Gold Nanoparticle as a Model Nanoparticle System for EfficientDelivery of Anticancer Drugs
Bibliographic record
Abstract
Only a small fraction of anticancer drugs gets (<0.01%) into the tumor when they are administered as free drugs to cancer patients.This results in many side effects to patients since drug molecules get into healthy, normal cells as well as tumour cells.We propose using gold nanoparticles (GNPs) for controlled and optimized delivery of drugs to overcome the side effects of poor distribution of anticancer drugs.Our studies show that normal cells take much less GNPs in contrast to tumor cells making them a more selective delivery vehicle for anticancer drugs.In this study, we have shown that GNPs offer the possibility of transporting major quantities of drugs due to their large surface-to-volume ratio.We have functionalized GNPs with natural peptides and polyethylene glycol for effective intracellular targeting and biocompatibility, respectively.In this in vitro study, we chose to use bleomycin (BLM) as the anticancer drug due to its limited therapeutic efficiency (harmful side effects).BLM was conjugated onto GNPs through a thiol bond.The effectiveness of BLM was observed by visualizing DNA double strand breaks and by calculating the survival fraction.The action of the drug (where the drug takes effect) is known to be in the nucleus, and our experiments have shown that some of the GNPs carrying BLM were present in the nucleus.The use of GNPs to deliver anticancer drugs increased the delivery and therapeutic efficacy compared to the free drug.Combined use of radiation therapy and chemotherapy is being used to treat locally advanced tumors.It is shown that GNPs can also be used as radiation dose enhancers.Therefore, this GNP-based drug carrier will make a paradigm change in achieving a significantly higher therapeutic ratio while minimizing side effects of both chemotherapy and radiotherapy while improving the quality of life of cancer patients.
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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) | 0.001 | 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 | 0.000 |
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".