A Porcine Adenovirus with Low Human Seroprevalence Is a Promising Alternative Vaccine Vector to Human Adenovirus 5 in an H5N1 Virus Disease Model
Bibliographic record
Abstract
Human adenovirus 5 (AdHu5) vectors are robust vaccine platforms however the presence of naturally-acquired neutralizing antibodies may reduce vector efficacy and potential for re-administration. This study evaluates immune responses and protection following vaccination with a replication-incompetent porcine adenovirus 3 (PAV3) vector as an alternative vaccine to AdHu5 using an avian influenza H5N1 disease model. Vaccine efficacy was evaluated in BALB/c mice following vaccination with different doses of the PAV3 vector expressing an optimized A/Hanoi/30408/2005 H5N1 hemagglutinin antigen (PAV3-HA) and compared with an AdHu5-HA control. PAV3-HA rapidly generated antibody responses, with significant neutralizing antibody titers on day 21, and stronger cellular immune responses detected on day 8, compared to AdHu5-HA. The PAV3-HA vaccine, administered 8 days before challenge, demonstrated improved survival and lower virus load. Evaluation of long-term vaccine efficacy at 12 months post-vaccination showed better protection with the PAV3-HA than with the AdHu5-HA vaccine. Importantly, as opposed to AdHu5, PAV3 vector was not significantly neutralized by human antibodies pooled from over 10,000 individuals. Overall, PAV3-based vector is capable of mediating swift, strong immune responses and offer a promising alternative to AdHu5.
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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 | 0.000 | 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".