An<i>in vitro</i>study of bare and poly(ethylene glycol)-co-fumarate-coated superparamagnetic iron oxide nanoparticles: a new toxicity identification procedure
Bibliographic record
Abstract
As the use of superparamagnetic iron oxide nanoparticles (SPION) in biomedical applications increases (e.g. for targeting drug delivery and imaging), patients are likely to be exposed to products containing SPION. Despite their high biomedical importance, toxicity data for SPION are limited to date. The aim of this study is to investigate the cytotoxicity of SPION and its ability to change cell medium components. Bare and poly(ethylene glycol)-co-fumarate (PEGF)-coated SPION with narrow size distributions were synthesized. The particles were prepared by co-precipitation using ferric and ferrous salts with a molar Fe3+/Fe2+ ratio of 2. Dulbecco's modified Eagle's medium (DMEM) and primary mouse fibroblast (L929) cell lines were exposed to the SPION. Variation of cell medium components and cytotoxicity due to the interactions with nanoparticles were analyzed using ultraviolet and visible spectroscopy (UV/vis) and the 3-[4,5-dimethylthiazol-2yl]-2,5-diphenyltetrazolium bromide (MTT) assay methods, respectively. The toxicity amount has been traditionally identified by changes in pH and composition in cells and DMEM due to the tendency of SPION to adsorb proteins, vitamins, amino acids and ions. For in vitro toxicity assessments, a new surface passivation procedure is proposed which can yield more reliable quantitative results. It is shown that a more reliable way of identifying cytotoxicity for in vitro assessments is to use particles with saturated surfaces via interactions with DMEM before usage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.000 | 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".