Injectable, pore-forming, self-healing, and adhesive hyaluronan hydrogels for soft tissue engineering applications
Bibliographic record
Abstract
Abstract Most existing injectable hydrogels are non-porous, thereby lacking a microporous structure to promote cell ingrowth. Also, most hydrogels do not effectively adhere to the host tissue. The present study describes an injectable double network hydrogel formed by combining two hyaluronic acid (HA) derivatives, namely dopamine grafted HA (DAHA) and methacrylated HA (HAMA). These constituents instantly form a physically crosslinked network through Fe3+-dopamine coordination, and confer fast gelation, pore formation, and self-healing properties to the hydrogel. Photocroslinked upon UV exposure, HAMA forms a chemically crosslinked network, thereby improving mechanical and degradation properties. The adhesive properties of this hydrogel are attributed to the presence of dopamine groups, inspired by mussel creatures. Proper modification of HA chains was confirmed by NMR spectroscopy. The physical, mechanical, rheological, and biological properties of the new hydrogels were quantified in wet laboratory conditions. The results revealed that the DAHA/HAMA hydrogel rapidly forms a self-healing microporous adhesive scaffold with a 26.9 µm pore size, 29.4 kPa compressive modulus, and 12.8 kPa adhesion strength in under 6 s. These findings suggest that the new hydrogel is a promising candidate for in situ repair of soft tissues, particularly mechanically dynamic ones such as the vocal folds, cartilage, and dermis.
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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".