Prevalence and molecular characterization of Vibrio cholerae from fruits and salad vegetables sold in Jakarta, Indonesia, using most probable number and PCR
Bibliographic record
Abstract
OBJECTIVE: Cholera is an intestinal infection caused by Vibrio cholerae, it is usually occurs in developing countries that lack of sanitation. In developing country including Indonesia, awareness importance of sanitation is still low. Unfortunately, research related to the detection of V. cholerae from fruit and vegetables in Indonesia is still rare. In this study, MPN method was used to determine the prevalence of V. cholerae followed by single and multiplex PCR to detect virulence genes, including toxR, ctxA, tcpA, hlyA, ace, ompU, and zot. RESULTS: We found 3 fruits and 2 vegetables positive for toxR gene. Fruit samples which were showed toxR positive found from East Jakarta while for vegetables, it was recovered from West Jakarta and Central Jakarta. Twenty-three isolates were recovered from toxR positive samples. The result of antibiotic resistance analysis showed that 4.35% of the isolates resistant to gentamicin, streptomycin (17.39%), trimethoprim (52.17%), ciprofloxacin (30.43%), ampicillin (13.04%), nalidixic acid (82.61%), and polymyxin B (91.30%). None of these isolates were resistant to kanamycin. Combination of MPN and Multiplex PCR method can be used to detect the prevalence and characterize the virulence properties of V. cholerae.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".