Molecular detection and characterization of the <i>Mycobacterium tuberculosis</i> complex subspecies responsible for bovine tuberculosis in Punjab, Pakistan
Bibliographic record
Abstract
ABSTRACT Bovine tuberculosis (bTB), traditionally associated with Mycobacterium bovis , presents significant public health and economic challenges worldwide. This study investigated the causative agents of bTB in slaughtered cattle and buffalo in Lahore, Pakistan. Of the 3,581 animals screened, 34 were identified with gross TB-like lesions. The lesions were processed for culture, PCR, and Sanger sequencing to identify the causative agents of the disease. The results identified 10 Mycobacterium orygis and 8 Mycobacterium tuberculosis sensu stricto isolates. Whole-genome sequencing was performed on two M . orygis isolates, and the sequences were phylogenetically compared to 93 publicly available M. orygis sequences. The results also demonstrated that the JB21 and JB22 primers, which have been previously commonly applied to detect M. bovis in Pakistan, are unable to distinguish between M. tuberculosis complex subspecies. The identification of M. orygis and M. tuberculosis as causative agents of bTB in this slaughterhouse in Punjab may have important implications in identifying cases of zoonotic TB in humans and applying appropriate molecular tools to identify the prevalence of the disease. The data from this study align with recent findings suggesting M. orygis is the predominant cause of bTB in South Asia. IMPORTANCE The study findings hold significant relevance to the Journal of Clinical Microbiology, as they directly impact the field. The first-time identification of Mycobacterium orygis and Mycobacterium tuberculosis as the predominant causative agents of bovine tuberculosis in Lahore, Pakistan underscores the urgent need for enhanced diagnostic methods. The study emphasizes the importance of improved assays for the accurate detection and differentiation of Mycobacterium subspecies. Additionally, the research addresses zoonotic risk assessment and public health implications, advocating for a multidisciplinary approach that integrates clinical microbiology with veterinary and human health sectors. These insights contribute to clinical microbiology knowledge, shaping effective strategies for disease prevention, surveillance, and control. The study’s potential to advance the field makes it well suited for publication in the Microbiology Spectrum journal.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".