Detection of fish pathogens with PCR-DGGE in non-lethal mucus samples and molecular typing of Aeromonas salmonicida using the 16s-23s internal transcribed spacers
Bibliographic record
Abstract
This thesis is an investigation of the detection of 'Aeromonas salmonicida' using nested PCR-Denaturing Gradient Gel Electrophoresis (PCR-DGGE) in non-lethal fish mucus samples. The technique was compared to conventional non-lethal and lethal methods. 'A. salmonicida' was detected by all techniques. Detection of 'A. salmonicida' by DGGE was based on co-migrating bands and was verified by sequencing. The mean level of detection was highest with culture of mucus on Coomassie brilliant blue agar in the Chinook (71.67% ± 7.64) and PCR-DGGE (69.10 ± 1.29) for the coho salmon. Mucus based techniques had a statistically significant agreement for detection of 'A. salmonicida. Flavobacterium psychrophilum, F. branchiophilun' and 'Yersinia ruckeri' were not detected with PCR-DGGE. Bands often co-migrated to these pathogens, but their nucleotide sequences belonged to other bacteria. Seeding 'Y. ruckeri ' in mucus showed that PCR-DGGE could detect the pathogen. In an attempt to detect strain differences, the 16S-23S rRNA internal transcribed spacers of 'A. salmonicida' were fingerprinted using RFLP and DGGE. These techniques verify the clonal nature of the pathogen, but do show the capacity to differentiate various 'Aeromonas' spp.
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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".