Characterization of Sudan Ebolavirus infection in ferrets
Bibliographic record
Abstract
// Andrea Kroeker 1, 2 , Shihua He 1, 2 , Marc-Antoine de La Vega 1, 3, 4 , Gary Wong 1, 5 , Carissa Embury-Hyatt 6 and Xiangguo Qiu 1, 2 1 Special Pathogens Program, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada 2 Department of Medical Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada 3 Department of Immunology, University of Manitoba, Winnipeg, Manitoba, Canada 4 Département de microbiologie-infectiologie et d’immunologie, Université Laval, Quebec City, Québec, Canada 5 CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China 6 Canadian Food Inspection Agency, National Centre for Foreign Animal Disease, Winnipeg, Manitoba, Canada Correspondence to: Xiangguo Qiu, email: xiangguo.qiu@phac-aspc.gc.ca Keywords: ferrets, Sudan, Ebolavirus, animal model, characterization Received: October 05, 2016 Accepted: March 30, 2017 Published: May 08, 2017 ABSTRACT Sudan virus (SUDV) outbreaks in Africa are highly lethal; however, the development and testing of novel antivirals and vaccines for this virus has been limited by a lack of suitable animal models. Non-human primates (NHP) remain the gold standard for modeling filovirus disease, but they are not conducive to screening large numbers of experimental compounds and should only be used to test the most promising candidates. Therefore, other smaller animal models are a valuable asset. We have recently developed a guinea-pig adapted SUDV virus that is lethal in guinea pigs. In our current study, we show that ferrets are susceptible to wild-type SUDV, providing a small animal model to directly study clinical isolates, screen experimental anti-SUDV compounds and potentially study viral transmission.
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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 | 0.000 | 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".