Restoration of renal function by a novel prostaglandin EP<sub>4</sub>receptor-derived peptide in models of acute renal failure
Bibliographic record
Abstract
Acute renal failure (ARF) is a serious medical complication characterized by an abrupt and sustained decline in renal function. Despite significant advances in supportive care, there is currently no effective treatment to restore renal function. PGE(2) is a lipid hormone mediator abundantly produced in the kidney, where it acts locally to regulate renal function; several studies suggest that modulating EP(4) receptor activity could improve renal function following kidney injury. An optimized peptidomimetic ligand of EP(4) receptor, THG213.29, was tested for its efficacy to improve renal function (glomerular filtration rate, renal plasma flow, and urine output) and histological changes in a model of ARF induced by either cisplatin or renal artery occlusion in Sprague-Dawley rats. THG213.29 modulated PGE(2)-binding dissociation kinetics, indicative of an allosteric binding mode. Consistently, THG213.29 antagonized EP(4)-mediated relaxation of piglet saphenous vein rings, partially inhibited EP(4)-mediated cAMP production, but did not affect Gα(i) activation or β-arrestin recruitment. In vivo, THG213.29 significantly improved renal function and histological changes in cisplatin- and renal artery occlusion-induced ARF models. THG213.29 increased mRNA expression of heme-oxygenase 1, Bcl2, and FGF-2 in renal cortex; correspondingly, in EP(4)-transfected HEK293 cells, THG213.29 augmented FGF-2 and abrogated EP(4)-dependent overexpression of inflammatory IL-6 and of apoptotic death domain-associated protein and BCL2-associated agonist of cell death. Our results demonstrate that THG213.29 represents a novel class of diuretic agent with noncompetitive allosteric modulator effects on EP(4) receptor, resulting in improved renal function and integrity following acute renal failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".