3D bioprinting of methacrylated hyaluronic acid (MeHA) hydrogel with intrinsic osteogenicity
Bibliographic record
Abstract
In bone regenerative medicine there is a need for suitable bone substitutes. Hydrogels have excellent biocompatible and biodegradable characteristics, but their visco-elastic properties limit their applicability, especially with respect to 3D bioprinting. In this study, we modified the naturally occurring extracellular matrix glycosaminoglycan hyaluronic acid (HA), in order to yield photo-crosslinkable hydrogels with increased mechanical stiffness and long-term stability, and with minimal decrease in cytocompatibility. Application of these tailor-made methacrylated hyaluronic acid (MeHA) gels for bone tissue engineering and 3D bioprinting was the subject of investigation. Visco-elastic properties of MeHA gels, measured by rheology and dynamic mechanical analysis, showed that irradiation of the hydrogels with UV light led to increased storage moduli and elastic moduli, indicating increasing gel rigidity. Subsequently, human bone marrow derived mesenchymal stromal cells (MSCs) were incorporated into MeHA hydrogels, and cell viability remained 64.4% after 21 days of culture. Osteogenic differentiation of MSCs occurred spontaneously in hydrogels with high concentrations of MeHA polymer, in absence of additional osteogenic stimuli. Addition of bone morphogenetic protein-2 (BMP-2) to the culture medium further increased osteogenic differentiation, as evidenced by increased matrix mineralisation. MeHA hydrogels demonstrated to be suitable for 3D bioprinting, and were printed into porous and anatomically shaped scaffolds. Taken together, photosensitive MeHA-based hydrogels fulfilled our criteria for cellular bioprinted bone constructs within a narrow window of concentration.
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".