Pediatrics

Deep and High-Throughput Proteomic Atlas of Chinese Bovine Colostrum Reveals Regional Signatures.

Bovine colostrum (BC) is essential for neonatal survival and human health, owing to its rich bioactive content. However, highly abundant proteins constrain the proteome depth of BC. By integrating magnetic nanoparticle enrichment of low-abundance proteins with a DIA-MS workflow, we achieved an unprecedented depth in BC proteome profiling. Across 463 samples from 10 Chinese provinces, our rapid DIA...

Generation of virtual abdominal aortic aneurysm shape evolution using a physics-based G&R model and its applications to aneurysm growth prediction.

The computational growth and remodeling (G&R) of aortic aneurysms has been extensively investigated to better understand vascular disease progression and improve surgical intervention strategies. This study introduces a framework to simulate abdominal aortic aneurysms (AAAs) shape evolution using a physics-based G&R model, addressing the limitation posed by the scarcity of large-scale clinical ima...

Objective assessment of academic performance using virtual reality and machine learning in early adolescents.

Early identification of academic deficits facilitates timely and effective interventions that are essential for individual development and societal pr...

Genomic and evolutionary factors influencing the prediction accuracy of optimal growth temperature in prokaryotes.

Bacteria and archaea have evolved diverse genomic adaptations to thrive across various temperatures. These adaptations include genome sequence optimiz...

Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.

BACKGROUND AND OBJECTIVE: Status epilepticus is a life-threatening neurological emergency. Ketamine combined with levetiracetam is a promising therapy...

Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images.

INTRODUCTION: Accurate segmentation of the lateral ventricles (LV) and choroid plexus (CP) in infant brain MRI is essential for understanding cerebros...

Machine learning-based prediction model for risk factors associated with conversion to open surgery in pediatric laparoscopic radical resection of choledochal cysts.

Identifying researcher characteristics driving growth in Japanese University-originated deep-tech startups: A machine learning approach.

Active involvement of researchers with strong outputs is essential for growth of deep-tech university-originated startups. We used the University-Orig...

Modeling and forecasting neonatal mortality in Ethiopia: a comparative study using statistical, machine learning, and deep learning approaches.

Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.

UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise...

Fuzzy-logic-driven predictions of multi-tissue ingrowth for rapid screening of mechanically instructive tissue-engineering scaffolds.

High retear rates in surgical intervention of interfacial/transitional tissues (bone-tendon, bone-ligament) drive a need to design tissue engineering ...

Generative artificial intelligence in medical scientific writing: Practical and ethical considerations.

Generative artificial intelligence (AI) is transforming scientific communication in health, offering opportunities to improve the quality and efficien...

Growth curve models for weight among children up to 2 years: a scoping review.

OBJECTIVE: The aim of this scoping review was to systematically identify and map the literature on statistical and mathematical approaches used to est...

Using Neural Networks to Complement Oximetry in Infant Obstructive Sleep Apnea: Working on Baby Steps.

Transcriptional signatures and topological reorganization of morphometric similarity networks in temporal lobe epilepsy with unilateral hippocampal sclerosis.

OBJECTIVE: To delineate morphometric similarity network (MSN) topological abnormalities and their underlying spatial transcriptomics in the normal-app...

Transforming Health Care AI Education Through Micro-learning: A Novel Partnership Model for Nursing Workforce Development.

Health care professionals face an urgent need for AI literacy as artificial intelligence technologies rapidly transform clinical practice, yet nursing...

Deep learning improves image quality in motion-robust and sedation-free pediatric brain MRI.

OBJECTIVES: Motion and limited compliance compromise diagnostic MR image quality, particularly in pediatric patients who frequently require sedation. ...

Individualized treatment strategies and long-term prognosis of congenital hydrocephalus: an integrated analysis based on multicenter retrospective data and machine learning.

INTRODUCTION: Hydrocephalus is a common pediatric neurological disorder characterized by abnormal head enlargement, intellectual disability, visual im...

Comparing Risk Profiles of Nicotine Pouch and Smokeless Tobacco Use in U.S. Adolescents: A Machine Learning Analysis.

Background: This study identified and compared the risk profiles associated with nicotine pouch (NP) and smokeless tobacco (ST) use among adolescents ...

Exploring the Ethical and Practical Considerations of Artificial Intelligence in Real-World Health Care Settings: Stakeholder Focus Group Study.

BACKGROUND: Artificial intelligence (AI) technologies continue to transform how we research human disease, diagnose and treat patients, and operate ho...