Phagomagnetic separation-quantitative PCR: A rapid, sensitive and specific surveillance tool for viable Mycobacterium avium ssp. paratuberculosis in bulk tank and individual cow milk samples
Bibliographic record
Abstract
Bulk tank milk samples from 392 Northern Ireland dairy farms and individual milk from animals (n = 293) on 4 of these farms were tested by a novel phagomagnetic separation (PhMS)-quantitative (q)PCR assay able to detect and quantify viable Mycobacterium avium ssp. paratuberculosis (MAP), to demonstrate its potential utility as a milk surveillance tool. Viable MAP were detected in 26.5% of the bulk tank milks, with MAP contamination levels ranging from 1 to 8,432 MAP/50 mL of milk; less than 2% of farms had MAP contamination levels >100 MAP/50 mL in their bulk tank milk. Follow-up PhMS-qPCR testing of milk from individual animals on 4 farms that had the highest numbers of MAP in their bulk tank milks indicated that 17 to 24% of animals in each herd were shedding viable MAP in their milk. Mean MAP numbers detected ranged between 6.7 and 42.1 MAP/50 mL of milk. No significant correlation was observed between the detection of viable MAP in bulk or individual milks by PhMS-qPCR and parallel milk ELISA results, or between PhMS-qPCR results and any other milk recording results (somatic cell count, total bacterial count, % butterfat, or % protein). Viable MAP was detected by IS900 qPCR in 52 (85.2%) Pozzato broth cultures of 61 PhMS-qPCR-positive individual milks after 12 wk of incubation, suggesting few PhMS-qPCR results were false positives. The mean sensitivities of the PhMS-qPCR assay and milk ELISA applied to individual milks were estimated by Bayesian latent class analysis to be 0.7096 and 0.2665, respectively, and mean specificities were similar (0.9626 and 0.9509). Our findings clearly demonstrate that the novel PhMS-qPCR assay could be a useful milk surveillance tool for dairy processors, or a milk monitoring tool for Johne's disease control or milk quality assurance programs.
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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 | 0.001 |
| 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 | 0.000 |
| 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 | 0.001 |
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".