Clustering of Beijing genotype Mycobacterium tuberculosis isolates from the Mekong delta in Vietnam on the basis of variable number of tandem repeat versus restriction fragment length polymorphism typing
Bibliographic record
Abstract
BACKGROUND: In comparison to restriction fragment length polymorphism (RFLP) typing, variable number of tandem repeat (VNTR) typing is easier to perform, faster and yields results in a simple, numerical format. Therefore, this technique has gained recognition as the new international gold standard in typing of Mycobacterium tuberculosis. However, some reports indicated that VNTR typing may be less suitable for Beijing genotype isolates. We therefore compared the performance of internationally standardized RFLP and 24 loci VNTR typing to discriminate among 100 Beijing genotype isolates from the Southern Vietnam. METHODS: Hundred Beijing genotype strains defined by spoligotyping were randomly selected and typed by RFLP and VNTR typing. The discriminatory power of VNTR and RFLP typing was compared using the Bionumerics software. RESULTS: Among 95 Beijing strains available for analysis, 14 clusters were identified comprising 34 strains and 61 unique profiles in 24 loci VNTR typing ((Hunter Gaston Discrimination Index (HGDI = 0.994)). 13 clusters containing 31 strains and 64 unique patterns in RFLP typing (HGDI = 0.994) were found. Nine RFLP clusters were subdivided by VNTR typing and 12 VNTR clusters were split by RFLP. Five isolates (5%) revealing double alleles or no signal in two or more loci in VNTR typing could not be analyzed. CONCLUSIONS: Overall, 24 loci VNTR typing and RFLP typing had similar high-level of discrimination among 95 Beijing strains from Southern Vietnam. However, loci VNTR 154, VNTR 2461 and VNTR 3171 had hardly added any value to the level of discrimination.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".