Microdosimetric considerations for radiation response studies using Raman spectroscopy
Bibliographic record
Abstract
Purpose Recent Raman spectroscopy (RS) studies of radiation response involve subcellular (μm‐scale) sampling volumes and macroscopic doses as low as 0.005 Gy. These studies ignore the stochastic nature of radiation transport and energy deposition, which can lead to considerable microdosimetric “spread” (i.e., variation in energy deposition). The goal of this work is to use Monte Carlo (MC) simulations to investigate the microdosimetric spread across populations of microscopic targets relevant for RS studies of cellular radiation response. Methods Simulation geometries involve populations of 1600 cells, with two sizes of sampling volumes (representative of recent RS studies) considered within each nucleus, as well as averaging over multiple sampling volumes in the same nucleus. To investigate variation in microdosimetric spread as a function of dose and target size, simple cubic voxel geometries are also considered. MC simulations are used to score energy imparted per unit mass (specific energy, z) in targets (nuclei, sampling volumes, and voxels), considering doses from a few mGy to several Gy. Three photon spectra are considered: 120 kVp x‐ray, cobalt‐60, and a 6 MV medical linac. Results For μm‐sized targets, there can be considerable variation in energy deposition across a population of targets: the specific energy distribution is skewed, a large fraction of targets receive no energy, and the standard deviation of the specific energy relative to the mean, , is considerable. These results vary with source energy and (macroscopic) dose: for 60Co with cylindrical nuclei of 12.8 μm height and diameter, is 17% at 0.02 Gy, decreasing to 2% at 2 Gy. In contrast, for cylindrical sampling volumes with 1 μm diameter and 4 μm height, is 170% at 0.02 Gy and 18% at 2 Gy. Results of MC simulations involving cubic voxel geometries are fit to an equation relating the relative standard deviation of the specific energy to the target volume and dose; additionally, specific energy distributions are compared with normal distributions. Conclusions Microdosimetric considerations are important for RS cellular radiation response studies, especially for low doses. The results of this work may motivate changes to current measurement and data analysis methods for RS experiments, and motivate future work comparing MC simulation results with RS measurements to advance understanding of radiation response.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".