Pathophysiology of hepatic encephalopathy: The concept of synergism
Bibliographic record
Abstract
Hepatic encephalopathy (HE) remains a severe neuropsychiatric complication of liver failure. Neuropathological evaluation of material from patients who died in hepatic coma reveals morphologic changes primarily to astrocytes (cytotoxic edema, Alzheimer Type II astrocytosis) accompanied by discreet neuronal changes. Liver failure results in the accumulation in brain of neurotoxic compounds (ammonia, manganese, proinflammatory cytokines, mercaptans, octanoic acid) that may act synergistically to impair neuropsychiatric function. Ammonia and manganese act synergistically to activate mitochondrial benzodiazepine receptors leading to increased production of neuroactive steroids, many of which (allopregnanolone, THDOC) have potent neuroinhibitory properties resulting from activation of a neuromodulatory site on the GABA-A receptor ("increased GABAergic tone"). New evidence demonstrates that proinflammatory cytokines such as tumor necrosis factor alpha (TNFalpha) and the interleukins (IL-1beta, and IL-6) are produced not only by the liver but also by the brain in liver failure. Ammonia and proinflammatory cytokines generated either by intercurrent infection or from hepatocyte necrosis in liver failure act synergistically to decrease the capacity of astrocytes to remove glutamate from the brain extracellular space leading to the activation of glutamate (NMDA) receptors that in turn results in alterations of cell-cell signalling and hyperexcitability. Therapy for HE continues to rely heavily on strategies aimed at reduction of gut ammonia production, increased ammonia removal and, ultimately, liver transplantation. A more complete understanding of pathophysiologic mechanisms has led to novel potential strategies aimed at decreasing both GABAergic tone, glutamate (NMDA) receptor activation and proinflammatory cytokines. Such strategies hold promise for new therapies for HE in the near future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".