<i>Mycobacterium marinum</i>infections in humans and tracing of its possible environmental sources
Bibliographic record
Abstract
The low frequency of nontuberculous mycobacterial infections, nonspecific symptoms for individual mycobacteria, and the lack of specific identification methods could alter correct diagnosis. This study presents a combined microbiology and molecular-based approach for Mycobacterium marinum detection in four aquarists with cutaneous mycobacterial infection. Simultaneously, ecology screening for M. marinum presence in the aquarists' fish tanks was performed. A total of 38 mycobacterial isolates originated from four human patients (n = 20), aquarium animals (n = 8), and an aquarium environment (n = 10). Isolate identification was carried out using 16S rRNA sequence analysis. A microbiology-based approach, followed by 16S rRNA sequence analysis, was successfully used for detection of M. marinum in all four patients. Animal and environmental samples were simultaneously examined, and a total of seven mycobacterial species were isolated: Mycobacterium chelonae , Mycobacterium fortuitum , Mycobacterium gordonae , Mycobacterium kansasii , Mycobacterium mantenii , Mycobacterium marinum , and Mycobacterium peregrinum . The presence of M. marinum was proven in the aquarium environments of two patients. Although M. marinum is described as being present in water, it was detected only in fish.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".