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Record W2114803960 · doi:10.1177/1076029610384114

An Anti-von Willebrand Factor Aptamer Reduces Platelet Adhesion Among Patients Receiving Aspirin and Clopidogrel in an Ex Vivo Shear-Induced Arterial Thrombosis

2010· article· en· W2114803960 on OpenAlexaff
Dabit Arzamendi, Firas Dandachli, Jean‐François Théorêt, Grégory Ducrocq, Mark Y. Chan, Walid Mourad, James C. Gilbert, Robert G. Schaub, Jean‐François Tanguay, Yahye Merhi

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2010
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsAbciximabVon Willebrand factorPlateletMedicineEx vivoClopidogrelAspirinTiclopidineInternal medicineAntithromboticGlycoprotein IbCardiologyPharmacologyIn vivoPercutaneous coronary interventionMyocardial infarctionBiology

Abstract

fetched live from OpenAlex

The von Willebrand factor (vWF) aptamer, ARC1779 that blocks the binding of vWF A1-domain to platelet glycoprotein 1b (GPIb) at high shear, may deliver a site-specific antithrombotic effect. We investigated the efficiency of ARC1779 on platelet function in patients with coronary artery disease (CAD) on double antiplatelet therapy. Blood from patients taking aspirin and clopidogrel and from normal volunteers was treated ex vivo with ARC1779 or abciximab, either prior to perfusion (pretherapy) or 10 minutes following the initiation of perfusion (posttherapy) on damaged arteries. Under pre- but not posttherapy, platelet adhesion was significantly reduced by ARC1779 at 83 and 250 nmol/L and by abciximab (100 nmol/L) versus placebo (4.8, 3.8, and 2.9 vs 7.3 platelets × 10(6)/cm(2), P < .05). In contrast to abciximab, ARC1779 did not significantly affect platelet aggregation, P-selectin expression, and platelet-leukocyte binding. These proof-of-concept data may constitute the framework for randomized clinical investigations of this novel antiplatelet therapy among patients with CAD.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.332
Teacher spread0.290 · 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.

Study designObservational
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

Citations24
Published2010
Admission routes1
Has abstractyes

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