Diet composition as a source of variation in experimental animal models of cancer cachexia
Bibliographic record
Abstract
BACKGROUND: A variety of experimental animal models are used extensively to study mechanisms underlying cancer cachexia, and to identify potential treatments. The important potential confounding effect of dietary composition and intake used in many preclinical studies of cancer cachexia is frequently overlooked. Dietary designs applied in experimental studies should maximize the applicability to human cancer cachexia, meeting the essential requirements of the species used in the study, matched between treatment and control groups as well as also being generally similar to human consumption. METHODS: A literature review of scientific studies using animal models of cancer and cancer cachexia with dietary interventions was performed. Studies that investigated interventions using lipid sources were selected as the focus of discussion. RESULTS: The search revealed a number of nutrient intervention studies (n = 44), with the majority including n-3 fatty acids (n = 16), mainly eicosapentaenoic acid and/or docosahexaenoic acid. A review of the literature revealed that the majority of studies do not provide information about dietary design; food intake or pair-feeding is rarely reported. Further, there is a lack of standardization in dietary design, content, source, and overall composition in animal models of cancer cachexia. A model is proposed with the intent of guiding dietary design in preclinical studies to enable comparisons of dietary treatments within the same study, translation across different study designs, as well as application to human nutrient intakes. CONCLUSION: The potential for experimental endpoints to be affected by variations in food intake, macronutrient content, and diet composition is likely. Diet content and composition should be reported, and food intake assessed. Minimum standards for diet definition in cachexia studies would improve reproducibility of pre-clinical studies and aid the interpretation and translation of results to humans with cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".