Regulation of Sequential Release of Growth Factors Using Bilayer Polymeric Nanoparticles for Cardiac Tissue Engineering
Bibliographic record
Abstract
AIM: Cardiac tissue engineering aims to develop engineered constructs for myocardial infarction repair, where a challenge is the control of growth factor (GF) sequential release. Herein, bilayer polymeric nanoparticles composed of a GF-encapsulating core surrounded by rate-regulating shell were developed for sequential GF release. MATERIALS & METHODS: Single and bilayer polymeric nanoparticles were fabricated, characterized and biologically assessed. A novel 'Geno-Neural model' was developed and validated for rate-programming of the nanoparticles. RESULTS: The bilayer nanoparticles featured low burst effect and time-delayed release, and allowed for sequential release of PDGF following co-release of VEGFand bFGF, which promoted angiogenesis. CONCLUSION: The nanoparticulate delivery system, along with the Geno-Neural model, offers great potential for spatiotemporal control of GF release for cardiovascular regenerative medicine.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".