Altered <scp>VEGF</scp>‐<scp>A</scp> and receptor m<scp>RNA</scp> expression profiles, and identification of <scp>VEGF</scp>144 in foetal rat calvaria cells, in coculture with microvascular endothelial cells
Bibliographic record
Abstract
Cellular proliferation and differentiation during angiogenesis and osteogenesis require the communication of different cell types through growth factors and their receptors. Vascular endothelial growth factor (VEGF-A) plays an important role in osteoblast and endothelial cell intercommunication. We have investigated the effect of monocultures and indirect coculture of foetal rat calvarial (FRC) osteoblasts and microvascular endothelial cells (ECs) on nodule formation, proliferation, and mRNA-expression of VEGF-A and its receptors during culturing. Despite increased nodule formation in the presence of dexamethasone (Dex) in monocultures, the number of nodules and alkaline phosphatise activity were decreased in cocultured FRCs. VEGF mRNA expression over the differentiation period showed the expression of most Vegf isoforms is biphasic in both FRCs and ECs, whereas receptor expression was quite variable; however, that of Np-2 in FRCs increased steadily and significantly from 8 h to 14 days after an initial drop in expression. Significant changes in the proportion of Vegfa by Day 14 were noted mainly in the matrix-bound variants Vegf144 and Vegf188 in ECs and osteoblasts, respectively. Less striking results were seen in the expression of the soluble isoforms in either cell type. These results have identified expression of Vegf144 in osteoblasts, suggesting a possible autocrine and/or paracrine role that is affecting osteoblast mineralisation along with Vegf188, as well as possible early roles of these isoforms in initial cell attachment. Further study of VEGF expression in coculture and Vegf144 will lead to better understanding of its role in cell-cell communication and bone development.
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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".