Critical Care

CLIC1 and IFITM2 expression in brain tissue correlates with cognitive impairment via immune dysregulation in sepsis and Alzheimer's disease.

BACKGROUND: Sepsis, a life-threatening condition driven by dysregulated host responses to infection, is associated with long-term cognitive impairments resembling Alzheimer's disease (AD). However, the molecular mechanisms linking sepsis-induced cognitive dysfunction and AD remain unclear. We hypothesized that shared genetic pathways underlie cognitive deficits in both conditions.

Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning.

Sepsis is a highly variable condition, and tracking leukocyte patterns may offer insights for tailored treatment and prognosis. We used the MIMIC-IV database to analyze patients diagnosed with Sepsis-3 within 24 h of ICU admission. Latent class mixed models (LCMM) were applied to leukocyte trajectories to identify sepsis subtypes. The primary outcome was 28-day all-cause mortality, with secondary ...

Tidal Volume Monitoring via Surface Motions of the Upper Body-A Pilot Study of an Artificial Intelligence Approach.

The measurement of tidal volumes via respiratory-induced surface movements of the upper body has been an objective in medical diagnostics for decades,...

Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and segmentation techniques.

Conventional manual or feature-based ECG analysis methods are limited by time inefficiencies and human error. This study explores the potential of tra...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential gene...

Revolutionizing Intensive Care Unit Care: A Scoping Review of Multimodal Family Engagement Technologies.

This scoping review systematically examines the emerging field of multimodal family engagement technologies in intensive care units (ICUs). Despite si...

Is artificial intelligence superior to traditional regression methods in predicting prognosis of adult traumatic brain injury?

Traumatic brain injury (TBI) is a significant global health issue with high morbidity and mortality rates. Recent studies have shown that machine lear...

DruGagent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction.

Advancements in large language models (LLMs) allow them to address diverse questions using human-like interfaces. Still, limitations in their training...

External validation and application of risk prediction model for ventilator-associated pneumonia in ICU patients with mechanical ventilation: A prospective cohort study.

BACKGROUND: Early identification and prevention of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) through reliable...

Monitoring effort and respiratory drive in patients with acute respiratory failure.

PURPOSE OF REVIEW: Accurate monitoring of respiratory drive and inspiratory effort is crucial for optimizing ventilatory support during acute respirat...

Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification.

In many challenging breast cancer pathology images, the proportion of truly informative tumor regions is extremely limited. The disparity between the ...

Artificial Intelligence for the Detection of Patient-Ventilator Asynchrony.

Patient-ventilator asynchrony (PVA) is a challenge to invasive mechanical ventilation characterized by misalignment of ventilatory support and patient...

5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respirat...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making...

Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection.

BACKGROUND: Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill p...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in...

Addressing model discrepancy in a clinical model of the oxygen dissociation curve.

Many mathematical models suffer from model discrepancy, posing a significant challenge to their use in clinical decision-making. In this article, we c...

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.

BACKGROUND: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Devel...

Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques.

Ventilator-associated pneumonia significantly increases morbidity, mortality, and healthcare costs among patients with traumatic brain injury. Accurat...

M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis.

Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide rang...