Back

Point of care testing for detection of coronaviruses including SARS-CoV-2 from saliva without treating RNA in advance

Muraoka, M.; Kawaguchi, O.; Mizukoshi, M.; Sejima, S.

2021-05-04 infectious diseases
10.1101/2021.05.01.21256441 medRxiv
Show abstract

Coronavirus disease 2019 (COVID-19) outbreak was reported to the WHO (World Health Organization) as an outbreak on end of 2019, afterwards pandemic on the worldwide in 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has be reported it to cause COVID-19, and highly transmissible. Therefore, it is important that it is rapidly and continuously detected and monitored on site so that the infection is prevented. Namely, POCT (point of care testing) may be important to control cross infection of SARS-CoV-2. At present, the routine confirmation of SARS-CoV-2 is based on detection of sequence unique in the virus RNA by nucleic acid amplification tests (NAATs) such as rRT-PCR. Taking POCT into account, it is clear that it takes time and labour very much or much. Thus, it was our purpose this time to contribute to develop POCT for microbes such as SARS-CoV-2, and thus needed to improve NAATs method. First, combining the mobile real-time RT-PCR (rRT-PCR) device PCR1100 with the appropriate rRT-PCR reagent, we found that it is possible to detect RNA of SARS-CoV-2 for less 14 minutes with equivalent accuracy to conventional devices. Next, we found that the above method made it possible for us to detect coronaviruses by direct rRT-PCR without pre-treatments. Furthermore, it also made clear that coronaviruses in saliva could be detected by the similar direct rRT-PCR method. Hence, it was concluded that this method made it possible to detect virus in saliva without treating in advance (extraction, purification, concentration, etc.), and moreover, samples would be able to be collected with non-invasive. For this reason, we suggest that this method is useful for POCT of coronaviruses including SARS-CoV-2.

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.