In Vitro Investigation of Vocal Fold Cellular Response to Variations in Hydrogel Porosity and Elasticity
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".