Combined Effects of Lipophilic Phycotoxins (Okadaic Acid, Azapsiracid-1 and Yessotoxin) on Human Intestinal Cells Models
Bibliographic record
Abstract
Phycotoxins are monitored in seafood because they can cause food poisonings in humans. Phycotoxins do not only occur singly but also as mixtures in shellfish. The aim of this study was to evaluate the in vitro toxic interactions of binary combinations of three lipophilic phycotoxins commonly found in Europe (okadaic acid (OA), yessotoxin (YTX) and azaspiracid-1 (AZA-1)) using the neutral red uptake assay on two human intestinal cell models, Caco-2 and the human intestinal epithelial crypt-like cells (HIEC). Based on the cytotoxicity of individual toxins, we studied the interactions between toxins in binary mixtures using the combination index-isobologram equation, a method widely used in pharmacology to study drug interactions. This method quantitatively classifies interactions between toxins in mixtures as synergistic, additive or antagonistic. AZA-1/OA, and YTX/OA mixtures showed increasing antagonism with increasing toxin concentrations. In contrast, the AZA-1/YTX mixture showed increasing synergism with increasing concentrations, especially for mixtures with high YTX concentrations. These results highlight the hazard potency of AZA-1/YTX mixtures with regard to seafood intoxication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".