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Record W2530003915 · doi:10.1021/acs.biomac.6b01148

An Injectable Hydrogel Prepared Using a PEG/Vitamin E Copolymer Facilitating Aqueous-Driven Gelation

2016· article· en· W2530003915 on OpenAlexafffund
Jianfeng Zhang, Ben B Muirhead, Megan Dodd, Lina Liu, Fei Xu, Nicole Mangiacotte, Todd Hoare, Heather Sheardown

Bibliographic record

VenueBiomacromolecules · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaHamilton Health Sciences
KeywordsSelf-healing hydrogelsEthylene glycolCopolymerPolymerPEG ratioChemical engineeringAqueous solutionMethacrylateChemistryPolymer chemistryPolymerizationSolventRadical polymerizationMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogels have been widely explored for biomedical applications, with injectable hydrogels being of particular interest for their ability to precisely deliver drugs and cells to targets. Although these hydrogels have demonstrated satisfactory properties in many cases, challenges still remain for commercialization. In this paper, we describe a simple injectable hydrogel based on poly(ethylene glycol) (PEG) and a vitamin E (Ve) methacrylate copolymer prepared via simple free radical polymerization and delivered in a solution of low molecular weight PEG and Ve as the solvent instead of water. The hydrogel formed immediately in an aqueous environment with a controllable gelation time. The driving force for gelation is attributed to the self-assembly of hydrophobic Ve residues upon exposure to water to form a physically cross-linked polymer network via polymer chain rearrangement and subsequent phase separation, a spontaneous process with water uptake. The hydrogels can be customized to give the desired water content, mechanical strength, and drug release kinetics simply by formulating the PEGMA-co-Ve polymer with an appropriate solvent mixture or by varying the molecular weight of the polymer. The hydrogels exhibited no significant cytotoxicity in vitro using fibroblasts and good tissue compatibility in the eye and when injected subcutaneously. These polymers thus have the potential to be used in a variety of applications where injection of a drug or cell containing depot would be desirable.

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 score0.016
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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

Citations39
Published2016
Admission routes2
Has abstractyes

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