Nerve Growth Factor, Brain-Derived Neurotrophic Factor, Neurotrophin-3 and Glial-Derived Neurotrophic Factor Enhance Angiogenesis in a Tissue-Engineered <i>In Vitro</i> Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Skin is a major source of secretion of the neurotrophic factors nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3), and glial-derived neurotrophic factor (GDNF) controlling cutaneous sensory innervation. Beside their neuronal contribution, we hypothesized that neurotrophic factors also modulate the cutaneous microvascular network. First, we showed that NGF, BDNF, NT-3, and GDNF were all expressed in the epidermis, while only NGF and NT-3 were expressed by cultured fibroblasts, and BDNF by human endothelial cells. We demonstrated that these peptides are highly potent angiogenic factors using a human tissue-engineered angiogenesis model. A 40% to 80% increase in the number of capillary-like tubes was observed after the addition of 10 ng/mL of NGF, 0.1 ng/mL of BDNF, 15 ng/mL of NT-3, and 50 ng/mL of GDNF. This is the first characterization of the direct angiogenic effect of NT-3 and GDNF. This angiogenic effect was mediated directly through binding with the neurotrophic factor receptors tropomyosin-receptor kinase A (TrkA), TrkB, GFRα-1 and c-ret that were all expressed by human endothelial cells, while this effect was blocked by addition of the Trk inhibitor K252a. Thus, if NGF, BDNF, NT-3, and GDNF may only moderately regulate the microvascular network in normal skin, they might have the potential to greatly increase angiogenesis in pathological situations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it