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Record W1903140244 · doi:10.1039/c5tb00808e

Surface modification of poly(dimethylsiloxane) with a covalent antithrombin–heparin complex for the prevention of thrombosis: use of polydopamine as bonding agent

2015· article· en· W1903140244 on OpenAlexafffund
Jennifer Leung, Leslie R. Berry, Helen Atkinson, Rena M. Cornelius, Darren Sandejas, Niels Rochow, P. Ravi Selvaganapathy, Christoph Fusch, Anthony K.C. Chan, John L. Brash

Bibliographic record

VenueJournal of Materials Chemistry B · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsThrombosis and Atherosclerosis Research InstituteMcMaster University
FundersCanada Research Chairs
KeywordsSurface modificationCovalent bondAntithrombinHeparinMaterials scienceThrombosisPolymer chemistryChemistryOrganic chemistryBiochemistryMedicineSurgery

Abstract

fetched live from OpenAlex

A modified poly(dimethyl siloxane) (PDMS) material is under development for use in an extracorporeal microfluidic blood oxygenator designed as an artificial placenta to treat newborn infants suffering from severe respiratory insufficiency. To prevent thrombosis triggered by blood-material contact, an antithrombin-heparin (ATH) covalent complex was coated on PDMS surface using polydopamine (PDA) as a "bioglue". Experiments using radiolabelled ATH showed that the ATH coating on PDA-modified PDMS remained substantially intact after incubation in plasma, 2% SDS solution, or whole blood over a three day period. The anticoagulant activity of the ATH-modified surfaces was also demonstrated: in contact with plasma the ATH-coated PDMS was shown to bind antithrombin (AT) selectively from plasma and to inhibit clotting factor Xa. It is concluded that modification of PDMS with polydopamine and ATH shows promise as a means of improving the blood compatibility of PDMS and hence of the oxygenator device.

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 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 score0.003
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.161
GPT teacher head0.358
Teacher spread0.197 · 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

Citations33
Published2015
Admission routes2
Has abstractyes

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