3D bioprinting of heterogeneous bi- and tri-layered hollow channels within gel scaffolds using scalable multi-axial microfluidic extrusion nozzle
Bibliographic record
Abstract
Abstract One of the primary focuses in recent years in tissue engineering has been the fabrication and integration of vascular structures into artificial tissue constructs. However, most available methodologies lack the ability to create multi-layered concentric conduits inside natural extracellular matrices (ECMs) and gels that replicate more accurately the hierarchical architecture of biological blood vessels. In this work, we present a new microfluidic nozzle design capable of multi-axial extrusion in order to 3D print and pattern bi- and tri-layered hollow channel structures. This nozzle allows, for the first time, for these structures to be embedded within layers of gels and ECMs in a fast, simple and low-cost manner. By varying flow rates (1–6 ml min −1 ), printspeeds (1–16 m min −1 ), and material concentration (25–175 mM and 1.5%–2.5% for calcium chloride and alginate, respectively) we are able to accurately determine the operational printing range as well as achieve a wide range of conduit dimensions (0.69–2.31 mm) that can be printed within a few seconds. Our scalable design allows for multi-axial extrusion and versatility in material incorporation in order to create heterogeneous structures. We demonstrate the ability to print distinct concentric layers of different cell types, namely endothelial cells and fibroblasts. By incorporating various layers of different cell-friendly materials (such as collagen and fibrin) alongside materials with high mechanical strength (i.e. alginate), we were able to increase long-term cell viability and growth without compromising the structural integrity. In this way, we can improve cellular adhesion in our biocompatible constructs as well as allow them to remain structurally sound. We are able to realize complex heterogeneous, hierarchical architectures that have strong potential for use not only in vascular tissue applications, but also in other artificially fabricated tubular or fiber-like structures such as skeletal muscle or nerve conduits.
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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) | 0.000 | 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".