Potential Thai Herbal Medicine for COVID-19
Bibliographic record
Abstract
SARS-CoV-2 is a cause of COVID-19 a contagious respiratory disease, in which there are many signs and symptoms such as fever, dry cough, shortness of breath, muscle ache, and pneumonia. Meanwhile, antiviral drug mechanisms which are being used to treat SARS-CoV-2 with Western drugs can be divided into three groups as follows: increasing acidic conditions by endosomal formation; viral replication; and affinity interaction with ACE-2 receptor via S-protein. Therefore, hydroxychloroquine/chloroquine, lopinavir, remdesivir, favipiravir, and molnupiravir which have been utilized to treat HIV and influenza via inhibiting viral replication and alkalinization could also modulate COVID-19 symptoms. However, antiviral drugs also have limited use in hospitalized and severe COVID-19 cases. The objective of this review is to provide a comprehensive analysis of Thai Herbal Medicine findings suggesting antiviral property potential that natural compounds derived from Thai plants could be further developed or provide mechanistic understanding of current drug treatment of COVID-19. Cinchona bark constituents create an alkaline environment to reduce viral replication and perfusion in cells. Certain medicinal plants which possess antiviral replication and blockage of the affinity binding between S-protein of SARS-CoV-2 and ACE2 receptor include Andrographis paniculata, Boesenbergia rotunda, Zingiber officinale, Phyllanthus amarus, Phylanthus emblica, Glycyrrhiza glabra, and Citrus medica. These plants were summarized for their potential in COVID-19 treatment. Integrating Thai Traditional Medicine principles with contemporary COVID-19 treatment mechanisms would certainly have valuable provide more efficient clinical therapy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".