Diabetes

Beyond the Cuff: Deep Learning-Based Analysis of Handheld Doppler Signals to Detect Masked Peripheral Ischemia in Diabetic Patients with Normal Ankle-Brachial Index.

OBJECTIVE: The Ankle-Brachial Index (ABI) remains the primary screening tool for Peripheral Arterial Disease (PAD). However, its diagnostic performance is severely compromised in patients with diabetes mellitus due to medial arterial calcification (MAC), leading to falsely normal or elevated results. We aimed to evaluate the efficacy of a smartphone-based Deep Learning (DL) model analyzing handhel...

Elucidating the therapeutic targets and multi-target mechanisms of salvianolic acid A for diabetic nephropathy.

Diabetic nephropathy (DN) is a leading cause of chronic kidney disease. Salvianolic acid A (SAA) has shown promising therapeutic potential against DN, yet its underlying mechanisms and precise molecular targets remain incompletely elucidated. Potential targets of SAA were predicted using SwissTargetPrediction and SuperPred, with its drug-like properties evaluated by ADMET analysis. Diabetic nephro...

Metabolomic signatures of brain aging: A multimodal and genetic study.

Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its meta...

Frailty index and type 2 diabetes with renal complications: insights from Mendelian randomization and retrospective observational study.

OBJECTIVES: To investigate the relationship between frailty and diabetic kidney disease (DKD)-related renal complications using Mendelian randomizatio...

Outer nuclear layer thinning as an in vivo biomarker for discriminating probable FTLD-tau from probable FTLD-TDP with PET-supported subtyping.

BACKGROUND AND OBJECTIVES: Outer nuclear layer (ONL) thinning has been identified in frontotemporal lobar degeneration (FTLD); however, its utility fo...

Precision Nutrition and Chronic Disease: Integrating Genomics, Microbiome, and Digital Health for Personalized Dietary Interventions.

Chronic non-communicable diseases (NCDs), including cardiovascular disease, type 2 diabetes, obesity, and metabolic syndrome, account for more than 70...

Quantifying the Economic Value of AI-Assisted Diabetic Retinopathy Screening: A Meta-Analysis Using the Incremental Net Benefit Framework.

OBJECTIVE: To quantitatively synthesize the economic value of artificial intelligence (AI)-assisted screening for diabetic retinopathy (DR), a leading...

Privacy-aware diabetic retinopathy grading and visual lesion-focused interpretability through mixture-of-experts federated deep learning with explainable AI.

Diabetic Retinopathy (DR) is still a major cause of vision loss that can be avoided. This means that we need automated screening systems that can work...

An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.

Diabetic foot is a severe chronic complication of diabetes, mainly resulting from peripheral neuropathy and vasculopathy, and may progress to ulcers, ...

Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion.

Branch retinal vein occlusion (BRVO) can cause persistent visual impairment, and predicting long-term best-corrected visual acuity (BCVA) after anti-v...

Cardiometabolic multimorbidity and atrial fibrillation: insights from traditional statistical and artificial intelligence approaches.

BACKGROUND: Cardiometabolic multimorbidity (CMM) is increasingly prevalent among patients with atrial fibrillation (AF), yet its independent impact on...

Archetypal analysis quantifies changes in patterns of visual field loss in eyes with acute non-arteritic anterior ischaemic optic neuropathy.

AIMS: We characterised visual field (VF) spatial loss patterns in acute non-arteritic anterior ischaemic optic neuropathy (NAION) using archetypal ana...

Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.

OBJECTIVES: Evidence regarding the effects of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors on carotid plaque regression in patient...

Integrating artificial intelligence and machine learning into risk prediction for type 2 diabetes: A model for early identification of high-risk populations.

Family history, body mass index (BMI), and ethnicity are three key, well-established determinants of susceptibility to type 2 diabetes mellitus (T2DM)...

Burden trajectories of stroke attributable to lipid-glucose dysfunction and biomarker validation: evidence from the global burden of disease (GBD) 2023 and the China health and retirement longitudinal study (CHARLS).

BACKGROUND: Metabolic dysfunction is an important contributor to stroke burden and may also help identify individuals at higher future risk of stroke....

An interpretable machine learning model for predicting postoperative hypotension in type 2 diabetes mellitus undergoing non‑cardiac surgery.

BACKGROUND: Postoperative hypotension (POH) is a common and serious complication in patients with type 2 diabetes mellitus (T2DM) undergoing non‑cardi...

A smart nutrition system for vitamin B12 deficiency management from sensing to precision intervention.

Vitamin B12 is essential for neural function, red blood cell formation, and DNA synthesis, yet its deficiency persists as a global health burden drivi...

Interpretable machine learning and incremental prognostic model for all-cause and cardiovascular mortality in obesity with MASLD population: Comorbidity insights from triglyceride-glucose and its obesity-related derivatives.

OBJECTIVE: To investigate comorbid correlation and prognostic value of triglyceride-glucose (TyG) and its obesity-related derivatives for all-cause an...

Pancreatic organoids and organoid-on-a-chip platforms: from disease modeling to precision therapy.

BACKGROUND: Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinica...

Incorporating dietary information to enhance polygenic prediction models with applications to body mass index and type 2 diabetes.

BACKGROUND: Polygenic predictors can enhance screening for metabolism-related traits such as body mass index (BMI) and type 2 diabetes (T2D). However,...