Review of methods for detection and characterization of non-tuberculous mycobacteria in aquatic organisms
Bibliographic record
Abstract
Mycobacteriosis is an emerging and often lethal disease of aquatic organisms caused by several non-tuberculous mycobacteria (NTM) species. Early diagnosis of mycobacteriosis in aquaculture and aquatic settings is critical; however, clinical diagnoses and laboratory detection are challenging, and the available literature is scarce. In an attempt to fill the gap, here we review the most relevant approaches to detect and characterize mycobacteria in clinical specimens of aquatic organisms. Emphasis is given to recent advances in molecular methods used to differentiate NTM species spanning from targeted gene sequencing to next-generation sequencing. Further, given that there are major gaps in our understanding of the prevalence of the different NTM species, partially because of their distinct requirements for in vitro growth, we also reviewed the most relevant NTM species reported to cause disease in aquatic organisms and their specific in vitro growth conditions. We also highlight that traditional bacterial culture continues to be relevant for NTM identification, particularly in non-automated laboratories. However, for NTM species discrimination, a high level of accuracy can be achieved with MALDI-TOF MS and molecular approaches, especially targeted gene sequencing applied from clinical specimens or from pure NTM isolates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".