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Record W1776308969 · doi:10.1039/c5tb00645g

Development of a hybrid gelatin hydrogel platform for tissue engineering and protein delivery applications

2015· article· en· W1776308969 on OpenAlexaff
Xiaodi Sun, Xin Zhao, Lili Zhao, Qing Li, Mathew D'Ortenzio, Brandon Nguyen, Xin Xu, Yong Wen

Bibliographic record

VenueJournal of Materials Chemistry B · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsRegional Municipality of WaterlooUniversity of Waterloo
Fundersnot available
KeywordsGelatinSelf-healing hydrogelsTissue engineeringScaffoldDrug deliveryMaterials scienceNanotechnologyBiomedical engineeringChemistryEngineeringPolymer chemistryBiochemistry

Abstract

fetched live from OpenAlex

In this study, to improve the cellular interaction and protein release of gelatin hydrogels, we reported the development of a new hybrid hydrogel platform as a promising tissue engineering scaffold and drug delivery carrier. The biodegradable, biocompatible hybrid hydrogel platform was fabricated from gelatin methacrylamide (Gel-MA) and arginine based unsaturated non-peptide polycations (Arg-UPEA) through UV photo-crosslinking, combining the favorable properties of gelatin and arginine. The hydrogels were systematically characterized based on their mechanical properties, swelling mechanics, interior morphology, and biodegradation capability. The in vitro biocompatibility study showed that the hybrid hydrogels show better performance than GelMA hydrogels, in terms of cell attachment and proliferation. Therapeutic proteins were loaded into the hydrogels and their release behavior was investigated. The loading and release profiles indicated that the new cationic gelatin hydrogels could significantly improve the protein loading capabilities, and release the proteins in vitro in a sustained manner. The structure-function study indicated that the material composition has a large effect on the properties of the hydrogels.

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 score0.067
Threshold uncertainty score0.438

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 head0.231
Teacher spread0.213 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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