Tuning Alginate Bioink Stiffness and Composition for Controlled Growth Factor Delivery and to Spatially Direct MSC Fate within Bioprinted Tissues
Bibliographic record
Abstract
Alginate is a commonly used bioink in 3D bioprinting. Matrix stiffness is a key determinant of mesenchymal stem cell (MSC) differentiation, suggesting that modulation of alginate bioink mechanical properties represents a promising strategy to spatially regulate MSC fate within bioprinted tissues. In this study, we define a printability window for alginate of differing molecular weight (MW) by systematically varying the ratio of alginate to ionic crosslinker within the bioink. We demonstrate that the MW of such alginate bioinks, as well as the choice of ionic crosslinker, can be tuned to control the mechanical properties (Young's Modulus, Degradation Rate) of 3D printed constructs. These same factors are also shown to influence growth factor release from the bioinks. We next explored if spatially modulating the stiffness of 3D bioprinted hydrogels could be used to direct MSC fate inside printed tissues. Using the same alginate and crosslinker, but varying the crosslinking ratio, it is possible to bioprint constructs with spatially varying mechanical microenvironments. Moreover, these spatially varying microenvironments were found to have a significant effect on the fate of MSCs within the alginate bioinks, with stiffer regions of the bioprinted construct preferentially supporting osteogenesis over adipogenesis.
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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 | 0.001 | 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".