A One Health approach to overcoming fungal disease and antifungal resistance
Bibliographic record
Abstract
The global burden of fungal disease poses a substantial threat to human, animal, and environmental health, endangering both human and livestock populations and creating vulnerabilities to food supplies world-wide. Antifungal drugs provide essential therapies to humans and animals against infections, while fungicides provide protection in agriculture. However, a limited arsenal of antifungal agents results in cross-use between agriculture and health, promoting the development of resistance, and drastically reducing our defenses against disease. Critically, antifungal resistant strains found ubiquitously within the natural environment demonstrate resistance to the same classes of antifungals used to treat human and animal infections, hindering effective treatment within the clinic. This interconnectivity supports the need for a One Health approach to combat fungal diseases and overcome antifungal resistance, ensuring that treatment and protection of a defined group does not inadvertently endanger or sacrifice other plants, animals, or humans. In this review, we present sources of antifungal resistance and discuss the integration of environmental and clinical resources to manage disease. Moreover, we explore opportunities for drug synergy and repurposing strategies, highlight fungal targets being investigated to overcome resistance, and propose technologies for the discovery of novel fungal targets. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".