Anti-inflammatory and immunoregulatory effects of pinolenic acid in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: In pre-clinical studies, pinolenic acid (PNLA), an omega-6-polyunsaturated fatty acid from pine nuts, has shown anti-inflammatory effects. We aimed to investigate the effect of PNLA in human cell lines and peripheral blood mononuclear cells (PBMCs) from RA patients and healthy controls (HCs). METHODS: A modified Boyden chamber was used to assess chemokine-induced migration of THP-1 monocytes. Macropinocytosis was assessed using Lucifer yellow and oxidized low-density lipoprotein (oxLDL) uptake using DiI-labelled oxLDL in THP-1 macrophages and human monocyte-derived macrophages (HMDMs). IL-6, TNF-α and prostaglandin E2 (PGE2) release by lipopolysaccharide (LPS)-stimulated PBMCs from RA patients and HCs was measured by ELISA. The transcriptomic profile of PNLA-treated, LPS-activated PBMCs was investigated by RNA-sequencing. RESULTS: PNLA reduced THP-1 cell migration by 55% (P < 0.001). Macropinocytosis and DiI-oxLDL uptake were reduced by 50% (P < 0.001) and 40% (P < 0.01), respectively, in THP-1 macrophages and 40% (P < 0.01) and 25% (P < 0.05), respectively, in HMDMs. PNLA reduced IL-6 and TNF-α release from LPS-stimulated PBMCs from RA patients by 60% (P < 0.001) and from HCs by 50% and 35%, respectively (P < 0.01). PNLA also reduced PGE2 levels in such PBMCs from RA patients and HCs (P < 0.0001). Differentially expressed genes whose expression was upregulated included pyruvate dehydrogenase kinase-4, plasminogen activator inhibitor-1, fructose bisphosphatase1 and N-Myc downstream-regulated gene-2, which have potential roles in regulating immune and metabolic pathways. Pathway analysis predicted upstream activation of the nuclear receptors peroxisome proliferator-activated receptors involved in anti-inflammatory processes, and inhibition of nuclear factor-κB and signal transducer and activator of transcription 1. CONCLUSIONS: PNLA has immune-metabolic effects on monocytes and PBMCs that are pathogenic in RA and atherosclerosis. Dietary PNLA supplementation may be beneficial in RA.
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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".