Pharmacologic activities of phytosteroids in inflammatory diseases: Mechanism of action and therapeutic potentials
Bibliographic record
Abstract
Natural products and their derivatives are known to be useful for treating numerous diseases since ancient times. Because of their high therapeutic potentials, the use of different medicinal plants is possible to treat varied inflammation-mediated chronic diseases. Among natural products, phytosteroids have emerged as promising compounds mostly because they have diverse pharmacological activities. Currently, available medications exert numerous systemic toxicities, including hypertension, immune suppression, osteoporosis, and metabolic abnormalities. Thus, further research on phytosteroids to subside these complications is of significant importance. In this study, the information on phytosteroids, their types, and actions against inflammation, and allergic complications was collected by a systematic survey of literature on several scientific search engines. The literature review suggested that phytosteroids exhibit antiinflammatory action via different modes through transrepression or selective COX-2 enzymes. Also, in silico ADMET analysis was carried out on available phytosteroids to uncover their pharmacokinetic properties. Our analysis has shown that eight compounds: withaferin A, stigmasterol, β-sitosterol, guggulsterone, diosgenin, sarsasapogenin, physalin A, and dioscin, -isolated from medicinal plants show similar pharmacokinetic properties as compared to dexamethasone, commercially available glucocorticoid. These phytosteroids could be useful for the treatment of inflammatory diseases, such as rheumatoid arthritis, inflammatory bowel diseases, multiple sclerosis, asthma, and cardiovascular diseases. Thus, systematic research is required to explore potent phytosteroids with lesser side effects, which might substitute the current medications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".