ABSTRACT
COVID-19 is detected using reverse transcription polymerase chain reaction (RT-PCR) of nasal swabs. A very sensitive and rapid detection technique using easily-collected fluids like saliva must be developed for safe and precise mass testing. Here, we introduce a metasurface platform for direct sensing of COVID-19 from unprocessed saliva. We computationally screen gold metasurfaces out of a pattern space of 2100 combinations for strongly-enhanced light-virus interaction with machine learning and use it to investigate the presence and concentration of the SARS-CoV-2. We use machine learning to identify the virus from Raman spectra with 95.2% sensitivity and specificity on 36 PCR positive and 33 negative clinical samples and to distinguish wild-type, alpha, and beta variants. Our results could pave the way for effective, safe and quantitative preventive screening and identification of variants.
Subject(s)
COVID-19ABSTRACT
Soluble ACE2 (sACE2) decoy receptors are promising agents to inhibit SARS-CoV-2 as they are not affected by common escape mutations in viral proteins. However, their success may be limited by their relatively poor potency. To address these challenges, we developed a highly active multimeric sACE2 decoy receptor via a SunTag system that could neutralize both pseudoviruses bearing SARS-CoV-2 spike protein and SARS-CoV-2 clinical isolates. This fusion protein demonstrated a neutralization efficiency nearly 250-fold greater than monomeric sACE2. SunTag in combination with a more potent version of sACE2 achieved near complete neutralization at a sub-nanomolar range, which is comparable with clinical monoclonal antibodies. We demonstrate that this activity is due to greater occupancy of the multimeric decoy receptors on Spike protein as compared to monomeric sACE2. Overall, these highly potent multimeric sACE2 decoy receptors offer a promising treatment approach against SARS-CoV-2 infections including its novel variants.