Growth Hormone Accelerates Recovery From Acetaminophen-Induced Murine Liver Injury
Bibliographic record
Abstract
Background and Aims: Acetaminophen (APAP) overdose is the leading cause of acute liver failure, with one available treatment, N-acetyl cysteine (NAC). Yet, NAC effectiveness diminishes about ten hours after APAP overdose, urging for therapeutic alternatives. This study addresses this need by deciphering a mechanism of sexual dimorphism in APAP-induced liver injury, and leveraging it to accelerate liver recovery via growth hormone (GH) treatment. GH secretory patterns, pulsatile in males and near-continuous in females, determine the sex bias in many liver metabolic functions. Here, we aim to establish GH as a novel therapy to treat APAP hepatotoxicity. Approach and Results: Our results demonstrate sex-dependent APAP toxicity, with females showing reduced liver cell death and faster recovery than males. Single-cell RNA sequencing analyses reveal that female hepatocytes have significantly greater levels of GH receptor expression and GH pathway activation compared to males. In harnessing this female-specific advantage, we demonstrate that a single injection of recombinant human GH protein accelerates liver recovery, promotes survival in males following sub-lethal dose of APAP, and is superior to standard-of-care NAC. Alternatively, slow-release delivery of human GH via the safe nonintegrative lipid nanoparticle-encapsulated nucleoside-modified mRNA (mRNA-LNP), a technology validated by widely used COVID-19 vaccines, rescues males from APAP-induced death that otherwise occurred in control mRNA-LNP-treated mice. Conclusions: Our study demonstrates a sexually dimorphic liver repair advantage in females following APAP overdose, leveraged by establishing GH as an alternative treatment, delivered either as recombinant protein or mRNA-LNP, to potentially prevent liver failure and liver transplant in APAP-overdosed patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".