Ayurveda and Siddha systems polyherbal formulations to treat COVID-19 caused by SARS-CoV-2 and brief insight on application of Molecular Docking and SWISS Target prediction tools to study efficacy of active molecules
Bibliographic record
Abstract
Ayurveda and Siddha systems are the two ancient medical systems originated in India more than 4000 years ago had given many formulary and treatment methods against influenza like infections. Kabasura churan from Siddha system and Maha sudharshan churan from the Ayurvedic system are the two major formulations along with many other individual herbs mentioned in the texts to treat Influenza like infections. Kabasura churan and Maha Sudarshan churan both have antipyretic, analgesic and anti-inflammatory effects. Both formulations were prepared according to Siddha and Ayurvedic texts. Herbs mentioned in both formulations like Turmeric, Tulsi (Basil), Kalmegh (Andrographis), Black Pepper, Liquorice (Mulethi), and Dronapushpi (Leucas) etc., had direct antiviral effect. Herbs like Aswagandha, Ginger, Guduchi (Tinospora), Kulanjan (Galangal) etc., had immunomodulatory and anti-inflammatory effect. Active compounds from different herbs were selected to study their antiviral activity through molecular docking algorithm. Application of modern of tools like Bioinformatics and Highthroughput screening methods can predict the efficacy of the ancient documented formulations and can be compared as per their literature. Compounds like curcumin, Glycyrrhizin, Ursolic acid, Quercetin, Andrographolide, Coumarins etc. were showed polyspecific activity like inhibition of Spike protein, Furin, Main Protease (Mpro) and Papain like Proteases (PLpro). Thus we propose use of Kabasura churan and Maha Sudharshan churan as alternative complementary medicine as a palliative treatment against COVID-19 caused by SARS-CoV-2 by conducting proper Randomized Clinical Trials
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".