TNF and IL-18 cytokines may regulate liver fat storage under homeostasis conditions
Bibliographic record
Abstract
The inflammation induced by obesogenic diets is associated with deposition of fat in the liver. On the other hand, anti-inflammatory and immunosuppressive therapies may impact in body fat storage and in liver lipid dynamics. It is important to study specific inflammatory mediators in this context, since their role on hepatic damage is not fully clarified. This study aimed to evaluate the role of interleukin (IL)-18 and tumor necrosis factor (TNF) receptor in liver dysfunction induced by diet. Male C57BL/6 wild-type (WT), IL-18, and TNF receptor 1 knockout mice (IL-18−/− and TNFR1−/−) were divided according to the experimental diets: chow diet or a high-refined carbohydrate-containing diet. Alanine aminotransferase was quantified by colorimetric analysis. Total fat content in the liver was determined by Folch methods. Levels of TNF, IL-6, IL-4, and IL-13 in liver samples were measured by ELISA assay. IL-18 and TNFR knockout mice fed with chow diet showed higher liver triglycerides deposition than WT mice fed with the same diet (WT: 131.9 ± 24.5; IL-18−/−: 239.4 ± 38.12*; TNF−/−: 179.6 ± 50.45*; *P < 0.01). Furthermore, these animals also showed a worse liver histopathological score and lower levels of TNF, IL-6, IL-4, and IL-13 in the liver. Interestingly, treatment with a high-carbohydrate diet did not exacerbate liver damage in IL-18−/− and TNFR1−/− mice. Our data suggest that IL-18 and TNF may be involved on hepatic homeostasis mainly in a context of a healthy diet.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".