Diabetes

Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.

Accurate segmentation of the pancreas from abdominal computed tomography (CT) images is challenging but essential for the diagnosis and treatment of pancreatic disorders such as tumours and diabetes. In this study, a dataset with 229 sets of high-resolution CT images was generated and annotated. We proposed a novel 3D segmentation model named nnTransfer (nonisomorphic transfer learning) net, which...

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities.

OBJECTIVE: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consis...

A combined analysis of TyG index, SII index, and SIRI index: positive association with CHD risk and coronary atherosclerosis severity in patients with NAFLD.

BACKGROUND: Insulin resistance(IR) and inflammation have been regarded as common potential mechanisms in coronary heart disease (CHD) and non-alcoholi...

Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence.

INTRODUCTION: Diabetic nephropathy (DN), distinguished by detrimental changes in the renal glomeruli, is regarded as the leading cause of death from e...

Development and deployment of a nationwide predictive model for chronic kidney disease progression in diabetic patients.

AIM: Chronic kidney disease (CKD) is a major complication of diabetes and a significant disease burden on the healthcare system. The aim of this work ...

Pain-free oral delivery of biologic drugs using intestinal peristalsis-actuated microneedle robots.

Biologic drugs hold immense promise for medical treatments, but their oral delivery remains a daunting challenge due to the harsh digestive environmen...

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Dev...

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different indiv...

Association between neutrophil-to-lymphocyte ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study.

AIMS: This investigation examined the possibility of a relationship between neutrophil-to-lymphocyte ratio (NLR) and diabetic kidney disease (DKD) in ...

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data.

OBJECTIVE: To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting ...

Intravitreal injection of methotrexate in persistent diabetic macular edema: a 6-month study.

BACKGROUND: Diabetic macular edema (DME) affects approximately 10% of patients with diabetes mellitus. This condition can cause blurred or distorted v...

Effects of exogenous energy on synthesis of steroid hormones and expression characteristics of the CREB/StAR signaling pathway in theca cells of laying hen.

Energy and the cAMP-response element binding protein (CREB)/steroidogenic acute regulatory protein (StAR) signaling pathway play important roles in st...

Artificial intelligence model for early detection of diabetes.

Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life ...

Contrast-Induced Nephropathy in Interventional Cardiology: Incidence, Risk Factors, and Identification of High-Risk Patients.

AIM: This study aimed to study contrast-induced nephropathy (CIN) or more recent nomenclature contrast-associated acute kidney injury (CI-AKI) in pati...

A Robust Machine Learning Model for Diabetic Retinopathy Classification.

Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usuall...

The Effect of Acute Hyperglycaemia Induced by Oral Glucose Load on Heart Rate Variability and Skin Microvascular Reactivity in Young Adults.

We aimed to elucidate the effects of acute hyperglycaemia, induced by an oral glucose tolerance test (OGTT), on the autonomic nervous system (ANS) and...

The Utility of ChatGPT in Diabetic Retinopathy Risk Assessment: A Comparative Study with Clinical Diagnosis.

PURPOSE: To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk.

-glucosidase and -amylase inhibitory activity of flavonols from (Burm.f.) Bedd.

(Burm.f.) Bedd., a fern species native to India, Southeast Asia, Polynesia, and Australia, has a long history of medical including as a diabetic ther...

The Management of Diabetes with Hyperuricemia: Can We Hit Two Birds with One Stone?

Serum urate (SU) is an independent predictor for the incidence of diabetes. In current diabetes treatment regimens, there is insufficient appreciation...

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification.

Background Elevated serum uric acid, associated with cardiovascular conditions such as atherosclerotic heart disease, hypertension, and heart failure,...