Back

Characterization of a fieldable process for airborne virus detection

Du, H.; Bruno, S.; Overholt, K. J.; Palacios, S.; Huang, H.-H.; Barajas, C.; Gross, B.; Lee, C.; Evile, H. K.; de Sousa, N. R.; Rothfuchs, A. G.; Del Vecchio, D.

2023-07-06 infectious diseases
10.1101/2023.07.03.23292170 medRxiv
Show abstract

Rapid, on-site, airborne virus detection is a requirement for timely action against the spread of air-transmissible infectious diseases. This applies both to future threats and to common viral diseases, such as influenza and COVID-19, which hit vulnerable populations yearly with severe consequences. The ultra-low concentrations of virus in the air make airborne virus detection difficult, yet readily infect individuals when breathed. Here, we propose a fieldable process that includes an enrichment step to concentrate collected genetic material in a small volume. The enrichment approach uses capillary electrophoresis and an RT-qPCR-compatible buffer, which allow enrichment of the RNA by about 5-fold within only 10 minutes of operation. Our detection process consists of air sampling through electrostatic precipitation, RNA extraction via heating, RNA enrichment, and RT-qPCR for detection. We optimized each step of the process and estimated a detection sensitivity of 3106 {+/-} 2457 genome copies (gc) per m3 of air. We then performed an integration experiment and confirmed a sensitivity of 5654 gc/m3 with a detection rate of 100% and a sensitivity of 4221 gc/m3 with a detection rate of 78.6%. When using fast RT-qPCR, the latency of the whole process is down to 61 minutes. Given that our sensitivity falls in the low range of influenza and SARS-CoV-2 concentrations reported in indoor spaces, our study shows that, with enrichment, airborne pathogen detection can be made sufficiently sensitive for practical use.

Matching journals

The top 9 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.