Raman spectroscopy of x-ray irradiated blood plasma: a pilot study
Bibliographic record
Abstract
Abstract Biodosimetry is a key technique for retrospective radiation dosimetry that provides individual estimates of the absorbed dose of ionising radiation based on the detection of biological indicators. A critical challenge confronting current biodosimetry methods is the time and technical expertise needed in processing and analysing samples, therefore new high-throughput techniques are required. In this pilot study, we demonstrate a technique based on RS and multivariate analysis of peripheral blood plasma from nine healthy male and female anonymous donors for the classification and biomarker identification of ex vivo irradiated blood exposed to 0 (control), 5 and 20 Gy of x-ray dose. After 4 h post-exposure, the blood was centrifuged, and the blood plasma samples were immediately frozen at −80 °C. Raman spectra were measured from thawed blood plasma using a custom benchtop Raman micro-spectroscopy setup. Data were preprocessed and analysed using partial least squares-discriminant analysis (PLS-DA). We applied a method based on a linear mixed-effects model to compensate for the differences in covariates such as gender, age and complete blood count between donors. After covariate adjustment, the application of PLS-DA to the residual Raman spectral intensities provided improved separation in the binary classification results (0 vs. 5 and 0 vs. 20 Gy). Raman spectral biomarkers responsible for the discrimination were extracted by evaluating the coefficients of the PLS-DA loading vectors. Sparse PLS-DA was demonstrated to be a promising method that offers the potential to further narrow down the regions in the Raman spectra that are dose discriminatory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".