Chemerin in a Mouse Model of Non-alcoholic Steatohepatitis and Hepatocarcinogenesis
Bibliographic record
Abstract
BACKGROUND/AIM: Non-alcoholic steatohepatitis (NASH) is a risk factor for hepatocellular carcinoma (HCC). The adipokine chemerin protects from HCC and is reduced in human HCC. In this study, chemerin expression was analyzed in a murine model of NASH-HCC. MATERIALS AND METHODS: Serum and hepatic chemerin, and ex vivo chemerin receptor activation were monitored in NASH and NASH-HCC in mice fed a low-methionine diet deficient in choline after initiation of tumors by injection of diethylnitrosamine. RESULTS: In non-tumorous liver tissues, the extent of hepatic steatosis, and the levels of proteins regulating hepatic lipids and liver fibrosis were similar in NASH and NASH-associated HCC. Systemic and hepatic chemerin, and chemerin receptor activation were not changed in HCC. Liver tumors only developed in diethylnitrosamine-injected mice and their number was increased in NASH. Chemerin protein was induced in liver in NASH, but was unchanged in HCC tissues. CONCLUSION: Hepatic and serum chemerin and ex vivo analyzed chemerin receptor activation do not differ in murine NASH-associated HCC when compared to NASH. Hepatic tumors still develop despite high endogenous levels of serum and liver chemerin protein.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".