The Ferret as a Model for Filovirus Pathogenesis and Countermeasure Evaluation
Bibliographic record
Abstract
The domestic ferret (Mustela putorius furo) has long been a popular animal model for evaluating viral pathogenesis and transmission as well as the efficacy of candidate countermeasures. Without question, the ferret has been most widely implemented for modeling respiratory viruses, particularly influenza viruses; however, in recent years, it has gained attention as a novel animal model for characterizing filovirus infections. Although ferrets appear resistant to infection and disease caused by Marburg and Ravn viruses, they are highly susceptible to lethal disease caused by Ebola, Sudan, Bundibugyo, and Reston viruses. Notably, unlike the immunocompetent rodent models of filovirus infection, ferrets are susceptible to lethal disease caused by wild-type viruses, and they recapitulate many aspects of human filovirus disease, including systemic virus replication, coagulation abnormalities, and a dysregulated immune response. Along with the stringency with which they reproduce Ebola disease, their relatively small size and availability make ferrets an attractive choice for countermeasure evaluation and pathogenesis modeling. Indeed, they are so far the only small animal model available for Bundibugyo virus. Nevertheless, ferrets do have their limitations, including the lack of commercially available reagents to dissect host responses and their unproven predictive value in therapeutic evaluation. Although the use of the ferret model in ebolavirus research has been consistent over the last few years, its widespread use and utility remains to be fully proven. This review provides a comprehensive overview of the ferret models of filovirus infection and perspective on their ongoing use in pathogenesis modeling and countermeasure evaluation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".