Postexercise serum from humans influences the biological tug of war of APP processing in human neuronal cells
Bibliographic record
Abstract
Neurodegenerative diseases such as Alzheimer’s disease (AD) are becoming more prevalent in our aging society. One specific neuropathological hallmark of this disease is the accumulation of amyloid-β (Aβ) peptides, which aggregate to form extraneuronal plaques. Increased Aβ peptides are often observed well before symptoms of AD develop, highlighting the importance of targeting Aβ-producing pathways early on in disease progression. Evidence indicates that exercise has the capacity to reduce Aβ peptide production in the brain; however, the mechanisms remain unknown. Exercise-induced signaling mediators could be the driving force behind some of the beneficial effects observed in the brain with exercise. The purpose of this study was to examine if postexercise serum and the factors it contains can alter neuronal amyloid precursor protein (APP) processing. Human SH-SY5Y neuronal cells were differentiated with retinoic acid for 5 days and treated with 10% pre- or postexercise serum from humans for 30 min. Cells were collected for analysis of acute (30 min; n = 6) or adaptive (24 h posttreatment; n = 6) responses. There were no statistical differences in a disintegrin and metalloproteinase 10 (ADAM10) and β-site amyloid precursor protein cleaving enzyme 1 (BACE1) mRNA or protein expression with postexercise serum treatment at either time point. However, there was an increase in the ratio of soluble amyloid precursor protein α (sAPPα) to soluble amyloid precursor protein β (sAPPβ) protein content ( P = 0.05) after 30 min of postexercise serum treatment. In addition, 30 min of postexercise serum treatment increased ADAM10 ( P = 0.01) and BACE1 ( P = 0.02) activity. These findings suggest that postexercise serum modulates important enzymes involved in APP processing, potentially pushing the cascade toward the nonamyloidogenic arm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".