The Synthetic Tie2 Agonist Peptide Vasculotide Protects Renal Vascular Barrier Function In Experimental Acute Kidney Injury
Bibliographic record
Abstract
Microvascular barrier dysfunction plays a major role in the pathophysiology of acute kidney injury (AKI). Angiopoietin-1, the natural agonist ligand for the endothelial-specific Tie2 receptor, is a non-redundant endothelial survival and vascular stabilization factor. Here we evaluate the efficacy of a polyethylene glycol-clustered Tie2 agonist peptide, vasculotide (VT), to protect against endothelial-cell activation with subsequent microvascular dysfunction in a murine model of ischemic AKI. Renal ischemia reperfusion injury (IRI) was induced by clamping of the renal arteries for 35 minutes. Mice were treated with VT or PEGylated cysteine before IRI. Sham-operated animals served as time-matched controls. Treatment with VT significantly reduced transcapillary albumin flux and renal tissue edema after IRI. The protective effects of VT were associated with activation of Tie2 and stabilization of its downstream effector, VE-cadherin in renal vasculature. VT abolished the decline in renal tissue blood flow, attenuated the increase of serum creatinine and blood urea nitrogen after IRI, improved recovery of renal function and markedly reduced mortality compared to PEG [HR 0.14 (95% CI 0.05-0.78) P < 0.05]. VT is inexpensive to produce, chemically stable and unrelated to any Tie2 ligands. Thus, VT may represent a novel therapy to prevent AKI 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".