Liver Innervation in Health and Disease: Neuroimmune–Neurovascular Interface and Future Therapeutic Implications
Bibliographic record
Abstract
The liver is intricately innervated by sympathetic, parasympathetic, and sensory fibres, forming a dynamic neurovascular and neuroimmune network that regulates hepatic function and contributes to disease pathogenesis. While traditionally underexplored, hepatic innervation is now recognised as a key modulator of metabolic homeostasis, immune surveillance, and vascular tone. Historically, the liver was not considered a major target of neural regulation, but recent advances in neurology and imaging have revealed complex and dynamic interactions between neural circuits and hepatic functions. This review provides a comprehensive overview of liver innervation, detailing its anatomical organisation and functional roles in both physiological and pathological contexts. We investigate the role of liver innervation in shaping immune responses, particularly in the context of metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, and autoimmune liver diseases, including autoimmune hepatitis and primary biliary cholangitis. Special attention is given to the neuroimmune crosstalk that governs inflammation, fibrosis, malignancy, and tissue remodelling. Furthermore, we examine how neural inputs influence hepatic blood flow, sinusoidal endothelial function, and portal hypertension, highlighting the interplay between neural and vascular systems. We highlight neuromodulatory approaches, including vagus nerve stimulation and other agents to modulate liver inflammation, vascular dysfunction, and immune dysregulation. Finally, we discuss emerging research models, including liver organoids, Artificial Intelligence-based digital twins and biomaterials as innovative platforms designed to study neural-liver interactions and test new therapeutic strategies. By integrating neuromorphology, immunology, and hepatology, this review aims to advance our understanding of liver innervation as a central player in hepatic health and disease and to identify novel targets for therapeutic intervention.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".