Increased Expression of Hepatic Stearoyl-CoA Desaturase (SCD)-1 and Depletion of Eicosapentaenoic Acid (EPA) Content following Cytotoxic Cancer Therapy Are Reversed by Dietary Fish Oil
Bibliographic record
Abstract
Cancer treatment evokes impediments to liver metabolism that culminate in fatty liver. This study determined hepatic fatty acid composition and expression of genes and mediators involved in lipid metabolism following chemotherapy treatment. Female rats bearing the Ward colon tumor were administered Irinotecan (CPT-11) +5-fluorouracil (5-FU) and maintained on a control diet or a diet containing eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA) (2.3 g/100 g fish oil). Healthy animals provided with a control diet served as a reference group. Livers were collected one week after chemotherapy. Triacylglycerol (TG), phospholipid (PL), ten lipid metabolism genes, leptin, and IL-4 were measured. Chemotherapy increased TG content and reduced EPA content in the liver. Expression of SCD1 was upregulated by chemotherapy, while dietary fish oil downregulated its expression. Dietary fish oil down-regulated expression of the fatty acid synthesis gene FASN, while restoring the long chain fatty acid converting genes FADS2 and ELOVL2, and genes involved in mitochondrial β-oxidation (CPT1α) and lipid transport (MTTP1), to values similar to reference animals. Neither leptin nor IL-4 were affected by chemotherapy or diet. Depletion of EPA is associated with pathways evoking enhanced TG accumulation in the liver. Restoring EPA through diet may pose a dietary strategy to attenuate chemotherapy-associated impediments in liver fatty acid metabolism.
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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".