3D biofabrication of vascular networks for tissue regeneration: A report on recent advances
Bibliographic record
Abstract
Rapid progress in tissue engineering research in past decades has opened up vast possibilities to tackle the challenges of generating tissues or organs that mimic native structures. The success of tissue engineered constructs largely depends on the incorporation of a stable vascular network that eventually anastomoses with the host vasculature to support the various biological functions of embedded cells. In recent years, significant progress has been achieved with respect to extrusion, laser, micro-molding, and electrospinning-based techniques that allow the fabrication of any geometry in a layer-by-layer fashion. Moreover, decellularized matrix, self-assembled structures, and cell sheets have been explored to replace the biopolymers needed for scaffold fabrication. While the techniques have evolved to create specific tissues or organs with outstanding geometric precision, formation of interconnected, functional, and perfused vascular networks remains a challenge. This article briefly reviews recent progress in 3D fabrication approaches used to fabricate vascular networks with incorporated cells, angiogenic factors, proteins, and/or peptides. The influence of the fabricated network on blood vessel formation, and the various features, merits, and shortcomings of the various fabrication techniques are discussed and summarized.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".