Assessment of the developmental toxicity of nanoparticles in an<i>ex vivo</i>3D model, the murine limb bud culture system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 it