Denaturing gradient gel electrophoresis for nonlethal detection of <i>Aeromonas salmonicida</i> in salmonid mucus and its potential for other bacterial fish pathogens
Bibliographic record
Abstract
Denaturing gradient gel electrophoresis (DGGE) of 16S rDNA was used to nonlethally detect Aeromonas salmonicida and other bacteria in salmonid skin mucus. Mucus samples from wild spawning coho salmon (Oncorhynchus kisutch) with endemic A. salmonicida and from cultured lake trout (Salvelinus namaycush) were tested by PCR-DGGE and were compared with mucus culture on Coomassie brilliant blue agar and internal organ culture. PCR-DGGE gave a highly reproducible 4-band pattern for 9 strains of typical A. salmonicida, which was different from other Aeromonas spp. Aeromonas salmonicida presence in mucus was evident as a band that comigrated with the bottom band of the A. salmonicida 4-band pattern and was verified by sequencing. PCR-DGGE found 36 of 52 coho salmon positive for A. salmonicida, compared with 31 positive by mucus culture and 16 by organ culture. Numerous other bacteria were detected in salmonid mucus, including Pseudomonas spp., Shewanella putrefaciens, Aeromonas hydrophila and other aeromonads. However, Yersinia ruckeri was not detected in mucus from 27 lake trout, but 1 fish had a sorbitol-positive Y. ruckeri isolated from organ culture. Yersinia ruckeri seeded into a mucus sample suggested that PCR-DGGE detection of this bacterium from mucus was possible. PCR-DGGE allows nonlethal detection of A. salmonicida in mucus and differentiation of some Aeromonas spp. and has the potential to allow simultaneous detection of other pathogens present in fish mucus.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".