Mycobacterium avium subspecies paratuberculosis (MAP) shedding and seropositivity in youngstock in MAP-infected dairy herds in Alberta, Canada
Bibliographic record
Abstract
Johne's disease (JD) control and eradication programs aim to reduce the prevalence of Mycobacterium avium subspecies paratuberculosis (MAP) infections and associated economic losses. Youngstock are particularly vulnerable to MAP infection-they can shed MAP in feces, develop antibody titers, and transmit the pathogen to pen mates. However, most JD control programs do not include youngstock in MAP testing strategies, potentially limiting their effectiveness by overlooking this transmission route. This study aimed to investigate the age at which youngstock ≤12 mo old in MAP-positive dairy herds start shedding MAP in feces and become ELISA-positive under field conditions. Eight dairy herds in Alberta, Canada, were sampled 4 times over 14 mo. Median MAP prevalence in animals >12 mo of age using quantitative PCR (qPCR) at the first and second sampling was 1% and 0%, respectively. Using the ELISA, median herd prevalence was 1% at both samplings. Blood and fecal samples were collected from all youngstock aged 2 to 12 mo. The MAP antibodies and fecal shedding were determined using ELISA and qPCR targeting the ISMAP02 insertion sequence. Thirteen percent of animals ≤12 mo of age shed MAP, with earliest shedding observed at 4 mo of age. ELISA seropositivity was first detected at 3 mo, with 4% of animals testing positive. These findings demonstrate that in MAP-positive commercial dairy herds, early MAP shedding and seroconversion can occur earlier than previously assumed under field conditions. Incorporating youngstock into JD testing and control strategies is essential. Monitoring MAP shedding in youngstock may reveal flaws in calf hygiene measures, enhance JD management, and reduce within-herd prevalence over time.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".