WNT signaling is required for peritoneal membrane angiogenesis
Bibliographic record
Abstract
The wingless-type mouse mammary tumor virus integration site family (WNT) signaling pathway is involved in wound healing and fibrosis. We evaluated the WNT signaling pathway in peritoneal membrane injury. We assessed WNT1 protein expression in the peritoneal effluents of 54 stable peritoneal dialysis (PD) patients and WNT-related gene expression in ex vivo mesothelial cell cultures from 21 PD patients. In a transforming growth factor-β (TGF-β)-mediated animal model of peritoneal fibrosis, we evaluated regulation of the WNT pathway and the effect of WNT inhibition on peritoneal fibrosis and angiogenesis. WNT1 and WNT2 gene expression were positively correlated with peritoneal membrane solute transport in PD patients. In the mouse peritoneum, TGF-β-induced peritoneal fibrosis was associated with increased expression of WNT2 and WNT4. Peritoneal β-catenin protein was significantly upregulated after infection with adenovirus expressing TGF-β (AdTGF-β) along with elements of the WNT signaling pathway. Treatment with a β-catenin inhibitor (ICG-001) in mice with AdTGF-β-induced peritoneal fibrosis resulted in attenuation of peritoneal angiogenesis and reduced vascular endothelial growth factor. Similar results were also observed with the WNT antagonist Dickkopf-related protein (DKK)-1. In addition to this, DKK-1 blocked epithelial-mesenchymal transition and increased levels of the cell adhesion protein E-cadherin. We provide evidence that WNT signaling is active in the setting of experimental peritoneal fibrosis and WNT1 correlates with patient peritoneal membrane solute transport in PD patients. Intervention in this pathway is a possible therapy for peritoneal membrane injury.
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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) | 0.002 | 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".