Role of P-Selectin and GPIbα in the Fast and Delayed Clearance of Transfused Platelets.
Bibliographic record
Abstract
Abstract Platelets become activated during preparation and storage of platelet concentrates (PCs) for transfusion. Flow cytometric assays of platelet activation can be employed for quantifying in vitro quality of PCs. It remains, however, unclear whether the level of in vitro platelet activation in stored PCs correlates with in vivo survival of the platelets after transfusion. Platelet surface glycoprotein (GP) Ibα and P-selectin (CD62) can be involved in regulation of posttransfusion PC clearance, mediating adhesive interactions of platelets with counter-receptors on leukocytes and endothelial cells. Recently, we described a rabbit model for analyzing posttransfusion kinetics of human PCs (Leytin et al, Transfusion42:711, 2002, Transfusion43:983, 2003). In the present work, we used this validated model for studying the implication of CD62 and GPIbα expression in posttransfusion PC clearance. Platelet activation in vitro was determined by flow cytometry using anti-CD62 and anti-GPIbα antibodies. PC clearance in vivo was evaluated in rabbits with inhibited reticuloendothelial system, as measured by 0.5 hr (R0.5), 24 hr (R24) and total (R∑) platelet recoveries, and survival time (ST). Correlations were analysed between in vitro assays of platelet activation and in vivo clearance of conventional (Day 2–5) and outdated (Day 7–8) PCs stored at 22°C, and refrigerated PCs. We found that the binding of anti-CD62 antibody was significantly increased in outdated and refrigerated PCs compared to conventional PCs, reflecting an increased exposure of CD62 on the platelet surface. In contrast, binding of anti-GPIbα antibody was significantly decreased during prolonged and refrigerated PC storage. The clearance of conventional (Day 2–5) PCs from the circulation can be described by a biphasic survival curve. The first (early) phase of platelet clearance is characterized by fast (≥ 14 x 109 platelets per hour) platelet removal, whereas the second (delayed) phase has a much slower rate of platelet clearance (approximately 0.4 x 109 platelets per hour). The biphasic survival curves were also obtained for outdated and refrigerated PCs, and were employed for determining fast (R0.5), delayed (ST and R24) and overall (fast + delayed; R∑) platelet clearance in vivo. We found that when stored PCs are cold-damaged, their in vivo viability decreased significantly, in comparison to conventional PCs, as reflected by the fast, delayed and overall platelet clearances. Viability of Day 7–8 PCs is also decreased, compared to Day 2–5 PCs, but only the fast and overall platelet clearance increased significantly. Negative correlation was observed between in vitro anti-CD62-binding to platelets and their fast, but not delayed, clearance. In contrast, anti-GPIbα-binding showed positive correlations with delayed, but not fast, platelet clearance. Overall clearance correlated better with anti-GPIbα- than with anti-CD62-binding. We also demonstrated that CD62 is shed from the platelet surface after transfusion, whereas GPIbα remains unchanged on the surface of circulating platelets. The data suggest that CD62 exposure during PC storage triggers fast CD62-mediated PC clearance. However, after CD62 shedding during platelet circulation, in vitro GPIbα alterations, such as cleavage, clustering or conformation changes, may determine long-term GPIbα-mediated PC clearance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".