Neuropharmaceutical Properties of Naringin Against Alzheimer's and Parkinson's Diseases
Bibliographic record
Abstract
Neurological complications are considered the leading cause of disability and the second cause of death worldwide. Although the most common neurological disorders affecting a large population are Alzheimer's (AD) and Parkinson's diseases (PD), no definitive treatment has been propounded in the clinic. As in recent years, special attention has been paid to medicinal herbal products as one of the ways to meet the challenges of treating diseases. This review study aimed to introduce the naringin neuroprotective effects as an abundant flavonoid in grapes and citrus fruits on the most common neurological disorders, including AD and PD. For this purpose, the specified keywords were searched in PubMed, Web of Science, Scopus, Embase, and Google Scholar, and the results were entered into the study after a concise overview. The findings show naringin can confront neurological disorders through several mechanisms such as modulating stress response pathways, preventing apoptosis, oxidative stress, and neuroinflammation, excessive chelating amounts of metal ions, thereby improving cognitive impairment and memory loss induced by neurological disorders. However, further studies, particularly on human, are critical for the final confirmation of obtained findings. [GMJ.2022;11:e2337].
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".