Non‐invasive monitoring of red blood cells during cold storage using handheld Raman spectroscopy
Bibliographic record
Abstract
BACKGROUND: The current best practices allow for the red blood cells (RBCs) to be stored for prolonged periods in blood banks worldwide. However, due to the individual-related variability in donated blood and RBCs continual degradation within transfusion bags, the quality of stored blood varies considerably. There is currently no method for assessing the blood product quality without compromising the sterility of the unit. This study demonstrates the feasibility of monitoring storage lesion of RBCs in situ while maintaining sterility using an optical approach. STUDY DESIGN AND METHODS: A handheld spatially offset Raman spectroscopy (RS) device was employed to non-invasively monitor hemolysis and metabolic changes in 12 red cell concentrate (RCC) units within standard sealed transfusion bags over 7 weeks of cold storage. The donated blood was analyzed in parallel by biochemical (chemical analysis, spectrophotometry, hematology analysis) and RS measurements, which were then correlated through multisource correlation analysis. RESULTS: ) were found to correlate strongly with bioanalytical data over the length of storage, with correlation values 0.98 (95% confidence interval [CI]: 0.86-1.00; p = .0001), 0.95 (95% CI: 0.71-0.99; p = .0008) and 0.97 (95% CI: 0.79-1.00; p = .0004) respectively. DISCUSSION: This study demonstrates the potential of collecting information on the clinical quality of blood units without breaching the sterility using Raman technology. This could significantly benefit quality control of RCC units, patient safety and inventory management in blood banks and hospitals.
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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 | 0.001 |
| 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) | 0.001 | 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".