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The mRNA-1273 Moderna COVID-19 vaccine was introduced to combat the COVID-19 global pandemic in 2020. Although the safety of the vaccine has been investigated worldwide, real-world safety data is scarce in Japan. An online, real-time survey of adverse events following immunization (AEFIs) with mRNA-1273 was conducted in the setting of a workplace vaccination program at the School of Pharmacy, Keio University from 26 June 2021, to 11 June 2022. Participants were requested to take four surveys during a seven-day follow-up period after each of the first, second, and third booster doses. The maximum number of responses, from 301 respondents, was obtained on day 0 (vaccination date) for the first dose. 98% of respondents reported local and systemic AEFIs for the second dose on day 1. No noticeable difference in local reactions was seen among the three doses. Females reported more AEFIs than males, and the young group (18–29 years) reported a higher rate than the middle age group (≥30 years) after the first dose. Age and gender differences in rates decreased at the second and third doses. This survey confirmed that the safety profile of mRNA-1273 in a real-world setting was similar to that derived from the clinical trials, and that the agent was well-tolerated. [ABSTRACT FROM AUTHOR]

This study focuses on the improvement of the accuracy and construction of a real-time, short-term rainfall prediction system in watershed, comparing the prediction results with radar observed rainfall intensity, to provide a reliable tool for disaster and water resource management. The short-term prediction of rainfall is very important for hydrologie forecasting for watershed with a short response time, especially under the global warming and extreme weather. Last September 9th (2015), heavy rain came a day after Tropical Storm Etau and triggered widespread flooding in Kinu Watershed. We use the Weather Research and Forecasting Model (WRF) to confirm the uncertainty of the rain prediction in Kinu watershed first, then will modify the WRF to suit the 6h, 12h, 24h rainfall prediction in Kinu watershed. Also, radar rainfall data will be employed to correct the WRF prediction in real-time meaning. Finally, we will check the possibility of general application of the system in other watersheds. We now are working on reproducing the heavy rain in Kinu watershed last September. We tried different microphysics, which describes the formation of cloud, to find one to reproduce the heavy rain in this case. The coverage, location of rain bands and distribution of heavy rainfall are applied to comparisons between calculations and observations by C-band radar. We found no Microphysics does well in this case and the area of heavy rain is underestimated by WRF, but the accumulated precipitation is overestimated. Detail insight at microphysics and modification are required. [ABSTRACT FROM AUTHOR]

The article discusses detection of pandemic influenza A (H1N1) 2009 virus RNA by real-time reverse transcription polymerase chain reaction. It arrive in Mexico in April 2009 and spread rapidly worldwide. The symptoms of this virus are severe illness, including pneumonia, which also led to hospitalization. Topics include patients and specimen collection, virus isolation, and real-time Reverse transcription Polymerase Chain Reaction (RT–PCR).