Paraquat induces oxidative stress, neuronal loss in substantia nigra region and Parkinsonism in adult rats: Neuroprotection and amelioration of symptoms by water-soluble formulation of Coenzyme Q10
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease, for which currently there is no cure, develops as a result of progressive loss of dopamine neurons in the brain; thus, identification of any potential therapeutic intervention for disease management is of a great importance. RESULTS: Here we report that prophylactic application of water-soluble formulation of coenzyme Q10 could effectively offset the effects of environmental neurotoxin paraquat, believed to be a contributing factor in the development of familial PD. In this study we utilized a model of paraquat-induced dopaminergic neurodegeneration in adult rats that received three weekly intra-peritoneal injections of the herbicide paraquat. Histological and biochemical analyses of rat brains revealed increased levels of oxidative stress markers and a loss of approximately 65% of dopamine neurons in the substantia nigra region. The paraquat-exposed rats also displayed impaired balancing skills on a slowly rotating drum (rotorod) evidenced by their reduced spontaneity in gait performance. In contrast, paraquat exposed rats receiving a water-soluble formulation of coenzyme Q10 in their drinking water prior to and during the paraquat treatment neither developed neurodegeneration nor reduced rotorod performance and were indistinguishable from the control paraquat-untreated rats. CONCLUSION: Our data confirmed that paraquat-induced neurotoxicity represents a convenient rat model of parkinsonian neurodegeneration suitable for mechanistic and neuroprotective studies. This is the first preclinical evaluation of a water-soluble coenzyme Q10 formulation showing the evidence of prophylactic neuroprotection at clinically relevant doses.
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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) | 0.000 | 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 | 0.001 | 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".