Molecular mechanisms of acquired antifungal drug resistance in principal fungal pathogens and EUCAST guidance for their laboratory detection and clinical implications
Bibliographic record
Abstract
The increasing incidence and changing epidemiology of invasive fungal infections continue to present many challenges to their effective management. The repertoire of antifungal drugs available for treatment is still limited although there are new antifungals on the horizon. Successful treatment of invasive mycoses is dependent on a mix of pathogen-, host- and antifungal drug-related factors. Laboratories need to be adept at detection of fungal pathogens in clinical samples in order to effectively guide treatment by identifying isolates with acquired drug resistance. While there are international guidelines on how to conduct in vitro antifungal susceptibility testing, these are not performed as widely as for bacterial pathogens. Furthermore, fungi generally are recovered in cultures more slowly than bacteria, and often cannot be cultured in the laboratory. Therefore, non-culture-based methods, including molecular tests, to detect fungi in clinical specimens are increasingly important in patient management and are becoming more reliable as technology improves. Molecular methods can also be used for detection of target gene mutations or other mechanisms that predict antifungal drug resistance. This review addresses acquired antifungal drug resistance in the principal human fungal pathogens and describes known resistance mechanisms and what in-house and commercial tools are available for their detection. It is emphasized that this approach should be complementary to culture-based susceptibility testing, given the range of mutations, resistance mechanisms and target genes that may be present in clinical isolates, but may not be included in current molecular assays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".