3D bioprinting technology for modeling vascular diseases and its application
Bibliographic record
Abstract
Abstract In vitro modeling of vascular diseases provides a useful platform for drug screening and mechanistic studies, by recapitulating the essential structures and physiological characteristics of the native tissue. Bioprinting is an emerging technique that offers high-resolution 3D capabilities, which have recently been employed in the modeling of various tissues and associated diseases. Blood vessels are composed of multiple layers of distinct cell types, and experience different mechanical conditions depending on the vessel type. The intimal layer, in particular, is directly exposed to such hemodynamic conditions inducing shear stress, which in turn influence vascular physiology. 3D bioprinting techniques have addressed the structural limitations of the previous vascular models, by incorporating supporting cells such as smooth muscle cells, geometrical properties such as dilation, curvature, or branching, or mechanical stimulation such as shear stress and pulsatile pressure. This paper presents a review of the physiology of blood vessels along with the pathophysiology of the target diseases including atherosclerosis, thrombosis, aneurysms, and tumor angiogenesis. Additionally, it discusses recent advances in fabricating in vitro 3D vascular disease models utilizing bioprinting techniques, while addressing the current challenges and future perspectives for the potential clinical translation into therapeutic interventions.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".