Tunable metacrylated hyaluronic acid-based hybrid bioinks for stereolithography 3D bioprinting
Bibliographic record
Abstract
Hyaluronic acid is a native extra-cellular matrix derivative that promises unique properties, such as anti-inflammatory response and cell-signaling with tissue-specific applications under its bioactive properties. Here, we investigate the importance of the duration of synthesis to obtain photocrosslinkable methacrylated hyaluronic acid (MeHA) with high degree of substitution. MeHA with high degree of substitution can result in rapid photocrosslinking and can be used as a bioink for stereolithographic (SLA) three dimensional 3D bioprinting. Increased degree of substitution results Our findings show that a ten-day synthesis results in an 88% degree of methacrylation (DM), whereas three-day and five-day syntheses result in 32% and 42% DM, respectively. The rheological characterization revealed an increased rate of photopolymerization with increasing DM. Further, we developed a hybrid bioink to overcome the non-cell-adhesive nature of MeHA by combining it with gelatin methacryloyl (GelMA) to fabricate 3D cell-laden hydrogel scaffolds. The hybrid bioink exhibited a 55% enhancement in stiffness compared to MeHA only and enabled cell-adhesion while maintaining high cell viability. Investigations also revealed that the hybrid bioink was a more suitable candidate for stereolithography (SLA) 3D bioprinting than MeHA because of its mechanical strength, printability, and cell-adhesive nature. This research lays out a firm foundation for the development of a stable hybrid bioink with MeHA and GelMA for first-ever use with SLA 3D bioprinting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".