Improvements of virulence factor phenotypic tests for <i>Aeromonas salmonicida</i> subsp. <i>salmonicida</i>, a major fish pathogen
Bibliographic record
Abstract
Aeromonas salmonicida subspecies salmonicida, a fish pathogen, expresses various virulence factors such as an A-layer, lipases and proteases during the infection process. Not all strains of this bacterium express the same virulence factors. It is important to be able to evaluate which factors are present when characterizing strains. The A-layer and secreted lipases and proteases are usually detected by agar-based tests that require long incubation (24 h and more) and may provide ambiguous results. In the present study, protocols have been optimized to determine the presence of these virulence factors using liquid tests. For A-layer detection, the optimized method stains the positive bacteria with Coomassie Brilliant Blue. The lipases are detected by a colorimetric biochemical reaction triggered by the degradation of p-nitrophenyl dodecanoate into a yellow product detectable by spectrophotometry, if the result is positive. Both of these tests show results in less than an hour. Finally, the protease activity is measured by clarification of a medium containing milk during an overnight bacterial growth. These new protocols provide opportunities for quicker characterization of A. salmonicida subsp. salmonicida strains and, particularly, provide more precise results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".