Extracellular matrix proteins in the regulation of thrombus formation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Exposure of subendothelial extracellular matrix (ECM) proteins to the circulation is the key event that initiates platelet accumulation and clot formation after vessel injury. Soluble ECM proteins are also essential for support and regulation of these events. This review discusses the current understanding of ECM proteins in thrombosis and hemostasis. RECENT FINDINGS: Fibrinogen and von Willebrand factor (VWF) are considered essential for thrombosis and hemostasis. Interestingly, αIIbβ3 integrin-dependent thrombus formation persists in the absence of fibrinogen and VWF, suggesting that other αIIbβ3 ligand (s), likely ECM proteins, can still mediate thrombosis. Fibronectin increases in platelets of fibrinogen-deficient humans and mice. This ECM protein can rapidly deposit onto the injured vessel wall prior to platelet accumulation. By switching from the soluble to insoluble form, plasma fibronectin supports hemostasis and inhibits excessive thrombosis. Fibrin, fibronectin, VWF, vitronectin, neutrophil extracellular traps, and other ECM proteins in the blood clot form a de-facto ECM, which interacts with various types of blood cells to regulate the evolution and resolution of the clot. SUMMARY: ECM proteins are intricately involved in major steps of thrombus formation. Further investigations of ECM proteins will reveal new therapeutic targets for treatment of thrombosis and bleeding disorders. VIDEO ABSTRACT: http://links.lww.com/COH/A12
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".