Endocrinology

On the road to fully automated insulin delivery: A systematic review of meal announcement free algorithms.

Fully automated insulin delivery (FAID) systems aim to regulate blood glucose levels in individuals with type 1 diabetes with minimal user input. A key challenge to achieving full automation is the need for manual meal announcements. This review focuses on algorithms developed to detect and compensate for unannounced meals. A systematic literature search was conducted across PubMed, Scopus, IEEE X...

Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.

BACKGROUND: Patients with type 2 diabetes mellitus (T2DM) prone to acute diabetic complications are at high risk for emergency department (ED) visits, which often precede hospitalization and mortality. Identifying these high-risk phenotypes before deterioration is critical for preventative care. We developed machine learning (ML) models using large-scale, real-world electronic medical records, inc...

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome.

BACKGROUND: Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). ...

SERSome-based metabolic profiling of aqueous humor for cataract subtyping with molecule-level interpretability.

Cataract is among the leading cause of vision impairment worldwide and it is essential to uncover molecule-level characteristics of different cataract...

Preoperative prediction of occult high-volume central lymph node metastasis in clinically node-negative papillary thyroid carcinoma using a multimodal practical model: development, temporal validation, and external validation.

PURPOSE: Preoperative risk estimation of occult high-volume central lymph node metastasis (CLNM) in clinically node-negative (cN0) papillary thyroid c...

Engineering EVs Via ZnBGs-Integrated 3D Dynamic Culture for Type II Diabetic Pressure Ulcer Therapy.

Pressure ulcers (PUs) under type II diabetic conditions pose a significant clinical challenge due to damage to the vascular and neural networks, a sit...

Spiky Magnetic Titania Particles for Integrated Exosome Capture and Metabolic Profiling Toward Cancer Diagnosis.

Exosomal metabolite profiling represents a promising non-invasive approach for cancer diagnosis. However, its widespread application has been constrai...

Plasticizer-related toxicogenomic signatures in gestational diabetes: Mendelian randomization, placental single-cell transcriptomics and trophoblast validation.

Plasticizer exposure has been associated with gestational diabetes mellitus (GDM), but the placental toxicogenomic mechanisms that may connect environ...

Evaluating the role of youth-onset diabetes mellitus in the context of food insecurity and preventive care: a repeated cross-sectional study using propensity score weighted machine learning analysis.

BACKGROUND: Youth-onset diabetes mellitus (DM) is an increasing public health concern, especially among adolescents facing socioeconomic challenges. F...

A Comparative Cross-sectional Study Assessing the Readability of ChatGPT and UpToDate Content on Multiple Endocrine Neoplasia Syndromes.

BACKGROUND: With growing reliance on digital health tools, the readability of online medical content is increasingly vital in patient-centered care. A...

Can Machine Learning Transform the Diagnostic Approach to Congenital Disorders of Adrenal Steroidogenesis?

Using machine learning to discriminate non-classical 21-hydroxylase deficiency from polycystic ovary syndrome: an external validation study.

INTRODUCTION: Nonclassical 21-hydroxylase deficiency (NC21OHD) is a rare autosomal recessive disorder that is frequently misdiagnosed as polycystic ov...

Heterogeneity, longitudinal decline, and metabolic risk in MRI-based quantification of 20 individual hip and thigh muscles.

Quantifying muscle health at scale has been limited by the difficulty of segmenting individual muscles on MRI. We developed an automated 3D deep-learn...

A comparative and interpretable machine learning framework for reliable diabetes risk prediction.

Diabetes mellitus is a common type of metabolic illness that is very common worldwide, and in most cases, it results in serious effects like heart dis...

Construction and validation of a machine learning model for predicting early pregnancy in patients with polycystic ovary syndrome: a retrospective cohort study.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is a leading cause of female infertility, and early pregnancy outcomes in affected women are affected by m...

Seamless human electronics interfacing through advanced skin attachable and implantable sensor technologies.

The transition from episodic clinical assessment to continuous physiological monitoring represents a transformative shift in biomedical engineering. T...

Current Landscape and Future Perspectives of Diabetic Retinopathy Therapy: Pharmacological Targets, Precision Laser Technology, and Clinical Evidence.

Diabetic retinopathy (DR), a major cause of blindness worldwide, poses a substantial and escalating burden on public health. The limitations of curren...

Hearing Loss in Older Adults: Consistent Determinants Across Two Community-Based Cohorts in Southern China.

BACKGROUND: Hearing loss (HL) is common in older adults and is associated with substantial functional decline, yet community-based evidence on its det...

A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.

OBJECTIVE: Based on multicenter clinical data, this study aimed to develop and validate a predictive model for chronic low back pain (CLBP) after lumb...

Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer and Their Caregivers.

OBJECTIVE: This study evaluated dyadic endocrine interdependence among cancer patients and their spousal caregivers using a machine learning (ML) fram...