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Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386399295> ?p ?o ?g. }

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• W4386399295 startingPage "13767" @default.
• W4386399295 abstract "The epidemic of coronaviruses has posed significant public health concerns in the last two decades. An effective disinfection scheme is critical to preventing ambient virus infections and controlling the spread of further outbreaks. Ultraviolet (UV) irradiation has been a widely used approach to inactivating pathogenic viruses. However, no viable framework or model can accurately predict the UV inactivation of coronaviruses in aqueous solutions or on environmental surfaces, where viruses are commonly found and spread in public places. By conducting a systematic literature review to collect data covering a wide range of UV wavelengths and various subtypes of coronaviruses, including severe acute respiratory syndrome 2 (SARS-CoV-2), we developed machine learning models for predicting the UV inactivation effects of coronaviruses in aqueous solutions and on environmental surfaces, for which the optimal test performance was obtained with R2 = 0.927, RMSE = 0.565 and R2 = 0.888, RMSE = 0.439, respectively. Besides, the required UV doses at different wavelengths to inactivate the SARS-CoV-2 to 1 Log TCID50/mL titer from different initial titers were predicted for inactivation in protein-free water, saliva on the environmental surface, or the N95 respirator. Our models are instructive for eliminating the ongoing pandemic and controlling the spread of an emerging and unknown coronavirus outbreak." @default.
• W4386399295 created "2023-09-04" @default.
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• W4386399295 date "2023-09-03" @default.
• W4386399295 modified "2023-10-14" @default.
• W4386399295 title "Preparing for the Next Pandemic: Predicting UV Inactivation of Coronaviruses with Machine Learning" @default.
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• W4386399295 doi "https://doi.org/10.1021/acs.est.3c03707" @default.
• W4386399295 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37660353" @default.
• W4386399295 hasPublicationYear "2023" @default.
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