Reduced cortical BACE1 content with one bout of exercise is accompanied by declines in AMPK, Akt, and MAPK signaling in obese, glucose-intolerant mice
Bibliographic record
Abstract
Obesity and type 2 diabetes are significant risk factors in the development of neurodegenerative diseases, such as Alzheimer's disease. A variety of cellular mechanisms, such as altered Akt and AMPK and increased inflammatory signaling, contribute to neurodegeneration. Exercise training can improve markers of neurodegeneration, but the underlying mechanisms remain unknown. The purpose of this study was to determine the effects of a single bout of exercise on markers of neurodegeneration and inflammation in brains from mice fed a high-fat diet. Male C57BL/6 mice were fed a low (LFD; 10% kcal from lard)- or a high-fat diet (HFD; 60% kcal from lard) for 7 wk. HFD mice underwent an acute bout of exercise (treadmill running: 15 m/min, 5% incline, 120 min) followed by a recovery period of 2 h. The HFD increased body mass and glucose intolerance (both P < 0.05). This was accompanied by an approximately twofold increase in the phosphorylation of Akt, ERK, and GSK in the cortex (P < 0.05). Following exercise, there was a decrease in beta-site amyloid precursor protein cleaving enzyme 1 (BACE1; P < 0.05) and activity (P < 0.001). This was accompanied by a reduction in AMPK phosphorylation, indicative of a decline in cellular stress (P < 0.05). Akt and ERK phosphorylation were decreased following exercise in HFD mice to a level similar to that of the LFD mice (P < 0.05). This study demonstrates that a single bout of exercise can reduce BACE1 content and activity independent of changes in adiposity. This effect is associated with reductions in Akt, ERK, and AMPK signaling in the cortex.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".