Water-soluble Coenzyme Q10 and Ashwagandha Root Extract as a Combinatorial Therapy for Parkinson's Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disease characterized by loss of dopaminergic neurons of the substatia nigra pars compacta region of the brain. PD patients initially display loss of movement coordination (resting tremors, postural instability, bradykinesia, and rigidity), eventually progressing to cognitive impairment, psychiatric irregularity, and death. Most PD cases are sporadic and caused by unknown factors. Although good progress has been made in providing a symptomatic relief such as with dopamine supplements or deep brain stimulation, there is no known available remedy to stop the progression of the disease. This study is based on findings that Ubisol-Q10, a water-soluble formulation of coenzyme-Q10 shows unprecedented near-complete protection against oxidative stress-induced cell death of cultured neurons. We have found that Ubisol-Q10 can neutralize Bax-induced dysfunction of mitochondria, which thus far can only be achieved by an anti-apoptotic protein Bcl2. We have also shown that Ubisol-Q10 exhibited neuroprotective effects in paraquat (PQ) exposed rats. Similarly, ethanolic root extract of ashwagandha extract (ASH) has shown neuroprotective efficacy in maneb-paraquat treated mice. Our objective is to combine ASH with Ubisol-Q10 and conduct a study with a multidisciplinary approach to examine whether post-injury intervention with ASH along with Ubisol-Q10 could slow/halt the progression of neurodegeneration in a PQ induced PD rat model. Our current behavioural tests have shown that PQ treated rats given Ubisol-Q10, ASH or a combination of both in drinking water have reduced motor impairment compared to rats given unsupplemented water. These interesting findings with details of behavioural and biochemical analysis will be presented.
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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 | 0.001 | 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".