Cell driven elastomeric particle packing in composite bioinks for engineering and implantation of stable 3D printed structures
Bibliographic record
Abstract
Geometric and structural integrity often deteriorate in 3D printed cell-laden constructs over time due to cellular compaction and hydrogel shrinkage. This study introduces a new approach that synergizes the advantages of cell compatibility of biological hydrogels and mechanical stability of elastomeric polymers for structure fidelity maintenance upon stereolithography and extrusion 3D printing. Enabling this advance is the composite bioink, formulated by integrating elastomeric microparticles from poly(octamethylene maleate (anhydride) citrate) (POMaC) into biologically derived hydrogels (fibrin, gelatin methacryloyl (GelMA), and alginate). The composite bioink enhanced the elasticity and plasticity of the 3D printed constructs, effectively mitigating tissue compaction and swelling. It exhibited a low shear modulus and a rapid crosslinking time, along with a high ultimate compressive strength and resistance to deformation from cellular forces and physical handling; this was attributed to packing and stress dissipation of elastomeric particles, which was confirmed via mathematical modelling. Enhanced functional assembly and stability of human iPSC-derived cardiac tissues and primary vasculature proved the utility of the composite bioink in tissue engineering. In vivo implantation studies revealed that constructs containing POMaC particles exhibited improved resilience against host tissue stress, enhanced angiogenesis, and infiltration of pro-reparative macrophages. • A bioink from POMaC elastomeric microparticles and biologically-derived hydrogels. • Enhanced elasticity and plasticity of 3D printed constructs mitigate compaction. • High compressive strength resists deformation from handling and implantation. • Improved assembly of bioengineered cardiac tissues and vasculature. • Enhanced in vivo vascularization and pre-regenerative macrophage infiltration.
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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".