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Background We aim to investigate the profile of acute antibody response in COVID-19 patients, and provide proposals for the usage of antibody test in clinical practice.Methods A multi-center cross-section study (285 patients) and a single-center follow-up study (63 patients) were performed to investigate the feature of acute antibody response to SARS-CoV-2. A cohort of 52 COVID-19 suspects and 64 close contacts were enrolled to evaluate the potentiality of the antibody test. ResultsThe positive rate for IgG reached 100% around 20 days after symptoms onset.The median day of seroconversion for both lgG and IgM was 13 days after symptoms onset. Seroconversion of IgM occurred at the same time, or earlier, or later than that of IgG. IgG levels in 100% patients (19/19) entered a platform within 6 days after seroconversion. The criteria of "IgG seroconversion" and "≥ 4-fold increase in the IgG titers in sequential samples" together diagnosed 82.9% (34/41) of the patients.Antibody test aided to confirm 4 patients with COVID-19 from 52 suspects who failed to be confirmed by RT-PCR and 7 patients from 148 close contacts with negative RT-PCR. ConclusionIgM and IgG should be detected simultaneously at the early phase of infection. The serological diagnosis criterion of seroconversion or the "≥ 4-fold increase in the IgG titer" is suitable for a majority of COVID-19 patients. Serologic test is helpful for the diagnosis of SARS-CoV-2 infection in suspects and close contacts.

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Recently, COVID-19 caused by the novel coronavirus SARS-CoV-2 has brought great challenges to the world. More and more studies have shown that severe patients may suffer from cytokine storm syndrome; however, there are few studies on its pathogenesis. Here we demonstrated that SARS-CoV-2 coding protein open reading frame 8 (ORF8) acted as a contributing factor to cytokine storm during COVID-19 infection. ORF8 could activate IL-17 signaling pathway and promote the expression of pro-inflammatory factors. Moreover, we demonstrated that treatment of IL17RA antibody protected mice from ORF8-induced inflammation. Our findings are helpful to understand the pathogenesis of cytokine storm caused by SARS-CoV-2, and provide a potential target for the development of COVID-19 therapeutic drugs.

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Discovering causal relationships from observational data is a crucial problem and it has applications in many research areas. The PC algorithm is the state-of-the-art constraint based method for causal discovery. However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e.g. gene expression datasets. On another note, the advancement of computer hardware in the last decade has resulted in the widespread availability of multi-core personal computers. There is a significant motivation for designing a parallelised PC algorithm that is suitable for personal computers and does not require end users' parallel computing knowledge beyond their competency in using the PC algorithm. In this paper, we develop parallel-PC, a fast and memory efficient PC algorithm using the parallel computing technique. We apply our method to a range of synthetic and real-world high dimensional datasets. Experimental results on a dataset from the DREAM 5 challenge show that the original PC algorithm could not produce any results after running more than 24 hours; meanwhile, our parallel-PC algorithm managed to finish within around 12 hours with a 4-core CPU computer, and less than 6 hours with a 8-core CPU computer. Furthermore, we integrate parallel-PC into a causal inference method for inferring miRNA-mRNA regulatory relationships. The experimental results show that parallel-PC helps improve both the efficiency and accuracy of the causal inference algorithm.

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