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https://www.selleckchem.com/mTOR.html The COVID-19 pandemic has caused huge impact on public health and significantly changed our lifestyle. This is due to the fast airborne oro-nasal transmission of SARS-CoV-2 from the infected individuals. The generation of liquid aerosolized particles occurs when the COVID-19 patients speak, sing, cough, sneeze, or simply breathe. We have developed a novel aerosol barrier mask (ABM) to mitigate the spread of SARS-CoV-2 and other infectious pathogens. This Aerosol Barrier Mask is designed for preventing SARS-CoV-2 transmission while transporting patients within hospital facilities. This mask can constrain aerosol and droplet particles and trap them in a biofilter, while the patient is normally breathing and administrated with medical oxygen. The system can be characterized as an oxygen delivery and mitigation mask which has no unfiltered exhaled air dispersion. The mask helps to prevent the spread of SARS-CoV-2, and potentially other infectious respiratory pathogens and protects everyone in general, especially healthcare professionals.The increase of social media usage across the globe has fueled efforts in digital epidemiology for mining valuable information such as medication use, adverse drug effects and reports of viral infections that directly and indirectly affect population health. Such specific information can, however, be scarce, hard to find, and mostly expressed in very colloquial language. In this work, we focus on a fundamental problem that enables social media mining for disease monitoring. We present and make available SEED, a natural language processing approach to detect symptom and disease mentions from social media data obtained from platforms such as Twitter and DailyStrength and to normalize them into UMLS terminology. Using multi-corpus training and deep learning models, the tool achieves an overall F1 score of 0.86 and 0.72 on DailyStrength and balanced Twitter datasets, significantly improving over previous

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