Mycobacteria Interspersed Repetitive Units-Variable Number of Tandem Repeat, Spoligotyping and Drug Resistance of Isolates from Pulmonary Tuberculosois Patients in Kenya
Bibliographic record
Abstract
Background: Molecular typing allows a rapid and precise species differentiation and is essential in investigating the spread of specific genotypes and any relationship with drug resistance. Methodology: To compare the discrimination power of 24-loci Mycobacteria interspersed repetitive units-variable number of tandem repeat (MIRU-VNTR) to spoligotyping in determining the circulating genotypes of Mycobacterium tuberculosis in isolates from pulmonary tuberculosis patients in Kenya, a total of 204 isolates were typed. Results: Spoligotyping identified 22 spoligo lineages; while 36(17.6%) isolates were not determined. MIRU-VNTR typing identified 12 genotypes; Kenya H37_Rv_ like, S-like that had never been reported before and which were not identified by spoligotyping were identified. Others were Uganda I and II, LAM, Beijing, TUR, EAI, Delhi/C, S and Haarlem. Only 8 (3.9%) were not defined by MIRU-VNTR. Delhi/CAS, EAI, S, S-like, LAM and Beijing had strains that showed resistance to all the five drugs tested. Two strains of EAI and 2 of S genotypes were resistant to all the five drugs tested. Beijing genotype commonly associated with drug resistance was found to be third in drug resistance (14.7%) after Delhi/CAS (28.9%) and LAM (17.6%) with the highest resistance towards isoniazid and pyrazinamide (3.9% each). MIRU-VNTR typing was more discriminative than spoligotyping; identifying 10 unique H37_Rv-like isolates designated KeniaH37_Rv_like genotype and 14 S-like genotype. Conclusion: MIRU-VNTR typing has not been reported in any other study in Kenya and its higher discrimination can help identify genotypes that cannot be determined by spoligotyping. Association of Beijing genotype drug resistance particularly isoniazid should be of concern since it may result in multidrug resistance in the patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".