Gold nanoparticle mediated combined cancer therapy
Bibliographic record
Abstract
The combined use of radiation therapy and chemotherapy is commonly being used in cancer treatment. The side effects of the treatment can be further minimized through targeted delivery of anticancer drugs and local enhancement of the radiation dose. Gold nanoparticles (GNPs) can play a significant role in this regard since GNPs can be used as radiation dose enhancers and anticancer drug carriers. Anticancer drug, bleomycin, was chosen as the model drug, since it could be easily conjugated onto GNPs through the gold–thiol bond. Gold nanoparticles of size 10 nm were synthesized using the citrate reduction method. The surface of The GNPs was modified with a peptide sequence (CKKKKKKGGRGDMFG) containing the RGD domain and anticancer drug, bleomycin. Human breast cancer cells (MDA-MB-231) were incubated with 0.3 nM concentration of GNP–drug complex for 16 h prior to irradiation with a 2 Gy single fraction of 6 MV X-rays. After the treatment, cells were trypsinized and seeded in 60 mm dishes for clonogenic assay. Damage to DNA was probed using immunofluorescence assay. Cancer cells internalized with the GNP–drug complex had a 32 ± 9% decrease in cell survival and statistically significant enhancement in DNA (deoxyribonucleic acid) damage as compared to control cells (irradiated with no GNPs) after receiving a radiation dose of 2 Gy with 6 MV photons. The experimental results demonstrate that GNP-mediated chemoradiation has the potential to improve cancer care in the near future through enhancement of the local radiation dose and controlled delivery of anticancer drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".