Comparison of repetitive-sequence-based polymerase chain reaction with random amplified polymorphic DNA analysis for rapid genotyping of nontuberculosis mycobacteria
Bibliographic record
Abstract
Nontuberculosis mycobacteria (NTM) are an important cause of human disease and infections. Though less notorious than tuberculosis, these infections are clinically significant and have been associated with outbreaks in various settings. To accommodate outbreak investigations for the numerous species of NTM, we evaluated a DiversiLab repetitive-sequence-based PCR (rep-PCR) kit for genotyping of mycobacteria. This kit was used to genotype both rapidly and slowly growing mycobacteria and was compared with other PCR-based genotyping methods, including random amplified polymorphic DNA (RAPD) analysis, hsp65 gene sequencing, and mycobacterial interspersed repetitive unit - variable number of tandem repeat (MIRU-VNTR) analysis. Compared with RAPD analysis, rep-PCR achieved better reproducibility in testing. When compared with hsp65 gene sequencing and MIRU-VNTR for Mycobacterium avium , rep-PCR provided results that agreed with these less discriminatory genotyping methods but provided a higher level of discrimination for situations such as outbreak investigations. We also evaluated the kit for its ability to identify closely related rapidly growing NTM. While rep-PCR was informative in some cases, a much larger library of isolates would be necessary to truly evaluate it as an identification tool. Overall, rep-PCR was able to provide improved reproducibility over RAPD and a discriminatory genotyping method for the isolates evaluated in this study.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".