Fibrinogen is required for maintenance of platelet intracellular and cell-surface P-selectin expression
Bibliographic record
Abstract
Platelet P-selectin plays important roles in inflammation and contributes to thrombosis and hemostasis. Although it has been reported that von Willebrand factor (VWF) affects P-selectin expression on endothelial cells, little information is available regarding regulation of platelet P-selectin expression. Here, we first observed that P-selectin expression was significantly decreased on platelets of fibrinogen and VWF double-deficient mice. Subsequently, we identified this was due to fibrinogen deficiency. Impaired P-selectin expression on fibrinogen-deficient platelets was further confirmed in human hypofibrinogenemic patients. We demonstrated that this impairment is unlikely due to excessive P-selectin shedding, deficient fibrinogen-mediated cell surface P-selectin binding, or impaired platelet granule release, but rather is due to decreased platelet P-selectin content. Fibrinogen transfusion completely recovered this impairment in fibrinogen-deficient (Fg(-/-)) mice, and engagement of the C-terminus of the fibrinogen gamma chain with beta3 integrin was required for this process. Furthermore, Fg(-/-) platelets significantly increased P-selectin expression following transfusion into beta3 integrin-deficient mice and when cultured with fibrinogen. These data suggest fibrinogen may play important roles in inflammation, thrombosis, and hemostasis via enhancement of platelet P-selectin expression. Since human fibrinogen levels vary significantly in normal and diseased populations, P-selectin as an activation marker on platelets should be used with caution.
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 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 | 0.001 |
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".