Concurrent and Sustained Delivery of FGF2 and FGF9 from Electrospun Poly(ester amide) Fibrous Mats for Therapeutic Angiogenesis
Bibliographic record
Abstract
Therapeutic angiogenesis has emerged as a potential strategy to treat ischemic vascular diseases. However, systemic or local administration of growth factors is usually inefficient for maintaining the effective concentration at the site of interest due to their rapid clearance or degradation. In this study, we report a differential and sustained release of an angiogenic factor, fibroblast growth factor-2 (FGF2), and an arteriogenic factor, fibroblast growth factor-9 (FGF9), from α-amino acid-derived biodegradable poly(ester amide) (PEA) fibers toward targeting neovessel formation and maturation. FGF2 and FGF9 were dual loaded using a mixed blend and emulsion electrospinning technique and exhibited differential and sustained release from PEA fibers over 28 days with preserved bioactivity. In vitro angiogenesis assays showed enhanced endothelial cell (EC) tube formation and directed migration of smooth muscle cells (SMCs) to platelet-derived growth factor (PDGF)-BB and stabilized EC/SMC tube formation. FGF2/FGF9-loaded PEA fibers did not induce inflammatory responses in vitro using human monocytes or in vivo after their subcutaneous implantation into mice. Histological examination showed that FGF2/FGF9-loaded fibers induced cell niche recruitment around the site of implantation. Furthermore, controlled in vivo delivery of FGF9 to mouse tibialis anterior (TA) muscle resulted in a dose-dependent expansion of mesenchymal progenitor-like cell layers and extracellular matrix deposition. Our data suggest that the release of FGF2 and FGF9 from PEA fibers offers an efficient differential and sustained growth factor delivery strategy with relevance to therapeutic angiogenesis.
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 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".