«In Vitro» evaluation of single walled carbon nanotubes as targeted drug delivery systems
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have become some of the most promising drug delivery systems due to their unique properties, especially their high surface area and their ease to penetrate cells.The aim of this thesis was to render single walled carbon nanotubes (SWNTs) more biocompatible, and to test their ability to selectively deliver suitable dosages of anti-cancer drugs to α v β 3 integrins and epidermal growth factor receptor (EGFR) expressing cancer cells used as delivery targets.Those two targets are important to consider, as they are highly present on the cell membrane of several cancer cells, such as colon, breast, leukemic, and lung cancer.Results reveal that the combination of covalent and noncovalent surface modification of SWNTs increased SWNTs biocompatibility towards RAW 264.7 and Caco-2 cells by 17.4% and 20.8%, respectively, compared to covalently modified SWNTs.Results also show that the delivery of the widely used anti-cancer drug, doxorubicin (DOX), was higher when targeted by the SWNTs.In fact, the concentration of targeted DOX was 1.4 (± 0.3) folds higher and 2 (± 0.7) folds higher than that of free DOX in Caco-2 and RAW 264.7 cells, respectively.Similarly, the cytotoxicity of the SWNT-targeted DOX on RAW 264.7 cells at 48h post exposure was 3.6 folds higher than that of free DOX.Thus, the study reveals that SWNTs are capable to enhance drug effect on cancer cell lines.Further in vivo studies are recommended to evaluate the full potentials.
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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".