New approaches to tackle a rising problem: Large-scale methods to study antifungal resistance
Bibliographic record
Abstract
Why the urgency?The scarcity of antifungal compounds available to treat infections makes the rise in incidence of both intrinsic and acquired resistance in clinical and field isolates an alarming situation [3].Key to facing this challenge is understanding how resistance to antifungals arises.In some species infecting humans, like Aspergillus fumigatus, resistance can be connected to the use of antifungals in agriculture [4], showing that the issue of resistance exists in a "One Health" context [2].While some of the causal genes are known (e.g., ERG11/CYP51, PDR1, FKS genes, TUB genes), resistance can still arise through a variety of mechanisms: coding sequence or promoter mutations, copy number alterations, aneuploidies or even epigenetic modifications, none of which have been fully cataloged [5].Furthermore, we have yet to uncover the precise mechanisms by which some species are intrinsically more resistant to certain antifungals, like Candida auris [6].This incomplete knowledge has important implications regarding next-generation approaches to tackle fungal pathogens.The switch to molecular diagnostics tools to detect resistance markers could accelerate the use of optimal treatment regimens but requires a deep understanding of the genotype-to-phenotype link [7].As new compounds are being discovered and progress through clinical trials [8], we also have the opportunity to map out evolutionary pathways to resistance and characterize tolerance before these compounds see widespread use, to maximize their efficacy.Finally, understanding the trade-offs in growth rate, stress resistance, or virulence associated with resistance to a particular antifungal could also help uncover vulnerabilities unique to resistant strains, leading to new strategies to bypass
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".