Molecular mechanisms governing antifungal drug resistance
Bibliographic record
Abstract
Abstract Fungal pathogens are a severe public health problem. The leading causative agents of systemic fungal infections include species from the Candida , Cryptococcus , and Aspergillus genera. As opportunistic pathogens, these fungi are generally harmless in healthy hosts; however, they can cause significant morbidity and mortality in immunocompromised patients. Despite the profound impact of pathogenic fungi on global human health, the current antifungal armamentarium is limited to only three major classes of drugs, all of which face complications, including host toxicity, unfavourable pharmacokinetics, or limited spectrum of activity. Further exacerbating this issue is the growing prevalence of antifungal-resistant infections and the emergence of multidrug-resistant pathogens. In this review, we discuss the diverse strategies employed by leading fungal pathogens to evolve antifungal resistance, including drug target alterations, enhanced drug efflux, and induction of cellular stress response pathways. Such mechanisms of resistance occur through diverse genetic alterations, including point mutations, aneuploidy formation, and epigenetic changes given the significant plasticity observed in many fungal genomes. Additionally, we highlight recent literature surrounding the mechanisms governing resistance in emerging multidrug-resistant pathogens including Candida auris and Candida glabrata . Advancing our knowledge of the molecular mechanisms by which fungi adapt to the challenge of antifungal exposure is imperative for designing therapeutic strategies to tackle the emerging threat of antifungal resistance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".