Creating 3D Angiogenic Growth Factor Gradients in Fibrous Constructs to Guide Fast Angiogenesis
Bibliographic record
Abstract
Fast angiogenesis in 3D fibrous constructs that mimic the morphology of the extracellular matrix remains challenging due to limited porosity in the densely packed constructs. We investigated whether mimicking the in vivo chemotaxis microenvironment for native blood vessel formation would stimulate angiogenesis in the fibrous constructs. The chemotaxis microenvironment was created by introducing 3D angiogenic growth factor gradients into the constructs. We have developed a technique that can quickly fabricate (∼40 min) such 3D gradients by simultaneously electrospinning polycaprolactone (PCL) fibers, encapsulating gradient amount of bFGF (stabilized by heparin) into poly(lactide-co-glycolide) (PLGA) microspheres, and electrospraying the microspheres into PCL fibers. Gradient formation was confirmed by fluorescence microscopy. Gradients with different steepnesses were obtained by modulating the initial concentration of the bFGF solution. All of the constructs were able to sustainedly release bioactive bFGF over a 28 day period. The release kinetics was dependent on the bFGF loading and steepness of the gradient. In vitro cell migration study demonstrated that bFGF gradients significantly increased the depth of cell migration. To assess the efficacy of bFGF gradients in inducing angiogenesis, we implanted constructs subcutaneously using mouse model. bFGF gradients significantly promoted cell penetration into the constructs. After 10 days of implantation, a high density of mature blood vessels (positive to both CD31 and α-SMA) were formed in the constructs. Vessel density was increased with the increase in steepness of the bFGF gradient. These gradient constructs may have potential to engineer vascularized tissues for various applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".