Combatting the evolution of antifungal resistance in <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Fungal infections are a global concern and the evolution of intrinsic resistance to current antifungals presents an alarming problem. For Cryptococcus neoformans, a human fungal pathogen of primarily immunocompromised individuals, resistance toward treatment strategies demands alternative approaches. Given the prevalence of virulence factor production during cryptococcal infection, an emerging and important field of research encompasses the development of novel antivirulence therapies proposed to improve host immune responses and promote fungal clearance. To accomplish this task, information regarding the presence and role of virulence factors, the mechanisms of action within the host, and the ability to influence fungal susceptibility to antifungals is pertinent. Research into mechanisms of antifungal resistance for C. neoformans is limited but extrapolation from successful studies in other fungal species can improve our understanding of mechanisms employed by C. neoformans and suggest targeted strategies to enhance our ability to combat the pathogen. In this Review, we highlight antifungal therapy options against Cryptococcus, explore current knowledge of underlying mechanisms promoting resistance, and present new opportunities for novel and effective strategies to overcome fungal infections and reduce, or possibly even reverse, the effects of resistance evolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".