NLRP3 receptor contributes to protection against experimental antigen-mediated cholangitis
Bibliographic record
Abstract
Inflammatory diseases of the bile ducts like primary sclerosing colangitis (PSC) are characterized by a robust cellular response targeting the biliary epithelium leading to chronic inflammation and fibrosis. Driving fibro-inflammatory diseases, NOD-like receptors such as NLRP3 have been identified as a central component to immune-mediated pathology. However, to date the role of NLRP3 in biliary diseases has been poorly explored. Here, we addressed the role of NLRP3 in the OVAbil mouse model of antigen-mediated cholangitis. As obesity continues to spread worldwide, we also evaluated the NLRP3 response in experimental cholangitis after high-fat diet exposure. We compared the extent of histopathological liver damage between OVAbil and OVAbilxNLRP3-/- mice after either a standard chow or a high-fat diet. Infiltrating immune cells were characterized by flow cytometry and levels of cytokines, chemokines and liver enzymes in blood samples were analyzed at the end of the experiment. We observed a more severe histopathological phenotype of cholangitis in absence of NLRP3, characterized by loss of bile ducts and larger inflammatory foci and higher levels of IL- 6 and CXCL10 as compared with NLRP3 sufficient mice. This phenotype was further exaggerated in the context of obesity, where cholangitis induced in NLRP3-deficient obese mice resulted in further exacerbated histopathology and increased levels of IL-13 and TNFα, suggesting a diet-specific profile. The absence of NLRP3 caused a supressed IL-17 response. In summary, our data suggest that activation of NLRP3 attenuates this antigen-mediated OVAbil model of cholangitis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".