Photocuring 3D printing technology as an advanced tool for promoting angiogenesis in hypoxia-related diseases
Bibliographic record
Abstract
Three-dimensional (3D) bioprinting has emerged as a promising strategy for fabricating complex tissue analogs with intricate architectures, such as vascular networks. Achieving this necessitates bioink formulations that possess highly printable properties and provide a cell-friendly microenvironment mimicking the native extracellular matrix. Rapid advancements in printing techniques continue to expand the capabilities of researchers, enabling them to overcome existing biological barriers. This review offers a comprehensive examination of ultraviolet-based 3D bioprinting, renowned for its exceptional precision compared to other techniques, and explores its applications in inducing angiogenesis across diverse tissue models related to hypoxia. The high-precision and rapid photocuring capabilities of 3D bioprinting are essential for accurately replicating the intricate complexity of vascular networks and extending the diffusion limits for nutrients and gases. Addressing the lack of vascular structure is crucial in hypoxia-related diseases, as it can significantly improve oxygen delivery and overall tissue health. Consequently, high-resolution 3D bioprinting facilitates the creation of vascular structures within three-dimensional engineered tissues, offering a potential solution for addressing hypoxia-related diseases. Emphasis is placed on fundamental components essential for successful 3D bioprinting, including cell types, bioink compositions, and growth factors highlighted in recent studies. The insights provided in this review underscore the promising prospects of leveraging 3D printing technologies for addressing hypoxia-related diseases through the stimulation of angiogenesis, complementing the therapeutic efficacy of cell therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".