Publications by authors named "J D Edgeworth"
Article Synopsis
- Nosocomial infections and antimicrobial resistance (AMR) pose serious global healthcare challenges, motivating the need for effective detection and treatment strategies.
- This study introduces a machine learning method called Multi-Objective Symbolic Regression (MOSR), which uses clinical data to predict bloodstream infections (BSI) and assess AMR while overcoming limitations of traditional ML approaches.
- Results show that MOSR significantly outperforms standard ML models in predicting BSI and AMR, achieving higher F1-Scores, thus serving as a potentially scalable solution to improve Antimicrobial Stewardship (AMS) practices.
Article Synopsis
- Clinical metagenomics enhances the detection of microorganisms in clinical samples through genomic sequencing while minimizing human DNA interference, using a rapid mechanical method for simultaneous RNA and DNA analysis.
- The method involves mechanically lysing human cells and employing nonspecific endonuclease to deplete human DNA, allowing the conversion of RNA to dsDNA for comprehensive sequencing.
- Results indicate high sensitivity and specificity in identifying various pathogens, with a promising concordance with traditional testing methods, suggesting potential for routine use in microbiology labs with further validation.
Background: Hospital drains and water interfaces are implicated in nosocomial transmission of pathogens. Metagenomics can assess the microbial composition and presence of antimicrobial resistance genes in drains ('the drainome') but studies applying these methods longitudinally and to assess infection control interventions are lacking.
Aim: To apply long-read metagenomics coupled with microbiological measurements to investigate the drainome and assess the effects of a peracetic-acid-containing decontamination product.