Identification of small-animal and primate models for evaluation of vaccine candidates for human metapneumovirus (hMPV) and implications for hMPV vaccine design
Bibliographic record
Abstract
Human metapneumovirus (hMPV), a recently identified paramyxovirus, is the causative agent of respiratory tract disease in young children. Epidemiological studies have established the presence of hMPV in retrospective as well as current clinical samples in Europe, USA, Canada, Hong Kong and Australia. The hMPV disease incidence rate varied from 7 to 12 %. This rate of disease attack places hMPV in severity between respiratory syncytial virus and human parainfluenza virus type 3, two common respiratory pathogens of young children, the elderly and immunosuppressed individuals. To evaluate the effectiveness and safety of future hMPV antiviral drugs, therapeutic and prophylactic monoclonal antibodies (mAbs), and vaccine candidates, it was necessary to identify small-animal and primate models that efficiently supported hMPV replication in the respiratory tract and produced neutralizing serum antibodies, commonly a clinical correlate of protection in humans. In this study, various rodents (mice, cotton rats, hamsters and ferrets) and two primate species, rhesus macaques and African green monkeys (AGMs), were evaluated for hMPV replication in the respiratory tract. The results showed that hamsters, ferrets and AGMs supported hMPV replication efficiently and produced high levels of hMPV-neutralizing antibody titres. Hamsters vaccinated with subgroup A hMPV were protected from challenge with subgroup A or subgroup B hMPV, which has implications for hMPV vaccine design. Although these animal models do not mimic human hMPV disease signs, they will nevertheless be invaluable for the future evaluation of hMPV antivirals, mAbs and vaccines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".