A rabbit model for monitoring in vivo viability of human platelet concentrates using flow cytometry
Bibliographic record
Abstract
BACKGROUND: Viability in vivo of novel platelet components cannot be readily determined in human transfusions. Elaboration of valid animal models may be useful for this purpose. STUDY DESIGN AND METHODS: Viability of platelet concentrates (PCs) WBC reduced before storage was determined by flow cytometry in rabbits whose reticuloendothelial system was inhibited by ethyl palmitate administration. PCs stored at 22 degrees C for 2 and 5 days (D2- and D5-PCs) or refrigerated PCs (3-6 days at 22 degrees C plus 1-4 days at 4 degrees C, RF-PCs) were transfused into rabbits. Five parameters of PC viability in vivo were calculated from human platelet survival curves: survival time, recovery 0.5 and 24 hours after transfusion (R0.5, R24), maximal recovery (Rmax), and total recovery for 0 to 24 hours (RSigma). RESULTS: No differences in viability of D2- and D5-PCs were found. In contrast, viability of RF-PCs was significantly lower than that of D2-PCs, as was revealed with diverse sensitivity by four parameters: RSigma > R24 > R0.5=survival time (p < 0.001, p < 0.01, and p < 0.05, respectively). CONCLUSION: The rabbit model elaborated is sufficiently sensitive to reveal differences in human platelet viability in vivo between conventional and cold-damaged PCs. It may be useful for comparing viability of different platelet components that cannot be readily tested in human transfusions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".