Crucial Ignored Parameters on Nanotoxicology: The Importance of Toxicity Assay Modifications and “Cell Vision”
Bibliographic record
Abstract
Until now, the results of nanotoxicology research have shown that the interactions between nanoparticles (NPs) and cells are remarkably complex. In order to get a deep understanding of the NP-cell interactions, scientists have focused on the physicochemical effects. However, there are still considerable debates about the regulation of nanomaterials and the reported results are usually in contradictions. Here, we are going to introduce the potential key reasons for these conflicts. In this case, modification of conventional in vitro toxicity assays, is one of the crucial ignored matter in nanotoxicological sciences. More specifically, the conventional methods neglect important factors such as the sedimentation of NPs and absorption of proteins and other essential biomolecules onto the surface of NPs. Another ignored matter in nanotoxicological sciences is the effect of cell "vision" (i.e., cell type). In order to show the effects of these ignored subjects, we probed the effect of superparamagnetic iron oxide NPs (SPIONs), with various surface chemistries, on various cell lines. We found thatthe modification of conventional toxicity assays and the consideration of the "cell vision" concept are crucial matters to obtain reliable, and reproducible nanotoxicology data. These new concepts offer a suitable way to obtain a deep understanding on the cell-NP interactions. In addition, by consideration of these ignored factors, the conflict of future toxicological reports would be significantly decreased.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".