Inhibition of Cellular Senescence in AD Models with Water Soluble CoQ10 via Upregulation in Autophagy
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder often associated with memory impairment. According to the World Health Organization, approximately 48 million people worldwide live with the disease and this number is expected to triple by 2050. Due to the increasing prevalence of AD and the rapidly aging global population, a preventative treatment is absolutely necessary. AD is a complex and poorly understood disease with no current treatments to stop its progression. Still, some characteristics of the disease are known including the formation of neurofibrillary tangles and amyloid plaques which have been linked to neuronal death in regions of the hippocampus and cerebral cortex. Additionally, studies have shown a number of mechanisms associated with the disease including impaired autophagy and mitochondrial dysfunction leading to oxidative stress. Elevated reactive oxygen species are a result of inefficiency in the electron transport chain and can induce premature cellular senescence. However, in previous in-vitro studies, ubisol-Q10, a water-soluble formulation of coenzyme-Q10, has been shown to stabilize the mitochondria and inhibit oxidative-stress by reducing the generation of free radicals. When this treatment was applied to a fibroblast cell model of AD and gene expression was analyzed and an increase in autophagy related gene expression was noted. This proposal focuses on determining if autophagy is activated by ubisol-Q10 during stressed induced cellular senescence by examining relative protein expression levels via Western Blot. This will be coupled with immunohistochemistry on an animal tissue model and live cell staining to confirm the role of autophagy as a pro-survival response.
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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 | 0.000 | 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 | 0.001 |
| 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".