Inhibition of proprotein convertase SKI-1 prevents blood vessel alteration after stroke
Bibliographic record
Abstract
Neutralizing factors involved in blood vessel dysfunction offer a promising strategy for stroke recovery. Many extracellular proteins need enzymatic activation to function, and blocking this activation is an untapped approach to restoring vessel integrity. Here we demonstrate that inhibition of the extracellular protease SKI-1 with PF-429242 restores blood vessel integrity and promotes functional recovery in both large and small animal models for stroke. Single-cell mRNA sequencing identified molecular signatures suggesting that PF-429242 restores the expression of genes involved in vessel integrity in endothelial cells. Moreover, we identify a mechanism whereby RGMa cleavage by SKI-1 is required for RGMa to interact with Neogenin and alter vessel integrity. Either preventing RGMa cleavage or deleting Neogenin on endothelial cells reduced blood vessel dysfunction, increased tissue preservation and restored brain function after stroke. This work identifies a much-needed therapeutic strategy that restores blood vessel integrity and functionality, showing efficacy in large and small animals. Shabanzadeh et al. identify and validate a pathway whereby RGMa cleavage by SKI-1 modifies gene expression related to blood–brain barrier (BBB) integrity after stroke. SKI-1 inhibition restores BBB integrity and neuronal function in mouse and rabbit stroke models.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".