The Emerging Role of Marine Natural Products for the Treatment ofParkinson’s Disease
Bibliographic record
Abstract
Parkinson's Disease (PD), known as Parkinsonism, is a neurodegenerative disease that mainly affects the elderly and is characterized by an extensive and progressive loss of dopaminergic neurons in the Substantia Nigra pars compacta (SNpc). Owing to genetic, environmental, and lifestyle changes, the incidence of PD has recently risen among adults. The most widely used PD treatment strategies include the use of dopamine agonists, anticholinergics, and enzyme inhibitors. The aquatic flora and fauna have become the emerging source of novel, structurally diverse bioactive compounds and, at present, the researchers concentrate their efforts on isolating, characterizing, and identifying many secondary metabolites of different nature to treat various disorders, including, neuroprotective marine natural products (MNPs). The bioactive peptides, tannins, carotenoids, alkaloids, polyunsaturated fatty acids (PUFA), and sulfated polysaccharides from the MNP's and their synthetic derivatives have demonstrated important neuroprotective activity in preclinical studies through multiple mechanisms. An extensive literature survey was carried out, and published articles from PubMed, Scifinder, Google Scholar, Web of Science, and Scopus were carefully reviewed to compile information on the MNPs to treat PD. This current review focus on neuroprotective MNPs and their probable biological pathways to treat PD based on their structure and bioactivities reported from 1990 to 2020.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".