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
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.
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) | 0.001 | 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 | 0.001 |
| 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".