Investigation of the Viability, Adhesion, and Migration of Human Fibroblasts in a Hyaluronic Acid/Gelatin Microgel‐Reinforced Composite Hydrogel for Vocal Fold Tissue Regeneration
Bibliographic record
Abstract
The potential use of a novel scaffold biomaterial consisting of cross-linked hyaluronic acid (HA)-gelatin (Ge) composite microgels is investigated for use in treating vocal fold injury and scarring. Cell adhesion integrins and kinematics of cell motion are investigated in 2D and 3D culture conditions, respectively. Human vocal fold fibroblast (hVFF) cells are seeded on HA-Ge microgels attached to a HA hydrogel thin film. The results show that hVFF cells establish effective adhesion to HA-Ge microgels through the ubiquitous expression of β1 integrin in the cell membrane. The microgels are then encapsulated in a 3D HA hydrogel for the study of cell migration. The cells within the HA-Ge microgel-reinforced composite hydrogel (MRCH) scaffold have an average motility speed of 0.24 ± 0.08 μm min(-1) . The recorded microscopic images reveal features that are presumably associated with lobopodial and lamellipodial cell migration modes within the MRCH scaffold. Average cell speed during lobopodial migration is greater than that during lamellipodial migration. The cells move faster in the MRCH than in the HA-Ge gel without microgels. These findings support the hypothesis that HA-Ge MRCH promotes cell adhesion and migration; thereby they constitute a promising biomaterial for vocal fold repair.
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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) | 0.000 | 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".