Podocyte Proteomics Revealed hUCMSC-Exosomes Ameliorate Diabetic Kidney Disease through Inhibiting Talin-1 Mediated EMT
Bibliographic record
Abstract
BACKGROUND: Podocytes injury drives proteinuria in diabetic kidney disease (DKD). Exosomes derived from human umbilical cord mesenchymal stem cells (hUCMSCs) have demonstrated therapeutic potential in kidney diseases. However, the effects of hUCMSCs on podocyte injury and the underlying mechanisms in DKD remain unexplored. METHODS: Four-dimensional label-free quantitative proteomics was performed on a global analysis of proteins in sorted podocytes from normal mice (NC group), db/db mice (DM group), and db/db mice treated with hUCMSCs (DMT group). RESULTS: HUCMSC-derived exosomes alleviated renal dysfunction and podocyte epithelial-mesenchymal transition (EMT). A total of 1765 proteins were quantified, with enrichment in pathways related to cytoskeleton organization, phagocytosis, oxidative stress, and apoptosis. Talin-1 was downregulated in diabetic podocytes but upregulated following hUCMSC treatment. Talin-1 silencing exacerbated high glucose-induced EMT. CONCLUSIONS: This study highlights the potential of hUCMSC-derived exosomes as a therapeutic strategy for DKD by ameliorating Talin-1-mediated podocyte EMT.
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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".