Voluntary wheel running alters markers of amyloid-beta precursor protein processing in an ovarian hormone depleted model
Bibliographic record
Abstract
Introduction: Aberrant cleavage of the transmembrane protein, amyloid-beta precursor protein (ABPP), results in the overproduction of amyloid-beta (AB) peptides which can form senile plaques in the brain. These plaques can get lodged within synapses and disrupt neuronal communication ultimately leading to rampant neuron death. The rate-limiting enzyme in AB production is beta-site ABPP cleaving enzyme 1 (BACE1). In females, estrogen loss is associated with increases in AB and BACE1 content and activity. Exercise is known to have anti-amyloidogenic effects and may be able to alter BACE1 in cases of ovarian hormone depletion. This study aimed to examine the effects of physical activity on BACE1 in intact and ovariectomized female mice. Methods: Female C57BL/6 mice (24 weeks old) underwent bilateral ovariectomy (OVX; n=20) or SHAM surgery (SHAM; n=20). Mice were assigned to one of four groups (n=10/group) for 8 weeks: (1) sham (SHAM), (2) sham with a wheel (SHAM VWR), (3) ovariectomized (OVX), or (4) ovariectomized with a wheel (OVX VWR). Results: Novel object recognition testing demonstrated that OVX mice had a lower percentage of novel object investigation time compared to SHAM. OVX mice also had higher prefrontal cortex BACE1 activity compared to SHAM (p<0.0001), while the OVX+VWR activity was not different from SHAM. Discussions: Our results demonstrate that voluntary wheel running in an ovariectomized model prevented increases in BACE1 activity, maintained memory recall, and may provide a method of slowing the progression of Alzheimer's disease.
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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) | 0.002 | 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".