Sepsis

Rapid Deployment of Antiviral Drugs Using Single-Virus Tracking and Machine Learning.

The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting phenotypic screening as a strategy for drug repurposing in antiviral research. However, traditional phenotypic screening methods typically require several days of experimental cycles and lack visual confirmation of a drug's ability to inhibit viral infect...

Analysis of HIV infection among voluntary blood donors based on HIV ELISA and nucleic acid detection.

OBJECTIVE: To analyze the epidemiological characteristics of human immunodeficiency virus (HIV) infection among voluntary blood donors and provide a foundation for improving the donor recruitment strategies and developing a more scientific and effective HIV screening strategy.

extract in combination with lytic phage cocktails: a promising therapeutic approach against biofilms of multi-drug resistant .

Antimicrobial resistance (AMR) poses a significant global threat to public health systems, rendering antibiotics ineffective in treating infectious di...

Antibacterial Activity of Crude , , and Methanolic Extracts on serovar Manilae.

BACKGROUND AND OBJECTIVE: Leptospirosis is a disease caused by pathogenic prevalent in tropical countries like the Philippines. Some studies have sho...

Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).

INTRODUCTION: In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challen...

Predicting intra-abdominal candidiasis in elderly septic patients using machine learning based on lymphocyte subtyping: a prospective cohort study.

OBJECTIVE: Intra-abdominal candidiasis (IAC) is difficult to predict in elderly septic patients with intra-abdominal infection (IAI). This study aimed...

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model.

BACKGROUND: Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characteri...

Antibiotic SERS spectral analysis based on data augmentation and attention mechanism strategy.

The analysis of Raman spectrum data has gradually transitioned into the era of machine learning. However, it is still constrained by the challenge of ...

Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections.

BACKGROUND: Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identif...

Possible biocontrol of bacterial blight in pomegranate using native endophytic spp. under field conditions.

Bacterial blight in pomegranate, caused by pv. (Xcp), is one of the most devastating diseases, leading to substantial economic losses in pomegranate...

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

UNLABELLED: Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study ai...

Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction.

OBJECTIVES: To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develop a ma...

Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data.

While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection...

Genomic analysis, culturing optimization, and characterization of bacteriophage OSYSP, previously studied as effective pathogen control on fresh produce.

Advances in bacteriophage genome sequencing and regulatory approvals of some bacteriophages in various applications have renewed interest in these ant...

Machine learning-enhanced assessment of potential probiotics from healthy calves for the treatment of neonatal calf diarrhea.

Neonatal calf diarrhea (NCD) remains a significant contributor to calf mortality within the first 3 weeks of life, prompting widespread antibiotic use...

A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.

This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensi...

Prediction of mortality in sepsis patients using stacked ensemble machine learning algorithm.

INTRODUCTION: Machine learning (ML) has been tried in predicting outcomes following sepsis. This study aims to identify the utility of stacked ensembl...

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is ...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to exist...

A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.

BACKGROUND: Acinetobacter baumanni infection is a leading cause of morbidity and mortality in the Intensive Care Unit (ICU). Early recognition of pati...