Experimental Evolution of Antifungal Resistance in <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Cryptococcus neoformans, an opportunistic yeast-like fungal pathogen, has demonstrated resistance to all major classes of antifungals used to treat cryptococcal meningitis. However, combatting this fungal disease is an ongoing challenge among clinicians due to the evolution of antifungal-resistant strains. The limited availability of clinically approved antifungals has heightened the urgency to investigate the molecular mechanisms underscoring resistance. Studying how a fungal pathogen evolves to an antifungal drug in vitro using experimental evolution provides a simple, yet powerful approach to study the mechanisms of antifungal resistance. Experimental evolution involves the serial passaging of microbial populations under laboratory conditions, such that adaptive mutations can occur and be monitored in real time. This technique plays a key role in investigating the mechanisms of antifungal resistance in C. neoformans, and this can help in developing novel strategies to combat the emergence of resistance. Here, we outline how to make overnight cultures of C. neoformans and how to perform experimental evolution, and we present a spectrophotometric analysis to evaluate the evolution of antifungal resistance. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Growth and sample preparation of Cryptococcus neoformans Basic Protocol 2: Experimental evolution of antifungal resistance Basic Protocol 3: Analyzing the evolution of antifungal resistance Basic Protocol 4: Glycerol stock preparation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".