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Record W2322706961 · doi:10.1021/bm201170h

Injectable Microgel-Hydrogel Composites for Prolonged Small-Molecule Drug Delivery

2011· article· en· W2322706961 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomacromolecules · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsDrug deliveryNanocompositeCationic polymerizationCopolymerAcrylic acidPolymer chemistryChemistryPolymerChemical engineeringDrug carrierIonic bondingSwellingKineticsMaterials scienceOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

The design and application of soft nanocomposite injectable hydrogels containing entrapped microgels for small-molecule drug delivery is demonstrated. Copolymer microgels based on N-isopropylacrylamide and acrylic acid were synthesized that exhibited both ionic and hydrophobic affinity for binding to bupivacaine, a cationic local anesthetic used as a model drug. Microgels were subsequently immobilized within an in situ-gelling hydrogel network cross-linked via hydrazide-aldehyde chemistry to generate hydrogel-microgel soft nanocomposites. Drug release could be sustained for up to 60 days from these nanocomposite hydrogels, significantly longer than that achievable using the constituent hydrogel or microgels alone (<1 week). Drug release kinetics could be readily tuned by varying the affinity of the microgel and hydrogel phases for drug-polymer interactions and the network density of the hydrogel phase.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.023
GPT teacher head0.227
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