Crosstalk between Platelets and the Immune System: Old Systems with New Discoveries
Bibliographic record
Abstract
Platelets are small anucleate cells circulating in the blood. It has been recognized for more than 100 years that platelet adhesion and aggregation at the site of vascular injury are critical events in hemostasis and thrombosis; however, recent studies demonstrated that, in addition to these classic roles, platelets also have important functions in inflammation and the immune response. Platelets contain many proinflammatory molecules and cytokines (e.g., P-selectin, CD40L, IL-1β, etc.), which support leukocyte trafficking, modulate immunoglobulin class switch, and germinal center formation. Platelets express several functional Toll-like receptors (TLRs), such as TLR-2, TLR-4, and TLR-9, which may potentially link innate immunity with thrombosis. Interestingly, platelets also contain multiple anti-inflammatory molecules and cytokines (e.g., transforming growth factor-β and thrombospondin-1). Emerging evidence also suggests that platelets are involved in lymphatic vessel development by directly interacting with lymphatic endothelial cells through C-type lectin-like receptor 2. Besides the active contributions of platelets to the immune system, platelets are passively targeted in several immune-mediated diseases, such as autoimmune thrombocytopenia, infection-associated thrombocytopenia, and fetal and neonatal alloimmune thrombocytopenia. These data suggest that platelets are important immune cells and may contribute to innate and adaptive immunity under both physiological and pathological conditions.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".