The modification of the ketogenic diet mitigates its stunting effects in rodents
Bibliographic record
Abstract
The high-fat and low-carbohydrate ketogenic diet (HFKD) is extensively studied within the fields of numerous diseases, including cancer and neurological disorders. Since most studies incorporate animal models, ensuring the quality of ketogenic rodent diets is important, both in the context of laboratory animal welfare as well as for the accuracy of the obtained results. In this study we implemented a modification to a commonly used ketogenic rodent chow by replacing non-resorbable cellulose with wheat bran. We assessed the effects of month-long treatment with either the unmodified or the modified HFKD on the growth and development of young male rats. Daily body weight, functional performance, and brain morphometric parameters were assessed to evaluate the influence of both applied diets on rodent development. Our results revealed that the unmodified ketogenic chow induced strong side effects that included weakness, emaciation, and brain undergrowth concomitant to growth inhibition. However, application of the ketogenic chow supplemented with wheat bran suppressed these adverse side effects, which was associated with the restoration of insulin-like growth factor 1 and a decrease in corticosterone levels. We have also shown that the advantageous results of the modified HFKD are not species- or sex-specific. Our data indicate that the proposed HFKD modification even allows for its application in young animals, without causing detrimental side 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) | 0.000 | 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".