Screening of Natural Antivirals Against the COVID-19 Pandemic- ACompilation of Updates
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19), named by WHO, is a public health disaster of the third millennium. This acute respiratory distress syndrome (ARDS) has severe complications like pneumonitis, respiratory failure, shock, multi-organ failure, and finally, death. Despite repurposing of broad-spectrum antivirals, vaccinations, use of mask sanitizers, social distancing, intermittent lockdowns and quarantine, long-term protection or eradication of coronavirus is yet to be achieved. Objectives: This comprehensive review makes a compilation of updates on the screening and evaluation of natural antivirals that are found to show anti-COVID potency. Methods: Literature mining was done in phytotherapy and food research journals, Pubmed, Scopus, Elsevier for collection of latest research updates focusing on screening and evaluation of anti-COVID natural antivirals. Results: In silico molecular docking studies have screened several phytochemicals and food bioactive principles with significant potencies against the corona virus. The anti-COVID potency of the phytochemicals is mostly by restricting the action of enzymes like the main protease (Mpro), 3-chymotrypsin like protease (3CLpro), spike proteins, papain-like protease (ACE2). Free radical scavenging, anti-inflammatory effect, DNA inhibition, prevention of viral attachment, and its penetration into the host body, inhibiting viral replication are other associated mechanisms of bioactive compounds present in plants, vegetables, fruits, spices and marine alga. Different formulations of Ayurveda, Siddha, Unani have shown their ameliorative effects. Many formulations of Traditional Chinese Medicine are under clinical trials. Conclusions: The immense potencies of bioactives that are omnipresent need to be properly utilized for immune-boosting and combat this deadly virus naturistically.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".