Sepsis

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.

BACKGROUND: Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.

Critical care studies using large language models based on electronic healthcare records: A technical note.

The integration of large language models (LLMs) in clinical medicine, particularly in critical care, has introduced transformative capabilities for an...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing ...

Miniature Robots for Battling Bacterial Infection.

Micro/nanorobots have shown great promise for minimally invasive bacterial infection therapy. However, bacterial infections usually form biofilms insi...

Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.

BACKGROUND: People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The aim of the study was to develop and validate an ex...

Plasma treated bimetallic nanofibers as sensitive SERS platform and deep learning model for detection and classification of antibiotics.

Design of a sensitive, cost-effective SERS substrate is critical for probing analyte in trace concentration in real field environment. Present work re...

Leveraging large-scale Mycobacterium tuberculosis whole genome sequence data to characterise drug-resistant mutations using machine learning and statistical approaches.

Tuberculosis disease (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global public health problem, resulting in > 1 million deaths each y...

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time.

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening technique...

Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning.

This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential d...

Adult Code Sepsis: A Narrative Review of its Implementation and Impact.

This narrative review explores the implementation and impact of sepsis code protocols, an urgent intervention strategy designed to improve clinical ou...

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research...

Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.

PURPOSE: Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted ki...

The infection post flexible UreteroreNoscopy (I-FUN) predictive model based on machine learning: a new clinical tool to assess the risk of sepsis post retrograde intrarenal surgery for kidney stone disease.

PURPOSE: To create a machine-learning model for estimating the likelihood of post-retrograde intrarenal surgery (RIRS) sepsis.

Essential blood molecular signature for progression of sepsis-induced acute lung injury: Integrated bioinformatic, single-cell RNA Seq and machine learning analysis.

In this study, we aimed to identify an essential blood molecular signature for chacterizing the progression of sepsis-induced acute lung injury using ...

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction.

Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of th...

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods ...

Neural parameter calibration and uncertainty quantification for epidemic forecasting.

The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus...

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data.

PURPOSE: Spontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any sur...

Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection.

OBJECTIVE: To evaluate the effectiveness of Monocyte Distribution Width (MDW) in predicting sepsis outcomes in emergency department (ED) patients comp...