The DOI matches its Crossref record; 35 of the 36 references it cites were found in a registry or an index.
Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections.
Open accessAbstract
Background: The outbreak of novel coronavirus pneumonia (NCP) caused by 2019-nCoV spread rapidly, and elucidating the diagnostic accuracy of different respiratory specimens is crucial for the control and treatment of this disease. Methods: Respiratory samples including nasal swabs, throat swabs, sputum and bronchoalveolar lavage fluid (BALF) were collected from Guangdong CDC confirmed NCP patients, and viral RNAs were detected using a CFDA approved detection kit. Results were analyzed in combination with sample collection date and clinical information. Findings: Except for BALF, the sputum possessed the highest positive rate (74.4%∼88.9%), followed by nasal swabs (53.6%∼73.3%) for both severe and mild cases during the first 14 days after illness onset (d.a.o). For samples collected ≥ 15 d.a.o, sputum and nasal swabs still possessed a high positive rate ranging from 42.9%∼61.1%. The positive rate of throat swabs collected ≥ 8 d.a.o was low, especially in samples from mild cases. Viral RNAs could be detected in all the lower respiratory tract of severe cases, but not the mild cases. CT scan of cases 02, 07 and 13 showed typical viral pneumonia with ground-glass opacity, while no viral RNAs were detected in first three or all the upper respiratory samples. Interpretation: Sputum is most accurate for laboratory diagnosis of NCP, followed by nasal swabs. Detection of viral RNAs in BLAF is necessary for diagnosis and monitoring of viruses in severe cases. CT scan could serve as an important make up for the diagnosis of NCP. Funding: National Science and Technology Major Project, Sanming Project of Medicine and China Postdoctoral Science Foundation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“SARS-CoV-2 can be detected from different body fluids and the rate of positive test results vary depending on the sample size, days after illness onset, and severity of illness.”
From Oral Microbiome and SARS-CoV-2: Beware of Lung Co-infection (2020) · cites this paper as Yang et al., 2020
In the citing paper
“Sputum appeared to be a good clinical sample with a high positive rate (74.4∼88.9%), followed by nasal swabs (53.6∼73.3%) and throat swabs (50∼ 61.3%).”
From Oral Microbiome and SARS-CoV-2: Beware of Lung Co-infection (2020) · cites this paper as Yang et al., 2020
In the citing paper
“According to a study on 213 confirmed COVID-19 patients, viral RNA could be detected in all BALF samples of severe cases, but not from mild cases.”
From Oral Microbiome and SARS-CoV-2: Beware of Lung Co-infection (2020) · cites this paper as Yang et al., 2020
In the citing paper
“When the onset time was greater than 14 days, the positive rate of BALF, sputum, and nasal swabs showed similar results, and were still much higher than throat swabs (Yang et al., 2020).”
From Oral Microbiome and SARS-CoV-2: Beware of Lung Co-infection (2020) · cites this paper as Yang et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationClinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Self-citationA novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263
Self-citationA pneumonia outbreak associated with a new coronavirus of probable bat origin
Zhou, P., Yang, X.-L., Wang, X.-G., Hu, B., Zhang, L., Zhang, W., Si, H.-R., Zhu, Y., Li, B., Huang, C.-L., et al. (2020) · Nature · doi:10.1038/s41586-020-2012-7 · Cited by 21,969
Self-citationWang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
Self-citationA new coronavirus associated with human respiratory disease in China
Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2008-3 · Cited by 11,688
Self-citationPathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Self-citation
Flagged first, then by citations
1 of the 36 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,228
Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019
Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., Wang, X., Yuan, J., Li, T., Li, J., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa344 · Cited by 2,607
Self-citationDiagnosing COVID-19: The Disease and Tools for Detection
Udugama, B. N., Kadhiresan, P., Kozlowski, H. N., Malekjahani, A., Osborne, M., Li, V. Y. C., Chen, H., Mubareka, S., Gubbay, J. B., & Chan, W. C. W. (2020) · ACS Nano · doi:10.1021/acsnano.0c02624 · Cited by 1,803
Kucirka, L. M., Lauer, S. A., Laeyendecker, O. B., Boon, D., & Lessler, J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-1495 · Cited by 1,414
Çevik, M., Tate, M., Lloyd, O., Maraolo, A. E., Schafers, J., & Ho, A. Y. W. (2020) · The Lancet Microbe · doi:10.1016/s2666-5247(20)30172-5 · Cited by 1,334
Real-time RT-PCR in COVID-19 detection: issues affecting the results
Tahamtan, A., & Ardebili, A. (2020) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2020.1757437 · Cited by 1,218
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
Rahman, T., Khandakar, A. A., Qiblawey, Y., Tahir, A., Serkan Kiranyaz, B., Kashem, S. B. A., Islam, M. T., Maadeed, S. A., Zughaier, S. M., Khan, M. S., et al. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104319 · Cited by 1,153
Coronavirus disease 2019: What we know?
