OC-008 Kupffer Cells Play a Dual Pro-Inflammatory and Protective Role in Non-Alcoholic Steatohepatitis
Bibliographic record
Abstract
Introduction Non-alcoholic fatty liver disease (NAFLD) is the leading liver disease in Europe and North America. 30% of patients with NAFLD are estimated to develop inflammatory non-alcoholic steatohepatitis (NASH) with the potential to lead to cirrhosis and hepatocellular carcinoma. Current evidence suggests that gut bacterial products can drive hepatic inflammation by activating specific innate pattern recognition receptors (PRRs) such as TLR4 and the NALP3 inflammasome. Both receptors are expressed by liver resident macrophages, Kupffer cells (KCs). Methods To determine the role of KCs and interactions with PRRs in NASH, mice were fed a methionine choline deficient (MCD) diet for three weeks to induce NASH. Liposomal clodronate was used to deplete KCs. Serum ALT levels were measured and hepatic inflammatory infiltrates characterised by flow cytometry. Real-time qPCR was used to assess changes in gene expression. Murine findings were correlated with human liver tissue from NASH patients. Groups were compared by one-way ANOVA and significance set at P < 0.05. Results NALP3 KO (knock-out), TLR4 KO or KC deficient WT mice on a MCD diet developed reduced liver damage and decreased T lymphocyte recruitment compared to WT MCD controls. Combined KC depletion and NLRP3 KO however lead to significantly worse liver injury and progressive fibrosis as measured by collagen expression. Further investigation revealed an as yet unrecognised role for KC expressed NALP6 activation which mediated anti-inflammatory responses and modulation of hepatic IL-22 responsiveness to reduce liver injury. Conclusion KC activation through NALP3 and TLR4 increases hepatic inflammation in the MCD model of NASH. KCs have a dual role in NASH as they also express NALP6 with anti-inflammatory properties and are able to reduce hepatic injury through modulation of IL-22 responsiveness modulation. Disclosure of Interest None Declared
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".