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In Vitro Investigation of Vocal Fold Cellular Response to Variations in Hydrogel Porosity and Elasticity

2024· article· en· W4398781143 on OpenAlex
Sara Nejati, Luc Mongeau

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

VenueACS Biomaterials Science & Engineering · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersFonds de recherche du Québec – Nature et technologies
KeywordsSelf-healing hydrogelsTissue engineeringBiomedical engineeringMaterials sciencePolyethylene glycolExtracellular matrixFibroblastBiophysicsMyofibroblastRheometryGelatinPEG ratioChemistryFibrosisPolymerComposite materialIn vitroPolymer chemistryBiochemistryPathology

Abstract

fetched live from OpenAlex

Tissue regeneration is intricately influenced by the dynamic interplay between the physical attributes of tissue engineering scaffolds and the resulting biological responses. A tunable microporous hydrogel system was engineered using gelatin methacryloyl (GelMA) and polyethylene glycol diacrylate (PEGDA), with polyethylene glycol (PEG) serving as a porogen. Through systematic variation of PEGDA molecular weights, hydrogels with varying mechanical and architectural properties were obtained. The objective of the present study was to elucidate the impact of substrate mechanics and architecture on the immunological and reparative activities of vocal fold tissues. Mechanical characterization of the hydrogels was performed using tensile strength measurements and rheometry. Their morphological properties were investigated using scanning electron microscopy (SEM) and confocal microscopy. A series of biological assays were conducted. Cellular morphology, differentiation, and collagen synthesis of human vocal fold fibroblasts (hVFFs) were evaluated using immunostaining. Fibroblast proliferation was studied using the WST-1 assay, and cell migration was investigated via the Boyden chamber assay. Macrophage polarization and secretions were also examined using immunostaining and ELISA. The results revealed that increasing the molecular weight of PEGDA from 700 Da to 10,000 Da resulted in decreased hydrogel stiffness, from 62.6 to 8.8 kPa, and increased pore dimensions from approximately 64.9 to 137.4 μm. Biological evaluations revealed that hydrogels with a higher stiffness promoted fibroblast proliferation and spreading, albeit with an increased propensity for fibrosis, as indicated by a surge in myofibroblast differentiation and collagen synthesis. In contrast, hydrogels with greater molecular weights had a softer matrix with expanded pores, enhancing cellular migration and promoting an M2 macrophage phenotype conducive to tissue healing. The findings show that the hydrogels formulated with a PEGDA molecular weight of 6000 Da are best among the hydrogels considered for vocal fold repair. The microporous hydrogels could be tuned to serve in other 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.

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)0.003
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 score0.010
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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