Detection, Enumeration, and Isolation of Vibrio parahaemolyticus and V. vulnificus from Seafood: Development of a Multidisciplinary Protocol
Bibliographic record
Abstract
Vibrio parahaemolyticus and V. vulnificus are bacterial foodborne pathogens that can cause illnesses in humans after ingestion or exposure to contaminated seafood or coastal waters. A procedure that combines microbiological, biochemical, and molecular methods was designed and optimized for the detection, enumeration, isolation, and characterization of these clinically significant Vibrio spp. Initially, microbiological culturing is used to resuscitate and isolate presumptive Vibrio spp. from chilled seafood samples. Biochemical tests are then used to analyze and select presumptive isolates at the species level, and, lastly, molecular methods, such as PCR targeting species-specific hemolysin genes, are used to confirm identification and assess the potential pathogenicity of presumptive isolates. By using artificially contaminated molluscan homogenates with known numbers of V. parahaemolyticus, this method yielded, on average, 90% recovery on complete agar media and 88% recovery on selective media. For V. vulnificus, the recovery rates were 86% (complete media) and 84% (selective media). Linearity of recovery of Vibrio spp. from artificially contaminated seafood homogenates supported the applicability of this method. Overall, this performance-tested protocol is easy to use, cost-effective, and fit-for-purpose, with potential for routine use in basic microbiological facilities.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".