Reversing PAI-1 deficiency in blood using mRNA lipid nanoparticles
Bibliographic record
Abstract
Plasminogen activator inhibitor-1 (PAI-1) deficiency is a rare disorder that causes moderate to severe bleeding and cardiac fibrosis, caused by mutation in the SERPINE-1 gene and no detectable circulating PAI-1 protein. There are currently no therapies that can effectively replace PAI-1 because the protein has a short half-life. An alternative approach to using recombinant protein is to endogenously increase circulating PAI-1 levels using mRNA therapy. Delivering mRNA encoding PAI-1 to the liver, a major site of PAI-1 synthesis, using lipid nanoparticles (mPAI-1) is a potential approach to increase circulating PAI-1 protein. Here, we developed mPAI-1, which induced expression of PAI-1 in vivo upon intravenous administration. In both wild-type (WT) mice and PAI-1 knockout mice, mPAI-1 induced supraphysiological circulating PAI-1 and inhibited fibrinolysis when measured ex vivo . In WT mice, plasma PAI-1 levels increased in a dose-dependent manner between 0.1 and 1 mg of mRNA per kg of body weight, peaking at 6 h post-injection and returning to baseline by 48 h. There was consistent production of PAI-1 after repeat dosing of mPAI-1 in the same mice. Expression of PAI-1 using mRNA-based approaches has the potential to be a preventive therapy for bleeding and cardiac fibrosis for PAI-1-deficient 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 | 0.000 |
| 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".