The Effect of Age on Serum Antibody Titers after Rabies and Influenza Vaccination in Healthy Horses
Bibliographic record
Abstract
BACKGROUND: The proportion of geriatric horses within the equine population has increased in the past decade, but there is limited information on the immune function of these animals. HYPOTHESIS: Aged horses will have a lesser increase in serum antibody response to vaccination. ANIMALS: Thirty-four aged healthy horses (> or = 20 years) and 29 younger adult horses (4-12 years) of various breeds. METHODS: All horses were vaccinated with vaccines of killed rabies and influenza virus. Horses in each age group were allocated to receive either rabies or influenza booster vaccine 4 weeks after the initial vaccination. Serum samples were taken at 0, 4, 8, and 24 weeks. Rabies serum neutralization titers and equine influenza virus specific antibody sub-isotypes (IgGa, IgGb, IgG(T), and IgA) as well as single radial hemolysis (SRH) titers were determined. RESULTS: Rabies antibody titers were similar in the 2 age groups at all sampling times. Aged horses had higher IgGa and IgGb influenza antibody titers before vaccination than younger horses but similar titers after vaccination (P= .004 and P= .0027, respectively). Younger horses had significantly greater increases in titer than aged horses at all sampling times for IgGa (P= .001) and at 8 and 24 weeks for IgGb (P= .041 and .01, respectively). There was no detectable serum IgG(T) at any time point. A significant booster vaccine effect was seen for both antirabies and anti-influenza titers. Anti-influenza titer before vaccination also had a significant effect on subsequent antibody response. CONCLUSIONS AND CLINICAL IMPORTANCE: Healthy aged horses generated a primary immune response to a killed rabies vaccine similar to that of younger adult horses. Aged horses had a significantly reduced anamnestic response to influenza vaccine.
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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 | 0.002 |
| 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".