Medicinal plants of Tamil Nadu (Southern India) are a rich source of antiviral activities
Bibliographic record
Abstract
In order to evaluate the potential of medicinal plants of Tamil Nadu as sources of antiviral activities, we used seven different viruses to evaluate the methanol extracts of 30 plants, derived from 22 families and recognized for their local medical applications. Antiviral activity was the minimum concentration of extracts required to completely inhibit viral cytopathic effects (CPE), i.e., MIC100 values. Many extracts showed strong activities against Herpes simplex virus (HSV) and mouse corona virus (MCV, the surrogate for human SARS virus). Some extracts were also active against influenza virus and Sindbis virus (SINV, surrogate for hepatitis C virus), but fewer were active against the non-membrane viruses feline calicivirus (FCV, the surrogate for Norovirus), rhinovirus (common cold virus), and poliovirus. The most potent extracts (low MIC100 and broad spectrum of activity) were obtained from Gymnema sylvestre R. Br. (Asclepiadaceae), Pergularia daemia (Forsskal) Chiov. (Asclepiadaceae), Sphaeranthus indicus L. (Asteraceae), Cassia alata L. (Caesalpiniaceae), Evolvulus alsinoides L. (Convolvulaceae), Clitoria ternatea L. (Fabaceae), Indigofera tinctoria L. (Euphorbiaceae), Abutilon indicum G. Don. (Malvaceae), Vitex trifolia L. (Verbenaceae), Clerodendrum inerme (L.) Gaertn (Verbenaceae), and Leucas aspera Spr. (Lamiaceae), which showed anti-MCV and anti-HSV activities at a concentration as low as 0.4 μg/mL. In some cases the activities were enhanced by light, suggesting the presence of photosensitizers. Some of these antiviral activities could contribute to the medicinal properties of the plants, and also provide more support for the concept of scientific validation of traditional plant medicines in the fight against infectious diseases.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".