Neutralization of IL-15 abrogates experimental immune-mediated cholangitis in diet-induced obese mice
Bibliographic record
Abstract
Obesity is a global epidemic affecting chronic inflammatory diseases. Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can occur as an extraintestinal manifestation of inflammatory bowel disease (IBD). Previously we reported that patients with PSC who are obese have a higher risk of advanced liver disease. Currently it is unknown how obesity accelerates or worsens PSC. We evaluated the progression of PSC in an antigen-driven cholangitis mouse model of diet-induced obesity. Obesity was induced in our murine model of immune-mediated cholangitis (OVAbil). OVAbil mice were fed standard chow or high-fat/sucrose diet for twelve weeks followed by induction of biliary inflammation by OVA-specific T cell transfer. Histopathological damage in portal tracts was scored and serum collected. Neutralizing antibodies against IL-15 were administered daily until study termination. Obese mice developed exacerbated liver inflammation and damage. Immune cell phenotyping in liver revealed greater numbers of neutrophils and CD8+ T cells in obese mice. Higher levels of cytokines and chemokines were found in obese mice with cholangitis. Immuno-neutralizing antibodies against IL-15 greatly attenuated cholangitis in obese mice. Obesity exacerbated experimental PSC in part by overproduction of IL-15. Timely targeting of IL-15 may slow the progression of PSC.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".