Endocrinology

Diabetes

Latest AI and machine learning research in diabetes for healthcare professionals.

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Adipsin and Leptin Levels in Type 2 Diabetic Patients on Sitagliptin and Metformin Versus Metformin Therapy.

OBJECTIVES: Adipsin and leptin are adipokines that link adipose tissue dysfunction and increased fat...

Evaluation of ChatGPT-4 Performance in Answering Patients' Questions About the Management of Type 2 Diabetes.

OBJECTIVES: Type 2 diabetes mellitus is a disease with a rising prevalence worldwide. Person-centere...

Urinary TYROBP and HCK as genetic biomarkers for non-invasive diagnosis and therapeutic targeting in IgA nephropathy.

BACKGROUND: IgA nephropathy (IgAN) is a leading cause of renal failure, but its pathogenesis remains...

Inhibition of the polyol pathway by extract: plausible implications for diabetic retinopathy treatment.

INTRODUCTION: Diabetic retinopathy is a significant microvascular disorder and the leading cause of ...

Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose.

BACKGROUND: Accurate fasting plasma glucose (FPG) trend prediction is important for management and t...

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prev...

Patient and practitioner perceptions around use of artificial intelligence within the English NHS diabetic eye screening programme.

AIMS: Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retino...

Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis.

INTRODUCTION: Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging populat...

Actuation-Mediated Compression of a Mechanoresponsive Hydrogel by Soft Robotics to Control Release of Therapeutic Proteins.

Therapeutic proteins, the fastest growing class of pharmaceuticals, are subject to rapid proteolytic...

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in tim...

An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.

BACKGROUND: Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifacete...

Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy.

Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring th...

Investigating the anti-obesity potential of leaf bioactive compounds through machine learning and computational biology methods.

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseas...

L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction.

Pre-training strategies based on self-supervised learning (SSL) have demonstrated success as pretext...

Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.

Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathop...

Improvement of an Edge-IoT Architecture Driven by Artificial Intelligence for Smart-Health Chronic Disease Management.

One of the health challenges in the 21st century is to rethink approaches to non-communicable diseas...

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