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Record W2996149427 · doi:10.1088/1748-605x/ab62ff

<i>In vivo</i> degradation behavior of enzyme-degradable poly(trimethylene carbonate)-based biohybrid networks of varying water content

2019· article· en· W2996149427 on OpenAlexafffund
Shadi Taghavi, Brian G. Amsden

Bibliographic record

VenueBiomedical Materials · 2019
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrimethylene carbonateDegradation (telecommunications)Materials scienceIn vivoCarbonateBiodegradationChemical engineeringPolymerComposite materialCopolymerOrganic chemistryChemistryComputer scienceBiotechnologyMetallurgyBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Polymeric biohybrid networks have significant potential as supportive materials for soft connective tissue regeneration. Their success in this regard is determined by their initial mechanical properties, which are dependent on their water content, as well as the rate at which these properties change with time due to cell mediated degradation. In this study the in vivo degradation and tissue response following implantation of matrix metalloproteinase (MMP)-degradable poly(trimethylene carbonate) (PTMC)-based biohybrid networks were assessed in a Wistar rat model. The networks examined varied in equilibrium water content from circa 20% to 70% w/w. The networks degraded through MMP secretion by inflammatory cells at the tissue-material interface, generating a mass loss profile consistent with surface erosion but modulus and sol content changes consistent with a bulk erosion process. This degradation profile was explained in terms of a population gradient in MMP concentration from the surface to the bulk of the networks due to diffusion restrictions. A histological analysis of the tissue surrounding the implants confirmed a moderate tissue response comparable to that observed towards a Vicryl TM suture, suggesting that these new materials can be considered biocompatible.

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 categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0040.000

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 head0.026
GPT teacher head0.231
Teacher spread0.205 · 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.

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

Citations3
Published2019
Admission routes2
Has abstractyes

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