Sci-Thurs AM: YIS-10: Raman Microscopy of Single Human Tumor Cells Irradiated <i>in Vitro</i>: A New Prospect for Experimental Radiobiology
Bibliographic record
Abstract
Continued investigation into radiation interactions with cells and tissues is necessary to shed light on outstanding radiobiological issues such as the variation in patient radiosensitivity, the inability to monitor a patient's radioresponse during the course of an extended treatment, and the failure of current models to predict cell survival or tumor control at single high doses. One technique that shows promise for radiobiological studies is Raman microscopy (RM). RM involves focusing an optical wavelength laser through a high power microscope objective onto a sample, inducing molecular vibrations and creating inelastically scattered photons with frequencies and intensities characteristic to the properties of the molecules in the sample. The resulting Raman spectrum collected provides a detailed description of the molecular composition within the sampling volume. In this study, human prostate tumor cells are cultured in vitro and exposed to single high doses (15–50 Gy) of 6 MV radiation. Irradiated and unirradiated cell cultures are re-incubated for varying amounts of time post-exposure, up to five days. Principal component analysis (PCA) is used to show that the Raman spectra collected from irradiated cells display a novel radiobiological effect that is correlated with both dose and post-exposure incubation time. The measurable effect is expressed as varying concentrations of lipids, nucleic acids, and conformational protein structures within irradiated cells as compared to unirradiated cells. PCA is shown to be useful in distinguishing the radiation-induced changes in cell spectra from the natural spectral variability within a cell culture due to cell cycle and other growth conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".