Red Blood Cells in Thrombosis: Active Participants in Clot Formation and Stability– A Systematic Review
Bibliographic record
Abstract
Abstract: Thrombosis, the formation of blood clots within blood vessels, has traditionally been attributed to platelets and clotting factors. Red blood cells (RBCs) play a significant role in thrombosis by impacting clot formation, stability, and fibrinolysis through mechanisms such as platelet margination, thrombin generation, and microvesicle release. However, their prothrombotic functions remain insufficiently studied. In this systematic review, which follows PRISMA guidelines, the aim is to explore how RBCs contribute to thrombus formation, stabilization, and resolution. This review analyzed peer-reviewed English-language studies and reviews on RBC involvement in thrombosis, focusing on clot formation, stability, and fibrinolysis. Studies in humans and relevant animal models were included, while case reports, non-English studies, and articles lacking methodological details were excluded. The research commenced in September 2024, utilizing PubMed, Scopus, SpringerLink, and Web of Science databases, with searches conducted up to that date. The risk of bias was assessed using the Newcastle-Ottawa Scale, and data were synthesized qualitatively. A total of 37 studies were included. RBCs contribute to thrombosis by influencing blood viscosity, interacting with platelets, and integrating into clots. Procoagulant activity induced by phosphatidylserine exposure and RBC-derived microvesicle products that promote thrombin generation and clot stability were also identified as key mechanisms. In conclusion, RBCs play an active role in thrombosis formation, contributing to clot formation and stability. Targeting RBC-mediated processes, such as aggregation, deformability, and microvesicle release, may offer novel strategies for thrombosis management. Further research and meta-analyses are needed to refine these therapeutic approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".