Murine liver repair via transient activation of regenerative pathways in hepatocytes using lipid nanoparticle-complexed nucleoside-modified mRNA
Bibliographic record
Abstract
Induction of intrinsic liver regeneration is an unmet need that can be achieved by temporally activating key hepatocyte regenerative pathways. Here, we establish an efficient, safe, non-integrative method to transiently express hepatocyte-growth-factor (HGF) and epidermal-growth-factor (EGF) in hepatocytes via nucleoside-modified, lipid-nanoparticle-encapsulated mRNA (mRNA-LNP) delivery in mice. We confirm specific hepatotropism of mRNA-LNP via intravenous injection of firefly luciferase encoding mRNA-LNP, with protein expression lasting about 3 days. In the liver, virtually all hepatocytes are transfected along with a subpopulation of endothelial and Kupffer cells. In homeostasis, HGF mRNA-LNP efficiently induce hepatocyte proliferation. In a chronic liver injury mouse model recapitulating non-alcoholic fatty liver disease, injections of both HGF and EGF mRNA-LNP sharply reverse steatosis and accelerate restoration of liver function. Likewise, HGF and EGF mRNA-LNP accelerate liver regeneration after acetaminophen-induced acute liver injury with rapid return to baseline ALT levels. This study introduces mRNA-LNP as a potentially translatable safe therapeutic intervention to harness liver regeneration via controlled expression of endogenous mitogens in vivo.
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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".