Dietary EPA+DHA Mitigate Hepatic Toxicity and Modify the Oxylipin Profile in an Animal Model of Colorectal Cancer Treated with Chemotherapy
Bibliographic record
Abstract
Irinotecan (CPT-11) and 5-fluorouracil (5-FU) are commonly used to treat metastatic colorectal cancer, but chemotherapy-associated steatosis/steatohepatitis (CASSH) frequently accompanies their use. The objective of this study was to determine effect of CPT-11+5-FU on liver toxicity, liver oxylipins, and cytokines, and to explore whether these alterations could be modified by dietary eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in the form of fish oil (EPA+DHA). Tumor-bearing animals were administered CPT-11+5-FU and maintained on a control diet or a diet containing EPA+DHA (2.3 g/100 g). Livers were collected one week after chemotherapy for the analysis of oxylipins, cytokines, and markers of liver pathology (oxidized glutathione, GSSH; 4-hydroxynonenal, 4-HNE, and type-I collagen fiber). Dietary EPA+DHA prevented the chemotherapy-induced increases in liver GSSH (p < 0.011) and 4-HNE (p < 0.006). Compared with the tumor-bearing animals, ten oxylipins were altered (three/ten n-6 oxylipins were elevated while seven/ten n-3 oxylipins were reduced) following chemotherapy. Reductions in the n-3 fatty-acid-derived oxylipins that were evident following chemotherapy were restored by dietary EPA+DHA. Liver TNF-α, IL-6 and IL-10 were elevated (p < 0.05) following chemotherapy; dietary EPA+DHA reduced IL-6 (p = 0.09) and eotaxin (p = 0.007) levels. Chemotherapy-induced liver injury results in distinct alterations in oxylipins and cytokines, and dietary EPA+DHA attenuates these pathophysiological effects.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".