Polymeric Microspheres Containing Human Vocal Fold Fibroblasts for Vocal Fold Regeneration
Bibliographic record
Abstract
OBJECTIVE: Most acellular injectable biomaterials for vocal fold (VF) wound treatment have limited regenerative potential due to their fast enzymatic degradation and limited recruitment of native cells postinjection. The injection of cells as therapeutic treatment often results in apoptosis due to stresses within the needle and the immune response of the host. Degradable microspheres may improve treatment effectiveness by increasing cell residence time, shielding cells during injection, and offering early protection against the immune system response. The objective of the present study was to investigate the potential of human VF fibroblasts encapsulated in polymeric microspheres as an injectable therapeutic treatment in vitro. METHODS: Alginate, alginate-poly-L-lysine, and alginate-chitosan microspheres were fabricated using electrospraying and characterized in terms of biocompatibility, swelling, and mechanical properties as well as cytokine production. RESULTS: Alginate microspheres were found to have the most desirable properties for VF regeneration. They were resistant to mechanical challenges. They were found to have a stiffness similar to that reported for native VF-lamina propria. They were found to be biocompatible and increased the proliferation of fibroblasts. Human VF fibroblasts encapsulated in alginate microspheres induced the production of interleukin (IL)-8 and IL-4 at 24 hours. CONCLUSION: The alginate microspheres fabricated in this study were found to offer potential advantages, as cell delivery tool. This study highlights the importance of combining biomaterials and cells to expedite the wound-healing process through cytokine production. Future work is aimed to further analysis of the wound-healing properties the microspheres. LEVEL OF EVIDENCE: NA Laryngoscope, 131:1828-1834, 2021.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".