Assessment of Extracellular Vesicles Isolated from the Culture Medium of Ischemic Renal Proximal Tubular Epithelial Cells
Bibliographic record
Abstract
Background: Extracellular vesicles (EVs) are membranous particles released by a cell into the extracellular environment. They play an emerging and important role in cell communication and have been implicated in a variety of pathological conditions including acute kidney injury and chronic kidney diseases. Our objective was to characterize the dynamics of extracellular vesicle release by proximal tubular epithelial cells exposed to hypoxia and reoxygenation (H/R). Methods: Primary human renal proximal tubule epithelial cells (hPTECs, n=3) were exposed to 24 hours of 1% hypoxia followed by 3 hours of reoxygenation. Proximal tubule phenotype was verified by megalin immunofluorescence and EVs were isolated from the conditioned culture medium by ultracentrifugation (100.000g for 1h30min). Extracellular vesicle size and quantity were assessed by nanoparticle tracking analysis (NTA) and EV protein quantification. Results: The immunofluorescence analysis indicated hPTECs were megalin-positive and this was not altered by H/R. NTA indicated that the H/R exposed cells released ˜2-fold more EVs than normoxia (p<0.05). This was further supported by an increase in EV protein in conditioned media of H/R vs normoxia (1.08±0.17 vs 0.75±0.12 μg/μl, p<0.05). The mean particle sizes were not significantly different between H/R (153.5nm) and normoxia (145.8nm) groups. Conclusions: Our initial data suggest that under hypoxia induces a shift in EV release from proximal tubule cells with greater EV release. These findings suggest that proximal tubule EV release may be altered in the context of ischemic kidney disease. Such changes may contribute to disease pathogenesis. Funding: Government Support - Non-U.S.
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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".