Dietary Lipid Type, Rather Than Total Number of Calories, Alters Outcomes of Enteric Infection in Mice
Bibliographic record
Abstract
Dietary lipids modulate immunity, yet the means by which specific fatty acids affect infectious disease susceptibility remains unclear. Deciphering lipid-induced immunity is critical to understanding the balance required for protecting against pathogens while avoiding chronic inflammatory diseases. To understand how specific lipids alter susceptibility to enteric infection, we fed mice isocaloric, high-fat diets composed of corn oil (rich in n-6 polyunsaturated fatty acids [n-6 PUFAs]), olive oil (rich in monounsaturated fatty acids), or milk fat (rich in saturated fatty acids) with or without fish oil (rich in n-3 PUFAs). After 5 weeks of dietary intervention, mice were challenged with Citrobacter rodentium, and pathological responses were assessed. Olive oil diets resulted in little colonic pathology associated with intestinal alkaline phosphatase, a mucosal defense factor that detoxifies lipopolysaccharide. In contrast, while both corn oil and milk fat diets resulted in inflammation-induced colonic damage, only milk fat induced compensatory protective responses, including short chain fatty acid production. Fish oil combined with milk fat, unlike unsaturated lipid diets, had a protective effect associated with intestinal alkaline phosphatase activity. Overall, these results reveal that dietary lipid type, independent of the total number of calories associated with the dietary lipid, influences the susceptibility to enteric damage and the benefits of fish oil during infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".