Assessment of Partial Sequencing of the 65-Kilodalton Heat Shock Protein Gene ( <i>hsp65</i> ) for Routine Identification of <i>Mycobacterium</i> Species Isolated from Clinical Sources
Bibliographic record
Abstract
We assessed the ability of an in-house database, consisting of 111 hsp65 sequences from putative and valid Mycobacterium species or described groups, to identify 689 mycobacterial clinical isolates from 35 species or groups. A preliminary assessment indicated that hsp65 sequencing confirmed the identification of 79.4% of the isolates from the 32 species examined, including all Mycobacterium tuberculosis complex isolates, all isolates from 13 other species, and 95.6% of all M. avium-M. intracellulare complex isolates. Identification discrepancies were most frequently encountered with isolates submitted as M. chelonae, M. fortuitum, M. gordonae, M. scrofulaceum, and M. terrae. Reexamination of isolates with discrepant identifications confirmed that hsp65 identifications were correct in a further 40 isolates. This brought the overall agreement between hsp65 sequencing and the other identification methods to 85.2%. The remaining 102 isolates had sequence matches below our acceptance criterion, had nondifferential sequence matches between two or more species, were identified by 16S rRNA sequencing as a putative taxonomic group not contained in our database, or were identified by hsp65 and 16S rRNA gene sequencing as a species not in our biochemical test database or had conflicting identifications. Therefore, to incorporate the unconfirmed isolates it was necessary to create 29 additional entries in our hsp65 identification database: 18 associated with valid species, 7 indicating unique sequences not associated with valid or putative species or groups, and 4 associated with unique, but currently described taxonomic groups. Confidence in the hsp65 sequence identification of a clinical isolate is best when sequence matches of 100% occur, but our data indicate that correct identifications can be confidently made when unambiguous matches exceeding 97% occur, but are dependent on the completeness of the database. Our study indicates that for hsp65 sequencing to be an effective means for identifying mycobacteria a comprehensive database must be constructed. hsp65 sequencing has the advantage of being more rapid and less expensive than biochemical test panels, uses a single set of reagents to identify both rapid- and slow-growing mycobacteria, and can provide a more definitive identification.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".