In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: SARS-CoV-2, a member of the coronavirus family, is responsible for the current COVID-19 pandemic. Based on our analysis of hepatitis C virus and coronavirus replication, and the molecular structures and activities of viral inhibitors, we previously demonstrated that three nucleotide analogues inhibit the SARS-CoV RNA-dependent RNA polymerase (RdRp). Here, using polymerase extension experiments, we have demonstrated that the active triphosphate form of Sofosbuvir (a key component of the FDA approved hepatitis C drug EPCLUSA), is incorporated by SARS-CoV-2 RdRp, and blocks further incorporation. Using the same molecular insight, we selected the active triphosphate forms of three other anti-viral agents, Alovudine, AZT (an FDA approved HIV/AIDS drug) and Tenofovir alafenamide (TAF, an FDA approved drug for HIV and hepatitis B) for evaluation as inhibitors of SARS-CoV-2 RdRp. We demonstrated the ability of these three viral polymerase inhibitors, 3'-fluoro-3'-deoxythymidine triphosphate, 3'-azido-3'deoxythymidine triphosphate and Tenofovir diphosphate (the active triphosphate forms of Alovudine, AZT and TAF, respectively) to be incorporated by SARS-CoV-2 RdRp, where they also terminate further polymerase extension. These results offer a strong molecular basis for these nucleotide analogues to be evaluated as potential therapeutics for COVID-19.

A: Nucleotide Analogues as Inhibitors of SARS-CoV-2 Polymerase
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Q: Background: Drainage systems and its role in sanitation related outbreaks are evident but still occluded once it has been installed. This current review evaluates if drainage systems can cause infections and thus be of clinical concern. Method: A review of the literature was analyzed. Papers, guidelines, and quality management systems have been considered. Results: Adequate sanitation is fundamental and a prerequisite for safe life and productivity. In contrast, malfunctioning sanitation has been reported to cause outbreaks all over the world. In areas with no sanitation, diarrheal mortality is high and has been shown to decrease by 36% after interventions to improve sanitation. Often, infections are faeces associated and when present in wastewater and sewage sludge poses a high risk of infection upon exposure. Hence, there are working safety guidelines and in industries where infection reduction is essential strict quality assurance systems, i.e. HACCP (hazard analysis critical control points) and GMP (Good Manufacturing Practice) must be complied. Healthcare has recently taken interest in the HACCP system in their efforts to reduce healthcare associated infections as a response to increasing number of ineffective antibiotics and the threat of mortality rate like the pre-antibiotic era. The last few years have called for immediate action to contain the emergence of increasing resistant microorganisms. Resistance is obtained as a result of overuse and misuse of antibiotics in both healthcare and agriculture. Also, by the discharge of antibiotics from manufacturers, healthcare and society. One mechanism of development of novel resistant pathogens has been shown to be by effortless sharing of genetic mobile elements coding for resistance from microbes in the environment to human microbes. These pathogens have been sampled from the drainage systems. These were noticed owing to their possession of an unusual antibiotic resistance profile linking them to the outbreak. Often the cause of sanitation related outbreaks is due to inadequate sanitation and maintenance. However, in general these infections probably go unnoticed. Conclusion: Drainage systems and its maintenance, if neglected, could pose a threat in both community and healthcare causing infections as well as emergence of multi-resistant bacteria that could cause unpredictable clinical manifestations.

A: Drainage systems, an occluded source of sanitation related outbreaks
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Q: Background: To control the spread of Corona Virus Disease , 30 screening large numbers of suspected cases for appropriate quarantine and 31 treatment is a priority. Pathogenic laboratory testing is the diagnostic gold standard 32 but it is time consuming with significant false negative results. Fast and accurate 33 diagnostic methods are urgently needed to combat the disease. Based on 34 COVID-19 radiographical changes in CT images, we aimed to develop a deep 35 learning method that could extract COVID-19's graphical features in order to provide 36 a clinical diagnosis ahead of the pathogenic test, thus saving critical time for 37 disease control. 38 Methods and Findings: We collected 1,065CT images of pathogen-confirmed 39 COVID-19 cases (325 images) along with those previously diagnosed with typical 40 viral pneumonia (740 images). We modified the Inception transfer-learning model 41 to establish the algorithm, followed by internal and external validation. The internal 42 validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity 43 of 0.87. The external testing dataset showed a total accuracy of 79.3% with 44 specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that 45 first two nucleic acid test results were negative, 46 were predicted as COVID-19 46 positive by the algorithm, with the accuracy of 85.2%. 47 Conclusion: These results demonstrate the proof-of-principle for using 48 artificial intelligence to extract radiological features for timely and accurate 49 COVID-19 diagnosis. 50

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Breast Cancer Prevention and Therapy, Ministry of Education
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