Research progress in laboratory detection of SARS-CoV-2.

BACKGROUND: Nucleic acid testing is a reliable method for diagnosing viral infection in clinical samples. However, when the number of cases is huge and there are individual differences in the virus itself, the probability of false-negative results increases. With the advancement in research on the new coronavirus, new detection technologies that use serum-specific antibodies as detection targets have been developed. These detection technologies have high efficiency and shorter turnaround time, which ultimately shortens the time required for diagnosis. This article summarizes the methods that have been reported to date for the detection of the new coronavirus and discusses their principles and technical characteristics. AIMS: Compare the advantages and disadvantages of various SARS-CoV-2 detection methods and analyze their principles. METHODS: Searched reports on SARS-CoV-2 detection methods published so far, extracted the data and analyzed them. Use the primer blast function of NCBI to analyze the primers used in qRT-PCR detection. RESULTS: The detection sensitivity was the highest when nucleocapsid protein gene was used as the target, reaching 96.6%. The detection efficiency of the remaining targets ranged from 66.7% to 96.0%. Various new detection methods, like Serum specific antibody detection, can speed up the test time. However, due to the complexity of the method and higher testing requirements, it seems that it cannot be used as a complete replacement for qRT-PRC testing. CONCLUSIONS: With the advancement of technology and the improvement of methods, the detection methods of SARSCoV-2 have become more mature. These advances provided great help to the detection of SARS-CoV-2.

Prevalence of comorbidities and its effects in coronavirus disease 2019 patients: A systematic review and meta-analysis

A review. An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, China;the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries. The aim of this meta-anal. was to assess the prevalence of comorbidities in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients and the risk of underlying diseases in severe patients compared to non-severe patients. A literature search was conducted using the databases PubMed, EMBASE, and Web of Science through Feb. 25, 2020. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random-effects models. Seven studies were included in the meta-anal., including 1 576 infected patients. The results showed the most prevalent clin. symptom was fever (91.3%, 95% CI: 86-97%), followed by cough (67.7%, 95% CI: 59-76%), fatigue (51.0%, 95% CI: 34-68%) and dyspnea (30.4%, 95% CI: 21-40%). The most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0-27.2%) and diabetes (9.7%, 95% CI: 7.2-12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8-13.8%) and respiratory system disease (1.5%, 95% CI: 0.9-2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46-3.83), 2.46 (95% CI: 1.76-3.44) and 3.42 (95% CI: 1.88-6.22) resp. We assessed the prevalence of comorbidities in the COVID-19 patients and found that underlying disease, including hypertension, respiratory system disease and cardiovascular disease, may be risk factors for severe patients compared with non-severe patients.

Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis.

BACKGROUND: An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, China; the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries. AIMS: The aim of this meta-analysis was to assess the prevalence of comorbidities in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients and the risk of underlying diseases in severe patients compared to non-severe patients. METHODS: A literature search was conducted using the databases PubMed, EMBASE, and Web of Science through February 25, 2020. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random-effects models. RESULTS: Seven studies were included in the meta-analysis, including 1 576 infected patients. The results showed the most prevalent clinical symptom was fever (91.3%, 95% CI: 86-97%), followed by cough (67.7%, 95% CI: 59-76%), fatigue (51.0%, 95% CI: 34-68%) and dyspnea (30.4%, 95% CI: 21-40%). The most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0-27.2%) and diabetes (9.7%, 95% CI: 7.2-12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8-13.8%) and respiratory system disease (1.5%, 95% CI: 0.9-2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46-3.83), 2.46 (95% CI: 1.76-3.44) and 3.42 (95% CI: 1.88-6.22) respectively. CONCLUSION: We assessed the prevalence of comorbidities in the COVID-19 patients and found that underlying disease, including hypertension, respiratory system disease and cardiovascular disease, may be risk factors for severe patients compared with non-severe patients.