Detection of non-pathogenic and pathogenic populations of Vibrio parahaemolyticus in various samples by the conventional, quantitative and droplet digital PCRs
Bibliographic record
Abstract
In this study, three generations of polymerase chain reaction (PCR) assays: (i) conventional PCR, (ii) qPCR and (iii) droplet digital PCR (ddPCR), were systematically tested for their abilities to detect non-pathogenic and pathogenic populations of Vibrio parahaemolyticus. The limit of detection (LOD) for the ddPCR was 1.1 pg/µL of purified DNA, followed by the qPCR (5.6 pg/µL) and the conventional PCR (8.8 pg/µL). Regarding the LOD for V. parahaemolyticus cells, the ddPCR assay was able to detect 29 cells, followed by the conventional PCR assay (58 cells) and the qPCR assay (115 cells). Regarding the sensitivities to detect this pathogen from PCR inhibition prone samples (naturally contaminated mussels), the ddPCR assay significantly outperformed the conventional PCR and qPCR. The ddPCR assay was able to consistently detect non-pathogenic and pathogenic populations of V. parahaemolyticus from naturally contaminated mussels, indicating its tolerance to various PCR inhibitors. This study also revealed the significant difference between conventional PCR and qPCR. The conventional PCR assay showed significantly greater sensitivity than that of the qPCR assay in detecting V. parahaemolyticus in crude samples, whereas the qPCR assay showed better sensitivity in detecting the presence of V. parahaemolyticus in purified DNA samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".