Publications by authors named "J E Afset"
Enteroviruses can infect various human organs, causing diseases such as meningitis, the common cold, hand-foot-and-mouth disease, myocarditis, pancreatitis, hepatitis, poliomyelitis, sepsis, and type 1 diabetes. Currently, there are no approved treatments for enterovirus infections. In this study, we identified a synergistic combination of orally available, safe-in-man pleconaril, AG7404, and mindeudesivir, that at non-toxic concentrations effectively inhibited enterovirus replication in human cell and organoid cultures.
View Article and Find Full Text PDFBloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St.
View Article and Find Full Text PDFspecies and subtypes in Norway: predominance of and emergence of .
Emerg Microbes Infect
December 2024
Article Synopsis
- Scientists studied how certain germs spread in Norway using advanced tests to check human and animal samples for infections.
- Most of the germs were found in kids and young adults, especially during late summer and early autumn, with 10 different types identified in people's samples.
- They discovered some small outbreaks, like one in a kindergarten, and found that most cases were caused by a couple of common germ types that were mostly spread in the country rather than from abroad.
Background: This study aimed to investigate a highly resistant strain of sp. isolated from a patient with bloodstream infection and determine its taxonomic classification.
Methods: The strain was isolated from blood culture from a 65-year-old male patient admitted to St.
Objective: This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).
Methods: Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS.
Results: Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.