Raman Spectroscopy of Human Lens Epithelial Cells Exposed to a Low-Dose Range of Ionizing Radiation
Bibliographic record
Abstract
Recent studies indicate that ionizing radiation induced opacification in the lens of the eye occurs at lower doses (< 2 Gy) than past protection guidelines had assumed.Research is currently focused on identifying early signs of the lens degradation that leads to cataract formation, and in developing non-invasive assays capable of detecting low dose exposures to the lens of the eye.Raman spectroscopy (RS) is a non-invasive, vibrational spectroscopic technique based on the inelastic scattering of light by molecular vibrations.It is capable of providing information on the molecular makeup of biological samples that can be used for classification purposes.This work focuses on the application of RS combined with multivariate statistical analysis to detect radiation induced changes in vitro within human lens epithelial (HLE) cells exposed to a broad dose-range (0.01-5 Gy).The development of a new Raman microscope which will increase data acquisition throughput is also discussed.I would like to begin by thanking all members of the Carleton Biophotonics Research Group (CBRG), both past and present.First and foremost, I would like to express my heartfelt gratitude to my supervisor, Dr. Sangeeta Murugkar, for providing invaluable advice and guidance, for keeping me on track throughout my work, and without whom this research would not have happened.I would like
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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".