Endocrinology

Effects of Dietary Gamma-Aminobutyric Acid (GABA) Inclusion on Acute Temperature Stress Responses in Juvenile Olive Flounder ().

This study investigated the potential of dietary gamma-aminobutyric acid (GABA) inclusion to mitigate acute temperature stress impacting the physiological resilience of juvenile olive flounder (). A total of 360 juvenile fish, with an average initial weight of 12.97 ± 0.1 g (mean ± SEM), were randomly assigned in triplicate to 18 tanks (20 fish per tank) and reared at 19.5 °C for 8 weeks, with bi-...

A Novel Mouse Model of Type 2 Diabetes Using a Medium-Fat Diet, Fructose, and Streptozotocin to Study the Complications of Human Disease.

The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal models, the most common of which involves the consumption of high-fat diets comprising 60% calories from fat. Although these models reproduce the onset and most complications associated with T2DM, they do not accurately mimic human dietary patterns, as they lack the addition of carbohydrates such as fru...

Early prediction of cardiovascular events following treatments in female breast cancer patients: Application of real-world data and artificial intelligence.

• Application of real-world data and artificial intelligence in detecting cardiotoxicity following cancer treatment. • Clinical features have been use...

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex complication, often characterized by potentially seve...

Equitable machine learning counteracts ancestral bias in precision medicine.

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease. ...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potent...

Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer.

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and ...

Transcriptome analysis reveals the potential role of neural factor EN1 for long-terms survival in estrogen receptor-independent breast cancer.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth fa...

Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.

BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having v...

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to deve...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal wa...

Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for effective treatment planning and prognosis evaluati...

Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease.

Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their ...

D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.

Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (...

A deep learning approach to understanding controlled ovarian stimulation and in vitro fertilization dynamics.

Infertility, recognized by the World Health Organization (WHO) as a disease affecting the male or female reproductive system, presents a global challe...

Circulating CCN6/WISP3 in type 2 diabetes mellitus patients and its correlation with insulin resistance and inflammation: statistical and machine learning analyses.

INTRODUCTION: Cellular Communication Network Factor 6 (CCN6) is an adipokine whose production undergoes significant alterations in metabolic disorders...

Comparison of the accuracy of GPT-4 and resident physicians in differentiating benign and malignant thyroid nodules.

OBJECTIVE: To assess the diagnostic performance of the GPT-4 model in comparison to resident physicians in distinguishing between benign and malignant...

Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.

Effect heterogeneity analyses using causal machine learning algorithms have gained popularity in recent years. However, the interpretation of estimate...

Performance of a Deep Learning Diabetic Retinopathy Algorithm in India.

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and di...

Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) models are being increasingly employed to predict the risk of developing and progressing diabetic kidney disease (DK...