Endocrinology

Cross-attention-based multimodal fusion for early diabetic retinopathy detection.

Early detection of diabetic retinopathy (DR) is crucial for preventing irreversible vision loss; however, existing automated methods often rely on single-modality inputs, thereby limiting the complementary diagnostic value of multimodal imaging. This study presents a cross-attention-based multimodal fusion framework that investigates cross-attention-based representation learning using semantically...

Differential Effects of Prenatal Depression and Anxiety on Infant Sleep: Dual-Pathway Mechanisms Involving the HPA Axis and the Gut-Brain Axis.

STUDY OBJECTIVES: Prenatal psychological distress is associated with adverse offspring outcomes, including infant sleep disturbances and altered gut microbiota, yet the mediating roles of neonatal gut microbiome and tryptophan metabolism remain underexplored. METHODS: This prospective birth cohort study enrolled 2288 mother-infant pairs, using questionnaires to assess prenatal anxiety/depression a...

Gradient-Boosting Threshold Identification for Developmental Toxicity: A BPAF Case-Study Framework for Early-Stage Risk Assessment.

Polymers are essential to modern infrastructure, yet their dependence on persistent chemical additives continues to raise unresolved environmental and...

Myokines in exercise‑mediated bone homeostasis: Molecular signaling mechanisms and therapeutic implications for bone disorders (Review).

Skeletal muscle functions as an endocrine organ, secreting myokines that mediate interorgan communication with bone. Exercise‑induced myokines regulat...

An interpretable predictive model for depression risk in diabetic patients: A web-based application using NHANES data.

Depression is a common comorbidity in individuals with diabetes and is associated with adverse clinical outcomes. Early identification of high-risk in...

Machine learning-based early kidney stone risk classification in diabetic-hypertensive patients using obesity composite indices.

Kidney stone disease (KSD) is increasingly prevalent among patients with diabetes mellitus and hypertension. Obesity-related metabolic abnormalities m...

Integrative analysis of machine learning and single-cell sequencing identifies IGHG1 as a therapeutic target for epimedium in diabetes-gastric cancer comorbidity.

BACKGROUND: The clinical comorbidity of diabetes mellitus (DM) and gastric cancer (GC) presents a significant healthcare challenge, as these two condi...

Machine Learning Algorithms to Accelerate Etiological Diagnosis of Congenital Disorders of Adrenal Steroidogenesis.

BACKGROUND: Early and accurate etiological diagnosis of congenital disorders of adrenal steroidogenesis (CDAS) is critical as timely targeted manageme...

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. Howev...

Using machine learning to identify individuals at elevated risk of diabetes from low-cost complete blood count data for opportunistic screening.

Type 2 diabetes and prediabetes remain substantially underrecognized, highlighting the need for practical screening approaches based on routinely avai...

Implementation of artificial intelligence in healthcare in Aotearoa New Zealand: learnings from the diabetic retinal screening use case.

Artificial intelligence (AI) tools in diabetic retinal screening (DRS) are currently in use overseas within public health systems, with growing eviden...

Artificial intelligence-based prediction of diseases among homeless populations in Bogotá: Implications for targeted interventions.

BACKGROUND: The homeless population in Bogotá exhibits complex social and health vulnerabilities, with a high prevalence of chronic and communicable d...

Corneal nerve regeneration: treatments and monitoring approaches.

BACKGROUND: Diagnostics and therapeutics for corneal nerve pathologies are rapidly evolving, with continual advancements in imaging, laser, machine le...

Association of glycolipid metabolism 6 factors index with cardiovascular and metabolic diseases, and its predictive value in disease progression.

BACKGROUND: The study aims to investigate the potential of glycolipid metabolism 6 factors (GLM6) and glycolipid metabolism 7 factors (GLM7) indexes i...

Metabolic and vascular outcomes after transition to the MiniMed 780G system in adults ≥65 years with type 1 diabetes: a randomised, single-centre study.

AIMS/HYPOTHESIS: Hybrid closed-loop insulin delivery systems are increasingly regarded as the preferred therapy for type 1 diabetes, although evidence...

Empowering digital health management with on-device large language models for glucose prediction: a model development and validation study.

BACKGROUND: Long-term management of chronic diseases such as diabetes is increasingly based on wearable technologies, particularly continuous glucose ...

Structure-based virtual screening identifies potential endogenous ligands of the human bitter taste receptor TAS2R46.

The sensing of bitter taste mediated by TAS2Rs serves as a defense mechanism against potentially harmful substances. The expression of TAS2Rs in extra...

MLMarker: a machine learning framework for tissue inference and biomarker discovery.

MLMarker is a machine learning tool that computes continuous tissue similarity scores for proteomics data, addressing the challenge of interpreting co...

Correlation between SHR and 28-day all-cause mortality risk in patients with spontaneous intracerebral hemorrhage.

BACKGROUND: The stress hyperglycemia ratio (SHR) has recently been suggested as a dependable indication for predicting adverse outcomes in critically ...

The systems medicine view of semaglutide: from clinical trials to molecular mechanisms.

INTRODUCTION: Semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), is a key therapy in managing type 2 diabetes (T2D), offering signific...