Shape Effect of Carbon Nanovectors on Angiogenesis
Bibliographic record
Abstract
Physically diverse carbon nanostructures are increasingly being studied for potential applications in cancer chemotherapy. However, limited knowledge exists on the effect of their shape in tuning the biological outcomes when used as nanovectors for drug delivery. In this study, we evaluated the effect of doxorubicin-conjugated single walled carbon nanotubes (CNT-Dox) and doxorubicin-conjugated spherical polyhydroxylated fullerenes or fullerenols (Ful-Dox) on angiogenesis. We report that CNTs exert a pro-angiogenic effect in vitro and in vivo. In contrast, the fullerenols or doxorubicin-conjugated fullerenols exerted a dramatically opposite antiangiogenic activity in zebrafish and murine tumor angiogenesis models. Dissecting the angiogenic phenotype into discrete cellular steps revealed that fullerenols inhibited endothelial cell proliferation, while CNTs attenuated the cytotoxic effect of doxorubicin on the endothelial cells. Interestingly, CNT promoted endothelial tubulogenesis, a late step during angiogenesis. Further, mechanistic studies revealed that CNTs, but not fullerenols, induced integrin clustering and activated focal adhesion kinase and downstream phosphoinositide-3-kinase (PI3K) signaling in endothelial cells, which can explain the distinct angiogenic outcomes. The results of the study highlight the function of physical parameters of nanoparticles in determining their activity in biological settings.
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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) | 0.000 | 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) | 0.001 | 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".