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Record W4405315795 · doi:10.1016/j.carbpol.2024.123140

Synthesis and characterization of photo-cross-linkable quince seed-based hydrogels for soft tissue engineering applications

2024· article· en· W4405315795 on OpenAlexafffund
Arman Jafari, Khushbu Bhatt, Seyyed Vahid Niknezhad, Abdellah Ajji, May Griffith, Grégor Andelfinger, Sidi A. Bencherif, Houman Savoji

Bibliographic record

VenueCarbohydrate Polymers · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsPolytechnique MontréalCentre Hospitalier Universitaire Sainte-JustineHôpital Maisonneuve-RosemontUniversité de Montréal
FundersChemical Sciences, Geosciences, and Biosciences DivisionBasic Energy SciencesFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDivision of Materials ResearchOffice of ScienceCentre de recherche du CHU Sainte-JustineU.S. Department of EnergyNational Institutes of HealthNational Science Foundation
KeywordsSelf-healing hydrogelsCharacterization (materials science)Tissue engineeringMaterials sciencePolymer scienceBiomedical engineeringNanotechnologyPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

H NMR and FTIR confirmed the successful methacrylation of QS. Hydrogels fabricated from QSGMs at a 0.5 wt% concentration exhibited high swelling ratios of 320 to 580 g/g, and compressive strengths between 0.6 ± 0.1 and 1.2 ± 0.3 kPa. No significant changes in the rheological properties of hydrogel precursors were observed. Moreover, QSGM-based hydrogels supported cell encapsulation for 14 days with minimal cytotoxicity and immune cell activation. Finally, as a proof of concept, the potential use of QSGM for 3D printing was demonstrated. Overall, the results highlight the significant potential of QSGMs as a biomaterial of choice for soft tissue engineering applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

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