Fortifying Angiogenesis in Ischemic Muscle with FGF9‐Loaded Electrospun Poly(Ester Amide) Fibers
Bibliographic record
Abstract
Delivery of angiogenic growth factors lessens ischemia in preclinical models but has demonstrated little benefit in patients with peripheral vascular disease. Augmenting the wrapping of nascent microvessels by mural cells constitutes an alternative strategy to regenerating a functional microvasculature, particularly if integrated with a sustained delivery platform. Herein, electrospun poly(ester amide) (PEA) nanofiber mats are fabricated for delivering a mural cell-targeting factor, fibroblast growth factor 9 (FGF9). Proof-of-principle is established by placing FGF9/FGF2-loaded PEA fiber mats on the chick chorioallantoic membrane and identifying enhanced angiogenesis by 3D power Doppler micro-ultrasound imaging. To assess the delivery system in ischemic muscle, FGF9-loaded PEA fiber mats are implanted onto the surface of the tibialis anterior muscle of mice with hindlimb ischemia. The system supplies FGF9 into the tibialis anterior muscle and yields a neo-microvascular network with enhanced mural cell coverage up to 28 days after injury. The regenerating muscle that receives FGF9 display near-normal sized myofibers and reduced interstitial fibrosis. Moreover, the mice demonstrate improved locomotion. These findings of locally released FGF9 from PEA nanofibers raise prospects for a microvascular remodeling approach to improve muscle health in peripheral vascular disease.
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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) | 0.001 | 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".