Experimental models of hepatic encephalopathy: ISHEN guidelines
Bibliographic record
Abstract
Objectives of the International Society for Hepatic Encephalopathy and Nitrogen Metabolism Commission were to identify well-characterized animal models of hepatic encephalopathy (HE) and to highlight areas of animal modelling of the disorder that are in need of development. Features essential to HE modelling were identified. The best-characterized animal models of HE in acute liver failure, the so-called Type A HE, were found to be the hepatic devascularized rat and the rat with thioacetamide-induced toxic liver injury. In case of chronic liver failure, surgical models in the rat involving end-to-side portacaval anastomosis or bile duct ligation were considered to best model minimal/mild (Type B) HE. Unfortunately, at this time, there are no satisfactory animal models of Type C HE resulting from end-stage alcoholic liver disease or viral hepatitis, the most common aetiologies encountered in patients. The commission highlighted the urgent need for such models and of improved models of HE in chronic liver failure in general as well as a need for models of post-transplant neuropsychiatric disorders. Studies of HE pathophysiology at the cellular and molecular level continue to benefit from in vitro and or ex vivo models involving brain slices or exposure of cultured cells (principally cultured astrocytes) to toxins such as ammonia, manganese and pro-inflammatory cytokines. More attention could be paid in the future to in vitro models involving the neurovascular unit, microglia and neuronal co-cultures in relation to HE pathogenesis.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".