Abstract 533: Lymph is a Vehicle for Extracellular Vesicles in Mice
Bibliographic record
Abstract
Introduction: Although for a long time considered as simple cellular debris, extracellular vesicles (EVs) are now known to be involved in many pathophysiological processes such as thrombosis, autoimmune diseases and inflammation. Due to their diversity and presence in different tissues, EVs are considered important biomarkers and thus, their precise detection in various biological fluids is important to better understand all their different functional activities. The lymphatic system works in close collaboration with the cardiovascular system to preserve fluid balance throughout the body. Lymphatic vessels are present in almost all vascularized tissues, including the brain and the artery wall, and their role in these organ-related pathologies are under intense investigations. Hypothesis: Since lymphatic vessels are often perceived as "sewers", due to their role in removing interstitial fluid and waste products from peripheral tissues such as the artery wall, we herein want to qualitatively and quantitatively assess the presence of EVs in circulating lymph. Methods and Results: Using several approaches such as a Zetasizer Nano S, electron microscopy and flow cytometry analysis, we have detected and characterized EVs in lymph of healthy animals, and found that these EVs are inclusively derived from red blood cells, platelets and lymphatic endothelial cells. Analysis of lymph from atherosclerotic mice (Ldlr -/- ) confirmed the idea that EVs number and origin varies according to the pathological setting. Conclusion: Herein, we show for the first time that EVs are present in lymph and that their level and origin vary in atherosclerosis. Our work will be setting the stage to a better understanding of the mechanism underlying EV accumulation in peripheral tissues during inflammation, and to better control related 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".