A Novel Therapy to Attenuate Acute Kidney Injury and Ischemic Allograft Damage after Allogenic Kidney Transplantation in Mice
Bibliographic record
Abstract
Ischemia followed by reperfusion contributes to the initial damage to allografts after kidney transplantation (ktx). In this study we tested the hypothesis that a tetrapeptide EA-230 (AQGV), might improve survival and attenuate loss of kidney function in a mouse model of renal ischemia/reperfusion injury (IRI) and ischemia-induced delayed graft function after allogenic kidney transplantation. IRI was induced in male C57Bl/6N mice by transient bilateral renal pedicle clamping for 35 min. Treatment with EA-230 (20-50mg/kg twice daily i.p. for four consecutive days) was initiated 24 hours after IRI when acute kidney injury (AKI) was already established. The treatment resulted in markedly improved survival in a dose dependent manner. Acute tubular injury two days after IRI was diminished and tubular epithelial cell proliferation was significantly enhanced by EA-230 treatment. Furthermore, CTGF up-regulation, a marker of post-ischemic fibrosis, at four weeks after IRI was significantly less in EA-230 treated renal tissue. To learn more about these effects, we measured renal blood flow (RBF) and glomerular filtration rate (GFR) at 28 hours after IRI. EA-230 improved both GFR and RBF significantly. Next, EA-230 treatment was tested in a model of ischemia-induced delayed graft function after allogenic kidney transplantation. The recipients were treated with EA-230 (50 mg/kg) twice daily i.p. which improved renal function and allograft survival by attenuating ischemic allograft damage. In conclusion, EA-230 is a novel and promising therapeutic agent for treating acute kidney injury and preventing IRI-induced post-transplant ischemic allograft injury. Its beneficial effect is associated with improved renal perfusion after IRI and enhanced regeneration of tubular epithelial cells.
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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) | 0.001 | 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".