Pediatrics

Charting human organogenesis across the Carnegie stages from a whole-embryo perspective.

The Carnegie stages represent a critical window in human development, during which organ primordia emerge, tissue identities diversify, and many congenital disorders are thought to originate. However, this period has remained difficult to study at the whole-embryo scale. Recent advances in spatial and single-cell genomics are beginning to close this gap. This article discusses the significance of ...

Nurse-Led Design and Prototype Development of a Smart Care Robot to Support Mental Well-Being in Nursing and Health Care Students: A Proof-of-Concept.

BACKGROUND: Smart care robots are rarely developed for younger adults, particularly nursing and health care students, and seldom integrate nursing expertise into design. PURPOSE: This study aimed to describe the codesign and proof-of-concept development of a nurse-led smart care robot to support students' emotional well-being and mental health, and to examine its relevance as an educational innova...

Predicting the antifungal effects of ethanolic propolis and geopropolis extracts on Aspergillus flavus growth using mechanistic and machine-learning models.

This study evaluated the antifungal potential of ethanolic propolis (EEP) and geopropolis (EEG) extracts against Aspergillus flavus, integrating mecha...

Development and Validation of an Integrated Artificial Intelligence-Powered Clinical Decision Support Application for Cardiac Critical Care: Combining Coagulation Management, Blood Gas Analysis, Mechanical Ventilation, Hemodynamic Assessment, and Extracorporeal Membrane Oxygenation.

This study describes the development and preliminary validation of an integrated clinical decision support application (Cardiac Critical Care Advisor ...

Targeted screening for heart failure in primary care: evidence, challenges and a pathway-first approach to implementation.

INTRODUCTION: Heart failure remains a leading cause of morbidity and mortality, yet diagnosis continues to occur late in routine practice, frequently ...

The Next Wave of Workplace Bullying in Health Care.

BACKGROUND AND INTRODUCTION: Workplace bullying and incivility have continued to escalate across health care and pose serious risks to workforce well-...

An AI-Enabled Predictive Harm Response Management Algorithmic Tool to Reduce Adverse Events in Health Care Settings (PreHaRM): Protocol for a Three-Phase Model Development and Pilot-Testing Study.

BACKGROUND: Current data analysis and coordination methods do not effectively support nurses and midwives in risk reduction, as retrospective reportin...

Usability and Usefulness of Machine Learning-Based Clinical Decision Support Software in Primary Care: Survey of Users in a Prospective Observational Study.

BACKGROUND: The successful implementation of decision support systems promises to enhance high-quality care. However, the successful implementation of...

Artificial intelligence analysis of temporalis muscle thickness for monitoring sarcopenia and clinical outcomes in individuals with paediatric brain tumours: a retrospective cohort study.

BACKGROUND: People with and who have survived paediatric brain tumour (PBT) have a poor quality of life due to physiological frailty, a primary compon...

[Technology-driven management of early-onset scoliosis:new technologies and concepts].

Early-onset scoliosis (EOS) refers to spinal deformity occurring in children younger than 10 years of age. The main goals of EOS treatment are not onl...

Machine learning-assisted meta-analysis reveals melatonin crosstalk regulating plant growth plasticity under heavy metal stress.

Heavy metal (HM) pollution is a traditional and ongoing global ecological issue that requires continuous attention. Although studies have focused on h...

A federated deep learning framework with distributed hybrid character-level and attention mechanisms for scalable and cost-efficient fake news detection.

Fake news detection is an essential task for media and news organizations to maintain the trust and reliability of the published content. Due to the r...

Reliability and readability of AI platforms for pediatric health advice: a comparative analysis.

Large language models (LLMs) are increasingly used as a first-line source of information for everyday questions, including pediatric health guidance, ...

Designing an Artificial Intelligence Transcription Tool to Support Multimodal Serious Illness Communication: Institutional Experience and Best Practices.

BACKGROUND: Communication during serious illness is often complex, emotionally charged, and cognitively demanding, particularly for patients and famil...

Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation.

BACKGROUND: YouTube is increasingly used for healthcasting, the sharing of evidence-based dietary and lifestyle interventions by domain experts. In th...

Artificial intelligence models for predicting ASA physical status classification: a systematic review and meta-analysis.

BACKGROUND: The American Society of Anesthesiologists (ASA) physical status classification remains the cornerstone of preoperative risk assessment but...

Building the adult growth hormone deficiency data mart: a Real-World model of AI-driven clinical data extraction in a single Italian center.

PURPOSE: Adult Growth Hormone Deficiency (AGHD) is a complex and under-recognized condition, mainly managed in outpatient settings and characterized b...

Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study.

BACKGROUND: Acute infectious symptoms are a leading cause of pediatric emergency department visits in Canada, many of which are low acuity and could b...

Addendum to "Predicting treatment pathways in Class II malocclusion patients using machine learning: A comparative study of four algorithms for classifying camouflage, growth modulation, and surgical decisions" [Int Orthod. 24 (2026) 101070].

OBJECTIVES: The aim of this study was to develop a machine learning model to assist in treatment decision-making for surgery, camouflage, and growth m...

Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality predicti...