Critical Care

Integrative transcriptomic and bioinformatic analyses predict candidate EMT-related genes in sepsis-associated acute lung injury.

Sepsis-associated acute lung injury (sepsis-ALI) is a complex pathological condition; its underlying mechanisms remain mostly obscure. Thus, in this study, we aimed to explore potential candidate molecular markers, infer the regulatory signaling pathways, and describe the immunological profiles of sepsis-ALI. We developed a comprehensive bioinformatics analytical workflow by combining human transc...

Ophiopogon japonicus alleviates pulmonary fibrosis through modulation of HIF1A/TUBB3 axis-associated M2 macrophage polarization and arginine metabolic alterations.

ETHNOPHARMACOLOGICAL RELEVANCE: Ophiopogon japonicus (Thunb.) Ker Gawl. is a traditional Chinese medicinal herb commonly used to "nourish yin and moisten the lung", and has long been applied for the prevention and treatment of lung-related disorders in classical medical texts such as the Treatise on Typhoid Fever and Item Differentiation of Warm Febrile Diseases. Pulmonary fibrosis (PF) is a chron...

A multidimensional risk prediction framework for malignant intestinal obstruction based on machine learning: computational model development and clinical validation.

Malignant intestinal obstruction (MIO) is a severe complication of advanced cancer. Traditional static assessment models struggle to capture its dynam...

Large language models for optimizing clinical trial recruitment in ICUs: application to ventilator-induced diaphragm dysfunction.

BACKGROUND: Ventilator-induced diaphragm dysfunction (VIDD) is a frequent and under-recognized consequence of prolonged mechanical ventilation in inte...

Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.

Patient-specific computational tools hold great promise for the development of more personalized treatment strategies for acute respiratory failure. S...

Comprehensive molecular analyses of a circadian rhythm-related gene diagnostic model and immune infiltration using machine learning methods in sepsis-associated acute kidney injury.

BACKGROUND: This study aimed to explore hub circadian rhythm-related genes (CRRGs) associated with sepsis-associated acute kidney injury (saAKI) using...

Smart feeding: the role of artificial intelligence and integrated nutrition platforms in the ICU.

PURPOSE OF REVIEW: Tremendous improvement in the use of artificial intelligence has opened new opportunities to analyze the data obtained from electro...

Functional Imaging in Hereditary Endocrine Neoplasms: Evolving Modalities and Clinical Implications.

Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and h...

A Spatiotemporal Transformer Framework on 4D-CT for Early Prediction of Progressive Pulmonary Fibrosis.

Progressive pulmonary fibrosis (PPF) remains difficult to predict because static imaging may not fully capture regional respiratory motion, ventilatio...

A Novel Model to Predict Progression to Death After Withdrawal of Care in Potential Donation-After-Circulatory-Death Heart Donors.

BACKGROUND: Predicting successful heart donation after circulatory death (DCD) remains a challenge. We developed a model to predict progression to cir...

High-throughput analysis of multimodal monitoring data: the role of machine learning in early warning systems for high-risk neonates.

The neonatal intensive care unit (NICU) generates vast amounts of high-throughput, multimodal monitoring data, offering unprecedented potential for id...

Feature selection methodology based on explainable AI: application in predicting extubation success in the intensive care unit.

High-dimensional medical data hinder predictive modeling because of noise and the curse of dimensionality, making robust feature selection (FS) essent...

FIRST-ICU: forecasting interventions and risk stratification in the ICU using graph neural network autoencoders.

Critically ill patients frequently require multiple concurrent interventions with complex interdependencies, yet existing prediction models treat thes...

Predicting Failure of Active Surveillance in Desmoid-Type Fibromatosis Using Radiomics: An International Multi-center Cohort Study.

BACKGROUND: Active surveillance (AS) is the first-line approach for desmoid-type fibromatosis (DTF). However, 30 % of patients require active treatmen...

Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.

Hemodialysis demand is rising as populations age and the chronic kidney disease burden increases, yet dialysis units face persistent workforce constra...

Targeting emerging respiratory pathogens with Clamp Peptides: broad-spectrum inhibition of viral entry in 3D human lung models.

Rapid urbanization, migration and climate change are accelerating the appearance and diversification of respiratory viruses, overwhelming the pace at ...

BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool.

SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interac...

Machine learning-based prediction of early invasive mechanical ventilation in ICU patients with pneumonia: Development and external validation.

BACKGROUND: Pneumonia is a common critical illness in the intensive care unit (ICU), and a subset of patients rapidly progresses to respiratory failur...

Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.

The application of machine learning (ML) models in healthcare management offers high potential. In particular, resource allocation and operational dec...

Development and validation of a machine learning-based model for predicting delirium risk in postoperative brain tumor patients in the Intensive Care Unit.

OBJECTIVE: This study aimed to clarify the incidence and influencing factors of delirium in ICU patients after brain tumor surgery, construct and vali...