<i>In vitro</i>screening of neuroprotective activity of Indian medicinal plant<i>Withania somnifera</i>
Bibliographic record
Abstract
Abstract Canine cognitive dysfunction (CCD) is an age-dependent neurodegenerative condition characterised by changes in decline in learning and memory patterns. The neurodegenerative features of CCD in ageing dogs and cats are similar to human ageing and Alzheimer's disease (AD). Discovering neuroprotective disease-modifying therapies against CCD and AD is a major challenge. Strong evidence supports the role of amyloid β peptide deposition and oxidative stress in the pathophysiology of CCD and AD. In both the human and canine brain, oxidative damage progressively increases with age. Dietary antioxidants from natural sources hold a great promise in halting the progression of CCD and AD. Withania somnifera ( WS ), an Ayurvedic tonic medicine, also known as ‘Indian ginseng’ or ashwagandha has a long history of use in memory-enhancing therapy but there is a dearth of studies on its neuroprotective effects. The objective of this study was to investigate whether WS extract can protect against Aβ peptide- and acrolein-induced toxicity. We demonstrated that treatment with WS extract significantly protected the human neuroblastoma cell line SK-N-SH against Aβ peptide and acrolein in various cell survival assays. Furthermore, treatment with WS extract significantly reduced the generation of reactive oxygen species in SK-N-SH cells. Finally, our results showed that WS extract is also a potent inhibitor of acetylcholinesterase activity. Thus, our initial findings indicate that WS extract may act as an antioxidant and cholinergic modulator and may have beneficial effects in CCD and AD therapy.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".