A novel therapeutic agent to prevent sepsis-induced acute kidney injury and mortality
Bibliographic record
Abstract
Acute kidney injury (AKI) occurs in about one-half of the patients who develop septic shock, and the mortality of AKI with sepsis is extremely high. An effective therapeutic intervention is urgently needed. In the present study we tested the ability of a novel tetrapeptide, EA-230, to improve survival and attenuate loss of kidney function in a clinically relevant model of sepsis – cecal ligation and puncture (CLP) in mice. Sepsis was induced in C57BL/6 mice by CLP. Four hours postoperatively, EA-230 was administered. Subsequently, animals were treated twice daily for four consecutive days intraperitoneally. The effects of 20, 30, 40, or 50 mg/kg were compared with those of saline. Survival and renal function were monitored. Inulin clearance and para -aminohippuric acid clearance were used to measure the glomerular filtration rate and renal blood flow. All saline-treated control animals died within 5 days of CLP, whereas EA-230 treatment improved survival significantly in a dose-dependent manner. The best result was obtained with 50 mg/kg EA-230 (43.8% survival after 2 weeks). Serum creatinine and blood urea nitrogen increased markedly 24 hours after CLP. EA-230 attenuated the increases in creatinine and blood urea nitrogen significantly in the 30 to 50 mg/kg treatment groups. Furthermore, the glomerular filtration rate and renal blood flow were significantly higher ( P < 0.05) 36 hours post CLP in EA-230-treated mice versus those treated with saline. EA-230 is a novel and promising therapeutic agent for preventing AKI in sepsis. Its beneficial effect is associated with an improvement in renal hemodynamics.
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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".