Development of a rapid identification method for<i>Aeromonas</i>species by multiplex-PCR
Bibliographic record
Abstract
Existing biochemical methods cannot distinguish among some species of Aeromonads, while genetic methods are labor intensive. In this study, primers were developed to three genes of Aeromonas: lipase, elastase, and DNA gyraseB. In addition, six previously described primer sets, five corresponding to species-specific signature regions of the 16S rRNA gene from A. veronii, A. popoffii, A. caviae, A. jandaei, and A. schubertii, respectively, and one corresponding to A. hydrophila specific lipase (hydrolipase), were chosen. The primer sets were combined in a series of multiplex-PCR (mPCR) assays against 38 previously characterized strains. Following PCR, each species was distinguished by the production of a unique combination of amplicons. When the assays were tested using 63 drinking water isolates, there was complete agreement in the species identification (ID) for 59 isolates, with ID established by biochemical assays. Sequencing the gyrB and the 16S rRNA gene from the remaining four strains established that the ID obtained by mPCR was correct for three strains. For only one strain, no consensus ID could be obtained. A rapid and reliable method for identification of different Aeromonas species is proposed that does not require restriction enzyme digestions, thus simplifying and speeding up the process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 0.001 |
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".