Intensivists

Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.

Navigating the vast chemical space remains a major challenge in the rational design of materials with tailored properties. Here, we investigate how the properties of the [6]helicene family can be effectively modelled using a local, data-driven AI framework. By predicting each molecule from its closest structural neighbours, we accurately estimate diverse photophysical and (chir)optical properties....

Functional-based multi-omics early prediction of radiation pneumonitis in NSCLC using AI-generated perfusion and ventilation from planning CT.

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and dosiomic features from functionally defined lung regions, using generated perfusion (Q) and ventilation (V) from pre-radiotherapy planning computed tomography (CT). ApproachWe retrospectively analyzed data from 121 patients with locally advanced...

Systematic review of Artificial Intelligence-based methods for glycemic control and risk prediction in intensive care units.

BACKGROUND: Achieving safe glycemic targets in intensive care remains difficult due to rapidly changing physiology, treatment effects, and measurement...

Infrared thermography in the detection of arthritis: a systematic review of diagnostic performance compared with joint ultrasound.

Early detection of arthritis in autoimmune rheumatic diseases (ARDs) is critical to prevent irreversible damage. Joint ultrasound (US) offers high sen...

Downregulated lysyl oxidase in plasma extracellular vesicles: a biomarker linked to brain metastasis risk in lung adenocarcinoma.

BACKGROUND: Brain metastasis (BrM) is a leading cause of mortality in patients with lung adenocarcinoma (LUAD). Extracellular vesicles (EVs), which ca...

[Annual progress in critical care of pulmonary medicine 2025].

This review systematically summarizes the annual research advances in the field of critical care of pulmonary, with a focus on pulmonary and critical ...

Modern machine learning techniques used in prediction of sepsis and bloodstream infection in burn patients: A systematic review.

INTRODUCTION: Burn patients are a group highly prone to sepsis and bloodstream infections (BSIs) due to immune dysregulation, skin barrier loss, and c...

Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.

OBJECTIVE: Machine learning models (ML) often require localization to perform optimally in local populations. We hypothesize that smaller community he...

A machine learning-based model to predict multi-time-point prognosis for acute-on-chronic hepatitis B liver failure.

Our prognostic model and mobile application enable multi-time-point prognostic evaluation for patients with acute-on-chronic hepatitis B liver failure...

Identifying and timing patient outcomes in clinician notes using large language models.

BACKGROUND: Key challenges in leveraging unstructured clinician notes for predictive models include identifying and timing patient outcomes. To addres...

Resveratrol ameliorates sepsis-induced immune dysfunction by targeting GADD45A to maintain hematopoietic stem cell homeostasis: A predictive study integrating ensemble machine learning, single-cell transcriptomics, and molecular dynamics simulation.

OBJECTIVE: To investigate the potential pharmacological mechanisms of resveratrol (RES) in ameliorating sepsis-associated immune dysfunction via the G...

An automated decision making framework for modern vehicles CO2 emissions using multi modal engine telemetry and feature interpretability.

Accurate prediction of vehicle CO₂ emissions is challenging due to heterogeneous engine characteristics, nonlinear interactions among fuel, mechanical...

Machine learning prediction model for delirium after heart valve replacement with cardiopulmonary bypass: A large-scale cohort study.

OBJECTIVE: Postoperative delirium (POD) is a common and severe complication following heart valve replacement (HVR) with cardiopulmonary bypass (CPB),...

The relationship between urinary tract infections and sepsis: results from Mendelian randomization and MIMIC data study.

Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.

Integrative multi-omics and machine learning identify mitochondrial biomarkers for pathogen-specific sepsis stratification and translational prioritization.

BACKGROUND: Sepsis is a leading cause of critical illness and mortality, yet substantial heterogeneity limits risk stratification and biomarker transl...

Explore Thermal and Mechanical Properties of Biobased Polyurethane Elastomers Through Machine Learning Models.

Mechanical and thermal properties are the core to determine the application scenarios of biobased polyurethane elastomers (BPUEs). Here, six core prop...

Field Foresight-Predicting the Need for Massive Transfusion, ICU Utilization, and Mechanical Ventilation for Traumatically Injured Patients.

INTRODUCTION: Expeditiously predicting outcomes is essential to allocating blood and intensive care resources. We hypothesize the use of external inju...

Ultrasonic-shear exfoliated multi-layer graphene flakes for enhanced triboelectric nanogenerator performance toward wearable self-powered applications.

Triboelectric nanogenerators (TENGs) can effectively harvest mechanical energy from the environment, offering a promising solution for a sustainable p...

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances in pharmacotherapy. Emerging evidence reveals a p...