Abstract
The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.
【초록키워드】 COVID-19, SARS CoV-2, coronavirus disease, Coronavirus disease 2019, mass spectrometry, pandemic, Clinical symptoms, Tuberculosis, Diagnosis, immunoassay, serum, sensitivity, specificity, PCR, Surveillance, Accuracy, outbreak, Data processing, Logistic regression, patients, False negative, Feature selection, COVID-19 patient, serum samples, global public health, healthy individuals, infected individuals, SARS CoV, false negative results, flight, serum sample, populations, highest, analyzed, detect, caused, the disease, false negative result, eight, were used, demonstrated, build, diagnosis of COVID-19, non-COVID-19 patient, 【제목키워드】 COVID-19, detection, Rapid, Profiling,