liver Fibrosis in Type 2 Diabetes: Protocol for a Pilot Crossover Trial.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease is highly prevalent in adults with type 2 diabetes, and advanced fibrosis is its ...

Biomarkers discovery of diabetic retinopathy through serum metabolomics - Retinal image association analysis with machine learning.

The aim of this study is to develop an innovative method of machine learning combining metabolomic and radiomic analyses for identifying biomarkers to...

Explainable machine learning integrating metabolic and inflammatory signatures for personalized prognosis in resected intrahepatic cholangiocarcinoma.

BACKGROUND: The precise prognostic stratification of intrahepatic cholangiocarcinoma (iCCA) remains challenging. We aimed to develop and validate inte...

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) pl...

Wooden-Tip Electrospray Ionization Mass Spectrometry Combined With Machine Learning for Differentiating Thyroid Tumours.

Direct mass spectrometry (MS) analysis of human tissues at the molecular level has great potential for clinical diagnosis and biomarker discovery. How...

DPA-Net: A dual-path attention neural network for estimating glycemic metrics from self-monitored blood glucose data.

Continuous glucose monitoring (CGM) provides dense and dynamic glucose profiles that enable reliable estimation of glycemic metrics, such as time-abov...

Action classification for endoscopic pituitary adenoma resection: a consensus-based study.

PURPOSE: Pituitary adenoma resection via the endoscopic transsphenoidal approach is technically demanding, with outcomes influenced by surgical skill....

Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.

BACKGROUND: Few studies have evaluated peripheral artery disease (PAD) and wound healing in patients with lower extremity wounds using a convolutional...

Exploring the mechanisms of diethyl phthalate-induced diabetic nephropathy through integrated network toxicology, machine learning, and experimental validation.

Phthalate plasticizers, especially diethyl phthalate (DEP), are linked to metabolic disorders, while their precise role and mechanism in diabetic neph...