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Record W4401177838 · doi:10.3390/pharmaceutics16081016

Synthesis of Gelatin Methacryloyl Analogs and Their Use in the Fabrication of pH-Responsive Microspheres

2024· article· en· W4401177838 on OpenAlexaff
Karolina Papera Valente, Geneviève N. Boice, Cameron Polglase, Roman G. Belli, Elaina Bourque, Afzal Suleman, Alexandre G. Brolo

Bibliographic record

VenuePharmaceutics · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGelatinSelf-healing hydrogelsMethacrylamidePolymerSwellingPolymer chemistryDrug deliveryAqueous solutionMaterials scienceChemical engineeringChemistryCopolymerNanotechnologyOrganic chemistryAcrylamideComposite material

Abstract

fetched live from OpenAlex

pH-responsive hydrogels have numerous applications in tissue engineering, drug delivery systems, and diagnostics. Gelatin methacryloyl (GelMA) is a biocompatible, semi-synthetic polymer prepared from gelatin. When combined with aqueous solvents, GelMA forms hydrogels that have extensive applications in biomedical engineering. GelMA can be produced with different degrees of methacryloyl substitution; however, the synthesis of this polymer has not been tuned towards producing selectively modified materials for single-component pH-responsive hydrogels. In this work, we have explored two different synthetic routes targeting different gelatin functional groups (amine, hydroxyl, and/or carboxyl) to produce two GelMA analogs: gelatin A methacryloyl glycerylester (polymer A) and gelatin B methacrylamide (polymer B). Polymers A and B were used to fabricate pH-responsive hydrogel microspheres in a flow-focusing microfluidic device. At neutral pH, polymer A and B microspheres displayed an average diameter of ~40 µm. At pH 6, microspheres from polymer A showed a swelling ratio of 159.1 ± 11.5%, while at pH 10, a 288.6 ± 11.6% swelling ratio was recorded for polymer B particles.

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 score0.237
Threshold uncertainty score0.218

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 communication0.0000.000
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 head0.060
GPT teacher head0.347
Teacher spread0.287 · 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
Published2024
Admission routes1
Has abstractyes

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