Clicktetrazine dECM–alginate hydrogels for injectable, mechanically mimetic, and biologically active vocal fold biomaterials
Bibliographic record
Abstract
Current injectable biomaterials for vocal fold disorders suffer from fast degradation and require frequent re-injection. Decellularized extracellular matrix (dECM) hydrogels are a tissue-derived, injectable biomaterial with intrinsic regenerative capacity. However, dECM hydrogels often exhibit mechanical instability and share the same problems with degradation as existing vocal fold biomaterials. In this work, we developed a composite dECM-alginate hydrogel with bioorthogonal click tetrazine ligation with improved stability, biocompatibility and regenerative capacity. dECM was extracted from two sources: tissue-specific vocal fold mucosa and scalable small intestinal submucosa for comparative analysis. Click dECM hydrogels from both sources were tunable and matched mechanical properties of native human vocal folds. The click dECM hydrogels showed capacity to resist contraction and modulate bioactive molecule secretion by fibroblasts, as well as stimulate the initial endothelial cell elongation phase of vasculogenesis. When injected subcutaneously into rats, both gels exhibit a strong initial immune response, followed by integration with the surrounding tissue by day 21. Overall, our click dECM hydrogels showed improved stability over previous dECM hydrogels and their performance was independent of tissue source.
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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) | 0.002 | 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".