Persistence of Serum Antibodies to Sarcocystis Neurona in Horses Moved from North America to India
Bibliographic record
Abstract
BACKGROUND: The study reported here was undertaken to assess the presence of antibodies to Sarcocystis neurona in the serum of horses of North American origin that had been relocated for 1 year or more to India (ie, outside of the known endemic areas for S. neurona). HYPOTHESIS: The presence or absence of such antibodies should provide information concerning the persistence of such antibodies, or support the presence of chronic infection, or both. ANIMALS: A total of 228 Thoroughbred horses were sampled in India, of which 86 were of North American origin that had been in India between 1 and 13 years, 124 were Indian-born horses that had never been out of India, 8 were of Irish origin, 8 were of English origin, and 2 were originally from France. METHODS: Sera were tested using established western blot analysis. RESULTS: Of the Indian-born horses, 0.8% were test positive, and of the North American horses, 42% were test positive. All of the English and Irish horses were test negative, and the 2 French horses were test positive. CONCLUSIONS AND CLINICAL IMPORTANCE: These data indicate that antibodies against S. neurona can be detected for many years after horses have been removed from an endemic area and that this may be attributable to long half-life of the antibodies or to chronic infection and ongoing antibody production, or both.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".