Clofibrate Attenuates ROS Production by Lipid Overload in Cultured Rat Hepatoma Cells
Bibliographic record
Abstract
PURPOSE: To investigate the effect of clofibrate on inducing liver fatty acid binding protein (FABP1) following a high-fat load in a hepatocyte cell culture model. METHODS: Rat hepatoma cells (CRL-1548) were treated with a fatty acid (FA) mixture consisting of oleate:palmitate (2:1) in the presence of 3% albumin. Cells were treated with 0, 0.5, 1, 2, or 3 mM FA for 24 and 48 hr, or further treated with 500 µM clofibrate (CLO) to induce FABP1 levels. Cytotoxicity was determined using the WST-1 assay. Intracellular lipid droplets were quantitated following staining with Nile Red. Dichlorofluorescein (DCF) was used to assess the extent of intracellular reactive oxygen species (ROS). RESULTS: Cell viability decreased (p < 0.01) with an increase in lipid concentration. Intracellular lipid droplets accumulated significantly (p < 0.001) with an increase in long-chain fatty acid load, which was associated with a statistical increase (p < 0.05) in ROS levels. Early clofibrate treatment showed significant increases in intracellular FABP1 levels with significant decreases in ROS levels (p < 0.05). Silencing FABP1 expression using siRNA revealed that FABP1 was the main contributor for the observed intracellular ROS clearance. CONCLUSIONS: Characteristic cellular damage resulted from released ROS following a high fat load to hepatoma cells. The damage was attenuated through early treatment with clofibrate, which may act as a hepatoprotectant by inducing FABP1 expression and in this manner, suppress intracellular ROS levels. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.000 |
| 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) | 0.001 | 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".