The Antidepressant Mirtazapine Inhibits Hepatic Innate Immune Networks to Attenuate Immune-Mediated Liver Injury in Mice
Bibliographic record
Abstract
Activation of the innate immune system, including tissue macrophages and associated neutrophil infiltration, is an important driver of subsequent adaptive immune responses in many autoimmune diseases, including autoimmune hepatitis (AIH). The antidepressant mirtazapine has a unique complex pharmacology, altering signaling through a number of serotonin and histamine receptors that can impact macrophage function; an effect potentially influencing AIH outcome. In the mouse model of concanavalin A (Con A) induced liver injury (mimics many aspects of human AIH), in which early innate immune activation (ie. stimulated hepatic macrophages recruit neutrophils and monocytes to the liver) critically drives immune-mediated hepatitis induction, mirtazapine strikingly and dose-dependently inhibited Con A-induced liver injury. This inflammation-suppressing effect of mirtazapine was linked to an attenuation of Con A-stimulated early innate immune responses within the liver, including inhibition of hepatic macrophage activation (as demonstrated by flow cytometry), decreased hepatic macrophage-derived pro-inflammatory cytokine (eg. TNFα) and chemokine (eg. CXCL1 and CXCL2) production, suppression of Con A-induced increases in the hepatic expression of the neutrophil relevant endothelial cell adhesion molecule ICAM-1, with the resultant significant reduction in neutrophil recruitment into the liver. Consistent with our findings in the Con A model, mirtazapine also significantly reduced activation-induced release of cytokine/chemokine mediators from human CD14+ monocytes/macrophages in vitro. Conclusion: Our data suggest that mirtazapine can attenuate hepatic innate immune responses that critically regulate the subsequent development of autoimmune liver injury. Therefore, given that it is a safe and widely used medication, mirtazapine may represent a novel therapeutic approach to autoimmune liver disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".