Vascularization Strategies in 3D Cell Culture Models: From Scaffold-Free Models to 3D Bioprinting
Bibliographic record
Abstract
The discrepancies between the findings in preclinical studies, and in vivo testing and clinical trials have resulted in the gradual decline in drug approval rates over the past decades. Conventional in vitro drug screening platforms employ two-dimensional (2D) cell culture models, which demonstrate inaccurate drug responses by failing to capture the three-dimensional (3D) tissue microenvironment in vivo. Recent advancements in the field of tissue engineering have made possible the creation of 3D cell culture systems that can accurately recapitulate the cell-cell and cell-extracellular matrix interactions, as well as replicate the intricate microarchitectures observed in native tissues. However, the lack of a perfusion system in 3D cell cultures hinders the establishment of the models as potential drug screening platforms. Over the years, multiple techniques have successfully demonstrated vascularization in 3D cell cultures, simulating in vivo-like drug interactions, proposing the use of 3D systems as drug screening platforms to eliminate the deviations between preclinical and in vivo testing. In this review, the basic principles of 3D cell culture systems are briefly introduced, and current research demonstrating the development of vascularization in 3D cell cultures is discussed, with a particular focus on the potential of these models as the future of drug screening platforms.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".