<i>Cyclospora</i> spp. in herbs and water samples collected from markets and farms in Hanoi, Vietnam
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of Cyclospora spp. oocysts in herb and water samples as well as in fecal specimens of clinical cases of diarrhoea in Hanoi, Vietnam. METHOD: From November 2004 to October 2005, water and herb samples collected from markets and farms in Hanoi were examined for the presence of Cyclospora spp. oocysts in concentrated sediments and washings using UV epifluorescence examination of a wet mount. In addition, hospital based surveillance studies were carried out using a structured questionnaire which focused on potential risk factors for cyclosporiasis. Stool specimens were collected from individuals with diarrhoea attending primary healthcare facilities and examined for Cyclospora spp. oocysts by modified acid fast smear and wet mount examination using both light and UV epifluorescence microscopy. RESULTS: Cyclospora spp. were found in 34/288 (11.8%) market water and herb samples, and in 24/287 (8.4%) farm samples. All varieties of herbs sold at the market and grown in farms were contaminated with Cyclospora spp. oocysts. A marked seasonal increase in Cyclospora spp. contamination was observed before the rainy season (39/288) from November to April compared to the rainy season (19/268) from May to October (chi(2) = 7.593, P = 0.006). However, Cyclospora spp. was not found in any stool samples collected in hospital-based surveillance studies. CONCLUSIONS: These results confirm the presence of Cyclospora spp. which varies seasonally in environmental samples (water and herbs collected from farms and markets) within the Hanoi metropolitan area.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".