Determination of the minimal level of neutralizing antibodies elicited following vaccination able to protect rabbits against virulent cowpox virus
Bibliographic record
Abstract
Background Serological assessment of antibody levels is a crucial measure of immunity in vaccinated animals. Establishing the level of antibodies considered protective is essential for vaccine standardization and evaluation of efficacy. The virus neutralization test (VNT), recognized as the gold standard for detecting virus-specific antibodies able to neutralize virus. Methods This study evaluated the effect of viral dose on the detection of humoral immune responses in rabbits vaccinated with a cowpox virus-based vaccine. Blood serum samples were collected on days 14, 21, and 28 post-vaccination. VNT was conducted using viral doses of 100, 50, 25, and 10 TCID50. Additionally, the infectious dose 50 (ID50) of the challenge virus was determined based on the induction of skin necrosis in 50% of infected animals. This dose (316 ID50 per 0.1 mL) was then used to challenge vaccinated rabbits in order to determine the protective antibody titer threshold. Results Lower viral doses (25 and 10 TCID50) demonstrated higher sensitivity, with neutralizing antibody titers detected at 1:16 and above, significantly exceeding those obtained using 50 and 100 TCID50. Based on these findings, 25 TCID50 was selected as the optimal dose for future VNT. Following cowpox virus challenge, rabbits with neutralizing titers ≥1:16 were protected from skin necrosis, while non-immunized animals developed characteristic lesions. Conclusion These results suggest that a low-dose (25 TCID50) VNT improves the sensitivity and that a titer of 1:16 can be considered a protective threshold. This approach provides a reliable laboratory model for assessing the immunogenicity and efficacy of cowpox virus vaccines. The results obtained in this study allow for an objective assessment of the immunity elicited from a cowpox vaccine using a laboratory model.
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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) | 0.000 | 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 | 0.001 |
| 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".