Concurrent and separate inside‐out transition of platelet apoptosis and activation markers to the platelet surface
Bibliographic record
Abstract
The cell plasma membrane is tightly coupled with the vital processes of apoptosis and activation. In the current study, we investigated exposure of the apoptosis marker phosphatidylserine (PS) and activation marker P-selectin (CD62) on the plasma membrane of anucleate platelets. We found that, depending on triggering stimuli, the plasma membrane of human platelets may exist in four states with predominant exposure of (i) PS but not CD62 (75·9 ± 2·8% of total cells), (ii) CD62 but not PS (86·2 ± 1·3%), (iii) both PS and CD62 (89·6 ± 1·0%) or (iv) neither PS nor CD62 (87·9-97·5%), when platelets were treated at optimal conditions with pro-apoptotic BH3 mimetic ABT-737, thrombin, calcium ionophore A23187 or control diluents, respectively. The dynamics of PS exposure induced by ABT-737 is a slow temperature-dependent process requiring 90 min treatment at 37°C rather than at room temperature for obtaining high levels of PS exposure. In contrast, thrombin-induced CD62 exposure and A23187-induced PS and CD62 exposure showed fast temperature-independent dynamics. This model of selective and concurrent stimulation of PS and/or CD62 transition to the platelet surface provides an experimental horizon for elucidating the roles of plasma membrane markers of platelet apoptosis and activation in platelet 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 | 0.000 | 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 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".