Assessment of urinary biomarkers of mycotoxin exposure in adults from Cameroon
Bibliographic record
Abstract
Abstract Background In Cameroon dietary staples are contaminated with diverse toxic fungal metabolites, known as mycotoxins. Aflatoxins and fumonisins are of particular public health concern, particularly in relation to cancer and/or early life stunting. Mixtures of these toxins are predicted from food measures, and in this work, the levels and frequencies of urinary mycotoxin biomarkers are reported in Cameroonian adults. Methods A single first void urine sample was collected from 89 adults (aged range: 28–85, male 39, female 50) from the city of Yaoundé, Centre Region, Cameroon. Urines were tested for eight distinct mycotoxins using measures of both parent compounds and/or their metabolites by Liquid Chromatography tandem mass spectrometry (LC-MS/MS). Results Altogether seven distinct mycotoxins, aflatoxin, fumonisin, deoxynivalenol, zearalenone, nivalenol, ochratoxin A and citrinin, (or their metabolites) were observed in urine samples. At least one mycotoxin was detected in all of the urine samples, 87 (97.8%) of which were above the method’s quantification limit. Aflatoxin M1 was detected in 42% (n.d. − 0.21 µg L− 1) of samples of which about a quarter additionally contained fumonisin B1. Of the remaining toxins deoxynivalenol, zearalenone, ochratoxin A, nivalenol and citrinin were present in 78%, 99% 95%, 53%, and 87% of the samples respectively. Alternariol was not detected in any sample. Mixtures of mycotoxins in the samples were frequently observed with 64 samples (72%) containing more than five mycotoxins. Estimates of intake exceeded the TDIs for fumonisin (n = 4), deoxynivalenol (n = 1) and zearalenone (n = 2), no TDI is set for aflatoxin. Conclusions This study reveals frequent co-exposure of Cameroonian individuals to a complex mixture of toxic and carcinogenic mycotoxins, with mixtures of aflatoxin and fumonisin a particular priority from a public health standpoint.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".