He, F., Deng, Y., & Li, W. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25766 · Cited by 1,066
Yang, Y., Shen, C., Li, J., Yuan, J., Wei, J., Huang, F., Wang, F., Li, G., Li, Y., Xing, L., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.04.027 · Cited by 872
Self-citationAntibody tests for identification of current and past infection with SARS‐CoV‐2
Geppert, J., Scandrett, K., Bigio, J., Sulis, G., Hettiarachchi, D., Mathangasinghe, Y., Weeratunga, P., Wickramasinghe, D., Bergman, H., Buckley, S. B., et al. (2022) · LSTM Online Archive (Liverpool School of Tropical Medicine) · Cited by 837
Covasim: An agent-based model of COVID-19 dynamics and interventions
Kerr, C. C., Stuart, R. M., Mistry, D., Abeysuriya, R., Rosenfeld, K., Hart, G. R., Núñez, R. C., Cohen, J. A., Selvaraj, P., Hagedorn, B., et al. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009149 · Cited by 729
Antibody tests for identification of current and past infection with SARS-CoV-2
Deeks, J. J., Dinnes, J., Takwoingi, Y., Davenport, C., Spijker, R., Taylor-Phillips, S., Adriano, A., Beese, S., Dretzke, J., di Ruffano, L. F., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013652 · Cited by 649
Liu, W., Liu, L., Kou, G., Zheng, Y., Ding, Y., Ni, W., Wang, Q., Tan, L., Wu, W., Tang, S., et al. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.00461-20 · Cited by 646
Self-citationChest CT Features of COVID-19 in Rome, Italy
Caruso, D., Zerunian, M., Polici, M., Pucciarelli, F., Polidori, T., Rucci, C., Guido, G., Bracci, B., Dominicis, C. D., & Laghi, A. (2020) · Radiology · doi:10.1148/radiol.2020201237 · Cited by 614
Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587
Roy, S., Menapace, W., Oei, S. P., Luijten, B., Fini, E., Saltori, C., Huijben, I. A. M., Chennakeshava, N., Mento, F., Sentelli, A., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2994459 · Cited by 597
Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection
Dinnes, J., Deeks, J. J., Berhane, S., Taylor, M., Adriano, A., Davenport, C., Dittrich, S., Emperador, D., Takwoingi, Y., Cunningham, J., et al. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013705.pub2 · Cited by 591
The genetic sequence, origin, and diagnosis of SARS-CoV-2
Wang, H., Li, X., Li, T., Zhang, S., Wang, L., Wu, X. Z., & Liu, J. (2020) · European Journal of Clinical Microbiology & Infectious Diseases · doi:10.1007/s10096-020-03899-4 · Cited by 502
Torrente-Rodríguez, R. M., Lukas, H., Tu, J., Min, J., Yang, Y., Xu, C., Rossiter, H. B., & Gao, W. F. (2020) · Matter · doi:10.1016/j.matt.2020.09.027 · Cited by 489
Self-citationSerology characteristics of SARS-CoV-2 infection after exposure and post-symptom onset
Lou, B., Li, T., Zheng, S., Su, Y., Li, Z., Liu, W., Yu, F., Ge, S., Zou, Q., Yuan, Q., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00763-2020 · Cited by 494
Tali, S. H. S., LeBlanc, J. J., Sadiq, Z., Oyewunmi, O. D., Camargo, C., Nikpour, B., Armanfard, N., Sagan, S. M., & Jahanshahi‐Anbuhi, S. (2021) · Clinical Microbiology Reviews · doi:10.1128/cmr.00228-20 · Cited by 430
COVID-19 diagnostics in context
Weissleder, R., Lee, H., Ko, J., & Pittet, M. J. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.abc1931 · Cited by 415
Nagura‐Ikeda, M., Imai, K., Tabata, S., Miyoshi, K., Murahara, N., Mizuno, T., Horiuchi, M., Kato, K., Imoto, Y., Iwata, M., et al. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.01438-20 · Cited by 396
Ravi, N., Cortade, D. L., Ng, E., & Wang, S. X. (2020) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112454 · Cited by 375
Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study
Zhao, Y., Cheng, S., Yu, X., & Xu, H. (2020) · Journal of Medical Internet Research · doi:10.2196/18825 · Cited by 366
Meng, Y., Wu, P., Lu, W., Liu, K., Ma, K., Huang, L., Cai, J., Zhang, H., Yu, Q., Sun, H., et al. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008520 · Cited by 342
COVID-19 in early 2021: current status and looking forward
Wang, C., Wang, Z., Wang, G., Lau, J. Y., Zhang, K., & Li, W. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00527-1 · Cited by 358
Self-citationKorth, J., Wilde, B., Dolff, S., Anastasiou, O. E., Krawczyk, A., Jahn, M., Cordes, S., Ross, B., Eßer, S., Lindemann, M., et al. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104437 · Cited by 332
Caring for patients with cancer in the COVID-19 era
van de Haar, J., Hoes, L. R., Coles, C. E., Seamon, K. B., Fröhling, S., Jäger, D., Valenza, F., de Braud, F., Petris, L. D., Bergh, J. C. S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0874-8 · Cited by 335
Gunraj, H., Wang, L., & Wong, A. M. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.608525 · Cited by 347
Uncertainty and COVID-19: how are we to respond?
Koffman, J., Gross, J., Etkind, S. N., & Selman, L. (2020) · Journal of the Royal Society of Medicine · doi:10.1177/0141076820930665 · Cited by 305
“positive for COVID-19 and those who have not, particularly if they present in emergency departments, a concern as the COVID-19 nasopharyngeal swab has a relatively high false-negative rate.(11) If patients are falsely screened-negative they may be sent to a ‘clean ward’ environment where they are potentially subjectin…”
BackgroundPadoan, A., Cosma, C., Sciacovelli, L., Faggian, D., & Plebani, M. (2020) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2020-0443 · Cited by 288
Emerging Telemedicine Tools for Remote COVID-19 Diagnosis, Monitoring, and Management
Lukas, H., Xu, C., Yu, Y., & Gao, W. F. (2020) · ACS Nano · doi:10.1021/acsnano.0c08494 · Cited by 276
Elslande, J. V., Houben, E., Depypere, M., Brackenier, A., Desmet, S., André, E., Ranst, M. A. V., Lagrou, K., & Vermeersch, P. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.05.023 · Cited by 256
COVID-19 in older people: a rapid clinical review
Lithander, F. E., Neumann, S., Tenison, E., Lloyd, K., Welsh, T. J., Rodrigues, J. C. L., Higgins, J. P. T., Scourfield, L., Christensen, H., Haunton, V. J., et al. (2020) · Age and Ageing · doi:10.1093/ageing/afaa093 · Cited by 250
CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT
Dadkhah, S., Neto, E. C. P., Ferreira, R., Molokwu, R. C., Sadeghi, S., & Ghorbani, A. A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101351 · Cited by 335
Recent advances and challenges of RT-PCR tests for the diagnosis of COVID-19
Teymouri, M., Mollazadeh, S., Mortazavi, H., Ghale-Noie, Z. N., Keyvani, V., Aghababaei, F., Hamblin, M. R., Abbaszadeh‐Goudarzi, G., Pourghadamyari, H., Hashemian, S. M. R., et al. (2021) · Pathology - Research and Practice · doi:10.1016/j.prp.2021.153443 · Cited by 228
Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection
Dinnes, J., Sharma, P., Berhane, S., van Wyk, S. S., Nyaaba, N., Domen, J., Taylor, M., Cunningham, J. A., Davenport, C. F., Dittrich, S., et al. (2022) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013705.pub3 · Cited by 247
Effectiveness of COVID-19 diagnosis and management tools: A review
Alsharif, W. M., & Qurashi, A. A. (2020) · Radiography · doi:10.1016/j.radi.2020.09.010 · Cited by 242
Viral Dynamics and Real-Time RT-PCR Ct Values Correlation with Disease Severity in COVID-19
