Bibliographic record
Abstract
This dissertation presents the effect of peptide-modified 10 nm gold nanoparticles (GNPs) with chemotherapeutic drugs, bleomycin and cisplatin, and 2 Gy of 6 MV X-ray irradiation in MDA-MB-231 cells. The GNPs were modified with a peptide sequence containing an ‘RGD’ amino acid motif. Bleomycin binds to the surface of the GNPs through a thiol bond and cisplatin has no known significant interaction with the GNP surface. No significant toxicity was induced by introducing GNPs to MDA-MB-231 cells at the 0.3 nM concentration used throughout this dissertation. The surface modification with ‘RGD’ peptides increased accumulation of the GNP constructs 6~7 fold compared to the unmodified counterparts. There was no significant difference in the accumulation of GNPs in the presence of bleomycin or cisplatin. These results suggest that the presence of chemotherapeutics do not affect the accumulation of peptide modified GNPs into cells. The effect of having GNPs with chemotherapeutics was examined. The presence of GNPs with bleomycin decreased the survival of MDA-MB-231 cells by 18 ± 3 % compared to treatment with the same concentration of free bleomycin. Treating cells with GNPs and cisplatin did not have a significant difference in survival compared to the same concentration of free cisplatin treatment. This suggests that conjugating chemotherapeutics onto the GNPs can result in a more efficient delivery of the drug. If the drug does not bind to the GNP surface, having GNPs in the media does not interfere with the uptake of the drug. The effect of radiosensitization in the presence of GNPs was studied by incubating cells with 0.3 nM GNPs prior to irradiation with 2 Gy of 6 MV X-rays. The survival fraction decreased by 19 ± 6 % compared to the irradiated control condition. Lastly, the triple combined effect of GNPs, chemotherapeutics, and irradiation was investigated. The presence of GNPs had an advantage to the combined chemotherapy and radiation therapy. Based on results from these studies, GNPs can be used in addition to combined chemotherapy and radiation therapy for improved outcomes in cancer treatment
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".