A real-time PCR assay for rapid identification of inducible and acquired clarithromycin resistance in Mycobacterium abscessus
Bibliographic record
Abstract
BACKGROUND: Mycobacterium abscessus is a rapidly growing mycobacteria involved in severe infections of the lung, skin, or soft tissue. Macrolides such as clarithromycin are the recommended first line drugs for treatment of M. abscessus infections. However, M. abscessus has dual mechanisms of resistance to macrolides, making treatment by macrolides difficult. A functional erm(41) gene confers for inducible resistance while acquired mutations on the 23S rRNA rrl gene confer for constitutive resistance. METHODS: We have developed a real-time PCR assay to detect both inducible and acquired resistance to clarithromycin, and compared the results to traditional erm(41) and rrl sequencing and phenotypic susceptibility testing using Sensititre™ plates. RESULTS: Of the total 126 M. abscessus isolates tested, truncated erm(41) was found in 23/126 (18.3%) of the samples, 27/126 (21.4%) had a T28C mutation in erm(41), and 2/126 (1.6%) had an acquired A2058C mutation in rrl. The phenotypic results correlated with the expected sequencing results in 121/126 samples (96%). Phenotypic testing compared to real-time PCR resolved 2 of these discrepancies by showing the existence of both erm(41) alleles in the isolates that sequencing missed. One culture was found to be mixed with two M. abscessus subsp. as per hsp65 sequencing and 2 isolates had discordance between molecular and phenotypic results. It was presumed that 3 isolates showed discrepancy between sequencing and real-time PCR, but one culture was mixed and other 2 detected both alleles by real-time PCR leading to 100% concordance when compared to sequencing. CONCLUSION: In conclusion, real-time PCR is more accurate for detection of both acquired and induced clarithromycin resistance, specifically when mixed genic profiles are present in a sample.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".