Single-Walled Carbon Nanotubes Mediated Neovascularity Targeted Antitumor Drug Delivery System
Bibliographic record
Abstract
PURPOSE: The aim of this study was to prepare a new neovascularity targeting antitumor drug delivery system mediated by single-walled carbon nanotubes (SWNTs). METHODS: In this study, antiangiogenesis agent 2-methoxyestradiol was loaded by SWNTs via π~π accumulation. The SWNTs were then linked with NGR (Asn-Gly-Arg) peptide, which could target tumor angiogenesis. This drug delivery system was characterized by transmission electron microscope, scanning electron microscopy, and atomic force microscope analysis. The suppression efficacy of tumor growth in cultured breast cancer cell line was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The in vivo antitumor activity was evaluated on the Sarcoma (S180) tumor-bearing mice model. RESULTS: The characteristics of this drug delivery system showed that the particle of complex was 190 ± 4.3 nm in size distribution and 23.56 ± 2.03 mV in zeta potential. The inhibition ratio of this SWNTs drug delivery system at 24, 48, and 72 h was about 57.7%, 83.6%, and 88.2%. Compared with normal saline group, the relative tumor volumes in the 2ME, SWNTs-2ME, and NGR-SWNTs-2ME groups were decreased 1 week after administration. CONCLUSION: This novel neovascularity targeting drug delivery system containing NGR-SWNTs-2ME may be beneficial to improve treatment efficacy and minimize side effects in future cancer therapy.
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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) | 0.000 | 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".