Understanding the mechanisms of resistance to azole antifungals in <i>Candida</i> species
Bibliographic record
Abstract
Abstract Cases of Candida infection have been on the rise in recent years. A comprehensive and clear understanding of the mechanisms of antifungal resistance is fundamental for developing novel therapies to address the current and emerging threat of fungal diseases. Certain Candida species can cause superficial or invasive infections in immunocompromised hosts, and invasive Candida infections are major contributors to infectious disease deaths. As fungi are eukaryotes like humans, there are only a limited number of unique molecular targets available for antifungal drug development. Until recently, there have only been four primary classes of antifungals used to treat systemic fungal infections. Among these, azole antifungals are globally used because they are both inexpensive and effective. Due to various factors, resistance to antifungal drugs—especially azole antifungals—has developed in many Candida species, posing a significant public health threat. This review discusses the known mechanisms of azole antifungal resistance in Candida albicans, Candida auris, Nakaseomyces glabrata, Candida tropicalis, Candida parapsilosis and explores strategies to overcome the resistance problem.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".