Protein is expressed in all major organs after intravenous infusion of mRNA-lipid nanoparticles in swine
Bibliographic record
Abstract
delivery of mRNA is promising for the study of gene expression and the treatment of diseases. Lipid nanoparticles (LNPs) enable efficient delivery of mRNA constructs, but protein expression has been assumed to be limited to the liver. With specialized LNPs, delivery to extrahepatic tissue occurs in small animal models; however, it is unclear if global delivery of mRNA to all major organs is possible in humans because delivery may be affected by differences in innate immune response and relative organ size. Furthermore, limited studies with LNPs have been performed in large animal models, such as swine, due to their sensitivity to complement activation-related pseudoallergy (CARPA). In this study, we found that exogenous protein expression occurred in all major organs when swine were injected intravenously with a relatively low dose of mRNA encapsulated in a clinically relevant LNP formulation. Exogenous protein was detected in the liver, spleen, lung, heart, uterus, colon, stomach, kidney, small intestine, and brain of the swine without inducing CARPA. Furthermore, protein expression was detected in the bone marrow, including megakaryocytes, hematopoietic stem cells, and granulocytes, and in circulating white blood cells and platelets. These results show that nearly all major organs contain exogenous protein expression and are viable targets for mRNA therapies.
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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) | 0.000 | 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".