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Record W1978815390 · doi:10.1160/th03-01-0045

Affinity and kinetics of P-selectin binding to heparin

2003· article· en· W1978815390 on OpenAlexaff
Jian-Guo Geng

Bibliographic record

VenueThrombosis and Haemostasis · 2003
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsInstitute for Biological Sciences
FundersChinese Academy of Sciences
KeywordsP-selectinHeparinSelectinChemistrySurface plasmon resonanceBiophysicsPlateletL-selectinEndotheliumAdhesionReceptor–ligand kineticsPlatelet membrane glycoproteinKineticsDissociation constantMolecular biologyGlycoproteinBiochemistryPlatelet activationImmunologyBiologyEndocrinologyReceptorMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.361
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2003
Admission routes1
Has abstractyes

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