Monocyte- and Endothelial-Derived Microparticles Induce an Inflammatory Phenotype in Human Podocytes
Bibliographic record
Abstract
BACKGROUND/AIMS: Proteinuria is associated with cardiovascular and chronic kidney disease. Microparticles (MPs) are bioactive vesicles shed from activated cells and also linked to cardiovascular disease. MP-like structures have been identified in the glomerular basement membrane, urinary space and between the glomerular basement membrane and the podocyte. We hypothesised that circulating MPs may provide a link between vascular injury and kidney diseases by inducing podocyte phenotypic alterations, thus propagating glomerular dysfunction and proteinuria. METHODS: Human umbilical vein endothelial cells and U937 monocytes were stimulated with TNF-α to produce MPs. These MPs were confirmed by electron microscopy, and added to differentiated podocyte monolayers to determine effects on podocyte albumin endocytosis and the production of soluble mediators. RESULTS: Monocyte and endothelial MPs upregulated podocyte production of pro-inflammatory mediators monocyte chemoattractant protein-1 (p < 0.001) and interleukin-6 (p < 0.001). Only monocyte MPs upregulated podocyte secretion of VEGF (p < 0.001), known to regulate glomerular permeability. Endothelial MPs decreased podocyte albumin endocytosis by 13% compared to control cells (p < 0.01). CONCLUSION: MPs alter endocytic functions of podocytes and induce secretion of pro-inflammatory cytokines, potentially leading to glomerular inflammation in vivo and the development of proteinuria. This study identifies a potential pathophysiological role for circulating MPs in the kidney through effects on the podocyte.
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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".