Detecting Neurodevelopmental Toxicity of Domoic Acid and Ochratoxin A Using Rat Fetal Neural Stem Cells
Bibliographic record
Abstract
Currently, animal experiments in rodents are the gold standard for developmental neurotoxicity (DNT) investigations; however, testing guidelines for these experiments are insufficient in terms of animal use, time, and costs. Thus, alternative reliable approaches are needed for predicting DNT. We chose rat neural stem cells (rNSC) as a model system, and used a well-known neurotoxin, domoic acid (DA), as a model test chemical to validate the assay. This assay was used to investigate the potential neurotoxic effects of Ochratoxin A (OTA), of which the main target organ is the kidney. However, limited information is available regarding its neurotoxic effects. The effects of DA and OTA on the cytotoxicity and on the degree of differentiation of rat rNSC into astrocytes, neurons, and oligodendrocytes were monitored using cell-specific immunofluorescence staining for undifferentiated rNSC (nestin), neurospheres (nestin and A2B5), neurons (MAP2 clone M13, MAP2 clone AP18, and Doublecortin), astrocytes (GFAP), and oligodendrocytes (A2B5 and mGalc). In the absence of any chemical exposure, approximately 46% of rNSC differentiated into astrocytes and neurons, while 40.0% of the rNSC differentiated into oligodendrocytes. Both non-cytotoxic and cytotoxic concentrations of DA and OTA reduced the differentiation of rNSC into astrocytes, neurons, and oligodendrocytes. Furthermore, a non-cytotoxic nanomolar (0.05 µM) concentration of DA and 0.2 µM of OTA reduced the percentage differentiation of rNSC into astrocytes and neurons. Morphometric analysis showed that the highest concentrations (10 μM) of DA reduced axonal length. These indicate that low, non-cytotoxic concentrations of DA and OTA can interfere with the differentiation of rNSC.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".