Bibliographic record
Abstract
P-selectin (CD62P), expressed on stimulated endothelial cells and activated platelets, reacts with P-selectin glycoprotein ligand-1 (PSGL-1, CD162) for leukocyte rolling. It also binds to heparin and heparan sulfate proteoglycans (HSPGs), which attenuates P-selectin mediated adhesions of leukocytes and cancer cells. Here we report that P-selectin mediated adhesion, but not rolling, of the HSPGs bearing human malignant melanoma A375 cells under shear stress. To understand its underlying molecular mechanism, we measured the biophysical properties of this interaction. Heparin inhibited the adhesion of A375 cells to immobilized P-selectin under flow (IC(50) = 3 microM heparin) and neutralized the binding of P-selectin to A375 cells (IC50 = 4 microM heparin). Using surface plasmon resonance technique, we found that P-selectin bound to heparin with a dissociation constant (K(d)) of 115 +/- 6 nM. The measured off rate (k(off)) was 3.15 +/- 0.34 x 10(-3) s(-1) and the calculated on rate (k(on)) was 2.75 x 10(4) M(-1) s(-1). Taken together, our data suggest that the very slow k(off) and the reduced k(on), but apparently not the K(d), are responsible for adhesion, but not rolling of A375 cells, to P-selectin under flow.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".