PSIII-22 Good vaccine practices influence immune priming against Bovine viral diarrhea virus in beef calves.
Bibliographic record
Abstract
Abstract Good vaccine practices (GVPs) contribute to successful vaccination programs. However, GVPs are underemphasized by vaccination programs for beef cattle. Therefore, this study quantified the effects of GVPs on priming protective immunity against respiratory pathogens. Beef calves (~205 days of age, n = 150) were randomly assigned to five groups at booster vaccination before weaning. Four groups received a commercial 5-way modified-live vaccine against PI3, BRSV, IBR, BVDV1, and BVDV2 as follows: vaccine prepared an hour before vaccination and stored in a cooler (SC, n = 34); vaccine prepared one hour before vaccination and stored at room temperature (SRT, n = 34); vaccine prepared six hours before vaccination and stored in a cooler (LC, n = 34); vaccine prepared six hours before vaccination and stored at room temperature (LRT, n = 34); and one group received no vaccines (control, n = 14). Blood samples from the experimental calves were collected before and four weeks after booster vaccination to assess antibody titers and cortisol levels. Data were analyzed using a generalized and a linear mixed model as well as a logistic regression. Experimental treatment group mean cortisol levels were not statistically significantly different (p = 0.13) pre- or post-vaccination. Booster vaccination increased (p < 0.01) antibody titers and virus neutralization against viral pathogens, compared to pre-vaccination, regardless of the GVP. Antibody titers to IBR were affected by the storage method of the prepared vaccine, regardless of the vaccine mixing time and sampling time, whereas viral neutralization (VN) titers against BVDV2 were affected by vaccine mixing time, regardless of sampling time or storage method. VN titers against BVDV1 were affected by the two-way interaction of vaccine mixing time-by-storage method of the prepared vaccine: the LRT group mean of BVDV1 titer was less than that of the SRT calves (p = 0.02). The number of calves immune-primed against BVDV1 decreased by 89% (OR 0.11, 95% CI 0.01-0.71; p = 0.05) and BVDV2 decreased by 86% (OR 0.14, 95% CI 0.01-0.89; p = 0.08) in the LRT group compared to SC group. The results motivate the conclusion that immune priming was not influenced by variations in stress-induced immune suppression among the treated calves, but the impact of GVPs varied based on the pathogen.
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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 | 0.001 |
| 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".