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: Cell that Ab8 is highly effective in preventing and treating SARS-CoV-2 infection in mice and hamsters. Its tiny size not only increases its potential for diffusion in tissues to better neutralize the virus, but also makes it possible to administer the drug by alternative routes, including inhalation. Importantly, it does not bind to human cells -- a good sign that it won't have negative side-effects in people.

Abstract:
SARS-CoV-2 rapidly spread around the globe after its emergence in Wuhan in December 2019.
With no specific therapeutic and prophylactic options available, the virus was able to infect millions of people.
To date, close to half a million patients succumbed to the viral disease, COVID-19.
The high need for treatment options, together with the lack of small animal models of infection has led to clinical trials with repurposed drugs before any preclinical in vivo evidence attesting their efficacy was available.
We used Syrian hamsters to establish a model to evaluate antiviral activity of small molecules in both an infection and a transmission setting.
Upon intranasal infection, the animals developed high titers of SARS-CoV-2 in the lungs and pathology similar to that observed in mild COVID-19 patients.
Treatment of SARS-CoV-2-infected hamsters with favipiravir or hydroxychloroquine (with and without azithromycin) resulted in respectively a mild or no reduction in viral RNA and infectious virus.
Micro-CT scan analysis of the lungs showed no improvement compared to non-treated animals, which was confirmed by histopathology.
In addition, both compounds did not prevent virus transmission through direct contact and thus failed as prophylactic treatments.
By modelling the PK profile of hydroxychloroquine based on the trough plasma concentrations, we show that the total lung exposure to the drug was not the limiting factor.
In conclusion, we here characterized a hamster infection and transmission model to be a robust model for studying in vivo efficacy of antiviral compounds.
The information acquired using hydroxychloroquine and favipiravir in this model is of critical value to those designing (current and) future clinical trials.
At this point, the data here presented on hydroxychloroquine either alone or combined with azithromycin (together with previously reported in vivo data in macaques and ferrets) provide no scientific basis for further use of the drug in humans.
{"verdict": "NEI", "evidence": []}