MétaCan
Menu
Back to cohort
Record W2072757180 · doi:10.1080/08927014.2013.787416

Proteome analysis of the plasma protein layer adsorbed to a rough titanium surface

2013· article· en· W2072757180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiofouling · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsWestern University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsChemistryBlood proteinsAdsorptionTitaniumFibrinogenProtein adsorptionAlbuminChromatographyFibronectinProteomeAdhesionLayer (electronics)Mass spectrometryBiophysicsAnalytical Chemistry (journal)BiochemistryCellOrganic chemistryBiology

Abstract

fetched live from OpenAlex

In this study a label-free proteomic approach was used to investigate the composition of the layer of protein adsorbed to rough titanium (Ti) after exposure to human blood plasma. The influence of the protein layer on the surface free energy (SFE) of the Ti was evaluated by contact angle measurements. Ti discs were incubated with blood plasma for 180 min at 37 °C, and the proteins recovered were subjected to liquid chromatography coupled to tandem mass spectrometry analysis. A total of 129 different peptides were identified and assigned to 25 distinct plasma proteins. The most abundant proteins were fibronectin, serum albumin, apolipoprotein A-I, and fibrinogen, comprising 74.54% of the total spectral counts. Moreover, the protein layer increased the SFE of the Ti (p < 0.05). The layer adsorbed to the rough Ti surface was composed mainly of proteins related to cell adhesion, molecule transportation, and coagulation processes, creating a polar and hydrophilic interface for subsequent interactions with host cells.

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.

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 score0.006
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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