A tissue-mimetic nano-fibrillar hybrid injectable hydrogel for potential soft tissue engineering applications
Bibliographic record
Abstract
While collagen type I (Col-I) is commonly used as a structural component of biomaterials, collagen type III (Col-III), another fibril forming collagen ubiquitous in many soft tissues, has not previously been used. In the present study, the novel concept of an injectable hydrogel with semi-interpenetrating polymeric networks of heterotypic collagen fibrils, with tissue-specific Col-III to Col-I ratios, in a glycol-chitosan matrix was investigated. Col-III was introduced as a component of the novel hydrogel, inspired by its co-presence with Col-I in many soft tissues, its influence on the Col-I fibrillogenesis in terms of diameter and mechanics, and its established role in regulating scar formation. The hydrogel has a nano-fibrillar porous structure, and is mechanically stable under continuous dynamic stimulation. It was found to provide a longer half-life of about 35 days than similar hyaluronic acid-based hydrogels, and to support cell implantation in terms of viability, metabolic activity, adhesion and migration. The specific case of pure Col-III fibrils in a glycol-chitosan matrix was investigated. The proposed hydrogels meet many essential requirements for soft tissue engineering applications, particularly for mechanically challenged tissues such as vocal folds and heart valves.
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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) | 0.001 | 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".