Platelet-derived microparticles bind to hematopoietic stem/progenitor cells and enhance their engraftment
Bibliographic record
Abstract
Because human CD34+ and murine Sca-1+ hematopoietic stem-progenitor cells (HSPCs) express platelet-binding sialomucin P-selectin (CD162) and integrin Mac-1 (CD11b-CD18) antigen, it was inferred that these cells might interact with platelets. As a result of this interaction, microparticles derived from platelets (PMPs) may transfer many platelet antigens (CD41, CD61, CD62, CXCR4, PAR-1) to the surfaces of HSPCs. To determine the biologic significance of the presence of PMPs on human CD34+ and murine Sca-1+ cells, their expressions on mobilized peripheral blood (mPB) and on nonmobilized PB- and bone marrow (BM)-derived CD34+ cells were compared. In addition, the effects of PMPs on the proliferation of CD34+ and Sca-1+ cells and on adhesion of HSPCs to endothelium and immobilized SDF-1 were studied. Finally, the hematopoietic reconstitution of lethally irradiated mice receiving transplanted BM mononuclear cells covered or not covered with PMPs was examined. It was found that PMPs are more numerous on mPB than on BM CD34+ cells, do not affect the clonogenicity of human and murine HSPCs, and increase adhesion of these cells to endothelium and immobilized SDF-1. Moreover, murine BM cells covered with PMPs engrafted lethally irradiated mice significantly faster than those not covered, indicating that PMPs play an important role in the homing of HSPCs. This could explain why in a clinical setting human mPB HSPCs (densely covered with PMPs) engraft more rapidly than BM HSPCs (covered with fewer PMPs). These findings indicate a new role for PMPs in stem cell transplantation and may have clinical implications for the optimization of transplantations.
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 | 0.000 |
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 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".