Anti-Leishmanial Activity of Artemisia persica, A. spicigera, and A. fragrance against Leishmania major
Bibliographic record
Abstract
Background: Neglected tropical diseases (NTDs) like zoonotic cutaneous leishmaniasis (ZCL), is a widespread infectious disease with high mortality and morbidity. Various medications are used for treating the disease, but several side effects and drug resistance have been reported. Herbal medicines are unlimited sources for discovering new medications to treat infectious diseases. We aimed to determine the leishmanicidal activity of three species of Iranian Artemisia herbal plant extracts in in-vitro. Methods: In-vitro anti-leishmanial activity of ethanolic extracts on both promastigotes and amastigotes was determined by using MTT method. IC50, CC50, EC50 and SI were calculated. The study was done in 2019-2020 in Iran University of Medical Sciences, Tehran, Iran. Results: All of the three Artemisia species significantly reduced the number of parasite promastigotes. Among them, A. persica had the highest leishmanicidal activity against parasite promastigotes. Cytotoxicity assay elucidated that the Artemisia had no toxicity to the host cells, and killed the L. major amastigotes very efficiently. By increasing the dose of extracts, the parasite number in both phases (promastigotes and amastigotes) was reduced significantly. Conclusion: These results indicated satisfactory anti-leishmanial activity of Artemisia extracts against ZCL in-vitro. Accordingly, Artemisia ethanolic extracts might be considered as a strong, effective and safe herbal compound for clearing the L. major with less toxicity to the host macrophages cells. Hence, it may be recognized as an excellent herbal therapy for treating the ZCL.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".