Back

Quickly and simply detection for coronaviruses including SARS-CoV-2 on the mobile Real-Time PCR without treating RNA in advance

Muraoka, M.; Tanoi, Y.; Tada, T.; Mizukoshi, M.; Kawaguchi, O.

2020-11-05 genetic and genomic medicine
10.1101/2020.08.06.20168294 medRxiv
Show abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported to WHO as an outbreak in Wuhan City, Hubei Province, China on end of 2019, afterwards epidemic in many countries, and pandemic on the worldwide in 2020. Usually detection of coronavirus including SARS-CoV-2 was detected by real-time RT-PCR method, but it must be long time that RNA is treated by extraction, concentration and purification, and detected by RT-PCR method. We modified various methods, of which evaluated if each method is short and simple enough. In one point of the evaluations, real-time RT-PCR could be finished in very short time with using mobile real-time PCR device PCR1100 (Nippon Sheet Glass Co. Ltd.). It was able to detect positive control RNA for 20 minutes by each method according to the National Institute of Infections Disease in Japan (NIID), and less than 13.5 minutes according to the Centers for Disease Control and Prevention in USA (CDC). In another point of the evaluations, surprisingly, Human coronavirus 229E, which was substituted for SARS-CoV-2, could be detected in crude state without treatment in advance of RNA. As that was, it was possible to detect coronavirus with direct RT-PCR. Therefore, it might eliminate wasteful time, avoid secondary infection and risk of contamination. In light of the above two points, SARS-CoV-2 might be detected more quickly and more simply. With using this mobile real-time PCR, these methods should be suitable for not only SARS-CoV-2 but also other various viruses and might save time compared to earlier detection methods.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.