Rabaan, A. A., Tirupathi, R., Sule, A. A., Aldali, J., Mutair, A. S. A., Alhumaid, S., Muzaheed, M., Gupta, N. R., Koritala, T., Adhikari, R., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11061091 · Cited by 233
Nicol, T., Lefeuvre, C., Serri, O., Pivert, A., Joubaud, F., Dubée, V., Kouatchet, A., Ducancelle, A., Lunel, F., & Guillou‐Guillemette, H. L. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104511 · Cited by 218
Unveiling COVID-19 from CHEST X-ray with deep learning: A hurdles race with small data
Tartaglione, E., Barbano, C. A., Berzovini, C., Calandri, M., & Grangetto, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17186933 · Cited by 211
Oral Microbiome and SARS-CoV-2: Beware of Lung Co-infection
Bao, L., Zhang, C., Dong, J., Zhao, L., Li, Y., & Sun, J. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.01840 · Cited by 207
Infectious Diseases Society of America Guidelines on the Diagnosis of COVID-19 (June 2020)
Hanson, K. E., Caliendo, A. M., Arias, C. A. D., Englund, J. A., Lee, M. J., Loeb, M. B., Patel, R., Alayli, A. E., Kalot, M. A., Falck-Ytter, Y. T., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa760 · Cited by 213
Jamal, A. J., Mozafarihashjin, M., Coomes, E. A., Powis, J. E., Li, A. X., Paterson, A., Anceva-Sami, S., Barati, S., Crowl, G., Faheem, A., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa848 · Cited by 207
False Negative Rate of COVID-19 PCR Testing: A Discordant Testing Analysis
Kanji, J. N., Zelyas, N., MacDonald, C., Pabbaraju, K., Khan, M. N., Prasad, A., Hu, J., Diggle, M., Berenger, B. M., & Tipples, G. (2021) · Virology Journal · doi:10.1186/s12985-021-01489-0 · Cited by 207
Diagnosis of COVID-19 for controlling the pandemic: A review of the state-of-the-art
Taleghani, N., & Taghipour, F. (2020) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2020.112830 · Cited by 212
Antibody tests for identification of current and past infection with SARS-CoV-2
Fox, T., Geppert, J., Dinnes, J., Scandrett, K., Bigio, J., Sulis, G., Hettiarachchi, D., Mathangasinghe, Y., Weeratunga, P. N., Wickramasinghe, D., et al. (2022) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013652.pub2 · Cited by 205
Kojima, N., Turner, F., Slepnev, V. I., Bacelar, A. G., Deming, L., Kodeboyina, S., & Klausner, J. D. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa1589 · Cited by 196
The role of peripheral blood eosinophil counts in COVID‐19 patients
Xie, G., Ding, F., Han, L., Yin, D., Lu, H., & Zhang, M. (2020) · Allergy · doi:10.1111/all.14465 · Cited by 186
Self-citationMohanty, S. K., Satapathy, A., Naidu, M. M., Mukhopadhyay, S., Sharma, S., Barton, L. M., Stroberg, E., Duval, E. J., Pradhan, D., Tzankov, A., et al. (2020) · Diagnostic Pathology · doi:10.1186/s13000-020-01017-8 · Cited by 185
COVID-19 diagnosis by routine blood tests using machine learning
Kukar, M., Gunčar, G., Vovko, T., Podnar, S., Černelč, P., Brvar, M., Zalaznik, M., Notar, M., Moškon, S., & Notar, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-90265-9 · Cited by 179
Highly accurate and sensitive diagnostic detection of SARS-CoV-2 by digital PCR
Dong, L., Zhou, J., Niu, C., Wang, Q., Pan, Y., Sheng, S., Wang, X., Zhang, Y., Yang, J., Liu, M., et al. (2020) · Talanta · doi:10.1016/j.talanta.2020.121726 · Cited by 183
Mick, E., Kamm, J., Pisco, A. O., Ratnasiri, K., Babik, J. M., Castañeda, G. R., DeRisi, J. L., Detweiler, A. M., Hao, S. L., Kangelaris, K. N., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19587-y · Cited by 176
Otolaryngological manifestations of hospitalised patients with confirmed COVID-19 infection
