Rate-programming of nano-particulate delivery systems for smart bioactive scaffolds in tissue engineering
Bibliographic record
Abstract
Development of smart bioactive scaffolds is of importance in tissue engineering, where cell proliferation, differentiation and migration within scaffolds can be regulated by the interactions between cells and scaffold through the use of growth factors (GFs) and extra cellular matrix peptides. One challenge in this area is to spatiotemporally control the dose, sequence and profile of release of GFs so as to regulate cellular fates during tissue regeneration. This challenge would be addressed by rate-programming of nano-particulate delivery systems, where the release of GFs via polymeric nanoparticles is controlled by means of the methods of, such as externally-controlled and physicochemically/architecturally-modulated so as to mimic the profile of physiological GFs. Identifying and understanding such factors as the desired release profiles, mechanisms of release, physicochemical characteristics of polymeric nanoparticles, and externally-triggering stimuli are essential for designing and optimizing such delivery systems. This review surveys the recent studies on the desired release profiles of GFs in various tissue engineering applications, elucidates the major release mechanisms and critical factors affecting release profiles, and overviews the role played by the mathematical models for optimizing nano-particulate delivery systems. Potentials of stimuli responsive nanoparticles for spatiotemporal control of GF release are also presented, along with the recent advances in strategies for spatiotemporal control of GF delivery within tissue engineered scaffolds. The recommendation for the future studies to overcome challenges for developing sophisticated particulate delivery systems in tissue engineering is discussed prior to the presentation of conclusions drawn from this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".