Severe acute respiratory syndrome vaccine efficacy in ferrets: whole killed virus and adenovirus-vectored vaccines
Bibliographic record
Abstract
Although the 2003 severe acute respiratory syndrome (SARS) outbreak was controlled, repeated transmission of SARS coronavirus (CoV) over several years makes the development of a SARS vaccine desirable. We performed a comparative evaluation of two SARS vaccines for their ability to protect against live SARS-CoV intranasal challenge in ferrets. Both the whole killed SARS-CoV vaccine (with and without alum) and adenovirus-based vectors encoding the nucleocapsid (N) and spike (S) protein induced neutralizing antibody responses and reduced viral replication and shedding in the upper respiratory tract and progression of virus to the lower respiratory tract. The vaccines also diminished haemorrhage in the thymus and reduced the severity and extent of pneumonia and damage to lung epithelium. However, despite high neutralizing antibody titres, protection was incomplete for all vaccine preparations and administration routes. Our data suggest that a combination of vaccine strategies may be required for effective protection from this pathogen. The ferret may be a good model for SARS-CoV infection because it is the only model that replicates the fever seen in human patients, as well as replicating other SARS disease features including infection by the respiratory route, clinical signs, viral replication in upper and lower respiratory tract and lung damage.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".