Phosphatidylserine Exposure after Vascular Injury-Platelet or Endothelial
Bibliographic record
Abstract
ABSTRACT Background Platelets membranes are considered the paramount site for the assembly of tenase and prothrombinase complex and are key players in localising coagulation to wound sites. However, the endothelium is also known to express phosphatidylserine (PS) and support the binding of recombinant FVa/FXa even beyond the site of injury. It thus remains unclear, what cell type play the preeminent role in the cellular control of coagulation after vascular injury. Approach To address this question, we utilised a model of haemostasis (full thickness 1mm excisional skin wounds) as well as tissues after injury in laser and ferric chloride models of thrombosis. Damage to the endothelium was assessed by the combined methods of picrosirius red staining, immunofluorescence and electron microscopy. Using multiphoton microscopy, we then compared the spatial distribution of PS on platelets and the endothelium. Results Platelets and detectable PS significantly co-localised compared with similar analysis of endothelial cell and exposed PS on wounded carotids arteries which was not significant. Point injury by laser induced restricted damage of the endothelium which was associated with limited platelets recruitment. In consistence with platelets response after FeCl 3 injury, platelets exposed most of the PS detected at the wound edge where skin vessels were transected in our haemostasis model (Correlation Coeff. 0.78 +/− 0.12 vs 0.35 +/− 0.23). Conclusions We surmised that data from the different models support a paradigm of graded haemostatic response to vascular injury, in which full platelets response is limited to wound sites exposing the sub-endothelial matrix.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".