Single-Step 3D Bioprinting of Alginate–Collagen Type I Hydrogel Fiber Rings to Promote Angiogenic Network Formation
Bibliographic record
Abstract
In the advent of tissue engineering and regenerative medicine, the demand for innovative approaches to biofabricate complex vascular structures is increasing. We describe a single-step 3D bioprinting method leveraging Aspect Biosystems RX1 technology, which integrates the crosslinking step at a flow-focusing junction, to biofabricate immortalized adult rat brain endothelial cell (SV-ARBEC)-encapsulated alginate-collagen type I hydrogel rings. This single-step biofabrication process involves the strategic layer-by-layer assembly of hydrogel rings, encapsulating SV-ARBECs in a spatially controlled manner while optimizing access to media and nutrients. The spatial arrangement of the SV-ARBECs within the rings promotes spontaneous angiogenic network formation and the constrained deposition of cells within the hydrogel matrix facilitates tissue-like organized vascular-like network development. This approach provides a platform that can be adapted to many different endothelial cell types and leveraged to better understand the mechanisms driving angiogenesis and vascular-network formation in 3D bioprinted constructs supporting the development of more complex tissue and disease models for advancing drug discovery, tissue engineering, and regenerative medicine applications.
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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".