White Blood Cell Subsets in Buffy Coat-Derived Platelet Concentrates: The Effect of Pre- and Poststorage Filtration
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Our objective was to study the effect of storage time on the filtration of platelet concentrates (PCs). We compared the total number of white blood cells (WBC), as well as the distribution of WBC subsets, in units filtered before and after storage. MATERIALS AND METHODS: Buffy coat-derived PCs were filtered either fresh or after 5 days of storage, and total WBC were enumerated by flow cytometry. WBC subsets were analyzed by flow cytometry with three-color fluorescence. RESULTS: The total number of white cells before filtration was significantly higher in fresh units compared with stored units, whereas in postfiltration samples the number of white cells was significantly lower in the fresh compared with the stored units. Although absolute numbers were significantly reduced, filtration also induced significant changes in the proportions of subsets in both fresh and stored units; the percentage of T cells was decreased, whereas the percentage of B cells and monocytes was increased after filtration. CONCLUSION: Our results suggest that prestorage WBC filtration of platelet concentrates is superior in reducing the absolute numbers of WBC. However, both pre- and poststorage WBC filtration significantly affect the proportions of WBC in the final product, decreasing the number of T cells while apparently increasing the proportion of MHC class II-positive cell populations.
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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 | 0.000 | 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 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".