Role of platelet surface glycoprotein Ibα and P‐selectin in the clearance of transfused platelet concentrates
Bibliographic record
Abstract
BACKGROUND: Role of P-selectin (CD62) and glycoprotein (GP) Ibalpha in posttransfusion clearance of platelet concentrates (PCs) is unclear. STUDY DESIGN AND METHODS: Platelet (PLT) activation in vitro was determined by flow cytometry using anti-CD62 and anti-GPIbalpha. PC clearance in vivo was evaluated in an animal model using rabbits with an inhibited reticuloendothelial system, as measured by 0.5-hour (R(0.5)), 24-hour (R(24)), and total (R( summation operator )) PLT recoveries, and survival time (ST). Correlations were analyzed between in vitro assays of PLT activation and in vivo clearance of conventional (Days 2-5), outdated (Days 7-8), and refrigerated PCs. RESULTS: Binding of anti-CD62 to the PLT surface was significantly increased and of anti-GPIbalpha decreased in outdated and refrigerated PCs compared to conventional PCs. Negative correlation was observed between in vitro anti-CD62 binding and the fast (R(0.5)) PLT clearance, but not with delayed (R(24) and ST) clearance. In contrast, anti-GPIbalpha binding showed positive correlations with delayed but not with fast PLT clearance. Overall (R( summation operator )) clearance correlated better with anti-GPIbalpha than with anti-CD62 binding. CD62 density on the PLT surface was decreased after PC transfusion, whereas GPIbalpha density remained unchanged. CONCLUSION: These data suggest that CD62 exposure on the PLT surface during PC storage triggers fast CD62-mediated PC clearance, whereas in vitro GPIbalpha changes are involved in delayed GPIbalpha-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 | 0.002 |
| 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".