Source or Sink: Examining the Role of Biofilms in Transmission of <i>Mycobacterium</i> spp. in Laboratory Zebrafish
Bibliographic record
Abstract
Zebrafish health is a primary research concern because diseases can have unintended impacts on experimental endpoints. Ideally, research would be conducted using disease-free fish or fish with known disease status. Mycobacteriosis is a common bacterial disease in wild and captive fishes, including zebrafish. Despite its prevalence, the dynamics of transmission and potential sources of mycobacterial infections in zebrafish are only partially understood. One suspected natural infection source is surface biofilms on tanks and other system components. This study investigates the role that tank biofilms play in mycobacteriosis in laboratory zebrafish by evaluating the establishment of biofilms from bacteria shed from fish, and conversely, the acquisition of infections in fish from surface biofilms. We found that zebrafish infected with Mycobacterium chelonae shed bacteria through feces, and bacteria are transmitted to tank biofilms from one to 16 weeks postinfection. We also found that zebrafish acquire M. chelonae infections as soon as 2 weeks when introduced to tanks with established M. chelonae biofilms. The results from this study highlight the role that tank biofilms play as both a reservoir and source of mycobacterial infections in zebrafish. Results support the inclusion of biofilm surveillance and prevention as part of a disease control program in zebrafish research facilities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".