Assessment of the relative sensitivity of milk ELISA for detection of Mycobacterium avium ssp. paratuberculosis infectious dairy cows
Bibliographic record
Abstract
Milk ELISA are commonly used for detection of Mycobacterium avium ssp. paratuberculosis (MAP) antibodies in dairy cows, due to low cost and quick processing for large numbers of samples. However, low sensitivity and variations from host and environmental factors can impede detection of MAP antibodies at early disease stages. The objectives of our study were to assess the sensitivity of milk ELISA in comparison with fecal tests and to evaluate how detectable antibody concentrations in milk vary with changes in fecal shedding of MAP, cow age, cow parity, days in milk, and time of year. To compare the sensitivity of a commercial milk ELISA with solid and broth fecal culture and with fecal real-time PCR, a longitudinal study was performed for the identification of MAP-infectious animals as determined by prior fecal testing for MAP shedding. In addition, associations between variation in milk MAP ELISA score and changes in fecal MAP shedding, host age, days in milk, and season were evaluated. Monthly milk and fecal samples were collected over 1 yr from 46 cows that were previously shedding MAP in their feces. Sensitivity of milk ELISA was 29.9% (95% CI: 24.8 to 35.1%), compared with 46.7% (40.7 to 52.7%) for fecal solid culture, 55.0% (49.3 to 60.7%) for fecal broth culture, and 78.4% (73.3 to 83.1%) for fecal direct real-time PCR. The effect of stage of lactation could not be separated from the effect of season, with increased milk ELISA scores at greater days in milk in winter. However, unpredictable monthly variations in results were observed among the 3 assays for individual cow testing, which highlights the importance of identifying patterns in pathogen and antibody detection over time in MAP-positive herds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".