Unraveling the Relevance of Tissue‐Specific Decellularized Extracellular Matrix Hydrogels for Vocal Fold Regenerative Biomaterials: A Comprehensive Proteomic and In Vitro Study
Bibliographic record
Abstract
Decellularized extracellular matrix (dECM) is a promising material for tissue engineering applications. Tissue-specific dECM is often seen as a favorable material that recapitulates a native-like microenvironment for cellular remodeling. However, the minute quantity of dECM derivable from small organs like the vocal fold (VF) hampers manufacturing scalability. Small intestinal submucosa (SIS), a commercial product with proven regenerative capacity, may be a viable option for VF applications. This study aims to compare dECM hydrogels derived from SIS or VF tissue with respect to protein content and functionality using mass spectrometry-based proteomics and in vitro studies. Proteomic analysis reveals that VF and SIS dECM share 75% of core matrisome proteins. Although VF dECM proteins have greater overlap with native VF, SIS dECM shows less cross-sample variability. Following decellularization, significant reductions of soluble collagen (61%), elastin (81%), and hyaluronan (44%) are noted in VF dECM. SIS dECM contains comparable elastin and hyaluronan but 67% greater soluble collagen than VF dECM. Cells deposit more neo-collagen on SIS than VF-dECM hydrogels, whereas neo-elastin (~50 μg/scaffold) and neo-hyaluronan (~ 6 μg/scaffold) are comparable between the two hydrogels. Overall, SIS dECM possesses reasonably similar proteomic profile and regenerative capacity to VF dECM. SIS dECM is considered a promising alternative for dECM-derived biomaterials for VF regeneration.
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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".