Pediatrics

Evolutionary Assembly and Future Design of Gibberellin Signaling.

Gibberellin (GA) signaling is widely regarded as a canonical hormone-controlled growth pathway, yet accumulating evidence suggests that it emerged through stepwise evolutionary assembly rather than as a pre-formed regulatory system. Here, we revisit the origin and diversification of the GA pathway from an evolutionary perspective. DELLA-mediated growth repression arose during early land plant evol...

Most digital psychological interventions for chronic pain fail to measure all 4 dimensions of the quadruple aim of value-based health care: a scoping review.

Psychological treatments are increasingly being developed and delivered using platforms such as mobile applications, online modules, virtual reality, and artificial intelligence chatbots. This scoping review aimed to examine how digital psychological interventions deliver outcomes that are valuable not only for patients but also for clinicians and the broader health system. Peer-reviewed studies e...

Trends and disparities in female authorship in surgical publications: A large-scale bibliometric study using natural language processing.

BACKGROUND: Gender disparities in academic surgery persist, particularly in authorship positions that signal leadership, mentorship, and scholarly inf...

Proteomic profiling and pathway analyses reveal molecular signatures and immune networks in pediatric sepsis.

BACKGROUND: Sepsis remains a leading cause of childhood mortality worldwide. Most deaths occur within the first few days of presentation, underscoring...

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes.

BACKGROUND: Accurate and rapid diagnosis is essential for controlling pulmonary tuberculosis (PTB) by enabling timely intervention and reducing diseas...

Artificial intelligence and radiomics for pediatric brain tumor classification and molecular characterization: a systematic review.

BACKGROUND: Artificial intelligence (AI) and radiomics are increasingly applied in pediatric neuroradiology to enhance diagnostic precision. However, ...

Enhancing Korean adolescent suicide risk prediction with TabR: A generative AI and explainable retrieval-based deep learning approach.

Adolescent suicide is a growing public health crisis, particularly in South Korea, which has one of the highest youth suicide rates among Organization...

Kenyan Neonatal Mortality Risk Predictor: Protocol for a User-Centered Design Evaluation.

BACKGROUND: Neonatal mortality remains a major public health challenge in low- and middle-income countries (LMICs), particularly in sub-Saharan Africa...

Artificial Intelligence-Assisted Pediatric Trauma Triage: Promising Innovation or Premature Implementation?

AVPENet: Pain estimation from audio-visual fusion of non-speech sounds.

Pain assessment in non-verbal patients, including neonates and unconscious adults, remains a critical challenge in clinical practice. Current pain sca...

Baseline symptom severity and response to multisession cathodal transcranial direct current stimulation in adolescents and young adults with autism: an exploratory analysis.

BACKGROUND: Identifying baseline clinical characteristics associated with treatment response is crucial for optimising intervention strategies in peop...

At the Edge of Chaos: Complexity Science and Critical Care in the Era of Artificial Intelligence.

Critical care unfolds at the crossroads of highly complex, nonlinear systems. The intersection of pathophysiology, human behavior, and clinical operat...

Letter to the Editor: Deep learning-derived features on neonatal abdominal radiographs-implications and next steps for early NEC diagnosis.

K2CO3-Fe2O3 catalyzes sludge ceramsite formation: ML-elucidated nucleation-growth kinetics.

Urban sewage sludge generation continues to rise annually, making its resource recovery into lightweight ceramsite a prominent research focus. This st...

Artificial intelligence in pediatric nursing and its education: A systematic review.

BACKGROUND: Artificial intelligence (AI) transforms healthcare delivery by improving patient outcomes and operational efficiencies. In pediatric nursi...

Internal and external validation of a machine learning algorithm to detect preeclampsia-related adverse outcomes in high-risk pregnancies.

OBJECTIVES: This study aimed to refine an existing machine learning (ML) algorithm for predicting preeclampsia-related adverse outcomes and to assess ...

3D ADHD-Net and DeepTrace: Decoding ADHD from EEG with neurophysiological insights.

EEG-based ADHD diagnosis models suffer from two persistent issues: data leakage and the lack of physiologically grounded interpretability, limiting cl...

Automating Adolescent Idiopathic Scoliosis Classification: An Interpretable, Workflow-Based Approach to Lenke Classification: Commentary on an article by Lingcong Xu, MD, et al.: "A Fully Automated Multistage Deep Learning System for Lenke Classification. Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis".

Using AI scribes in New Zealand primary care consultations: an exploratory survey.

INTRODUCTION: AI scribes have had a rapid uptake in primary care across New Zealand (NZ). The benefits of this new technology must be weighed against ...

Enhancing autonomous agriculture control systems in greenhouses for sustainable resource usage using deep learning techniques.

Greenhouse climate control is essential for optimizing crop growth while minimizing resource consumption in controlled environment agriculture. Tradit...