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Record W2113018052 · doi:10.5539/ijc.v6n2p1

In vitro and in vivo Evaluation of Hemocompatibility of Silk Fibroin Based Artificial Vascular Grafts

2014· article· en· W2113018052 on OpenAlexvenueno aff
Derya Aytemiz, Yu Suzuki, Tomoko Shindo, Toshiki Saotome, Ryou Tanaka, Tetsuo Asakura

Bibliographic record

VenueInternational Journal of Chemistry · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsnot available
FundersTokyo University of Agriculture and TechnologyTokyo University of AgricultureMinistry of Agriculture, Forestry and Fisheries
KeywordsFibroinThrombogenicityIn vivoChemistryBiocompatibilityFibrinPlateletBiomedical engineeringIn vitroIntimal hyperplasiaVascular graftPlatelet activationPlatelet adhesivenessImmunologySILKMaterials scienceMedicineInternal medicineBiochemistryPlatelet aggregation

Abstract

fetched live from OpenAlex

Artificial vascular grafts with low thrombogenicity are generally required to avoid blood platelet adhesion and to minimize intimal hyperplasia, thus retaining vascular patency. In this study, we aimed to determine the acute and subacute hemocompatibility of silk fibroin (SF) grafts by in vitro and in vivo evaluation. Blood contact reaction with SF grafts was examined by thrombin-anti-thrombin III complex (TAT) formation, platelet activation level by beta-thromboglobulin (beta-TG), complement system response (C3a and SC5b-9), platelet and fibrin deposition and compared with commercially available polyethylene terephthalate (PET) artificial grafts in vitro. The biocompatibility and coagulation-inducing effect of coating materials were evaluated by in vivo implantation in rats. Two weeks after implantation, SF grafts showed low subacute coagulation. All blood parameters evaluated for animals implanted with SF-coated grafts showed almost the same values as those for sham-operated animals. Our results support the suggestion that SF will be a suitable material for vascular regeneration in future.

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)0.000
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 score0.010
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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