Isolation and Histopathological Changes Associated with Non-Tuberculous Mycobacteria in Lymph Nodes Condemned at a Bovine Slaughterhouse
Bibliographic record
Abstract
Background: non-tuberculous mycobacteria (NTM) infect humans and animals and have a critical confounding effect on the diagnosis of bovine tuberculosis. The Official Mexican Standard (Norma Oficial Mexicana, NOM-ZOO-031-1995) for food safety regulates Mycobacterium bovis in cattle, but not the NTM species. The study’s objective was to isolate and identify the NTM present in condemned bovine lymph nodes in a slaughterhouse, characterize the histological lesions, and correlate bacteriological and microscopic findings with the antemortem tuberculin skin test. Methods: from 528 cattle, one or two pooled samples of lymph nodes from each animal were cultured for Mycobacteria spp. and processed for histopathology. Results: mycobacteria were isolated from 54/528 (10.2%) of the condemned lymph nodes; 25/54 (46.2%) of these isolates were NTM; 4 bacteriological cultures with fungal contamination were discarded. Granulomatous and pyogranulomatous inflammation were present in 6/21 (28.6%) and 7/21 (33.3%) of the NTM-positive lymph nodes, respectively. The species of NTM associated with granulomatous lymphadenitis were M. scrofulaceum, M. triviale, M. terrae, and M. szulgai, while those causing pyogranulomatous lesions were M. szulgai, M. kansasii, M. phlei, and M. scrofulaceum. Conclusions: the NTM infections can cause false-positive results in the tuberculin test because of cross immune reactivity and interference with the postmortem identification of M. bovis in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".