Detection of Mycobacterium avium Subspecies paratuberculosis (MAP) Microorganisms Using Antigenic MAP Cell Envelope Proteins
Bibliographic record
Abstract
(MAP) that are antigenically distinct from closely related mycobacterial species are potentially useful for Johne's Disease (JD) diagnosis. We evaluated the potential of ELISAs, based on six antigenically distinct recombinant MAP cell envelope proteins (SdhA, FadE25_2, FadE3_2, Mkl, DesA2, and hypothetical protein MAP1233) as well as an extract of MAP total cell envelope proteins, to detect antibodies against MAP in the sera of infected cattle. The sensitivity (Se) and specificity (Sp) of an ELISA based on MAP total cell envelope proteins, when analyzing 153 bovine serum samples, was 75 and 96%, respectively. Analysis of the same samples, using a commercial serum ELISA resulted in a Se of 56% and Sp of 99%. Results of ELISA analysis using plates coated with recombinant cell envelope proteins ranged from a highest Se of 94% and a lowest Sp of 79% for Sdh A to a lowest Se of 67% and a highest Sp of 95% for hypothetical protein MAP1233. Using polyclonal antibodies to MAP total cell envelope proteins, immunohistochemical analysis of intestinal and lymph node tissues from JD-positive cattle detected MAP organisms whereas antibodies to recombinant proteins did not. Finally, polyclonal antibodies to MAP total cell envelope protein and to recombinant SdhA, FadE25_2, and DesA2 proteins immunomagnetically separated MAP microorganisms spiked in PBS. These results suggest that antigenically distinct MAP cell envelope proteins and antibodies to these proteins may have potential to detect MAP infection in dairy cattle.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".