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Record W2776491925 · doi:10.1039/c7tb02968c

The bioconjugation mechanism of purine cross-linkers affects microstructure and cell response to ultra rapidly gelling purine–chitosan sponges

2017· article· en· W2776491925 on OpenAlexafffund
Laı̈la Benameur, Timothée Baudequin, Mina Mekhail, Maryam Tabrizian

Bibliographic record

VenueJournal of Materials Chemistry B · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMitacsCalifornia HIV/AIDS Research Program
KeywordsBioconjugationChitosanPurineMaterials scienceMechanism (biology)BiophysicsNanotechnologyChemistryBiochemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

C-NMR revealed the formation of a quadruplex structure among the four GDP molecules confined between the chitosan backbone. This resulted from the ability of guanine to form intermolecular hydrogen bonds, while adenosine in ADP lacks this capacity. The formation of a more organized structure in GDP-chitosan sponges also increased the crystallinity of the sponge as shown by X-ray diffraction data. Further, physicochemical analyses with SEM and μCT indicated a more open-pore architecture and increased porosity. Although an active population of encapsulated cells was maintained in all chitosan sponges overtime, the GDP-based sponges provided a 6-fold increase in the activity of MC-3T3 cells and significantly enhanced their proliferation due to a more appropriate microstructure. Overall, these findings suggest that slight changes in the chemical structure of the cross-linker in the preparation of chitosan-based biomaterials will have a significant impact on the structural properties of the chitosan. This important parameter can be utilized to modulate cell response and to understand the cell signaling pathway of chitosan-based biomaterials in the context of their applications in tissue engineering.

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)0.001
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 score0.003
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.009
GPT teacher head0.272
Teacher spread0.263 · 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

Citations17
Published2017
Admission routes2
Has abstractyes

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