Neuroprotective efficacy of a combination of fish oil and ferulic acid against 3-nitropropionic acid-induced oxidative stress and neurotoxicity in rats: behavioural and biochemical evidence
Bibliographic record
Abstract
The beneficial effects of fish oil (FO) supplements on the central nervous system have been adequately demonstrated. However, FO supplementation at higher doses for longer duration is likely to cause oxidative stress in vivo. To overcome this, attempts have been made to enrich FO with known antioxidants/phytochemicals. In the present study, we examined the hypothesis that a combination of FO with ferulic acid (FA), a naturally occurring phenolic compound, is likely to provide higher degree of neuroprotection. This was examined by employing 3-nitropropionic acid (NPA), a well-known neurotoxin used to mimic behavioural and neurochemical features of Huntington's disease. Growing male rats administered with NPA (25 mg/kg of body weight (bw) for 4 days) were provided with either FO (2 mL/kg bw), FA (50 mg/kg bw) or FO+FA for 2 weeks. Interestingly, FO+FA not only offered significant protection against NPA-induced behavioural impairments, but also markedly attenuated oxidative stress in brain regions (striatum/cerebellum) as evidenced by the reduction in reactive species, malondialdehyde, hydroperoxides and nitric oxide (NO) levels. Further, FO+FA combination restored the activities of various antioxidant enzymes and the levels of cytosolic calcium. In striatum, activity levels of acetylcholinesterase enzyme and dopamine levels were markedly restored among FO+FA rats. Interestingly, NPA-induced mitochondrial dysfunctions were also attenuated among FO+FA rats. Collectively, our findings suggest the advantage of co-treatment of FO with known antioxidants to achieve a higher therapeutic benefit in the treatment of oxidative stress-mediated neurodegenerative conditions.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".