Priming by DNA immunization augments T-cell responses induced by modified live bovine herpesvirus vaccine
Bibliographic record
Abstract
DNA vaccines have several advantages over conventional vaccines. One of the most important characteristics is the presentation of antigen via both MHC class I and class II receptors. Although this generally results in strong T-cell responses, antibody production and protection achieved by DNA immunization are unfortunately not always adequate. In contrast, modified live virus (MLV) vaccines usually induce adequate antibody and moderate cellular responses, whereas killed vaccines tend to elicit weak immune responses in general. A DNA prime-MLV boost regimen should result in enhanced cellular immunity and possibly improved antibody production. To test this hypothesis, plasmids encoding bovine herpesvirus-1 (BHV-1) glycoproteins B and D were delivered by gene gun to the genital mucosa of cattle prior to immunization with modified live BHV-1 vaccine. The immune responses induced were compared to those of an MLV-vaccinated group and a negative control group. Although significantly enhanced T-cell responses were induced by priming with the DNA vaccine, there was no increase in antibody titres. Similar levels of protection were induced by the MLV vaccine alone and the DNA prime and MLV boost regimen, which suggests that there is no correlation between the induction of T-cell responses and protection from BHV-1 challenge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".