Standardisation of high throughput microdilution antifungal susceptibility testing for Candida albicans and Cryptococcus neoformans
Bibliographic record
Abstract
Abstract The Clinical and Laboratory Standards Institute (CLSI) M27 guidelines are the recommended and most commonly used protocols for broth microdilution antifungal susceptibility testing of yeasts. However, these guidelines are limited to the use of 96-well assay plates, limiting assay capacity. With the increased risk of fungal resistance emerging in the community, it is important to have alternative protocols available, that offer higher throughput and can screen more than eight to ten potential antifungal compounds per plate. This study presents an optimised broth microdilution minimum inhibitory concentration (MIC) method for testing the susceptibility of yeasts in an efficient high throughput screening setup, with minimal growth variability and maximum reproducibility. We extend the M27 guidelines and optimise the conditions for 384-well plates. Validation of the assay was performed with ten clinically used antifungals (fluconazole, amphotericin B, 5-fluorocytosine, posaconazole, voriconazole, ketoconazole, itraconazole, caspofungin diacetate, anidulafungin and micafungin) against Candida albicans and Cryptococcus neoformans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".