Back

Assessment of the use and quick preparation of saliva for rapid microbiological diagnosis of COVID-19.

Vila, J.; Fernandez-Pittol, M.; Hurtado, J. C.; Moreno-Garcia, E.; Rubio Garcia, E.; Navarro, M.; Valiente, M.; Peiro, A.; Seijas, N.; Capon, A.; Martinez, M. J.; Casals-Pascual, C.

2020-06-29 microbiology
10.1101/2020.06.25.172734 bioRxiv
Show abstract

The objective of this study was to assess the performance of direct real time RT-PCR detection of SARS-CoV-2 in heated saliva samples, avoiding the RNA isolation step. Oropharyngeal and nasopharyngeal swabs together with saliva samples were obtained from 51 patients clinically diagnosed as potentially having COVID-19. Two different methods were compared: 1. RNA was extracted from 500 l of sample using a MagNA Pure Compact Instrument with an elution volume of 50l and 2. 700{micro}L of saliva were heat-inactivated at 96{degrees}C for 15 minutes, and directly subjected to RT-PCR. One step real time RT-PCR was performed using 5 l of extracted RNA or directly from 5 l of heated sample. RT-PCR was performed targeting the SARS-CoV-2 envelope (E) gene region. Diagnostic performance was assessed using the results of the RT-PCR from nasopharyngeal and oropharyngeal swabs as the gold standard. The overall sensitivity, specificity, positive and negative predictive values were 81.08%, 92.86%, 96.77% and 65.00%, respectively when RNA extraction was included in the protocol with saliva, whereas sensitivity, specificity, positive and negative predictive values were 83.78%, 92.86%, 68.42% and 96.88%, respectively, for the heat-inactivation protocol. However, when the analysis was performed exclusively on saliva samples with a limited time from the onset of symptoms (<9 days, N=28), these values were 90%, 87.5%, 44% and 98.75% for the heat-inactivation protocol. The study showed that RT-PCR can be performed using saliva in an RNA extraction free protocol, showing good sensitivity and specificity.

Matching journals

The top 10 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.