Sepsis

Biomimetic Electrochemical Chip Integrated with Closed-Loop AI for Dynamic Dopamine Decoding and Neuromodulation.

Bioelectronic systems integrated with artificial intelligence (AI) are transforming neurochemical diagnostics, enabling intelligent, real-time decoding of brain chemistry. Here, we present an AI-driven biomimetic electrochemical chip with intestine-inspired wrinkled MoS2 electrodes, enabling dynamic in vivo dopamine monitoring and neuromodulation. This wrinkled structural design promotes efficient...

AI-enhanced therapeutic drug monitoring for vancomycin and β-lactam antibiotics in critical care: from population PK to bedside algorithms.

INTRODUCTION: Although sepsis and other severe infections in intensive care are common and deadly, obtaining safe and effective exposure for vancomycin and broad-spectrum β-lactams is difficult because of substantial kinetic variability, operational difficulties with AUC-guided therapeutic drug monitoring (TDM)//model‑informed precision dosing (MIPD), and the limited effectiveness of current tools...

Incisional hernia prediction using machine learning models.

BACKGROUND: One of the main complications after laparotomy is incisional hernia (IH), with an incidence of 40% in specific risk groups. There is no co...

Fcer1g and St3gal1: Macrophage-associated angiogenesis biomarkers and therapeutic targets in sepsis-induced acute lung injury.

BACKGROUND: Acute lung injury (ALI) involves the release of growth factors and inflammatory mediators from damaged pulmonary tissues, fostering endoth...

Acute respiratory infection (COVID-19) risk prediction in travelers: A random forest model.

BACKGROUND: Early screening during outbreaks of acute respiratory infections (ARIs) is critical for controlling disease spread among international tra...

Early hemodynamic phenotyping in sepsis using transthoracic echocardiography: A proof-of-concept study in a north African ICU.

BACKGROUND: Cardiovascular dysfunction in sepsis is heterogeneous and contributes to poor outcomes. Hemodynamic phenotyping may delineate pathophysiol...

Machine learning analysis of s-EASIX for predicting 30-day mortality in sepsis patients from MIMIC-IV.

Endothelial dysfunction is an important risk factor for the progression of sepsis. The simplified endothelial activation and stress index (s-EASIX) se...

Integrating machine learning techniques for critical node identification in complex networks.

Identifying the most prominent nodes in complex networks becomes more critical for applications such as information propagation, epidemic control, and...

Construction of interpretable machine-learning diagnostic models for erectile dysfunction based on routine blood and biochemical detection data.

Despite its high incidence, the diagnosis of erectile dysfunction (ED) is impeded by current diagnostic constraints and patient hesitancy in seeking m...

Development and validation of a predictive model for profiling antibiotic resistance phenotypes of Acinetobacter baumannii strains via clinical MALDI-TOF mass spectra: A multicenter study.

Acinetobacter baumannii is a significant pathogen responsible for healthcare-acquired infections (HAIs), posing challenges due to its rising resistanc...

Balancing Innovation in Artificial Intelligence With Practical Sepsis Definitions.

Operationalizing Sepsis Definitions With Artificial Intelligence: Toward Smarter Antibiotic Use.

Discovery of H2 Receptor Antagonists as Colistin Enhancers by Targeting Acid Stress Response.

Plasmid-mediated colistin resistance gene mcr has markedly diminished the effectiveness of colistin in managing multidrug-resistant (MDR) Gram-negativ...

A machine learning model for prediction of early-onset neonatal sepsis in low-income and middle-income countries: development and validation study.

OBJECTIVE: Early-onset sepsis (EOS), which occurs within the first 72 hours of life, can often be fatal for neonates. Machine learning (ML) models dem...

Nomogram based on the pre-treatment systemic oxidative stress score to predict the prognosis of surgically treated patients with oral squamous cell carcinoma: competing risk model analysis.

The aim of this study was to explore the prognostic predictive importance of the pre-treatment systemic oxidative stress score (SOS) in surgically tre...

Explainable multimodal deep learning models for variable-length sequences in critically ill patients.

OBJECTIVE: Deep learning models have shown strong performance in predicting clinical events in critical care using structured electronic health record...

A Surface-Enhanced Raman Scattering-Digital Microfluidics Biosensing Platform Integrated with a 1D-Convolutional Neural Network for Noninvasive Detection of Inflammation Markers.

The noninvasive detection of multiple inflammatory markers (IMs) for early sepsis diagnosis is challenging. Therefore, herein, we innovatively develop...

Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study.

BACKGROUND: Mortality prediction in intensive care unit (ICU) patients with ischemic stroke complicated by intracranial artery stenosis or occlusion r...

Integration of single-cell and bulk transcriptomics with machine learning identifies LDHA as a lactate-related diagnostic and prognostic biomarker in sepsis.

Improved outcomes and donor utilization in heart transplantation with 10 °C static cold storage.

OBJECTIVE: To compare 10 °C static cold storage (SCS) with traditional ice for cardiac allograft preservation, its impact on post-transplant outcomes,...