Rapamycin reduces renal hypoxia, interstitial inflammation and fibrosis in a rat model of unilateral ureteral obstruction
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore effects of rapamycin on renal hypoxia, interstitial inflammation and fibrosis, and the expression of transforming growth factor β1 (TGF-β1), vascular endothelial growth factor (VEGF), Flk-1 and Flt-1 in a rat model of unilateral ureteral obstruction (UUO). METHODS: Male Sprague-Dawley rats (n=36) were randomly divided into three groups (n=12 per group): sham surgery, UUO and UUO plus rapamycin (0.2 mg/kg/d). Serum creatinine (Scr), blood urea nitrogen, uric acid, triglycerides, cholesterol and 24-h urine protein levels were measured. The extent of interstitial fibrosis was determined by Masson's trichrome staining. ED-1 positive macrophages, type III collagen, hypoxia, TGF-1, VEGF, Flk-1, and Flt-1 mRNA and protein expressions were detected using immunohistochemical staining, real-time PCR and Western blot. RESULTS: UUO induced an elevation in Scr, renal hypoxia, inflammation, interstitial fibrosis, TGF-β1, VEGF, Flk-1, and Flt-1 mRNA and protein expression levels (P < 0.05). Rapamycin alleviated the UUO-induced renal hypoxia, infiltration of inflammatory cells and tubulointerstitial fibrosis (at days 3 and 7). Rapamycin also down-regulated the UUO-induced elevated expression levels of TGF-β1 and Flt-1 mRNA and protein (P < 0.05). Rapamycin decreased VEGF mRNA and protein expression at day 3, and increased Flk-1 mRNA and protein expression at day 7, compared with the UUO group (P < 0.05). CONCLUSION: Rapamycin shows beneficial effects by reducing UUO-induced renal hypoxia, inflammation and tubulointerstitial fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".