Emerging Antifungal Resistance in Falco Species: A Novel Model for Human Medicine
Bibliographic record
Abstract
Abstract Antifungal resistance is a growing concern in the medical community, as many fungal infections are becoming increasingly difficult to treat. The most isolated fungi in this study were A. fumigatus, A. flavus, A. niger, and A. terreus, all of which can cause aspergillosis in falcons. Isavuconazole, posaconazole, and voriconazole had the lowest MICs among the drugs tested, suggesting that they may be effective treatment options. However, this study showed that 34% of the isolates were resistant to itraconazole, which is an increase from 21% in 2006. Voriconazole was not found to be resistant in 2006 and 2011, but its resistance rate increased to 9% in 2023. Similarly, the resistance of posaconazole and isavuconazole was 0% in 2011, but it increased to 4.7% and 5.8%, respectively, in 2023. Amphotericin B, which showed a 51% resistance rate in 2006, became even more resistant with an 80% rate in 2011, leading to its discontinuation from the treatment of falcons against aspergillosis. This study highlights a significant rise in antifungal resistance, which is a challenging problem in both falcon and human medicine. Fungal diseases are emerging in association with post-COVID-19, as the COVID-19 virus can weaken the immune system, making individuals more susceptible to fungal infections. Thus, it is crucial to develop new and effective antifungal treatments to address this growing concern.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".