Cross-Reactive Neutralizing Antibodies Directed against Pandemic H1N1 2009 Virus Are Protective in a Highly Sensitive DBA/2 Mouse Influenza Model
Bibliographic record
Abstract
Our ability to rapidly respond to an emerging influenza pandemic is hampered somewhat by the lack of a susceptible small-animal model. To develop a more sensitive model, we pathotyped 18 low-pathogenic non-mouse-adapted influenza A viruses of human and avian origin in DBA/2 and C57BL/6 mice. The majority of the isolates (13/18) induced severe morbidity and mortality in DBA/2 mice upon intranasal challenge with 1 million infectious doses. Also, at a 100-fold-lower dose, more than 50% of the viruses induced severe weight loss, and mice succumbed to the infection. In contrast, only two virus strains were pathogenic for C57BL/6 mice upon high-dose inoculation. Therefore, DBA/2 mice are a suitable model to validate influenza A virus vaccines and antiviral therapies without the need for extensive viral adaptation. Correspondingly, we used the DBA/2 model to assess the level of protection afforded by preexisting pandemic H1N1 2009 virus (H1N1pdm) cross-reactive human antibodies detected by a hemagglutination inhibition assay. Passive transfer of these antibodies prior to infection protected mice from H1N1pdm-induced pathogenicity, demonstrating the effectiveness of these cross-reactive neutralizing antibodies in vivo.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".