Sepsis

A scale-down mini-bioreactor for the acceleration of data-driven bioprocess optimisation in cell therapy.

Cell therapies have demonstrated great potential for treating a broad range of diseases where conventional treatments have failed. However, long development times and sub-optimal processing conditions often hinder their clinical translation. To efficiently develop optimal bioprocesses, a large number of experiments are required, making the screening process lengthy, costly, and resource-intensive....

Targeted Surface-Enhanced Raman Scattering for Highly Accurate Identification of Bacterial Species and Finding Spectral Signatures with Explainable Artificial Intelligence.

The analysis of complex Raman spectra from biological samples has traditionally relied on conventional chemometric-based methods, the performance of which has been further improved by artificial intelligence (AI). The accurate identification of bacteria is critical to preventing sepsis, even in advanced clinical settings. Among several methods, Raman scattering has shown great promise in overcomin...

[Comparative observation of analgesic efficacy between liposomal bupivacaine and "cocktail" therapy following artificial intelligence-assisted direct anterior approach total hip arthroplasty: A prospective randomized controlled study].

OBJECTIVE: To compare the analgesic efficacy and safety of liposomal bupivacaine (LB) versus ropivacaine for surgical incision local anesthesia after ...

Predicting Postoperative Outcomes in Pediatric Ureteroscopy Using Machine Learning and Explainable AI-EAU Endourology Vision-AI Study.

AIM OF THE STUDY: This study aimed to evaluate the performance of machine learning (ML) algorithms integrated with explainable artificial intelligence...

Comparison of AI-based Chatbot Performance in Analyzing Clinical Scenarios versus Medical Residents: A Novel Approach in Chest Diseases Education.

OBJECTIVE: Rapid advancements in artificial intelligence (AI) technologies offer new opportunities in medical education. The aim of this study is to c...

Predicting Nosocomial Infections in Hematologic Patients: A Machine Learning Model Based on Dynamic Body Temperature Trajectories.

AIMS: To identify body temperature dynamic patterns and develop a machine learning model for the early detection of nosocomial infections. DESIGN: A r...

Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.

Hypotension Prediction Index-Guided Hemodynamic Management on Postoperative Organ Outcome: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.

Intraoperative hypotension (IOH) is a frequent occurrence during noncardiac surgery. Hypotensive episodes can compromise tissue perfusion, and cumulat...

Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.

Development of a Predictive Model for the Latency Period in Preterm Premature Rupture of Membranes Before 32 Weeks' Gestation.

BACKGROUND: Only 13.7% of corticosteroid treatments occurred within the most effective treatment time window for preterm premature rupture of membrane...

Performance of Machine Learning Models for Sepsis and Stroke Detection Using EMS Data.

OBJECTIVES: Early sepsis and stroke recognition by emergency medical services (EMS) improves triage, treatment, and patient outcomes. Machine learning...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

Microbiome and multi-omics insights into sustainable aquaculture: A triennial systematic review.

Aquaculture is the fastest-growing food production sector, yet intensive practices drive disease outbreaks, antibiotic resistance, and environmental d...

Association between the monocyte-to-lymphocyte ratio and 28-day all-cause mortality in sepsis-associated delirium patients: a retrospective study and machine learning.

The emergence of superficial dermatophytosis due to Trichophyton indotineae and Trichophyton mentagrophytes genotypes VII and II* in New York: a need for comprehensive testing approaches.

We report a rapid increase in dermatophyte infections in New York City and surrounding counties from 2022 to 2024. Reported cases include 135 of Trich...

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning.

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotic targets and metabolic engineering. Unlike single ...

Integrative bioinformatics and machine learning identify shared molecular mechanisms and diagnostic biomarkers between Helicobacter pylori infection and atrial fibrillation.

BACKGROUND: Helicobacter pylori (H. pylori) infection and atrial fibrillation(AF) are major global health concerns. Emerging evidence has suggested a ...

Predicting hepatocellular carcinoma in people with hepatitis B: a comparison between Cox proportional hazard and machine learning models.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with chronic hepatitis B virus (HBV) infectio...

Machine learning for early detection and prediction of sepsis: explainability and key sepsis biomarkers representation-A systematic review.

OBJECTIVE: To systematically review machine learning-based sepsis prediction studies, examining model explainability and the extent to which explanati...

Mechanistic Design of Graphdiyne-Based Multimodal Sensing Integrating Machine Learning and Photothermal Dynamics for Precision Recognition and On-Demand Inactivation.

Rapid and accurate detection of pathogenic bacteria remains essential for infection control and timely treatment. Here, we report a graphdiyne (GDY)-b...