Potential Thai Herbal Medicine for COVID-19
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
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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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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