Lipid and fatty acid profiles in rats consuming different high‐fat ketogenic diets
Bibliographic record
Abstract
High-fat ketogenic diets are used to treat intractable seizures in children, but little is known of the mechanism by which these diets work or whether fats rich in n-3 polyunsaturates might be beneficial. Tissue lipid and fatty acid profiles were determined in rats consuming very high fat (80 weight%), low-carbohydrate ketogenic diets containing either medium-chain triglyceride, flaxseed oil, butter, or an equal combination of these three fat sources. Ketogenic diets containing butter markedly raised liver triglyceride but had no effect on plasma cholesterol. Unlike the other fats, flaxseed oil in the ketogenic diet did not raise brain cholesterol. Brain total and free fatty acid profiles remained similar in all groups, but there was an increase in the proportion of arachidonate in brain total lipids in the medium-chain triglyceride group, while the two groups consuming flaxseed oil had significantly lower arachidonate in brain, liver, and plasma. The very high dietary intake of alpha-linolenate in the flaxseed group did not change docosahexaenoate levels in the brain. Our previous report based on these diets showed that although ketosis is higher in rats consuming a ketogenic diet based on medium-chain triglyceride oil, seizure resistance in the pentylenetetrazol model is not clearly related to the degree of ketosis achieved. In combination with our present data from the same seizure study, it appears that ketogenic diets with widely differing effects on tissue lipids and fatty acid profiles can confer a similar amount of seizure protection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".