Korkmaz, M. Ö., Eğilmez, O. K., Özçelik, M. A., & Güven, M. (2020) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-020-06396-8 · Cited by 186
Mostafa, H. H., Fissel, J. A., Fanelli, B. C., Bergman, Y., Gniazdowski, V., Dadlani, M., Carroll, K. C., Colwell, R. R., & Simner, P. J. (2020) · mBio · doi:10.1128/mbio.01969-20 · Cited by 165
Mallett, S., Allen, A. J., Graziadio, S., Taylor, S. A., Sakai, N. S., Green, K., Suklan, J., Hyde, C., Shinkins, B., Zhelev, Z., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01810-8 · Cited by 175
Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people
Hopkinson, N. S., Rossi, N., Moustafa, J. S. E.-S., Laverty, A. A., Quint, J. K., Freidin, M. B., Visconti, A., Murray, B. A. K., Marc Modat, A., Sebastien Ourselin, A., et al. (2021) · Thorax · doi:10.1136/thoraxjnl-2020-216422 · Cited by 162
Laboratory diagnosis of COVID-19
Goudouris, E. (2020) · Jornal de Pediatria · doi:10.1016/j.jped.2020.08.001 · Cited by 165
Estimating the false-negative test probability of SARS-CoV-2 by RT-PCR
Wikramaratna, P. S., Paton, R. S., Ghafari, M., & Lourenço, J. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.50.2000568 · Cited by 313
Review of Current COVID-19 Diagnostics and Opportunities for Further Development
Mardian, Y., Kosasih, H., Karyana, M., Neal, A., & Lau, C. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.615099 · Cited by 153
SARS-CoV-2 detection in different respiratory sites: A systematic review and meta-analysis
Mohammadi, A., Esmaeilzadeh, E., Li, Y., Bosch, R. J., & Li, J. Z. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102903 · Cited by 144
Self-citationRole of computed tomography in COVID-19
Pontone, G., Scafuri, S., Mancini, M. E., Agalbato, C., Guglielmo, M., Baggiano, A., Muscogiuri, G., Fusini, L., Andreini, D., Mushtaq, S., et al. (2020) · Journal of cardiovascular computed tomography · doi:10.1016/j.jcct.2020.08.013 · Cited by 139
Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning
Lopez‐Rincon, A., Tonda, A. P., Mendoza‐Maldonado, L., Mulders, D. G. J. C., Molenkamp, R., Perez-Romero, C. A., Claassen, E., Garssen, J., & Kraneveld, A. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-80363-5 · Cited by 137
SARS-CoV-2 and the COVID-19 disease: a mini review on diagnostic methods
Oliveira, B. A., de Oliveira, L. C., Sabino, É. C., & Okay, T. S. (2020) · Revista do Instituto de Medicina Tropical de São Paulo · doi:10.1590/s1678-9946202062044 · Cited by 128
COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia
Liu, M., Zeng, W., Wen, Y. D., Zheng, Y., Lv, F., & Xiao, K. (2020) · European Radiology · doi:10.1007/s00330-020-06928-0 · Cited by 122
Young, S. A., Taylor, S. N., Cammarata, C. L., Varnado, K. G., Roger-Dalbert, C., Montano, A., Griego-Fullbright, C., Burgard, C., Fernandez, C., Eckert, K., et al. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.02338-20 · Cited by 122
John, G., Sahajpal, N., Mondal, A. K., Ananth, S., Williams, C., Chaubey, A., Rojiani, A. M., & Kolhe, R. B. (2021) · Current Issues in Molecular Biology · doi:10.3390/cimb43020061 · Cited by 131
CT features of novel coronavirus pneumonia (COVID-19) in children
Duan, Y.-N., Zhu, Y.-Q., Tang, L.-L., & Qin, J. (2020) · European Radiology · doi:10.1007/s00330-020-06860-3 · Cited by 119
A Comprehensive Review of Detection Methods for SARS-CoV-2
Eftekhari, A., Alipour, M., Chodari, L., Dizaj, S. M., Ardalan, M., Samiei, M., Sharifi, S., Vahed, S. Z., Huseynova, I. M., Khalilov, R. I., et al. (2021) · Microorganisms · doi:10.3390/microorganisms9020232 · Cited by 126
