A Systematic Review To Unveil Therapeutic Potential Of Some Common Green Seaweeds
Bibliographic record
Abstract
The increasing demand of mankind for a healthier life and increased longevity has led humans to consume functional foods with plenteous sources of biochemical compounds and other nutraceuticals. Among these famous natural resources, seaweeds are considered as one of the most precious natural resources for macronutrients (carbohydrates, proteins, and lipids), micronutrients (vitamins and minerals), and other significant bioactive substances for animal and human health. Green seaweeds are commonly classified as “Chlorophyta” because of their green pigment dominance. Green seaweeds also known as green algae are considered as an important marine biological resource possess a variety of medicinal and biological actions including anti-inflammatory, anti-oxidant, analgesic, immuno-regulatory etc. They are a rich source of nutritional and bioactive components which are responsible for numerous therapeutic activities. Despite of the magnificent advancement in alternative medicines, land plants are more extensively, frequently, and commonly explored for biological and medicinal activities as compared to marine plants. This systematic review is constituted after comprehensively reviewing peer-reviewed publications of renowned and authentic online databases including Elsevier, Scopus, Web of Science, Pubmed, etc. Records of present and potential medicinal, bio-medical and pharmaceutical uses of green seaweeds from 2000 to the present had been reviewed and considered to constitute this review. This review will highlight some common green seaweeds including Bryopsis plumosa, Chaetomorpha antennina, Acrosiphonia orientalis, Ulva fasciata and Ulva prolifera. The basic purpose of this review is to provide updated knowledge and information about marine green seaweeds which will be valuable for the scientists working in the field of pharmacology, pharmacognosy and biomedicine. This review will also help the marine scientists and pharmacologists to explore these seaweeds further to evaluate and discover their true efficacy and therapeutic potential in human beings and in different disease models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".