Sequencing of<i>hsp65</i>Distinguishes among Subsets of the<i>Mycobacterium avium</i>Complex
Bibliographic record
Abstract
The Mycobacterium avium complex consists of epidemiologically distinct subsets. The classification of these subsets is complicated by a number of factors, including the ambiguous results obtained with phenotypic and genetic assays and the recent appreciation that human and avian strains appear to be distinct. In previous work, sequencing based on a 441-bp portion of the hsp65 gene has proven to efficiently classify isolates within the Mycobacterium genus but provides low resolution for distinguishing among members of the M. avium complex. Therefore, in this study, we have targeted the more variable 3' region of the hsp65 gene to determine whether it can effectively discriminate M. avium complex isolates at the levels of species and subspecies. Primers designed for this target consistently generated amplicons for all organisms classified as M. avium complex. Sequences obtained indicate that M. intracellulare is genetically divergent from M. avium organisms, and distinct sequevars were obtained for M. avium subsets, including M. avium subsp. avium (bird type), M. avium subsp. hominissuis, and M. avium subsp. paratuberculosis. In addition, sequence differences served to distinguish bovine from ovine strains of M. avium subsp. paratuberculosis. A unique profile for M. avium subsp. silvaticum was not obtained. These results indicate that sequencing the 3' region of the hsp65 gene can simply and unambiguously distinguish species and subspecies of the M. avium complex.
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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) | 0.001 | 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".