The Regulation of Cytokine Gene Expression by Sphingosine 1‐Phosphate in Endothelial Cells
Bibliographic record
Abstract
Angiogenesis is the formation of new blood vessels from pre‐existing vasculature. Signaling mechanisms initiated by vascular endothelial growth factor (VEGF) and the lipid bioactivator of angiogenesis, sphingosine‐1‐phosphate (S1P) have been linked in recent studies. In human umbilical vein endothelial cells (HUVECs) stimulated with either VEGF or S1P, Janus kinases (JAKs) become activated and stimulate downstream signal transducers and activators of transcription (STAT) proteins that affect gene expression. The S1P signaling to JAKs is mediated by the transactivation of VEGF receptors. Given that VEGF and S1P are strong inducers of angiogenesis and that the JAK/STAT pathway mediates gene expression, an initial expression profile of 27 soluble growth factors and cytokines in S1P‐ and VEGF‐treated HUVECs was elucidated using Bio‐Plex technology. Treatment with either VEGF or S1P induced increased expression of the proangiogenic and proinflammatory factors IL‐6, IL‐8 and MCP‐1. Expression of an additional subset of 13 factors was increased with VEGF alone while S1P did not induce expression of any unique genes. This upregulation is dependent on treatment dose. While the extracellular matrix (ECM) that cells were grown on did not induce expression, specific ECM proteins did affect expression levels in treated cells. These findings reveal a potential role for JAK/STAT signaling in the angiogenic response.
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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".