First Canadian report of transmission of fluconazole-resistant <i>Candida parapsilosis</i> within two hospital networks confirmed by genomic analysis
Bibliographic record
Abstract
ABSTRACT Candida parapsilosis is a common cause of non - albicans candidemia. It can be transmitted in healthcare settings resulting in serious healthcare-associated infections and can develop drug resistance to commonly used antifungal agents. Following a significant increase in the percentage of fluconazole (FLU)-nonsusceptible isolates from sterile site specimens of patients in two Ontario acute care hospital networks, we used whole genome sequence (WGS) analysis to retrospectively investigate the genetic relatedness of isolates and to assess potential in-hospital spread. Phylogenomic analysis was conducted on all 19 FLU-resistant and seven susceptible-dose dependent (SDD) isolates from the two hospital networks, as well as 13 FLU susceptible C. parapsilosis isolates from the same facilities and 20 isolates from patients not related to the investigation. Twenty-five of 26 FLU-nonsusceptible isolates (resistant or SDD) and two susceptible isolates from the two hospital networks formed a phylogenomic cluster that was highly similar genetically and distinct from other isolates. The results suggest the presence of a persistent strain of FLU-nonsusceptible C. parapsilosis causing infections over a 5.5-year period. Results from WGS were largely comparable to microsatellite typing. Twenty-seven of 28 cluster isolates had a K143R substitution in lanosterol 14-α-demethylase (ERG11) associated with azole resistance. As the first report of a healthcare-associated outbreak of FLU-nonsusceptible C. parapsilosis in Canada, this study underscores the importance of monitoring local antimicrobial resistance trends and demonstrates the value of WGS analysis to detect and characterize clusters and outbreaks. Timely access to genomic epidemiological information can inform targeted infection control measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".