Neutralization of Tumor Necrosis Factor-Alpha Reduces Renal Fibrosis and Hypertension in Rats with Renal Failure
Bibliographic record
Abstract
BACKGROUND: Increased production of tumor necrosis factor-α (TNF-α) in chronic kidney disease may be involved in the progression of renal failure and injury, and cardiovascular disease. We investigated the effect of TNF-α neutralization on renal failure, inflammation and fibrosis, and blood pressure in rats with renal failure. METHODS AND RESULTS: Renal failure was induced by renal mass reduction and the animals were treated with PEG-sTNFR1, a pegylated form of soluble TNF type 1 receptor that neutralizes TNF-α, for 6 weeks. Systolic, diastolic and mean arterial pressures were higher in renal failure rats that were associated with increased serum creatinine, albuminuria and renal injury comprised of blood vessel media hypertrophy, focal and segmental glomerulosclerosis, tubular atrophy and interstitial inflammation and fibrosis. These changes were associated with greater levels of TNF-α, transforming growth factor (TGF)-β1, nuclear transcription factor NF-ĸB and cytosolic phospho-IĸB-α, and inflammatory markers expression (ICAM-1, VCAM-1 and MCP-1). Moreover, endothelin (ET)-1 production was also increased, whereas nitric oxide (NO) release was decreased. TNF-α neutralization reduced hypertension, albuminuria and renal inflammation and fibrosis, which were coupled to a reduction in renal NF-ĸB activation, inflammatory markers expression, TGF-β1 and ET-1 production, and an increase in NO release. CONCLUSION: Neutralization of TNF-α in rats with renal failure decreases NF-ĸB activity that is associated with a reduction in renal TGF-β1 and ET-1 production, and an improvement of NO release. These effects likely reduce renal inflammation and fibrosis, and blood pressure indicating a pivotal role for TNF-α, at least, in the progression of renal 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) | 0.001 | 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) | 0.001 | 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".