Endocrinology

1H NMR-Based Plasma Metabolomics and Machine Learning Reveal Distinct Metabolic Signatures of Breast Cancer in Nigerian Women.

Untargeted 1H NMR metabolomics offers a noninvasive means to identify biomarkers in breast cancer (BC) patients; however, metabolic signatures specific to Nigerian women remain poorly understood. This study aimed to identify plasma metabolomic and lipidomic biomarkers associated with BC in Nigerian patients, evaluate their diagnostic performance using machine learning (ML), and identify dysregulat...

Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes.

Emerging evidence implicates cerebellar degeneration-related protein 1 antisense transcript (CDR1as) and alpha-synuclein (α-Syn) in diabetes and cognitive decline, suggesting their potential to serve as biomarkers. This study aimed to evaluate their diagnostic efficacy for type 2 diabetes mellitus-associated mild cognitive impairment (T2DM-MCI) and establish a corresponding diagnostic model using ...

MetaboNet: The Largest Publicly Available Consolidated Data Set for Type 1 Diabetes Management.

BACKGROUND: Progress in type 1 diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D ma...

Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition.

BACKGROUND: Continuous glucose monitoring (CGM) provides real-time glucose data, aiding diabetes management. Identifying glucose patterns is difficult...

Evaluation of large language models for carbohydrate counting in type 1 diabetes mellitus: a comparative study with patient estimations.

AIMS: To evaluate large language models (LLMs) accuracy in carbohydrate (CHO) counting (CC) and compare their performance with estimations by patients...

Lactate-albumin ratio predicts in-hospital mortality in critically Ill patients with congestive heart failure and diabetes.

OBJECTIVE: To evaluate the predictive utility of the initial lactate-to-albumin ratio (LAR) measured within 24 h of admission for in-hospital all-caus...

Prediction of trajectories and outcomes in early-stage metabolic dysfunction-associated steatotic liver disease: a narrative review.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disorder, with manifestations ranging from steato...

Deep learning-based multimodal radiopathomics for preoperative prediction of lymph node metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) exhibits a high incidence and a strong propensity for lymph node metastasis (LNM). Accurate preoperative assessment ...

Deep learning for predicting pituitary neuroendocrine tumour lineage and high-risk subtypes from histology.

Pituitary neuroendocrine tumours (PitNETs) exhibit significant heterogeneity, posing challenges for clinical management. We developed a deep learning ...

Prognostic value of stress hyperglycemia ratio, hemoglobin glycation index, and glycemic variability for postoperative atrial fibrillation: a machine learning-based prediction model.

Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.

Hyperglycemia is a major risk factor for chronic kidney disease (CKD). This multicenter prospective study developed and validated a machine learning (...

Leveraging routine clinical data for dementia risk prediction using machine learning.

BackgroundEarly diagnosis of dementia is essential for enabling timely interventions that may slow disease progression, improve patient outcomes, and ...

Machine Learning Analysis of Serum Metabolomics for Multi-Disease Classification and Proteome-Metabolome Pathway Insights in T2D, IBD, and CRC.

Type 2 diabetes (T2D), inflammatory bowel disease (IBD), and colorectal cancer (CRC) share overlapping metabolic alterations that hinder early, diseas...

Machine learning analysis of continuous glucose monitoring identifies a novel dysglycemic phenotype found in most people with cystic fibrosis.

BACKGROUND: Cystic fibrosis related diabetes (CFRD) is a common complication in people with cystic fibrosis (PwCF), yet traditional diagnostic tools s...

Updates on tuberculosis imaging.

Tuberculosis (TB) remains a leading infectious cause of morbidity and mortality worldwide, and major diagnostic and therapeutic challenges persist des...

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical and angiographic characteristics. We hypothesized ...

Marker-Free, Automated Eyelid Assessment in Thyroid Eye Disease Using Artificial Intelligence: A Multicenter Validation Study.

OBJECTIVE: To validate "Glandy LID," a novel marker-free, deep learning-based software designed for the automated assessment of eyelid morphology usin...

Screening of metabolic-related biomarkers linking intervertebral disc degeneration and type 2 diabetes based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: Intervertebral disc degeneration (IVDD) is a prominent etiology of lower back pain. Type 2 diabetes (T2D), the most prevalent metabolic di...

Detection of young-onset type 2 diabetes using deep learning across primary and secondary care: a nationwide, retrospective cohort study.

BACKGROUND: Once considered a disease observed in older adults, type 2 diabetes is now increasingly seen in youth and adolescence. Young-onset (<40 ye...

Integrative Proteomic and Machine Learning Analysis Identifies Novel Predictors and Risk Model for Diabetic Macrovascular Complications.

OBJECTIVE: Diabetic macrovascular complications continue to drive substantial morbidity, yet early detection tools and deeper mechanistic understandin...