Application of High-Z Nanoparticles to Enhance Current Radiotherapy Treatment
Bibliographic record
Abstract
Radiotherapy is an essential component of the treatment regimens for many cancer patients. Despite recent technological advancements to improve dose delivery techniques, the dose escalation required to enhance tumor control is limited due to the inevitable toxicity to the surrounding healthy tissue. Therefore, the local enhancement of dosing in tumor sites can provide the necessary means to improve the treatment modality. In recent years, the emergence of nanotechnology has facilitated a unique opportunity to increase the efficacy of radiotherapy treatment. The application of high-atomic-number (Z) nanoparticles (NPs) can augment the effects of radiotherapy by increasing the sensitivity of cells to radiation. High-Z NPs can inherently act as radiosensitizers as well as serve as targeted delivery vehicles for radiosensitizing agents. In this work, the therapeutic benefits of high-Z NPs as radiosensitizers, such as their tumor-targeting capabilities and their mechanisms of sensitization, are discussed. Preclinical data supporting their application in radiotherapy treatment as well as the status of their clinical translation will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".