Multiple Antiviral Activities of Endemic Medicinal Plants Used by Berber Peoples of Morocco
Bibliographic record
Abstract
We investigated the antiviral activities of methanol extracts of 75 Moroccan plants (64 genera of 35 families), used traditionally to treat diseases that could be caused by viruses and microbes. The plants included many endemic to Morocco and used by Berber as well as Arab peoples. They were evaluated against three mammalian viruses: herpes simplex virus, Sindbis virus and poliovirus, at non-cytotoxic concentrations. Five extracts were very active against the three viruses, 16 were active against two viruses, and 24 were only active against one virus. Thirty-two extracts showed light enhanced and two showed light-dependent activities. Punica granatum extract, which was the most active, inhibited all three viruses at a concentration of only 1.5 µg/ml, although these activities were not light enhanced. The extracts of Acacia gummifera, Juglans regia, Thymus maroccanus, Lawsonia inermis, Pinus halepensis, and Rosa canina inhibited Sindbis virus at a minimum concentration of 1.5 µg/ml. Thymus maroccanus and Rosa canina activities were light enhanced. Pistacia lentiscus and Thymus maroccanus were very active against herpes simplex virus. The extracts most active against poliovirus were those from Pinus halepensis and Punica granatum. These were active at minimum concentrations of 6.5 µg/ml, but were not light enhanced. These results indicate that some of these plants are potential potent medicines against infectious diseases caused by viruses. Their discriminatory effect against specific microorganisms suggests the presence of different chemical compounds. Light is a determining factor in the activity of photosensitizers and should be definitely taken into account in this kind of test.
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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 | 0.000 | 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) | 0.002 | 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".