In-Vitro Cell Culture for Efficient Assessment of Mycotoxin Exposure, Toxicity and Risk Mitigation
Bibliographic record
Abstract
Mycotoxins are toxic secondary fungal metabolites that commonly contaminate crops and food by-products and thus, animal feed. Ingestion of mycotoxins can lead to mycotoxicosis in both animals and humans, and at subclinical concentrations may affect animal production and adulterate feed and animal by-products. Mycotoxicity mechanisms of action (MOA) are largely unknown, and co-contamination, which is often the case, raises the likelihood of mycotoxin interactions. Mitigation strategies for reducing the risk of mycotoxicity are diverse and may not necessarily provide protection against all mycotoxins. These factors, as well as the species-specific risk of toxicity, collectively make an assessment of exposure, toxicity, and risk mitigation very challenging and costly; thus, in-vitro cell culture models provide a useful tool for their initial assessment. Since ingestion is the most common route of mycotoxin exposure, the intestinal epithelial barrier comprised of epithelial cells (IECs) and immune cells such as macrophages, represents ground zero where mycotoxins are absorbed, biotransformed, and elicit toxicity. This article aims to review different in-vitro IEC or co-culture models that can be used for assessing mycotoxin exposure, toxicity, and risk mitigation, and their suitability and limitations for the safety assessment of animal foods and food by-products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".