Gold nanoparticles and their alternatives for radiation therapy enhancement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Radiation therapy is one of the most commonly used treatments for cancer. The dose of delivered ionizing radiation can be amplified by the presence of high-Z materials via an enhancement of the photoelectric effect; the most widely studied material is gold (atomic number 79). However, a large amount is needed to obtain a significant dose enhancement, presenting a challenge for delivery. In order to make this technique of broader applicability, the gold must be targeted, or alternative formulations developed that do not rely solely on the photoelectric effect. One possible approach is to excite scintillating nanoparticles with ionizing radiation, and then exploit energy transfer between these particles and attached dyes in a manner analogous to photodynamic therapy (PDT). Doped rare-earth halides and semiconductor quantum dots have been investigated for this purpose. However, although the spectrum of emitted light after radiation excitation is usually similar to that seen with light excitation, the yield is not. Measurement of scintillation yields is challenging, and in many cases has been done only for bulk materials, with little understanding of how the principles translate to the nanoscale. Another alternative is to use local heating using gold or iron, followed by application of ionizing radiation. Hyperthermia pre-sensitizes the tumors, leading to an improved response. Another approach is to use chemotherapeutic drugs that can radiosensitize tumors. Drugs may be attached to high-Z nanoparticles or encapsulated. This article discusses each of these techniques, giving an overview of the current state of nanoparticle-assisted radiation therapy and future directions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it