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Record W2030830165 · doi:10.1039/b719371h

Adsorption of fibrinogen on a biomedical-grade stainless steel 316LVM surface: a PM-IRRAS study of the adsorption thermodynamics, kinetics and secondary structure changes

2008· article· en· W2030830165 on OpenAlexafffund
Marie-Josee Desroches, Sasha Omanovic

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2008
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsAdsorptionKineticsThermodynamicsChemistryPhysical chemistryMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Polarization-modulation infrared reflection-absorption spectroscopy (PM-IRRAS) was employed to investigate the interaction of serum protein fibrinogen with a biomedical-grade 316LVM stainless steel surface, in terms of the adsorption thermodynamics, kinetics and secondary structure changes of the protein. Apparent Gibbs energy of adsorption values indicated a highly spontaneous and strong adsorption of fibrinogen onto the surface. The kinetics of fibrinogen adsorption were successfully modeled using a pseudo first-order kinetic model. Deconvolution of the amide I bands indicated that the adsorption of fibrinogen on 316LVM results in significant changes in the protein's secondary structure that occur predominantly within the first minute of adsorption. Among the investigated structures, the alpha-helix structure undergoes the smallest changes, while the beta-sheet and beta-turns structures undergo significant changes. It was shown that lateral interactions between the adsorbed molecules do not play a role in controlling the secondary structure changes. An increase in temperature induced changes in the secondary structure of the protein, characterized by a loss of the alpha-helical content and its transformation into the beta-turns structure.

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 score0.011
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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
Published2008
Admission routes2
Has abstractyes

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