Combined Ubisol-Q10 and Ashwagandha Root Extract Target Multiple Biochemical Mechanisms and Reduces Neurodegeneration in a Paraquat-Induced Rat Model of Parkinson’s Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is characterized by progressive neurodegeneration in the substantia nigra (SN) region resulting in loss of movement coordination. Current therapies only provide symptomatic relief, and there is no agent to halt the progression of PD. Previously, Ubisol-Q10, a water-soluble formulation of coenzyme-Q10, and ethanolic root extract of ashwagandha (ASH) have been shown to inhibit PD pathology in rodent models when used alone. Here, we evaluated the neuroprotective efficacy of oral administration of ASH and Ubisol-Q10 alone and in combination in a paraquat-induced PD rat model. The combined treatment resulted in better-preserved neuron morphology compared to Ubsiol-Q10 or ASH alone. The combination treatment enhanced activation of pro-survival astroglia and inhibited pro-inflammatory microglia. While anti-oxidative effects were seen with both agents, Ubisol-Q10 activated autophagy, whereas ashwagandha showed a better anti-inflammatory response. Thus, the combined treatment caused inhibition of oxidative stress, autophagy activation, inhibition of pro-inflammatory microglia, and activation of pro-survival astroglia. Consequently, paraquat (PQ)-treated rats given the combination treatment in drinking water did not show motor impairment. Based on these interesting observations, the combined treatment containing two well-tolerated natural compounds could be a more effective strategy to halt the progression of PD.
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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 | 0.001 | 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) | 0.001 | 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".