Oncostatin M and TLR-4 Ligand Synergize to Induce MCP-1, IL-6, and VEGF in Human Aortic Adventitial Fibroblasts and Smooth Muscle Cells
Bibliographic record
Abstract
Accumulating evidence suggests that adventitial fibroblasts play a significant role in contributing to inflammation of the arterial wall and pathogenesis of atherosclerosis. The effects of gp130 cytokines on these cells (including oncostatin M-[OSM] and IL-6), some of which have been implicated in atherosclerosis, are currently unknown. Experiments were performed to determine whether gp130 cytokines regulate human aortic adventitial fibroblasts (HAoAFs) or smooth muscle cells (HAoSMCs) alone or in context of TLR-4 ligands (also implicated in atherosclerosis). HAoAFs and HAoSMCs were stimulated with LPS and/or one of OSM, IL-6, IL-11, IL-31, or LIF. ELISAs performed on cell supernatants showed that stimulation with OSM alone caused increased MCP-1, IL-6, and VEGF levels. When combined, LPS and OSM synergized to increase MCP-1, IL-6, VEGF protein, and mRNA expression as assessed by qRT-PCR, in both HAoAFs and HAoSMCs, while LPS-induced IL-8 levels were reduced. Such effects were not observed with other gp130 cytokines. Signalling pathways including STATs, MAPKinases, and NF κ B were activated, and LPS induced steady state mRNA levels of the OSM receptor chains OSMR β and gp130. The results suggest that OSM is able to synergize with TLR-4 ligands to induce proinflammatory responses by HAoAFs and HAoSMCs, supporting the notion that OSM regulation of these cells contributes to the pathogenesis of atherosclerosis.
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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".