Application of Raman spectroscopy and multivariate analysis to detect ionizing radiation-induced changes in blood plasma
Bibliographic record
Abstract
The risk of large-scale radiological/nuclear events has notably increased in recent years. Biodosimetry is considered an essential tool for emergency management following such unplanned exposures to ionizing radiation. For example, by assessing an individual’s received dose to blood, biodosimetry can support medical screening and individual health management. Current biodosimetry techniques, such as the dicentric chromosome assay, are based on the analysis of chromosomal aberrations Although highly accurate, these methods are time-consuming and labour-intensive. We recently developed a new high-throughput approach based on Raman spectroscopy of blood combined with covariate-adjusted multivariate analysis for the detection of irradiated blood. We found that the protein bands in the Raman spectra were the main sources of discrimination between unirradiated (control) and irradiated blood. In this follow up work, we explored the application of Raman spectroscopy and multivariate analysis to blood plasma to avoid dominant hemoglobin contributions. Peripheral blood drawn from a healthy volunteer was irradiated at 0 (control), 5 and 20 Gy using 250 kV X-rays. After a 4 hour incubation time, plasma centrifuged from the blood sample was immediately frozen at -80 deg C. Raman measurements were performed in triplicate on thawed blood plasma samples. Partial least squares-discriminant analysis (PLS-DA) was utilized for multi-class differentiation between Raman spectra of 0, 5 and 20 Gy irradiated plasma. Sparse PLS-DA (sPLS-DA) provided improved dose classification after combining Raman spectral data from different batches. Biomarker information related to radiation-induced changes in blood plasma was also extracted from sPLS-DA. The outcomes of these initial studies highlight the value of Raman spectroscopy to support biodosimetry.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".