A comprehensive Review of the Morphology, Phytochemistry and Medicinal Applications of Asteraceae Family Plants.
Bibliographic record
Abstract
The Asteraceae family, commonly known as the aster or sunflower family, comprises one of the largest and most diverse plant families in the world.This review study provides an extensive overview of various plants within the Asteraceae family, emphasizing their phytochemical constituents and medicinal applications.In this review, we systematically examine the phytochemistry of selected Asteraceae species, focusing on the identification and characterization of secondary metabolites, including terpenoids, flavonoids, alkaloids, and essential oils.These compounds contribute to the pharmacological properties that make Asteraceae plants valuable in traditional and modern medicine.Furthermore, the review outlines the diverse range of medicinal uses associated with Asteraceae family plants, such as anti-inflammatory, antimicrobial, analgesic, antioxidant, and immunomodulatory activities.The study also sheds light on the challenges and opportunities in the utilization of Asteraceae species in medicine, addressing issues related to sustainability, conservation, and standardization.This review provides a comprehensive understanding of the phytochemistry and medicinal applications of Asteraceae family plants, highlighting their significance in the realm of natural products and traditional healing.It serves as a valuable resource for researchers, ethnobotanists, and pharmaceutical professionals interested in harnessing the rich phytochemical diversity of this plant family for various health-related applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".