Endocrinology

Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.

Nasal polyps (NP) are benign mucosal outgrowths associated with chronic inflammation that can signif...

Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for preci...

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk.

BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflamm...

Intervention through an intelligent technological platform for socio-emotional development and health promotion in adolescents with Type 1 Diabetes Mellitus (emoTICare): A study protocol for randomized controlled trial.

BACKGROUND: During adolescence, individuals face various challenges that require adequate psychosoci...

A multidimensional prediction model for overtraining risk in youth soccer players: Integrating physiological and psychological markers.

Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods ...

Hub biomarkers and their clinical relevance in glycometabolic disorders: A comprehensive bioinformatics and machine learning approach.

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and...

Understanding stress-induced illegitimate aggression: the role of physiological and psychological factors in police cadets.

To better understand the consequences of stress in realistic scenarios, police cadets were tasked wi...

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considera...

Association of DNA methylation epigenetic markers with all-cause mortality and cardiovascular disease-related mortality in diabetic population: a machine learning-based retrospective cohort study.

BACKGROUND: Diabetes has a large and diverse population, with individuals exhibiting significant het...

Machine learning-based stratification of prediabetes and type 2 diabetes progression.

BACKGROUND: Diabetes mellitus, a global health concern with severe complications, demands early dete...

A Novel Three-Stage AI-Assisted Approach for Accurate Differential Diagnosis and Classification of NIFTP and Thyroid Neoplasms.

The recent introduction of the term non-invasive follicular thyroid neoplasm with papillary-like nuc...

Deep Learning-Based Adrenal Gland Volumetry for the Prediction of Diabetes.

BACKGROUND: The long-term association between adrenal gland volume (AGV) and type 2 diabetes (T2D) r...

Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance matrices.

Thyroid hormones control crucial physiological activities, such as metabolism, oxidative stress, ery...

Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy.

BACKGROUND: Thyroid diseases are the second most common hormonal disorders, necessitating accurate d...

The Future of Automated Insulin Delivery Systems.

Automated insulin delivery (AID) systems have revolutionized diabetes care by integrating continuous...

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electroni...

When the Slide Knows the Sequence: Artificial Intelligence-Assisted Pathology Predicts Mutations in Thyroid Cancer.

Interpretable deep fuzzy network-aided detection of central lymph node metastasis status in papillary thyroid carcinoma.

PURPOSE: The non-invasive assessment of central lymph node metastasis (CLNM) in patients with papill...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed....

High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for...

Multi-omics analysis unveils dysregulation of the tumor immune microenvironment and development of a machine learning-based multi-gene classifier for predicting lateral lymph node metastasis in papillary thyroid carcinoma.

PURPOSE: Lateral lymph node metastasis (LNM) critically influences surgical decision-making in papil...