Systematic Review of Anti-Inflammatory and Antiviral Properties of <i>Glycyrrhiza</i>
Bibliographic record
Abstract
Glycyrrhiza spp., a commonly used medicinal herb originating from plants of the genus Glycyrrhiza spp., exhibits significant anti-inflammatory and antiviral activities.The primary bioactive components of licorice include flavonoids, triterpenoids (including glycyrrhizic acid), polysaccharides, and other secondary metabolites.Many studies have reported that these compounds intervene in inflammation and viral infections through regulating various inflammatory mediators such as TNF-α, IL-1β, IL-6, key signaling pathways such as NF-κB, MAPK, JAK/STAT immune cell functions, and antioxidant and cytoprotective effects.The molecular mechanisms underlying the antiviral activity of licorice against viral replication and invasion have also been systematically explored both in vitro and in vivo.In recent years, critical roles of multi-omics approaches (genomics, proteomics, metabolomics), systems biology, and network pharmacology have been demonstrated in the study of the action mechanism of licorice bioactives, while the application of artificial intelligence and big data provides new instruments in the research of natural products.Licorice has the potential to be used in anti-inflammatory and antiviral therapy, as indicated by clinical studies, although its pharmacokinetics, bioavailability, standardized extraction, and safety need further assessment.The study will systematically summarize the progress, mechanisms, and application prospects of the anti-inflammatory and antiviral activities of licorice, providing theoretical guidance for the development of natural drugs, public health, and clinical use.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.006 | 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".