Foodborne Pathogens in Leafy Vegetables Grown and Consumed Locally in Yaounde, Cameroon: A Public Health Concern
Bibliographic record
Abstract
This study sought to understand the health risks of foodborne pathogens in fresh leafy vegetables that are grown and consumed locally in Yaounde, Cameroon. Through a survey, 200 respondents were recruited to relate possible food-related illnesses to leafy vegetable consumption. Additionally, a total of 168 vegetable samples consisting of six leafy vegetables and 15 irrigated water samples from five water sources were collected from farms and local markets for microbiological analysis. Using a high-fidelity DNA polymerase, five potential bacterial pathogens, namely, Shiga-toxin producing Escherichia coli (STEC), Campylobacter spp., Salmonella spp., Listeria monocytogenes and Yersinia enterocolitica were also examined. The mean counts of total viable count and total coliforms followed decreasing trends from vegetables obtained on the farms to the local markets, and these ranged from 4.98-8.74 log cfu/g and 1.77-7.42 log cfu/g respectively. All pathogens detected were of significant concern to public health showing high occurrence in some vegetables: STEC (20%) and Yersinia enterolitica (13%) in cabbage, Campylobacter spp. (21%) in lettuce, Listeria monocytogenes (15%) in African nightshade, and Salmonella spp. (15%) in amaranth. Importantly, 42% of respondents highlighted that they frequently got sick from eating leafy vegetables from the study area. These microbiological and qualitative results along with certain vegetable farming and vending practices (such as the use of untreated sewage water for crop irrigation, the sales of physically dirty, muddy, and unpackaged vegetables) indicated that foodborne diseases could be occurring among leafy vegetable-consuming populations in Cameroon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".