An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.
The rapidly progressing of coronavirus disease 2019 (COVID-19) pandemic has become a global concern. This meta-analysis aimed at evaluating the efficacy and safety of current option of therapies for severe acute respiratory syndrome (SARS), Middle Eastern respiratory syndrome (MERS) besides COVID-19, in an attempt to identify promising therapy for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients. We searched PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and WANFANG DATA for randomized controlled trials (RCTs), prospective cohort, and retrospective cohort studies that evaluated therapies (hydroxychloroquine, lopinavir/ ritonavir-based therapy, and ribavirin-based therapy, etc.) for SARS, MERS, and COVID-19. The primary outcomes were mortality, virological eradication and clinical improvement, and secondary outcomes were improvement of symptoms and chest radiography results, incidence of acute respiratory disease syndrome (ARDS), utilization of mechanical ventilation, and adverse events (AEs). Summary relative risks (RRs) and 95% confidence intervals (CIs) were calculated using random-effects models, and the quality of evidence was appraised using GRADEpro. Eighteen articles (5 RCTs, 2 prospective cohort studies, and 11 retrospective cohort studies) involving 4,941 patients were included. Compared with control treatment, anti-coronary virus interventions significantly reduced mortality (RR 0.65, 95% CI 0.44-0.96; I 2 = 81.3%), remarkably ameliorate clinical improvement (RR 1.52, 95% CI 1.05-2.19) and radiographical improvement (RR 1.62, 95% CI 1.11-2.36, I 2 = 11.0 %), without manifesting clear effect on virological eradication, incidence of ARDS, intubation, and AEs. Subgroup analyses demonstrated that the combination of ribavirin and corticosteroids remarkably decreased mortality (RR 0.43, 95% CI 0.27-0.68). The lopinavir/ritonavir-based combination showed superior virological eradication and radiographical improvement with reduced rate of ARDS. Likewise, hydroxychloroquine improved radiographical result. For safety, ribavirin could induce more bradycardia, anemia and transaminitis. Meanwhile, hydroxychloroquine could increase AEs rate especially diarrhea. Overall, the quality of evidence on most outcomes were very low. In conclusion, although we could not draw a clear conclusion for the recommendation of potential therapies for COVID-19 considering the very low quality of evidence and wide heterogeneity of interventions and indications, our results may help clinicians to comprehensively understand the advantages and drawbacks of each anti-coronavirus agents on efficacy and safety profiles. Lopinavir/ritonavir combinations might observe better virological eradication capability than other anti-coronavirus agents. Conversely, ribavirin might cause more safety concerns especially bradycardia. Thus, large RCTs objectively assessing the efficacy of antiviral therapies for SARS-CoV-2 infections should...
Coronavirus disease 2019 (COVID-19) has become a public health emergency. The reverse transcriptase real-time quantitative PCR (qRT-PCR) test is currently considered as the gold standard in the laboratory for the etiological detection of COVID-19. However, qRT-PCR results could be false-negative due to the inadequate sensitivity of qRT-PCR. In this study, we have developed and evaluated a novel one-step single-tube nested quantitative real-time PCR (OSN-qRT-PCR) assay for the highly sensitive detection of SARS-CoV-2 targeting the ORF1ab and N genes. The sensitivity of the OSN-qRT-PCR assay was 1 copy/ reaction and 10-fold higher than that of the commercial qRT-PCR kit (10 copies/reaction). The clinical performance of the OSN-qRT-PCR assay was evaluated using 181 clinical samples. Among them, 14 qRT-PCR-negative samples (7 had no repetitive results and 7 had no cycle threshold (CT) values) were detected by OSN-qRT-PCR. Moreover, the 7 qRT-PCR-positives in the qRT-PCR gray zone (CT values of ORF1ab ranged from 37.48 to 39.07, and CT values of N ranged from 37.34 to 38.75) were out of the gray zone and thus were deemed to be positive by OSN-qRT-PCR, indicating that the positivity of these samples is confirmative. Compared to the qRT-PCR kit, the OSN-qRT-PCR assay revealed higher sensitivity and specificity, showing better suitability to clinical applications for the detection of SARS-CoV-2 in patients with low viral load.
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