COVID-19 infection map generation and detection from chest X-ray images
Degerli, A., Ahishali, M., Yamaç, M., Serkan Kiranyaz, B., Chowdhury, M. E. H., Hameed, K., Hamid, T., Mazhar, R., & Gabbouj, M. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00146-8 · Cited by 120
Comparing the analytical performance of three SARS-CoV-2 molecular diagnostic assays
Uhteg, K., Jarrett, J., Richards, M., Howard, C., Morehead, E., Geahr, M., Gluck, L., Hanlon, A., Ellis, B. C., Kaur, H. B., et al. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104384 · Cited by 112
Estimate False-Negative RT-PCR Rates for SARS-CoV-2: A Systematic Review and Meta-Analysis
Pecoraro, V., Negro, A., Pirotti, T., & Trenti, T. (2021) · European Journal of Clinical Investigation · doi:10.1111/eci.13706 · Cited by 117
Neilan, A. M., Losina, E., Bangs, A. C., Flanagan, C. F., Panella, C., Eskibozkurt, G. E., Mohareb, A. M., Hyle, E. P., Scott, J. A., Weinstein, M. C., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa1418 · Cited by 110
Utilisation of deep learning for COVID-19 diagnosis
Aslani, S., & Jacob, J. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.11.006 · Cited by 119
Yang, Y., Yang, M., Peng, Y., Liang, Y., Wei, J., Xing, L., Guo, L., Li, X., Li, J., Wang, J., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-021-01051-2 · Cited by 111
Self-citationCOVID-19: A challenge for electrochemical biosensors
Kudr, J., Michálek, P., Ilieva, L., Adam, V., & Zítka, O. (2021) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2021.116192 · Cited by 112
Reddy, K. P., Shebl, F. M., Foote, J. H. A., Harling, G., Scott, J. A., Panella, C., Fitzmaurice, K. P., Flanagan, C. F., Hyle, E. P., Neilan, A. M., et al. (2020) · The Lancet Global Health · doi:10.1016/s2214-109x(20)30452-6 · Cited by 107
Clustered cases of acral perniosis: Clinical features, histopathology, and relationship to COVID‐19
Cordoro, K. M., Reynolds, S. D., Wattier, R. L., & McCalmont, T. H. (2020) · Pediatric Dermatology · doi:10.1111/pde.14227 · Cited by 106
Humoral Immune Responses in COVID-19 Patients: A Window on the State of the Art
Siracusano, G., Pastori, C., & Lopalco, L. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01049 · Cited by 110
Wee, S. K., Sivalingam, S. P., & Yap, P. H. (2020) · Genes · doi:10.3390/genes11060664 · Cited by 108
Gunraj, H., Sabri, A., Koff, D., & Wong, A. M. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.729287 · Cited by 116
Singh, N. K., Ray, P., Carlin, A. F., Magallanes, C. G., Morgan, S., Laurent, L. C., Aronoff-Spencer, E. S., & Hall, D. A. (2020) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113111 · Cited by 105
Detection of serum IgM and IgG for COVID-19 diagnosis
Zhong, L., Chuan, J., Gong, B., Shuai, P., Zhou, Y., Zhang, Y., Jiang, Z., Zhang, D., Liu, X., Ma, S., et al. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1688-9 · Cited by 100
Self-citationCOVID-2019: update on epidemiology, disease spread and management
Sahu, K. K., Mishra, A. K., & Lal, A. (2020) · Monaldi Archives for Chest Disease · doi:10.4081/monaldi.2020.1292 · Cited by 100
Lammers, M. J. W., Lea, J., & Westerberg, B. D. (2020) · Journal of Otolaryngology - Head and Neck Surgery · doi:10.1186/s40463-020-00429-2 · Cited by 97
National French Survey of Coronavirus Disease (COVID-19) Symptoms in People Aged 70 and Over
Annweiler, C., Sacco, G., Salles, N., Aquino, J.-P., Gautier, J., Berrut, G., Guérin, O., & Gavazzi, G. (2021) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa792 · Cited by 95
Inequality of fear and self-quarantine: Is there a trade-off between GDP and public health?
