Reply to Comment on ‘Implications on clinical scenario of gold nanoparticle radiosensitization in regards to photon energy, nanoparticle size, concentration and location’
Bibliographic record
Abstract
In a comment on a recent paper by Lechtman et al (2011 Phys. Med. Biol. 56 4631–47), McMahon critiques one of Lechtman's conclusion that gold nanoparticle radiosensitization may not be applicable to megavoltage radiotherapy. He refers to recently published experimental studies showing radiosensitization with 6 MV x-rays and low gold concentrations. However those published studies show conflicting results, presenting survival curves with a small cell death increased with gold and some with no difference. In regards to gold nanoparticle radiosensitization physical, chemical, pharmacological and biological constraints all interplay. There are plenty of experimental and theoretical data to confirm the strong dependence to the primary photon energy and gold concentration. The manuscript of Lechtman added the dependence to microscopic localization, analysing the spatial distribution and the quality of secondary electrons, as a major player in the feasibility of the technique. We agree that radiobiological dose modification factor should be considered, but it is unlikely that accounting for a maximum RBE of 2 can compensate for the drastic decrease of photoelectric events shifting from kV to MV. Lechtman calculated that to achieve similar radiosensitization for low energy beams and intra-cellular gold concentration of 0.5%, concentrations 300 times higher are required for 6 MV beams. To date it seems unlikely that concentration higher than 1% could be achieved such that it is unlikely that megavoltage would yield a measurable clinical effect.
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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.075 | 0.069 |
| Insufficient payload (model declined to judge) | 0.007 | 0.011 |
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".