Protective Effects of Hepatocyte Stress Defenders, Nrf1 and Nrf2, Against MASLD Progression
Bibliographic record
Abstract
Background: Progression of metabolic dysfunction associated steatotic liver disease (MASLD) to steatohepatitis (MASH) is driven by stress-inducing lipids that promote liver inflammation and fibrosis. MASH can lead to cirrhosis and hepatocellular carcinoma. We showed coordinated defenses regulated by transcription factors, nuclear factor erythroid 2 related factor-1 (Nrf1) and -2 (Nrf2), protect against hepatic lipid stress. Here, we investigated protective effects of hepatocyte Nrf1 and Nrf2 against MASLD-induced liver fibrosis and tumorigenesis. Methods: Using mice fed MASH diet for 24-52 weeks, we examined MASLD in mice with hepatocyte specific Nrf1, Nrf2, or combined deletion, and compared this to control. In a separate study, mice received weekly injections of carbon tetrachloride to induce liver fibrosis. From week 16-24, mice were treated with Nrf2 activating drug bardoxolone, hepatocyte overexpression of human NRF1 (hNRF1), or both, and groups were compared to control. Results: Hepatocyte Nrf2 deficiency had no effect. Hepatocyte Nrf1 and combined deficiency caused MASH but only hepatocyte Nrf1 deficiency in male mice increased tumor number. Bardoxolone reduced liver steatosis, fibrosis, inflammation, and proliferation, and this effect when combined with hNRF1 was greater than bardoxolone alone. Conclusion: Physiologic Nrf1 delays MASLD progression, Nrf2-induction alleviates MASH, and combined enhancement synergistically protects against steatosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".