Investigation into Efficacy and Mechanisms of Neuroprotection of Ashwagandha Root Extract and Water-Soluble Coenzyme Q10 in a Transgenic Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's Disease (AD) is one of the most prevalent neurodegenerative disorders and the most common form of dementia. Although current treatments examine disease progression, many have side effects and primarily target symptomatic relief as opposed to halting further neurodegeneration. OBJECTIVE: The current study aims to determine the neuroprotective effects of water-soluble coenzyme Q10 (Ubisol-Q10) and an ethanolic Ashwagandha extract (E-ASH) on a transgenic mouse model of AD. METHODS: A variety of immunofluorescence staining of biomarkers was conducted to assess mechanisms commonly implicated in the disease. Additionally, spatial and non-spatial memory tests evaluated cognitive functions at two timepoints throughout the progression of the disease. RESULTS: A substantial reduction in microglial activation and amyloid-β (Aβ) plaques when treated with a combination of natural health products (NHPs), Ubisol-Q10 and E-ASH. Moreover, activation of autophagy was upregulated in both the Ubisol-Q10 and combination (Ubisol-Q10+E-ASH given as a combined "Tonic" solution) groups. Oxidative stress was decreased across treated groups, while astrocyte activation was elevated in both the E-ASH and Tonic group. The Tonic group expressed an elevation in the fluorescent intensity of neuronal nuclei (NeuN) and brain-derived neurotrophic factor (BDNF) levels. Interestingly, treatment with E-ASH and Ubisol-Q10 enhanced synaptic vesicle formation compared to controls. Pre-mortem memory tests revealed the treatments to be effective at preserving cognitive abilities. CONCLUSIONS: Based on these findings, the combination of E-ASH and Ubisol-Q10 may effectively mitigate the various mechanisms implicated in AD and ultimately prevent further disease progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".