Review on Curcumin Compounds in Turmeric Plants for the Treatment of COVID-19
Bibliographic record
Abstract
Coronavirus Disease (COVID-19) is an infectious disease that has a high fatality rate and is spreading quickly throughout the world. The WHO claims that SARS-CoV-2, a brand-new coronavirus strain, is to blame for this outbreak (Severe Acute Respiratory Syndrome Corona Virus-2) and that COVID-19 must be treated with both conventional medical therapy and a combination of modern medicine. The technique of this study, a review of the literature, focused on numerous investigations looking at the potential of curcumin molecules from turmeric to cure the COVID-19 disease. Primary data for scientific papers is gathered from national and international journals through searches on electronic search engines like Google Scholar, Sciencedirect, or PubMed and selected publications are assessed, evaluated, and interpreted by authors. Turmeric contains substances that are immune system boosters, anti-inflammatory, antitumor, antiviral, and antioxidants. Curcumin may prevent a number of viral infections, according to evidence. In vitro testing has shown that the SARS-CoV virus is resistant to curcumin's antiviral properties. It's possible that curcumin can halt viral replication. Curcumin has the potential to treat COVID-19 effectively. Curcumin has antiviral activity that can fight the SARS-CoV-two virus. Treatment with curcumin can change the virus top protein structure, preventing the virus from entering the body and from budding. Future study on the use of curcumin as SARS-Cov-2 virus inhibitory agent is necessary in order to employ it as a novel and long-lasting therapy option for COVID-19 patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".