Assessment of the developmental toxicity of nanoparticles in an<i>ex vivo</i>3D model, the murine limb bud culture system
Bibliographic record
Abstract
The rapid growth of nanotechnological products for biomedical applications has exacerbated the need for suitable biological tests to evaluate the potential toxic effects of nanomaterials. The possible consequences of exposure during embryo and fetal development are of particular concern. The limb bud culture is an ex vivo 3D model in which growth, cell differentiation, and tissue organization occur and both molecular and functional endpoints can be quantitatively assessed. We employed this model to assess biochemical and morphological changes induced during organogenesis by two classes of nanostructured materials: quantum dot nanocrystals and organic polyglycerol sulfate dendrimers (dPGS). We show that quantum dots carrying mercaptopropionic acid (QD-MPA) on the surface, commonly used in biological studies, inhibit the development of limb buds from CD1 wildtype and Col2a1; Col10a1; Col1a1 triple transgenic fluorescent reporter mice, as revealed by changes in several morphological and biochemical markers. QD-MPA interfere with chondrogenesis and osteogenesis and disrupt the expression of COL10A1 and COL1A1, key markers of differentiation. In contrast, equivalent (3-100 nM) concentrations of dPGS do not adversely affect limb development. Neither QD-MPA nor dPGS-Cy5 alters the expression of several markers of cell proliferation or apoptosis. Collectively, these results suggest that murine limb buds in culture constitute a convenient, inexpensive and reliable developmental model for the assessment of the nanotoxicological effects of nanocrystals and polymers. In these 3D cultures, any effect that is observed can be directly ascribed to the nanostructures per se or a degradation component released from the complex nanostructure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".