Genetic Analyses of Amphotericin B Susceptibility in Aspergillus fumigatus
Bibliographic record
Abstract
Aspergillus fumigatus is a ubiquitous saprophytic mold that can cause a range of clinical syndromes, from allergic reactions to invasive infections. Amphotericin B (AMB) is a polyene antifungal drug that has been used to treat a broad range of systemic mycoses since 1958, including as a primary treatment option against invasive aspergillosis in regions with high rates (≥10%) of environmental triazole resistance. However, cases of AMB-resistant A. fumigatus strains have been increasingly documented over the years, and high resistance rates were recently reported in Brazil and Canada. The objective of this study is to identify candidate mutations associated with AMB susceptibility using a genome-wide association analysis of natural strains, and to further investigate a subset of the mutations in their putative associations with differences in AMB minimum inhibitory concentration (MIC) and in growths at different AMB concentrations through the analysis of progeny from a laboratory genetic cross. Together, our results identified a total of 34 candidate single-nucleotide polymorphisms (SNPs) associated with AMB MIC differences—comprising 18 intergenic variants, 14 missense variants, one synonymous variant, and one non-coding transcript variant. Importantly, progeny from the genetic cross allowed us to identify putative SNP–SNP interactions impacting progeny growth at different AMB concentrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".