Abstract 375: Interaction of Plasma Autotaxin and Extracellular Vesicles in Rheumatoid Disease Patients Induces Circulating Cell Activation
Bibliographic record
Abstract
Background: the frequency and severity of cardiovascular disease (CVD) is higher in patients suffering from Rhumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) than healthy individuals, likely due to the direct impact of systemic inflammation on the atherosclerotic plaque. Several factors can contribute to cardiovascular damage, including pro-inflammatory lipid mediators such as lysophosphatidic acid (LPA) produced by a lysophospholipase enzyme named autotaxin (ATX). Extracellular vesicles (EVs) are also abundant in blood from patient developing atherosclerotic plaque, and recently EVs from arthritic synovial fluid were found to interact with phospholipase and release inflammatory lipids. Objective: we therefore sought to understand how EVs and ATX may cooperate in the context of rheumatoid disease to promote inflammation. Results: we observed that plasma from RA and SLE patients, analysed by ELISA, contained high concentrations of ATX compared to the plasma from healthy people (pvalue =0.0001). The plasma of these patients, analysed by nanoparticle flow cytometry, also contained high concentration of EVs and these EVs are decorated by ATX, on their surface. As the presence of EVs indicated cell activation due to different endogenous stimuli such as LPA, we found in vitro that different species of LPA activate circulating blood cell to produce EVs (observed by flow cytometry and electronic microscopy), suggesting that LPA serves as an activating signal for EV production. In summary, our results show that ATX bind EVs and produces LPA that activates circulating cells in RA and SLE patients. ATX and LPA could serve as biomarkers and therapeutic target in accelerated CVD in patients with rheumatoid diseases.
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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) | 0.005 | 0.001 |
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".