Bibliographic record
Abstract
It is becoming increasingly evident that peripheral organ-centered inflammatory diseases, including chronic inflammatory liver diseases, are associated with changes in central neural transmission that result in alterations in behavior. These behavioral changes include sickness behaviors, such as fatigue, cognitive dysfunction, mood disorders, and sleep disturbances. While such behaviors have a significant impact on quality of life, the changes within the brain and the communication pathways between the liver and the brain that give rise to changes in central neural activity are not fully understood. Traditionally, neural and humoral communication pathways have been described, with the three cytokines TNFα, IL-1β, and IL-6 receiving the most attention in mediating communication between the periphery and the brain, in the setting of peripheral inflammation. However, more recently, we described an immune-mediated communication pathway in experimentally induced liver inflammation whereby, in response to activation of resident immune cells in the brain (i.e., the microglia), peripheral circulating monocytes transmigrate into the brain, leading to development of sickness behaviors. These signaling pathways drive changes in behavior by altering central neurotransmitter systems. Specifically, changes in serotonergic and corticotropin-releasing hormone neurotransmission have been demonstrated and implicated in liver inflammation-associated sickness behaviors. Understanding how the liver communicates with the brain in the setting of chronic inflammatory liver diseases will help delineate novel therapeutic targets that can reduce the burden of symptoms in patients with 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".