Genetic heterogeneity among <i>Vibrio alginolyticus</i> strains, and design of a PCR-based identification method using <i>gyrB</i> gene sequence
Bibliographic record
Abstract
Vibrio alginolyticus, a pathogen among humans and marine animals, is ubiquitous in marine environments. The aims of this study were to analyze the relationships between genetic diversity and origins, and to develop new primers based on the gyrB sequence to identify V. alginolyticus isolated from various sources. To determine the genetic diversity of this bacterium, an arbitrarily primed polymerase chain reaction (AP-PCR) technique was performed on 36 strains of V. alginolyticus isolated from diarrhea patients and from diseased marine animals and environments in southern Thailand. The results showed distinct DNA fingerprints of all strains, indicating that they are genetically heterogeneous. For species-specific identification of V. alginolyticus, primers targeting the gyrB gene of V. alginolyticus were developed. Thirty reference Vibrio spp., 13 non-Vibrio spp., and 160 strains of V. alginolyticus isolated from various sources in southern Thailand were used to evaluate the specificity of these primers. Our results showed that the gyrB primers could specifically identify V. alginolyticus from all sample types. In addition, the detection limit of the PCR was at least 95 pg of DNA template. Therefore, we concluded that the newly designed gyrB primers are rapid, highly sensitive, and specific to identify V. alginolyticus isolated from various sources.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".