Harnessing <i>Jasminum</i> Bioactive Compounds: Updated Insights for Therapeutic and Food Preservation Innovations
Bibliographic record
Abstract
ABSTRACT The Jasminum genus, renowned for its aromatic flowers, has been used in traditional medicine across various cultures for its therapeutic properties. Recently, scientific interest has focused on the bioactive compounds present in Jasminum species, highlighting their potential applications in health and food preservation. This review evaluates the phytochemical composition of Jasminum species, emphasizing their therapeutic and preservative roles while identifying research gaps. A comprehensive literature search was conducted using major scientific databases, including PubMed, Scopus, and Web of Science, focusing on studies from the last two decades. The review includes peer‐reviewed articles that provide robust methodologies and detailed results regarding the biological activities of Jasminum species. Findings reveal that Jasminum is rich in bioactive compounds such as terpenoids, flavonoids, and phenolic acids, contributing to significant antioxidant, anti‐inflammatory, antimicrobial, and anticancer properties. Scientific evidence supports traditional uses, such as treating headaches and infections. Additionally, Jasminum extracts have shown promise as natural food preservatives due to their potent antimicrobial activity. However, the review identifies significant variability in study methodologies and a lack of clinical trials, which limit the generalizability and application of these findings. Jasminum species possess a diverse phytochemical profile that holds promise for advancing health and food preservation applications. Future research should prioritize standardizing methodologies and conducting clinical trials to validate their efficacy. Bridging the gap between traditional knowledge and modern science will unlock the full potential of Jasminum as a multifaceted resource for health and nutrition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".