Pathogenesis of renal fibrosis: Role of Proteinase‐activated Receptor‐2
Bibliographic record
Abstract
OBJECTIVES To evaluate the role of proteinase‐activated receptor‐2 (PAR2) in renal fibrosis. METHODS We used both cultured acutely isolated human‐derived proximal tubular cells (HPTCs) and an in‐vivo murine unilateral ureteral obstruction (UUO) model of renal fibrosis, with wild‐type and PAR2 null mice (Jackson Labs). HPTCs at passage 3 were activated with a PAR2‐activating peptide (PAR2‐AP) and trypsin without or with co‐stimulation by transforming growth factor‐ β (TGF‐ β) and the expression level of connective tissue growth factor (CTGF) was measured by western blot analysis. Cells were activated with and without the signal pathway inhibitors for MAPKinase (PD98059) and Rho‐kinase (Y‐27632). Kidneys from wild‐type and PAR2‐null mice with or without (sham) UUO were obtained at 7, 14, 21 and 28 days after UUO. Renal tissue was fixed and evaluated histopathologically for morphology (H & E), collagen deposition (Masson's Trichrome) and biochemically (western blot) for α‐smooth muscle actin. RESULTS (1) PAR2‐AP activation of HPTCs alone significantly upregulated CTGF expression and did so synergistically to augment TGF‐β‐induced CTGF production. This synergy was reduced by MAPKinase but not Rho‐kinase inhibition. (2) PAR2 null UUO mice had less tubular injury and fibrosis and reduced alpha‐smooth muscle actin at day 7 but not at 28 days. SUMMARY Our results support a role for PAR2 in acute renal fibrosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".