Rapamycin inhibits transforming growth factor β‐induced peritoneal angiogenesis by blocking the secondary hypoxic response
Bibliographic record
Abstract
Patients with end-stage kidney disease on peritoneal dialysis often develop progressive scarring of the peritoneal tissues. This manifests as submesothelial thickening and is associated with increased vascularization that leads to ultrafiltration dysfunction. Hypoxia induces a characteristic series of responses including angiogenesis and fibrosis. We investigated the role of hypoxia in peritoneal membrane damage. An adenovirus expressing transforming growth factor (TGF) β was used to induce peritoneal fibrosis. We evaluated the effect of the mTOR inhibitor rapamycin, which has been previously shown to block hypoxia-inducible factor (HIF) 1α. We also assessed the effect of HIF1α independently using an adenovirus expressing active HIF1α. To identify the TGFβ1-independent effects of HIF1α, we expressed HIF1α in the peritoneum of mice lacking the TGFβ signalling molecule Smad3. We demonstrate that TGFβ-induced fibroproliferative tissue is hypoxic. Rapamycin did not affect the early angiogenic response, but inhibited angiogenesis and submesothelial thickening 21 days after induction of fibrosis. In primary mesothelial cell culture, rapamycin had no effect on TGFβ-induced vascular endothelial growth factor (VEGF) but did suppress hypoxia-induced VEGF. HIF1α induced submesothelial thickening and angiogenesis in peritoneal tissue. The fibrogenic effects of HIF1α were Smad3 dependent. In summary, submesothelial hypoxia may be an important secondary factor, which augments TGFβ-induced peritoneal injury. The hypoxic response is mediated partly through HIF1α and the mTOR inhibitor rapamycin blocks the hypoxic-induced angiogenic effects but does not affect the direct TGFβ-mediated fibrosis and angiogenesis.
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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 | 0.001 |
| 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".