Fish oil mitigates myosteatosis and improves chemotherapy efficacy in a preclinical model of colon cancer
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess whether feeding a diet containing fish oil was efficacious in reducing tumor- and subsequent chemotherapy-associated myosteatosis, and improving tumor response to treatment. METHODS: Female Fischer 344 rats were fed either a control diet for the entire study (control), or switched to a diet containing fish oil (2.0 g /100 g of diet) one week prior to tumor implantation (long term fish oil) or at the start of chemotherapy (adjuvant fish oil). Chemotherapy (irinotecan plus 5-fluorouracil) was initiated 2 weeks after tumor implantation (cycle-1) and 1 week thereafter (cycle-2). Reference animals received no tumor or treatment and only consumed the control diet. All skeletal muscle measures were conducted in the gastrocnemius. To assess myosteatosis, lipids were assessed histologically by Oil Red O staining and total triglyceride content was quantified by gas chromatography. Expression of adipogenic transcription factors were assessed at the mRNA level by real-time RT-PCR. RESULTS: Feeding a diet containing fish oil significantly reduced tumor- and subsequent chemotherapy-associated increases in skeletal muscle neutral lipid (p<0.001) and total triglyceride content (p<0.03), and expression of adipogenic transcription factors (p<0.01) compared with control diet fed animals. The adjuvant fish oil diet was as effective as the long term fish oil diet in mitigating chemotherapy-associated skeletal muscle fat content, and in reducing tumor volume during chemotherapy compared with control fed animals (p<0.01). CONCLUSION: Long term and adjuvant fish oil diets are equally efficacious in reducing chemotherapy-associated myosteatosis that may be occurring by reducing expression of transcription factors involved in adipogenesis/lipogenesis, and improving tumor-response to chemotherapy in a neoplastic model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".