Schwann cells promote endothelial cell migration
Bibliographic record
Abstract
Directed cell migration is a crucial orchestrated process in embryonic development, wound healing, and immune response. The underlying substrate can provide physical and/or chemical cues that promote directed cell migration. Here, using electrospinning we developed substrates of aligned poly(lactic-co-glycolic acid) nanofibres to study the influence of glial cells on endothelial cells (ECs) in a 3-dimensional (3D) co-culture model. ECs build blood vessels and regulate their plasticity in coordination with neurons. Likewise, neurons construct nerves and regulate their circuits in coordination with ECs. In our model, the neuro-vascular cross-talk was assessed using a direct co-culture model of human umbilical vein endothelial cells (HUVECs) and rat Schwann cells (rSCs). The effect of rSCs on ECs behavior was demonstrated by earlier and higher velocity values and genetic expression profiles different of those of HUVECs when seeded alone. We observed 2 different gene expression trends in the co-culture models: (i) a later gene expression of angiogenic factors, such as interleukin-8 (IL-8) and vascular endothelial growth factor (VEGF), and (ii) an higher gene expression of genes involved in actin filaments rearrangement, such as focal adhesion kinase (FAK), Mitogen-activated protein kinase-activated protein kinase 13 (MAPKAPK13), Vinculin (VCL), and Profilin (PROF). These results suggested that the higher ECs migration is mainly due to proteins involved in the actin filaments rearrangement and in the directed cell migration rather than the effect of angiogenic factors. This co-culture model provides an approach to enlighten the neurovascular interactions, with particular focus on endothelial cell migration.
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) | 0.000 | 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) | 0.003 | 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 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".