Bibliographic record
Abstract
Extracellular vesicles (EVs) are a means of cell-to-cell communication and can facilitate the exchange of a broad array of molecules between adjacent or distant cells. Platelets are anucleate cells derived from megakaryocytes and are primarily known for their role in maintaining hemostasis and vascular integrity. Upon activation by a variety of agonists, platelets readily generate EVs, which were initially identified as procoagulant particles. However, as both platelets and their EVs are abundant in blood, the role of platelet EVs in hemostasis may be redundant. Moreover, findings have challenged the significance of platelet-derived EVs in coagulation. Looking beyond hemostasis, platelet EV cargo is incredibly diverse and can include lipids, proteins, nucleic acids, and organelles involved in numerous other biological processes. Furthermore, while platelets cannot cross tissue barriers, their EVs can enter lymph, bone marrow, and synovial fluid. This allows for the transfer of platelet-derived content to cellular recipients and organs inaccessible to platelets. This review highlights the importance of platelet-derived EVs in physiological and pathological conditions beyond hemostasis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".