Screening of Natural Antivirals Against the COVID-19 Pandemic- ACompilation of Updates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it