Diabetes

Hexagonal Hollow-Core Photonic Crystal Fiber for High-Sensitivity Terahertz Glucose Detection.

Diabetes mellitus is a rapidly escalating global health concern, often leading to severe complications such as neuropathy, vision impairment, vascular dysfunction, and renal failure. In this work, we present a newly designed hexagonal photonic crystal fiber (HPhCF) sensor for the noninvasive detection of urine glucose, offering a compact, fabrication-friendly design. The proposed HPhCF demonstrate...

Diagnostic accuracy of image-based deep learning for glaucomatous optic neuropathy detection: a systematic review and meta-analysis.

BACKGROUND: Several studies have explored the use of deep learning (DL) algorithms based on fundus photography (FP) and optical coherence tomography (OCT) for the detection of glaucomatous optic neuropathy (GON). However, high-quality evidence regarding their diagnostic accuracy remains insufficient. Therefore, this study systematically evaluated the diagnostic performance of DL algorithms for GON...

Development and validation of a nomogram for predicting hemoptysis risk in patients with bronchiectasis: A study based on Random Forest algorithm.

Hemoptysis is a severe and potentially life-threatening complication of bronchiectasis. There is currently a lack of reliable tools for the individual...

Mitochondria-associated protein UQCRH serves as a potential target in mild cognitive impairment of type 1 diabetes mellitus mice.

Type 1 diabetes mellitus (T1DM) and cognitive impairment (CI) are diseases impacting human health. Mild cognitive impairment (MCI), a critical stage o...

Joint Multi-Task Deep Learning with Cross-Task Attention for Simultaneous Lesion Segmentation, Detection, and Severity Grading of Diabetic Retinopathy on Fundus Images: An External Validation Study.

BACKGROUND: Diabetic retinopathy (DR) severity is graded from the type and distribution of retinal lesions, yet most automated methods address grading...

Predictors of atrial fibrillation recurrence after catheter ablation: a systematic review.

BACKGROUND: Recurrence of atrial fibrillation (AF) following catheter ablation is a major clinical problem, and there is considerable variability in p...

Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis.

OBJECTIVE: Cost analysis of autonomous artificial intelligence (AI)-based screening of diabetic retinopathy (DR) for adults with diabetes at a primary...

Predicting medication non-adherence using machine learning: Incorporating Complementary and Alternative Medicine (CAM) beliefs in Malaysian chronic disease patients.

Medication non-adherence among chronic disease patients remains a major contributor to poor health outcomes and medication wastage, particularly in mu...

Development of a machine learning-based noninvasive diagnostic model for liver fibrosis in metabolic-associated steatotic liver disease.

BACKGROUND: To address the lack of simple tools for assessing fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD), this study...

AI-derived oocyte morphology and follicular fluid biomarkers in donors.

Oocyte quality is a key determinant of reproductive success, yet its assessment in assisted reproduction largely relies on subjective morphological cr...

Content-based retrieval of fundus images and diabetic retinopathy detection using variants of local texture features.

Content-based retinal image analysis plays a crucial role in the early diagnosis of ocular diseases. In this study, we proposed a novel approach for e...

Neural networks improve repeatability of intravoxel incoherent motion (IVIM) parameter estimation in pancreatic diffusion-weighted MRI.

Intravoxel incoherent motion (IVIM) analysis in diffusion-weighted MRI (DWI-MRI) shows potential for characterizing pancreatic tissue, but its clinica...

Identification of key biomarkers for ferroptosis in diabetic kidney disease using machine learning and WGCNA.

OBJECTIVE: Early detection of diabetic kidney disease (DKD) remains challenging because currently available clinical markers mainly reflect establishe...

The hard exudate pathway: Biological mechanisms, imaging correlates, and clinical implications.

Hard exudates are common fundus findings in retinal vascular disease, but are frequently regarded as passive remnants of vascular leakage rather than ...

Organelle proteomics of human, neonatal pig, healthy and diabetic mouse islets.

Pancreatic islets, in which β cells constitute 50%-80% of the endocrine mass, are the body's site for glucose-regulated insulin secretion. Pancreatic ...

The role of machine learning in early detection, accurate diagnosis, and timely treatment of diseases.

The contemporary healthcare landscape is experiencing a significant transformation driven by the rapid growth of digital health data and advancements ...

Dichotomous associations of circulating short-chain organic acids with cognitive impairment in older adults: An epidemiological and network analysis study.

BackgroundShort-chain organic acids (SCOAs) may have paradoxical effects on neurocognition, acting as either neuroprotective bioenergetic substrates o...

Data-driven endocrine-metabolic phenotypes in young women with polycystic ovary syndrome and associations with cardiometabolic risk markers.

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder associated with reproductive dysfunction and increased cardiometabolic risk. Co...

INSIGHT: Ingest and Standardize Glucose Harmonization Tool.

BACKGROUND: Publicly available continuous glucose monitoring (CGM) datasets are fragmented across schemas, limiting reuse, pooling, and machine learni...

An integrated deep learning and Bayesian computational method-based quantification of angiogenesis in the chick embryo model.

Angiogenesis is the process of the formation of new blood vessels from existing vessels, and it sits directly at the intersection of normal physiology...