Evaluating triple inactivated vaccine-induced immunity from a large-scale study in feline population
Bibliographic record
Abstract
Feline panleukopenia virus (FPV), feline calicivirus (FCV), and feline herpesvirus-1 (FHV-1) substantially impact feline health. Field evidence on vaccine-induced population immunity remains limited. We evaluated humoral responses in 4,736 vaccinated domestic cats across 24 provinces in China (July 2024-June 2025), quantifying neutralizing antibody titers to FPV, FCV, and FHV-1 after a trivalent inactivated vaccine (Meowonder™). For cats aged 3 months or older, two primary doses achieved high antibody positivity rates across pathogens; a third dose yielded marginal additional benefit, while boosters-maintained titers. Antibody peaks occurred in late summer and early winter, with a decline beginning in February and a rebound in spring. Maternal-derived antibodies (MDAs) interfered most in kittens < 4 months. A subset with very high FPV titers but sub-threshold FCV/FHV-1 titers had recent environmental FPV based on pet owners' feedback. The variability of individual immune responsiveness, potentially influenced by feline leukocyte antigen (FLA) polymorphisms, likely contributed to heterogeneous responses. These results support a two-dose primary series for cats aged ≥ 3 months, followed by periodic boosters, emphasizing the importance of vaccine schedules and consideration of genetic and environmental factors for effective disease management.
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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 | 0.002 | 0.002 |
| 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".