2D, 3D and 4D Active Compound Delivery in Tissue Engineering and Regenerative Medicine
Bibliographic record
Abstract
Recent advances in tissue engineering and regenerative medicine have shown that controlling cells microenvironment during growth is a key element to the development of successful therapeutic system. To achieve such control, researchers have first proposed the use of polymeric scaffolds that were able to support cellular growth and, to a certain extent, favor cell organization and tissue structure. With nowadays availability of a large pool of stem cell lines, such approach has appeared to be rather limited since it does not offer the fine control of the cell micro-environment in space and time (4D). Therefore, researchers are currently focusing their efforts on developing strategies that include active compound delivery systems in order to add a fourth dimension to the design of 3D scaffolds. This review will focus on recent concepts and applications of 2D and 3D techniques that have been used to control the load and release of active compounds used to promote cell differentiation and proliferation in or out of a scaffold. We will first present recent advances in the design of 2D polymeric scaffolds and the different techniques that have been used to deposit molecular cues and cells in a controlled fashion. We will continue presenting the recent advances made in the design of 3D scaffolds based on hydrogels as well as polymeric fibers and we will finish by presenting some of the research avenues that are still to be explored.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".