Docosapentaenoic acid monoacylglyceride reduces inflammation and vascular remodeling in experimental pulmonary hypertension
Bibliographic record
Abstract
n-3 Polyunsaturated fatty acids (n-3 PUFA) have been shown to reduce inflammation and proliferation of pulmonary artery smooth muscle cells under pathophysiological conditions. However, the anti-inflammatory effect of the newly synthesized docosapentaenoic acid monoacylglyceride (MAG-DPA) on key signaling pathways in pulmonary hypertension (PH) pathogenesis has yet to be assessed. The aim of the present study was to determine the effects of MAG-DPA on pulmonary inflammation and remodeling occurring in a rat model of PH, induced by a single injection of monocrotaline (MCT: 60 mg/kg). Our results demonstrate that MAG-DPA treatment for 3 wk following MCT injection resulted in a significant improvement of right ventricular hypertrophy (RVH) and a reduction in Fulton's Index (FI). Morphometric analyses revealed that the wall thickness of pulmonary arterioles was significantly lower in MCT + MAG-DPA-treated rats compared with controls. This result was further correlated with a decrease in Ki-67 immunostaining. Following MAG-DPA treatments, lipid analysis showed a consistent increase in DPA together with lower levels of arachidonic acid (AA), as measured in blood and tissue samples. Furthermore, in MCT-treated rats, oral administration of MAG-DPA decreased NF-κB and p38 MAPK activation, leading to a reduction in MMP-2, MMP-9, and VEGF expression levels in lung tissue homogenates. Altogether, these data provide new evidence regarding the mode of action of MAG-DPA in the prevention of pulmonary hypertension induced by MCT.
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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) | 0.001 | 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".