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Biological activity of multicomponent bio-hydrogels loaded with tragacanth gum

2022· article· en· W4283709797 on OpenAlexaff
Roberta Teixeira Polez, Maria Morits, Christopher Jonkergouw, Josphat Phiri, Juan José Valle‐Delgado, Markus B. Linder, Thaddeus Maloney, Orlando J. Rojas, Monika Österberg

Bibliographic record

VenueInternational Journal of Biological Macromolecules · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of British Columbia
FundersDivision of Materials ResearchAalto-YliopistoAcademy of Finland
KeywordsTragacanthSelf-healing hydrogelsTissue engineeringChemistryPolysaccharideChemical engineeringRheologyCelluloseMaterials scienceBiomedical engineeringPolymer chemistryComposite materialBiochemistryFood science

Abstract

fetched live from OpenAlex

Producing hydrogels capable of mimicking the biomechanics of soft tissue remains a challenge. We explore the potential of plant-based hydrogels as polysaccharide tragacanth gum and antioxidant lignin nanoparticles in bioactive multicomponent hydrogels for tissue engineering. These natural components are combined with TEMPO-oxidized cellulose nanofibrils, a material with known shear thinning behavior. Hydrogels presented tragacanth gum (TG) concentration-dependent rheological properties suitable for extrusion 3D printing. TG enhanced the swelling capacity up to 645% and the degradation rate up to 1.3%/day for hydrogels containing 75% of TG. Young's moduli of the hydrogels varied from 5.0 to 11.6 kPa and were comparable to soft tissues like skin and muscle. In vitro cell viability assays revealed that the scaffolds were non-toxic and promoted proliferation of hepatocellular carcinoma HepG2 cells. Therefore, the plant-based hydrogels designed in this work have a significant potential for tissue engineering.

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)0.000
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 score0.053
Threshold uncertainty score0.596

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.032
GPT teacher head0.285
Teacher spread0.252 · 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

Citations45
Published2022
Admission routes1
Has abstractyes

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