Molecular characterization of drug-resistant Mycobacterium tuberculosis isolates from Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: Ontario bears the greatest burden of tuberculosis in Canada, with 40% of all cases and 60% of multidrug-resistant cases. The purpose of this study was to genotypically characterize isoniazid- and rifampicin-resistant isolates and compare these results with phenotypic drug susceptibility testing data. This is the first Canadian study to examine gene mutations that contribute to multidrug-resistant tuberculosis. METHODS: A total of 751 tuberculosis isolates were tested for drug resistance using phenotypic antimicrobial susceptibility testing methods. Isolates were then characterized using molecular methods. Following DNA extraction, PCR amplification and sequence analysis were performed on the rifampicin resistance region of rpoB, as well as the region surrounding katG315 and the inhA promoter region associated with isoniazid resistance. RESULTS: Eighteen different mutation types were found in the rpoB region of rifampicin-resistant isolates. Isolates with mutations at residues rpoB531 (64.1%), rpoB526 (15.2%) and rpoB516 (8.7%) were the most common. In addition, an insertion was found at residue 514. Three phenotypically rifampicin-resistant isolates (3.3%) were genotypically wild-type. In isoniazid-resistant strains, mutations were found most commonly at katG315 (45.4%) as well as at the inhA promoter region (28.6%). Thirty-nine isolates (25.3%) were phenotypically isoniazid-resistant but genotypically wild-type. The katG315 mutation was statistically associated with multidrug-resistant isolates. CONCLUSIONS: This study expands the knowledge of mutations that potentially contribute to drug resistance in tuberculosis and lays the foundation for developing molecular-based tests to determine drug resistance in clinical tuberculosis isolates.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".