siRNA-mediated reduction of a circulating protein in swine using lipid nanoparticles
Bibliographic record
Abstract
delivery of nucleic acids. Swine are a preferred animal model for studying the cardiovascular and immune systems, but there are limited strategies for genetic manipulation. Lipid nanoparticles (LNPs) efficiently deliver small interfering RNA (siRNA) to knock down circulating proteins, but swine are sensitive to LNP-induced complement activation-related pseudoallergy (CARPA). We hypothesized that appropriately administering optimized siRNA-LNPs could knock down circulating levels of plasminogen, a blood protein synthesized in the liver. siRNA-LNPs against plasminogen (siPLG) reduced plasma plasminogen protein and hepatic plasminogen mRNA levels to below 5% of baseline values. Functional assays showed that reducing plasminogen levels modulated systemic blood coagulation. Clinical signs of CARPA were not observed, and occasional mild and transient hepatotoxicity was present in siPLG-treated animals at 5 h post-infusion, which returned to baseline by 7 days. These findings advance siRNA-LNPs in swine models, enabling genetic engineering of blood and hepatic proteins, which can likely expand to proteins in other tissues in the future.
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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".