Diabetes

Interfacial impedance engineering in ionic hydrogels for integrated enzymatic and electrophysiological sensing.

Interfacial charge-transfer resistance remains a critical bottleneck in wearable enzymatic biosensors, particularly in soft, deformable systems, where impedance instability limits signal amplification. Here, we introduce an impedance-engineered dual-network ionic hydrogel designed to deliberately modulate ionic conductivity and charge-transfer resistance (Rct) at the bioelectronic interface. Incor...

Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.

OBJECTIVE: To explore clinically meaningful phenotypes of preeclampsia using unsupervised machine learning. DESIGN: Prospective cohort study. SETTING: BCNatal, a tertiary maternal-foetal medicine centre (Barcelona, Spain). POPULATION: A total of 482 pregnant women diagnosed with preeclampsia between August 2013 and April 2024. METHODS: Maternal demographic, clinical, ultrasound and laboratory data...

Machine learning-driven identification of circulating biomarkers and therapeutic compounds for diabetic retinopathy.

BACKGROUND: Diabetic retinopathy (DR) is a major cause of severe visual impairment, where early diagnosis and intervention are crucial to prevent irre...

Precise design of clean-label starch: Mechanisms underlying the regulation of multiscale structure induced by non-thermal physical modification.

Amid the global shift toward clean-label and sustainable food production, this review highlights green, non-thermal physical modification (NTPM) techn...

EXPRESS: Artificial Intelligence in Diabetes Care: Toward Precision Diagnosis and Personalized Management.

AimsDiabetes mellitus is a global health challenge requiring innovative solutions for early diagnosis, personalized treatment, and ongoing management....

Non-traditional metabolic indices predict incident circadian syndrome in middle-aged and older Chinese adults: a nationwide prospective cohort study and machine learning analysis.

BACKGROUND: Circadian syndrome (CircS) augments the conventional metabolic syndrome construct by adding disturbed sleep and depressive features. Wheth...

Tetrahedral framework nucleic acids as a promising vehicle for small molecule drugs of traditional Chinese medicine: application to disease prevention and treatment.

The modernization of Traditional Chinese Medicine (TCM) faces considerable challenges, primarily due to the poor water solubility, low chemical stabil...

Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.

Retinopathy of prematurity (ROP) remains a leading cause of preventable childhood blindness globally, particularly in regions with limited screening r...

Underperformance of Machine Learning Algorithms Predicting Extended Lengths of Stay and Readmission in Underrepresented Patient Cohorts After Primary Total Hip Arthroplasty.

BACKGROUND: The demand for total hip arthroplasty (THA) is increasing, yet disparities in access and outcomes persist across racial, ethnic, and socio...

Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers.

Diabetic foot ulcers are serious skin wounds that affect many people with diabetes, often leading to severe infections or even the loss of a limb. Thi...

Integrative bioinformatic analysis prioritizes TIMP1 and FN1 as angiogenesis-related candidate genes in diabetic foot ulcers.

BACKGROUND: Diabetic foot ulcers (DFUs) represent a severe chronic complication of diabetes and are characterized by persistent impairment of wound he...

Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis.

BACKGROUND: Hospital readmission following stroke poses a significant challenge for healthcare systems. Machine learning (ML) offers the potential to ...

Glycolytic reprogramming in host response to Borrelia burgdorferi: A gene signature revealed by integrative bioinformatics analysis and machine learning.

Lyme disease (LD), a multifaceted condition caused by Borrelia burgdorferi (Bb), remains poorly understood, particularly regarding metabolic pathways....

Thrombospondin-2 is a performant biomarker of at-risk MASH and advanced MASH fibrosis in a large multicentre European cohort.

BACKGROUND: Non-invasive biomarkers with biological rationale are needed for identifying patients with progressive metabolic dysfunction-associated st...

Identifying gut microbiome signatures of type 1 diabetes using machine learning and evolutionary feature selection.

BACKGROUND: Type 1 Diabetes Mellitus (T1D) has been increasingly associated with alterations in the gut microbiome. However, the impact of taxonomic r...

Experiences and Perceived Influence of the Artificial Intelligence-Based Health Education Accurately Linking System (AI-HEALS) on Health Behaviors Among Patients With Type 2 Diabetes: Qualitative Study.

BACKGROUND: The management of type 2 diabetes requires sustained self-management across diet, physical activity, medication adherence, and blood gluco...

Clinically interpretable nomogram incorporating radiomics and deep learning feature fusion from abdominal CT for preclinical type 2 diabetes.

OBJECTIVE: To develop and externally validate a multimodal artificial intelligence framework for opportunistic detection of preclinical type 2 diabete...

Public Expectations for Food and Drug Administration Approval of AI-Based Clinical Decision Support Tools: Quantitative Study.

BACKGROUND: Regulation of artificial intelligence (AI) has been slow relative to the pace of its integration into health care. Several AI diagnostic t...

Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review.

BACKGROUND: Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden on health care systems. Prevention and early detec...

Peripheral retinal haemorrhage density on ultra-widefield imaging as a novel biomarker for predicting diabetic retinopathy progression: a 2-year longitudinal study in Asians.

AIMS: To evaluate whether predominantly peripheral lesions (PPLs) and other imaging biomarkers on ultra-widefield (UWF) photography can predict the pr...