A Raman Spectroscopic Study of Cell Response to Clinical Doses of Ionizing Radiation
Bibliographic record
Abstract
The drive toward personalized radiation therapy (RT) has created significant interest in determining patient-specific tumor and normal tissue responses to radiation. Raman spectroscopy (RS) is a non-invasive and label-free technique that can detect radiation response through assessment of radiation-induced biochemical changes in tumor cells. In the current study, single-cell RS identified specific radiation-induced responses in four human epithelial tumor cell lines: lung (H460), breast (MCF-7, MDA-MB-231), and prostate (LNCaP), following exposure to clinical doses of radiation (2-10 Gy). At low radiation doses (2 Gy), H460 and MCF-7 cell lines showed an increase in glycogen-related spectral features, and the LNCaP cell line showed a membrane phospholipid-related radiation response. In these cell lines, only spectral information from populations receiving 10 Gy or less was required to identify radiation-related features using principal component analysis (PCA). In contrast, the MDA-MB-231 cell line showed a significant increase in protein relative to nucleic acid and lipid spectral features at doses of 6 Gy or higher, and high-dose information (30, 50 Gy) was required for PCA to identify this biological response. The biochemical nature of the radiation-related changes occurring in cells exposed to clinical doses was found to segregate by status of p53 and radiation sensitivity. Furthermore, the utility of RS to identify a biological response in human tumor cells exposed to therapeutic doses of radiation was found to be governed by the extent of the biochemical changes induced by a radiation response and is therefore cell line specific. The results of this study demonstrate the utility and effectiveness of single-cell RS to identify and measure biological responses in tumor cells exposed to standard radiotherapy doses.
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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) | 0.000 | 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".