Generation of an adenoviral vaccine vector based on simian adenovirus 21
Bibliographic record
Abstract
Adenoviral vectors can be used to generate potent humoral and cellular immune responses to transgene products. Use of adenoviral vectors based on non-human isolates may allow for their utilization in populations harbouring neutralizing antibodies to common human serotypes. A vector chimera was constructed using simian adenovirus 22 (a serotype belonging to the species Human adenovirus E) and simian adenovirus 21 (a serotype belonging to the species Human adenovirus B) expressing the Ebola (Zaire) virus glycoprotein (Ad C5/C1-ZGP). This chimeric adenovirus vector was used as a model to test its efficacy as a genetic vaccine and comparisons were made to a vector based on the commonly used human adenovirus C serotype 5 (Adhu5-ZGP). Ebola glycoprotein-specific T- and B-cell responses were measured in B10BR mice vaccinated with either Adhu5-ZGP or Ad C5/C1-ZGP vectors. Both vectors resulted in Ebola glycoprotein-specific gamma interferon-expressing T cells, although the Ad C5/C1-ZGP vector appeared to induce lower frequencies with kinetics slower than those elicited by the Adhu5-ZGP vector. The total immunoglobulin G response to Ebola glycoprotein was similar in sera from mice vaccinated with either vector. Two rhesus macaques vaccinated with the Ad C5/C1-ZGP vector were found to mount T-cell and antibody responses to the Ebola glycoprotein. It was found that a single administration of the chimeric Ad C5/C1-ZGP vector protected mice against a lethal challenge with a mouse-adapted strain of the Ebola (Zaire) virus.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".