You will be shown a claim about public health, and the abstract of a biomedical research paper. Each sentence from the abstract will be on a separate line. Your task is to return a JSON object with two fields:

- "verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". If the abstract does not provide enough information to arrive at a verdict, write "NEI" (for "not enough information").
- "evidence": An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract. If the verdict was "NEI", then return an empty array.

For instance, if the model were given the claim "wearing masks can prevent the spread of COVID", the output might be
{
  "verdict": "SUPPORT",
  "evidence": ["Our findings indicate that mass mask-wearing reduces the transmission rate for COVID-19."]
}

Claim: Hot matcha green tea (with EGCG) can do magic for covid-19

Abstract:
Coronavirus disease 2019 (COVID-19) is a viral respiratory disease which caused global health emergency and announced as pandemic disease by World Health Organization.
Lack of specific drug molecules or treatment strategy against this disease makes it more devastating.
Thus, there is an urgent need of effective drug molecules to fight against COVID-19.
The main protease (Mpro) of SARS CoV-2, a key component of this viral replication, is considered as a prime target for anti-COVID-19 drug development.
In order to find potent Mpro inhibitors, we have selected eight polyphenols from green tea, as these are already known to exert antiviral activity against many RNA viruses.
We have elucidated the binding affinities and binding modes between these polyphenols including a well-known Mpro inhibitor N3 (having binding affinity -7.0 kcal/mol) and Mpro using molecular docking studies.
All eight polyphenols exhibit good binding affinity toward Mpro (-7.1 to -9.0 kcal/mol).
However, only three polyphenols (epigallocatechin gallate, epicatechingallate and gallocatechin-3-gallate) interact strongly with one or both catalytic residues (His41 and Cys145) of Mpro.
Molecular dynamics simulations (100 ns) on these three Mpro-polyphenol systems further reveal that these complexes are highly stable, experience less conformational fluctuations and share similar degree of compactness.
Estimation of total number of intermolecular H-bond and MM-GBSA analysis affirm the stability of these three Mpro-polyphenol complexes.
Pharmacokinetic analysis additionally suggested that these polyphenols possess favorable drug-likeness characteristics.
Altogether, our study shows that these three polyphenols can be used as potential inhibitors against SARS CoV-2 Mpro and are promising drug candidates for COVID-19 treatment.
Communicated by Ramaswamy H. Sarma.
{"verdict": "CONTRADICT", "evidence": ["Lack of specific drug molecules or treatment strategy against this disease makes it more devastating.", "Thus, there is an urgent need of effective drug molecules to fight against COVID-19."]}