Aquatic Plants With Anti-Inflammatory And Anti-Oxidant Activities
Bibliographic record
Abstract
Nature has rewarded the human beings with uncountable nutritious and medicinal plants. These medicinal plants are a nature’s gift to us so that we can live a healthy and disease free life. Aquatic plants are those natural herbs that usually grow in or near water/aquatic environment and are considered as one of the most ancient source of food and medicine used by the human beings. These aquatic plants are unique in their nutritional composition and therapeutic potential and are widely used in traditional medicinal system in treatment of different unhealthy conditions. This review is designed to discuss some aquatic plants with established anti-oxidant and anti-inflammatory activities along with their possible mechanism of action and part responsible to possess this action. The recently updated information was collected from scientific journals, books, and globally accepted scientific databases via a library and electronic search such as PubMed, Elsevier, Google Scholar, Springer, Scopus, Web of Science, Wiley online library. All of the full-text articles and abstracts were screened. The most important and relevant articles were carefully chosen for study in this review. This review will help the researchers, traditional medical practitioners and marine pharmacologists to explore these aquatic plants in future to evaluate their true role and efficacy in acute and chronic inflammatory 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".