Intensivists

Lactate-Associated Gene Signatures as Predictors: A Comprehensive Analysis of Immune Profiles in Sepsis.

BackgroundSepsis is a complex disorder characterized by a dysregulated immune response to infection. Elevated lactic acid levels and lactylation modification may induce changes in gene expression and immune cell infiltration in sepsis.MethodsRNA-seq data and clinical information related to sepsis were obtained from GEO datasets. Differential expression analysis identified genes that are differenti...

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...

Multifunctional Bioengineered Scaffolds and Adjunct Therapeutic Strategies for Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) are chronic, non-healing wounds that affect up to 34% of diabetic patients. DFUs are complicated by infection in nearly 60...

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...

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...

Machine-Learning-Enhanced Printed Vertical Magnetoresistive Sensors for Transparent, Flexible, Multimodal Interactive Magnetoelectronics.

To meet the increasingly stringent demands of next-generation electronic systems, magnetoresistive sensors are required to simultaneously deliver envi...

Integrating artificial intelligence into paediatric interventional radiology: a review of emerging applications and future directions.

Artificial intelligence (AI) is heralded to revolutionise healthcare by improving efficiency, personalising care, and enhancing clinical outcomes. Alt...

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...

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...

Prospective and external evaluation of an AI model for continuous and early prediction of moderate and severe AKI in critically ill patients.

BACKGROUND: Acute kidney injury (AKI) is a major complication in critically ill patients, burdening both patients and healthcare systems. We previousl...

Association between the PaO2/FiO2-to-PaCO2 ratio and mortality in patients with sepsis: a retrospective study with dual-cohort validation.

BACKGROUND: Sepsis patients face a high mortality risk. Available prognostic biomarkers have certain limitations. This study explored the prognostic u...

Prediction of ICU length of stay, hospital discharge outcomes and discharge location among ICU-admitted patients diagnosed with viral hepatitis using machine learning: a retrospective cohort study of the MIMIC-IV database.

BACKGROUND: Hepatitis, a disease characterised by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million...

Deep learning approach for probabilistic pulmonary function estimation from chest X-ray and peak expiratory flow rate.

BACKGROUND: Spirometry remains the gold standard for assessing pulmonary function. Deep learning models have demonstrated potential for estimating mea...

Artificial intelligence in pediatric intensive care units: current applications in sepsis management.

BACKGROUND: Early detection of sepsis in pediatric intensive care units (PICUs) is critical, but challenging due to its nonspecific clinical presentat...