A novel fluorinated stilbene exerts hepatoprotective properties in CCl<sub>4</sub>-induced acute liver damage
Bibliographic record
Abstract
There has been a recently increase in the development of novel stilbene-based compounds with in vitro anti-inflamatory properties. For this study, we synthesized and evaluated the anti-inflammatory properties of 2 fluorinated stilbenes on carbon tetrachloride (CCl₄)-induced acute liver damage. To achieve this, CCl₄ (4 g·kg(-1), per os) was administered to male Wistar rats, followed by either 2-fluoro-4'-methoxystilbene (FME) or 2,3-difluoro-4'-methoxystilbene (DFME) (10 mg·kg(-1), per os). We found that although both of the latter compounds prevented cholestatic damage (γ-glutamyl transpeptidase activity), only DFME showed partial but consistent results in the prevention of necrosis, as assessed by both alanine aminotransferase activity and histological analysis. Since inflammatory responses are mediated by cytokines, mainly tumour necrosis factor α (TNF-α), we used the Western blot technique to determine the action of FME and DFME on the expression level of this cytokine. The observed increase in the level of TNF-α caused by CCl₄ administration was only prevented by treatment with DFME, in agreement with our biochemical findings. This result was confirmed by measuring interleukin-6 (IL-6) levels, since the expression of this protein depends on the level of TNF-α. In this case, DFME completely blocked the CCl₄-induced increase of IL-6. Our results suggest that DFME possesses greater anti-inflammatory properties in vivo than FME. DFME constitutes a possible therapeutic agent for liver disease and could serve as a template for structure optimization.
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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) | 0.001 | 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".