Combining Gold Nanoparticles with Other Radiosensitizing Agents for Unlocking the Full Potential of Cancer Radiotherapy
Bibliographic record
Abstract
About half of cancer patients (50%) receive radiotherapy (RT) for the treatment of local tumors. However, one of the main obstacles in RT is the close proximity of adjacent organs at risk, resulting in treatment doses being limited by significant tissue toxicity, hence preventing the necessary dose escalation that would guarantee local control. Effective local cancer therapy is needed to avoid progression of tumors and to decrease the development of systemic metastases which may further increase the possibility of resection. In an effort to do so, radiosensitizing agents are introduced to further increase damage to the tumor while minimizing normal tissue toxicity. Cisplatin and docetaxel (DTX) are currently being used as radiation dose enhancers in RT. Recent research shows the potential of gold nanoparticles (GNPs) as a radiosensitizing agent. GNPs are biocompatible and have been tested in phase I clinical trials. The focus will be on exploring the effects of adding other radiosensitizing agents such as DTX and cisplatin to the GNP-RT platform. Therefore, a combined use of local radiosensitizing agents, such as GNPs, with currently available radiosensitizing drugs could make a significant impact in future RT. The ultimate goal is to develop treatments that have limited or nonexistent side effects to improve the quality of life of all 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".