Novel, accurate pathogen sensors for fast detection of SARS-CoV-2 in the aerosol in seconds for a breathalyzer platform

Xiaoling Shi, Pardis Sadeghi, Nader Lobandi, et al.

Biosensors and Bioelectronics: X, vol. 14, art. 100369, 2023.

Abstract

Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.

Cite

@article{shi2023NovelAccurate,
  author  = {Shi, Xiaoling and Sadeghi, Pardis and Lobandi, Nader and Emam, Shadi and Seyed Abrishami, Seyed Mahdi and Martos-Repath, Isabel and Mani, Natesan and Nasrollahpour, Mehdi and Sun, William and Rones, Stav and Kwok, Joshua and Shah, Harsh and Charles, Joseph and Khan, Zulqarnain and Pagsuyoin, Sheree and Rojjanapinun, Akarapan and Liu, Ping and Chae, Jeongmin and Ferreira Da Costa, Maxime and Li, Jianxiu and Sun, Xin and Yang, Mengdi and Li, Jiahe and Dy, Jennifer and Wang, Jennifer and Luban, Jeremy and Chang, {ChingWen} and Finberg, Robert and Mitra, Urbashi and Cash, Sydney and Robbins, Gregory and Hodys, Cole and Lu, Hui and Wiegand, Patrick and Rieger, Robert and Sun, Nian X.},
  title   = {Novel, accurate pathogen sensors for fast detection of {SARS}-{CoV}-2 in the aerosol in seconds for a breathalyzer platform},
  journal = {Biosensors and Bioelectronics: X},
  volume  = {14},
  pages   = {100369},
  year    = {2023},
  doi     = {10.1016/j.biosx.2023.100369}
}