Fengting Yu1,2, Liting Yan1,2, Nan Wang3,4, Siyuan Yang1,2, Linghang Wang1,2, Yunxia Tang1,2, Guiju Gao1,2, Sa Wang1,2, Chengjie Ma1,2, Ruming Xie1,2, Fang Wang5, Chianru Tan5, Lingxiang Zhu3, Yong Guo5, Fujie Zhang1,2. 1. Beijing Ditan Hospital, Capital Medical University, Beijing, China. 2. Clinical Center for HIV/AIDS, Capital Medical University, Beijing, China. 3. Human Genetic Resource Center, National Research Institute for Health and Family Planning, Beijing, China. 4. Chinese Academy of Medical Sciences, Graduate School of Peking Union Medical College, Beijing, China. 5. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a public health emergency. The widely used reverse transcription-polymerase chain reaction (RT-PCR) method has limitations for clinical diagnosis and treatment. METHODS: A total of 323 samples from 76 COVID-19-confirmed patients were analyzed by droplet digital PCR (ddPCR) and RT-PCR based 2 target genes (ORF1ab and N). Nasal swabs, throat swabs, sputum, blood, and urine were collected. Clinical and imaging data were obtained for clinical staging. RESULTS: In 95 samples that tested positive by both methods, the cycle threshold (Ct) of RT-PCR was highly correlated with the copy number of ddPCR (ORF1ab gene, R2 = 0.83; N gene, R2 = 0.87). Four (4/161) negative and 41 (41/67) single-gene positive samples tested by RT-PCR were positive according to ddPCR with viral loads ranging from 11.1 to 123.2 copies/test. The viral load of respiratory samples was then compared and the average viral load in sputum (17 429 ± 6920 copies/test) was found to be significantly higher than in throat swabs (2552 ± 1965 copies/test, P < .001) and nasal swabs (651 ± 501 copies/test, P < .001). Furthermore, the viral loads in the early and progressive stages were significantly higher than that in the recovery stage (46 800 ± 17 272 vs 1252 ± 1027, P < .001) analyzed by sputum samples. CONCLUSIONS: Quantitative monitoring of viral load in lower respiratory tract samples helps to evaluate disease progression, especially in cases of low viral load.
BACKGROUND:Coronavirus disease 2019 (COVID-19) has become a public health emergency. The widely used reverse transcription-polymerase chain reaction (RT-PCR) method has limitations for clinical diagnosis and treatment. METHODS: A total of 323 samples from 76 COVID-19-confirmed patients were analyzed by droplet digital PCR (ddPCR) and RT-PCR based 2 target genes (ORF1ab and N). Nasal swabs, throat swabs, sputum, blood, and urine were collected. Clinical and imaging data were obtained for clinical staging. RESULTS: In 95 samples that tested positive by both methods, the cycle threshold (Ct) of RT-PCR was highly correlated with the copy number of ddPCR (ORF1ab gene, R2 = 0.83; N gene, R2 = 0.87). Four (4/161) negative and 41 (41/67) single-gene positive samples tested by RT-PCR were positive according to ddPCR with viral loads ranging from 11.1 to 123.2 copies/test. The viral load of respiratory samples was then compared and the average viral load in sputum (17 429 ± 6920 copies/test) was found to be significantly higher than in throat swabs (2552 ± 1965 copies/test, P < .001) and nasal swabs (651 ± 501 copies/test, P < .001). Furthermore, the viral loads in the early and progressive stages were significantly higher than that in the recovery stage (46 800 ± 17 272 vs 1252 ± 1027, P < .001) analyzed by sputum samples. CONCLUSIONS: Quantitative monitoring of viral load in lower respiratory tract samples helps to evaluate disease progression, especially in cases of low viral load.
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Authors: Gunjan L Shah; Susan DeWolf; Yeon Joo Lee; Roni Tamari; Parastoo B Dahi; Jessica A Lavery; Josel Ruiz; Sean M Devlin; Christina Cho; Jonathan U Peled; Ioannis Politikos; Michael Scordo; N Esther Babady; Tania Jain; Santosha Vardhana; Anthony Daniyan; Craig S Sauter; Juliet N Barker; Sergio A Giralt; Cheryl Goss; Peter Maslak; Tobias M Hohl; Mini Kamboj; Lakshmi Ramanathan; Marcel Rm van den Brink; Esperanza Papadopoulos; Genovefa Papanicolaou; Miguel-Angel Perales Journal: J Clin Invest Date: 2020-12-01 Impact factor: 14.808
Authors: Maciej M Kowalik; Piotr Trzonkowski; Magdalena Łasińska-Kowara; Andrzej Mital; Tomasz Smiatacz; Miłosz Jaguszewski Journal: Cardiol J Date: 2020-05-07 Impact factor: 2.737