Aum, S., Lee, S. Y., & Shin, Y. (2021) · Journal of Public Economics · doi:10.1016/j.jpubeco.2020.104354 · Cited by 100
Kinloch, N. N., Ritchie, G., Brumme, C. J., Dong, W. W. Y., Dong, W., Lawson, T., Jones, R. B., Montaner, J. S. G., Leung, V. C. M., Romney, M. G., et al. (2020) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiaa370 · Cited by 94
Resource planning strategies for healthcare systems during a pandemic
Fattahi, M., Keyvanshokooh, E., Kannan, D., & Govindan, K. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.01.023 · Cited by 94
Kashefi‐Kheyrabadi, L., Nguyen, H. V., Go, A., Baek, C., Jang, N., Lee, J. M., Cho, N., Min, J., & Lee, M. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113649 · Cited by 92
LeBlanc, J. J., Heinstein, C., MacDonald, J., Pettipas, J., Hatchette, T. F., & Patriquin, G. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104442 · Cited by 91
Zhang, Z., Bi, Q., Fang, S., Wei, L., Wang, X., He, J., Wu, Y., Liu, X., Gao, W., Zhang, R., et al. (2021) · The Lancet Microbe · doi:10.1016/s2666-5247(20)30200-7 · Cited by 95
Self-citationLu, F., Nguyen, A. T., Link, N., Molina, M., Davis, J. T., Chinazzi, M., Xiong, X., Vespignani, A., Lipsitch, M., & Santillana, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008994 · Cited by 82
Kundu, R., Singh, P. K., Ferrara, M., Ahmadian, A., & Sarkar, R. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11319-8 · Cited by 88
Lu, F. S., Nguyen, A. T., Link, N. B., Davis, J. T., Chinazzi, M., Xiong, X., Vespignani, A., Lipsitch, M., & Santillana, M. (2020) · doi:10.1101/2020.04.18.20070821 · Cited by 87
“There are varying estimates for the false negative rate for the RT-PCR used in COVID-19 tests, with some reports suggesting rates as high as 2530% [54, 55].”
COVID-19 classification of X-ray images using deep neural networks
Keidar, D., Yaron, D., Goldstein, E., Shachar, Y., Blass, A., Charbinsky, L., Aharony, I., Lifshitz, L., Lumelsky, D., Neeman, Z., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08050-1 · Cited by 86
Li, Y., Hu, Y., Yu, Y., Zhang, X., Li, B., Wu, J., Li, J., Wu, Y., Xia, X., Tang, H., et al. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25905 · Cited by 82
Self-citationValidation of a chemiluminescent assay for specific SARS-CoV-2 antibody
Tré‐Hardy, M., Wilmet, A., Beukinga, I., Dogné, J., Douxfils, J., & Blairon, L. (2020) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2020-0594 · Cited by 82
Design of a Low-Cost Miniature Robot to Assist the COVID-19 Nasopharyngeal Swab Sampling
Wang, S., Wang, K., Tang, R., Qiao, J., Liu, H., & Hou, Z. (2020) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2020.3036461 · Cited by 82
Metadata
- Authors
- Yang, Y., Yang, M., Shen, C., Wang, F., Yuan, J., Li, J., Zhang, M., Wang, Z., Xing, L., Wei, J., Peng, L., Wong, G., Zheng, H., Wu, W., Liao, M., Feng, K., Li, J., Yang, Q., Zhao, J., Zhang, Z., Liu, L., & Liu, Y.
- Affiliations
- Southern University of Science and Technology (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Zhang, Zheng
- ORCID
- Venue
- The Innovation
- Publisher
- openRxiv
- Published
- 2020-02-12
- Volume
- 1
- Issue
- 3
- Pages
- 100061-100061
- ISSN
- 2666-6758 (ISSN-L)
- PMC
- PMC7609236
- OpenAlex
- W3107529852
- Cited by
- 836
- References
- 36
- Citations by year
- 2026: 2 · 2025: 18 · 2024: 23 · 2023: 62 · 2022: 114 · 2021: 246 · 2020: 370
- FWCI
- 25.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- The Innovation (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2020YFC0846300)
- China Postdoctoral Science Foundation (2019M660836, 2019T120147)
- National Science and Technology Major Project (2017ZX10204401, 2018ZX10711-001, 2017ZX10103011)
- Sanming Project of Medicine in Shenzhen (SZSM201512005, SZSM201412003)
- National Major Science and Technology Projects of China (2018ZX10711001, 2017ZX10103011)
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- SARS-CoV-2, COVID-19 pandemic, viral shedding, sputum, nasal swabs, bronchoalveolar lavage fluid, viral RNA detection, diagnostic accuracy, disease severity, ground-glass opacities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
