MétaCan
Menu
Back to cohort
Record W2075573385 · doi:10.1002/jbm.a.10017

Interactions of fibrinolytic system proteins with lysine‐containing surfaces

2003· article· en· W2075573385 on OpenAlexaff
W. Glenn McClung, David L. Clapper, Aron B. Anderson, D.E. Babcock, John L. Brash

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLysinePlasminogen activatorBiophysicsMaterials scienceTissue plasminogen activatorBlood proteinsPlasma protein bindingAdsorptionPolymer chemistryBiochemistryChemistryAmino acidOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Studies on the interactions of tissue plasminogen activator (tPA) and plasminogen with polyurethane surfaces containing epsilon-lysine moieties (epsilon-amino group free) are reported. These surfaces are considered to have the potential to dissolve nascent clots that may be formed on them. For adsorption from both single protein solutions and plasma, the surfaces were found to have a high capacity for tPA as well as plasminogen. A significant fraction of preadsorbed tPA was displaced from the epsilon-lysine surfaces upon contact with plasma. These surfaces, when preadsorbed with tPA and then incubated with plasma, were able to dissolve incipient clots formed around them. However, the clot-dissolving capacity diminished as the time of plasma incubation increased, presumably due to loss of tPA. It was also shown that in plasma, preadsorbed tPA is displaced from these surfaces largely by plasminogen, which thus appears to have a greater binding affinity than tPA for the epsilon-lysine moieties. Finally, it was found that in plasma, the epsilon-lysine surfaces interact with plasminogen in a dynamic manner, and that about 70% of the bound plasminogen is exchanging continuously with plasminogen in the plasma.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.037
GPT teacher head0.330
Teacher spread0.293 · 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

Citations48
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part ASame topicProtease and Inhibitor MechanismsFrench-language works237,207