Neutralization of transforming growth factor-β attenuates hypertension and prevents renal injury in uremic rats
Bibliographic record
Abstract
OBJECTIVE: We investigate the role of transforming growth factor-beta (TGF-beta) in hypertension and renal failure progression in uremic rats, and whether it modulates the endothelin (ET) system. DESIGN: Following renal mass reduction, uremic rats (Nx) received the pan-specific TGF-beta neutralizing antibody 1D11 (0.5 mg/kg, three times/week), the isotype control antibody 13C4 or the AT1 antagonist losartan (10 mg/kg per day) for 6 weeks. RESULTS: Before treatment, the blood pressure was higher in Nx rats and increased further over time in Nx+13C4 rats. At the end of the study, Nx+13C4 rats exhibited increased serum creatinine, proteinuria and renal expression and excretion of TGF-beta1 and ET-1. ET-1 concentrations were greater in vascular and renal tissues, whereas the ETB receptor expression was reduced. Renal injuries were comprised of blood vessel hypertrophy, glomerular sclerosis, tubular atrophy and interstitial fibrosis, which was associated with increased alpha-smooth muscle actin expression. Treatment of uremic rats with the 1D11 antibody attenuated the increase in blood pressure and the decline in renal function. Losartan normalized the blood pressure and significantly attenuated the increase in serum creatinine and proteinuria. However, both treatments prevented renal TGF-beta1 and ET-1 overexpression, and prevented all renal histological injuries. The 1D11 antibody only improved ETB receptor expression. CONCLUSIONS: Neutralization of TGF-beta attenuates hypertension and renal failure progression in uremic animals, in part, by preventing renal injury processes. These effects may be related to the modulation of the ET system, preventing renal ET-1 overproduction and the reduction of ETB receptor expression. Our data also suggest that TGF-beta1 is involved, at least in part, in the pathological effects related to angiotensin II in chronic renal failure.
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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) | 0.001 | 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) | 0.002 | 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".