Reduction of chronic rejection of renal allografts by anti-transforming growth factor-β antibody therapy in a rat model
Bibliographic record
Abstract
There is no effective treatment for chronic rejection (CR) that largely limits long-term survival of kidney transplants. Transforming growth factor (TGF)-β is a fibrogenic factor for tissue fibrosis. This study was to test the efficacy of an anti-TGF-β antibody in preventing the CR of renal allografts in a preclinical model. Male Lewis rats (RT1¹) were orthotopically transplanted with donor kidneys from male Fischer 344 (RT11v1) rats and were treated with either anti-TGF-β or a control antibody. The CR of renal allografts was assessed by semiquantitative histological analyses, and intragraft cytokines and fibrosis-related genes ware examined by PCR arrays. Compared with the control antibody, anti-TGF-β antibody treatment significantly reduced recipients' proteinuria (P = 0.0002), and CR in renal transplants, which was indicated by the fewer injured renal tubules, glomeruli, and interlobular arterioles or arteries, and by less mononuclear cell infiltration and interstitial fibrosis in the anti-TGF-β antibody-treated group (P < 0.05), but not significantly attenuate the ratios of different infiltrating leukocytes. These pathological changes were associated with downregulation of TGF-β1, TGF-β2, and proinflammatory cytokines, or with upregulation of anti-fibrotic HGF, BMP5, and BMP7. The therapeutic effect of the anti-TGF-β antibody was further confirmed by its prevention of graft dysfunction, indicated by lower levels of serum creatinine and blood urea nitrogen or higher creatinine clearance in anti-TGF-β antibody-treated recipients compared with those in control recipients (P < 0.05). In conclusion, the anti-TGF-β antibody (1D11) treatment significantly reduces CR of renal allografts in rats, suggesting the therapeutic potential of this antibody therapy for treating CR of kidney transplants in patients.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".