Effect of a ketogenic diet on motor performance and amyloid beta accumulation in a mouse model of inclusion body myositis
Bibliographic record
Abstract
?‐Amyloid (A ?) accumulation is a hallmark of Alzheimer's disease (AD) and is thought to induce the onset of disease. Similarly, A ? accumulates in muscle in inclusion body myositis (IBM), an age‐related disease. Our lab is interested in the potentially shared aspects of the disease process, particularly those involving regulation of expression. There is considerable evidence that diet impacts the progression of AD, a theory supported by the correlation between diabetes and AD. A "ketogenic" diet has been used to treat childhood epilepsy and has shown efficacy in AD models. In this study, we fed aged (18‐19 mo.) IBM transgenic mice either a ketogenic (80% fat) or control (10% fat) diet for 2 months, significantly enhancing ketone levels (0.33 ± 0.06 mM vs. 0.68 ± 0.08 mM). Motor performance was measured longitudinally and tissue was collected after euthanasia. Plasma insulin and cholesterol decreased in mice fed the ketogenic diet (0.93 ± 0.3 ng/mL vs. 0.65 ± 0.08 ng/mL and 2.16 ± 0.41 mg/mL vs. 1.55 ± 0.1 mg/mL respectively), while adiponectin, leptin, and resistin were unaffected. The ketogenic diet markedly prolonged wire suspension times (1.33 ± 0.21 sec. vs. 6.6 ± 2.8 sec.) and induced a trend towards improved grip strength (0.55 ± 0.06 N vs. 0.62 ± 0.04 N) and exploratory behavior (17.6 ± 3.2 meters vs. 21.1 ± 4.3 meters). These data suggest that dietary interventions may have some therapeutic value for both AD and IBM. Supported by NIH NS058382.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".