Identification of <i>Mycobacterium</i> Species by Multiple-Fluorescence PCR–Single-Strand Conformation Polymorphism Analysis of the 16S rRNA Gene
Bibliographic record
Abstract
Identification of mycobacteria to the species level by growth-based methodologies is a process that has been fraught with difficulties due to the long generation times of mycobacteria. There is an increasing incidence of unusual nontuberculous mycobacterial infections, especially in patients with concomitant immunocompromised states, which has led to the discovery of new mycobacterial species and the recognition of the pathogenicity of organisms that were once considered nonpathogens. Therefore, there is a need for rapid and sensitive techniques that can accurately identify all mycobacterial species. Multiple-fluorescence-based PCR and subsequent single-strand conformation polymorphism (SSCP) analysis (MF-PCR-SSCP) of four variable regions of the 16S rRNA gene were used to identify species-specific patterns for 30 of the most common mycobacterial human pathogens and environmental isolates. The species-specific SSCP patterns generated were then entered into a database by using BioNumerics, version 1.5, software with a pattern-recognition capability, among its multiple uses. Patient specimens previously identified by 16S rRNA gene sequencing were subsequently tested by this method and were identified by comparing their patterns with those in the reference database. Fourteen species whose SSCP patterns were included in the database were correctly identified. Five other test organisms were correctly identified as unique species or were identified by their closest relative, as they were not in the database. We propose that MF-PCR-SSCP offers a rapid, specific, and relatively inexpensive identification tool for the differentiation of mycobacterial species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".