Extracellular vesicles are present in mouse lymph and their level differs in atherosclerosis
Bibliographic record
Abstract
The lymphatic system works in close collaboration with the cardiovascular system to preserve fluid balance throughout the body and is essential for the trafficking of antigen-presenting cells and lymphocytes to lymphoid organs. Recent findings have associated lymphatic dysfunction with the pathogenesis of cardiovascular-related diseases such as atherosclerosis, inflammation and obesity. Whether lymphatic dysfunction is a cause or a consequence of these diseases, as well as how, is under intensive investigation. Extracellular vesicles (EVs) are submicron vesicles released by diverse cell types upon activation or apoptosis and are considered important biomarkers for several inflammatory diseases. Thus, it is critical to characterize the presence of EVs in various biological tissues and fluids to delineate their origins and, subsequently, their functions. In the past few years, new techniques allowing the quantitative and qualitative analysis of EVs have emerged, thus facilitating the onset of studies bridging these vesicles to the lymphatic system. Using several state-of-the-art approaches, this article reports the presence of diverse EVs inclusively derived from red blood cells and platelets in lymph of healthy animals. Our results suggest that lymph from atherosclerotic mice displays a higher concentration of EVs, bringing forward the concept that EVs contained in lymph could either be a biomarker for lymphatic dysfunction or, conversely, for inflammatory disease progression.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".