Resolvin E<sub>1</sub>normalizes contractility, Ca<sup>2+</sup>sensitivity and smooth muscle cell migration rate in TNF-α- and IL-6-pretreated human pulmonary arteries
Bibliographic record
Abstract
Pulmonary hypertension (PH) is a rare disease in which pathophysiology is characterized by an increase in proinflammatory mediators, chronic endothelial dysfunctions, and a high migration rate of smooth muscle cells (SMC). Over the course of the last decade, various treatments have been proposed to relax the pulmonary arteries, none of which have been effective in resolving PH. Our hypothesis is that artery-relaxing drugs are not the long-term solution, but rather the inhibition of tissue inflammation, which underlies human pulmonary artery (HPA) dysfunctions that lead to abnormal vasoconstriction. The goal of the present study was to assess the anti-inflammatory effects of resolvin E1 (RvE1) with concomitant effects on SMC migration and on HPA reactivity. The role and mode of action of RvE1 and its precursor, monoacylglyceride eicosapentaenoic acid were assessed on HPA under proinflammatory conditions, involving a combined pretreatment with 10 ng/ml TNF-α and 10 ng/ml IL-6. Our results show that TNF-α and IL-6 treatment induced hyperreactivity and Ca(2+) hypersensitivity in response to pharmaco-mechanical stimuli, including 80 mM KCl, 1 μM phorbol 12-13-dibutyrate, and 30 nM U-46619. Furthermore, the proinflammatory treatment increased the migration rate of SMC isolated from HPA. The phosphorylation level of regulatory contractile proteins (CPI-17, MYPT-1), and proinflammatory signaling pathways (c-Fos, c-Jun, NF-κB) were also significantly increased compared with control conditions. Conversely, 300 nM RvE1 was able to normalize all of the above abnormal events triggered by proinflammation. In conclusion, RvE1 can resolve human arterial hyperreactivity via the resolution of inflammatory markers.
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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) | 0.001 | 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".