The Significance of Natural Product Derivatives and Traditional Medicine for COVID-19
Why this work is in the frame
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Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, there have been more than 10 million reported cases, more than 517,000 deaths in 215 countries, areas or territories. There is no effective antiviral medicine to prevent or treat COVID-19. Natural products and traditional medicine products with known safety profiles are a promising source for the discovery of new drug leads. There is increasing number of publications reporting the effect of natural products and traditional medicine products on COVID-19. In our review, we provide an overview of natural products and their derivatives or mimics, as well as traditional medicine products, which were reported to exhibit potential to inhibit SARS-CoV-2 infection in vitro, and to manage COVID-19 in vivo, or in clinical reports or trials. These natural products and traditional medicine products are categorized in several classes: (1) anti-malaria drugs including chloroquine and hydroxychloroquine, (2) antivirals including nucleoside analogs (remdesivir, favipiravir, β-D-N4-hydroxycytidine, ribavirin and among others), lopinavir/ritonavir and arbidol, (3) antibiotics including azithromycin, ivermectin and teicoplanin, (4) anti-protozoal drug, emetine, anti-cancer drug, homoharringtonine, and others, as well as (5) traditional medicine (Lian Hua Qing Wen Capsule, Shuang Huang Lian Oral Liquid, Qingfei Paidu Decoction and Scutellariae Radix). Randomized, double-blind and placebo-controlled large clinical trials are needed to provide solid evidence for the potential effective treatment. Currently, drug repurposing is a promising strategy to quickly find an effective treatment for COVID-19. In addition, carefully combined cocktails need to be examined for preventing a COVID-19 pandemic and the resulting global health concerns.
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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 | 0.259 |
| 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 | 0.001 |
| 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 it