IL-8 reduces VCAM-1 secretion of smooth muscle cells by increasing p-ERK expression when 3-D cocultured with vascular endothelial cells
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to investigate the crosstalk between vascular endothelial cells (ECs) and smooth muscle cells (SMCs) using a three-dimensional (3-D) co-culture model. In addition, the role of IL-8 in this crosstalk was investigated. METHODS: A 3-D co-culture model was constructed using a Transwell chamber system and type I collagen gel. Human umbilical artery smooth muscle cells (HUASMCs) were suspended in the gel and added to the upper compartment of the Transwell. Human umbilical vein endothelial cells (HUVECs) were then grown on the surface of the gel. The growth of HUASMCs was tested with a CFDA SE cell proliferation kit. IL-8 and other bioactive substances were investigated by ELISA and real-time PCR. The alteration of p-ERK expression related to the change in IL-8 levels was also examined by Western blot analysis. RESULTS: The proliferation rate of HUASMCs in the 3-D co-culture model was 0.679 ± 0.057. Secretion and transcription of VEGF, t-PA, NO and VCAM-1 in the 3-D co-culture model were different than in single (2-D) culture. When 3-D co-cultured, IL-8 released by HUVECs was significantly increased (2.35 ± 0.16 fold) (P﹤0.05) and the expression of VCAM-1 from HUASMCs was reduced accordingly (0.55±0.09 fold). In addition, increasing or decreasing the level of IL-8 changed the level of p-ERK and VCAM-1 expression. The reduction of VCAM-1, resulting from increased IL-8, could be blocked by the MEK inhibitor, PD98059. CONCLUSION: Crosstalk between HUVECs and HUASMCs occurred and was probably mediated by IL-8 in this 3-D co-culture model.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".