Neural cells enhance angiogenesis <i>in vitro</i> through local neurotrophins secretion
Bibliographic record
Abstract
Nerves and blood vessels are closely associated. Moreover, sensory nerves were shown to determine the branching pattern of the vascular network in skin and to promote the arterial differentiation of blood vessels. We developed a unique in vitro model featuring a pre‐formed three‐dimensional neurites network on which a capillary‐like network was allowed to organize and mature. Sensory neurons and glial cells were co‐cultured with fibroblasts in a collagen‐chitosan sponge to reconstruct the neural network for 14 days and human endothelial cells were then seeded on the tissue to build a capillary‐like network for 17 additional days. Neural cells induced a 27% increase in the number of capillary‐like tubes (CLT) formed in the tissue. This effect was abolished when K252a, an inhibitor of the TrkA, B and C receptors for the NGF, BDNF and NT‐3 neurotrophins respectively was added to the culture medium. Moreover, we demonstrated that when 10 ng/ml of NGF, 0.1 ng/ml of BDNF, 15 ng/ml of NT3 and 50 ng/ml of GDNF were added to our endothelialized reconstructed connective tissue model, a major increase from 40 to 80% in the number of CLT was observed. This is the first in vitro demonstration of a direct angiogenic effect of peripheral neural cells on human endothelial cells through the release of neurotrophic fators and of the angiogenic potential of NT‐3 and GDNF, the latter belonging to an other family of neurotrophic factors.
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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".