New soluble angiopoietin analog of Hepta‐ANG1 prevents pathological vascular leakage
Bibliographic record
Abstract
Vascular leak is a key driver of organ injury in diseases, and strategies that reduce enhanced permeability and vascular inflammation are promising therapeutic targets. Activation of the angiopoietin-1 (ANG1)-Tie2 tyrosine kinase signaling pathway is an important regulator of vascular quiescence. Here we describe the design and construction of a new soluble ANG1 mimetic that is a potent activator of endothelial Tie2 in vitro and in vivo. Using a chimeric fusion strategy, we replaced the extracellular matrix (ECM) binding and oligomerization domain of ANG1 with a heptameric scaffold derived from the C-terminus of serum complement protein C4-binding protein α. We refer to this new fusion protein biologic as Hepta-ANG1, which forms a stable heptamer and induces Tie2 phosphorylation in cultured cells, and in the lung following intravenous injection of mice. Injection of Hepta-ANG1 ameliorates vascular endothelial growth factor- and lipopolysaccharide-induced vascular leakage, in keeping with the known functions of Angpt1-Tie2 in maintaining quiescent vascular stability. The new Hepta-ANG1 fusion is easy to produce and displays remarkable stability with high multimericity that can potently activate Tie2. It could be a new candidate ANG1 mimetic therapy for treatments of inflammatory vascular leak, such as acute respiratory distress syndrome and sepsis.
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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".