Using Raman spectroscopy to assess hemoglobin oxygenation in red blood cell concentrate: an objective proxy for morphological index to gauge the quality of stored blood?
Bibliographic record
Abstract
Blood banking is an essential aspect of modern medical care. When red blood cells (RBCs) are stored, they become damaged by various chemical processes, such as accumulation of their own waste products and oxidative injury, among others. These processes lead to the development of the RBC storage lesion, a complex condition where the severity is reflected through the morphology of the stored cells. It was hypothesized that Raman spectroscopy could be used to monitor certain structural and compositional changes associated with such ageing effects and that a relationship between these features and traditional morphology (as measured using a morphology index) could be observed. The hypothesis was tested by measuring spectral features associated with hemoglobin oxygenation from dry-fixed smears and liquid RBCs for twenty-nine different donors (combined), and comparing the trends with morphological scoring from seven of these donors. After appropriately fitting the two data sets to either power or linear curves, the oxygenation state was shown to change in a manner that was donor-dependent and that closely tracked morphological changes. This study suggests Raman analysis has promise for providing a rapid and objective measure of the cell quality of stored RBCs through measurements of hemoglobin oxygenation that is comparable to traditional morphological assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".