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Record W3193537619 · doi:10.3390/polym13162772

Modulating the Thermoresponse of Polymer-Protein Conjugates with Hydrogels for Controlled Release

2021· article· en· W3193537619 on OpenAlexafffund
Vincent Huynh, Natalie Ifraimov, Ryan G. Wylie

Bibliographic record

VenuePolymers · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceCanada Foundation for Innovation
KeywordsLower critical solution temperatureSelf-healing hydrogelsEthylene glycolPolymerControlled releasePEG ratioConjugateThermoresponsive polymers in chromatographyMethacrylateChemistryPolymer chemistryDrug carrierChemical engineeringDrug deliveryMaterials scienceCopolymerChromatographyNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Sustained release is being explored to increase plasma and tissue residence times of polymer-protein therapeutics for improved efficacy. Recently, poly(oligo(ethylene glycol) methyl ether methacrylate) (PEGMA) polymers have been established as potential PEG alternatives to further decrease immunogenicity and introduce responsive or sieving properties. We developed a drug delivery system that locally depresses the lower critical solution temperature (LCST) of PEGMA-protein conjugates within zwitterionic hydrogels for controlled release. Inside the hydrogel the conjugates partially aggregate through PEGMA-PEGMA chain interactions to limit their release rates, whereas conjugates outside of the hydrogel are completely solubilized. Release can therefore be tuned by altering hydrogel components and the PEGMA's temperature sensitivity without the need for traditional controlled release mechanisms such as particle encapsulation or affinity interactions. Combining local LCST depression technology and degradable zwitterionic hydrogels, complete release of the conjugate was achieved over 13 days.

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 score0.008
Threshold uncertainty score0.444

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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