Performance comparison of two nucleic acid amplification systems for SARS-CoV-2 detection: A multi-center study.

BACKGROUND: Many rapid nucleic acid testing systems have emerged to halt the development and spread of COVID-19. However, so far relatively few studies have compared the diagnostic performance between these testing systems and conventional detection systems. Here, we performed a retrospective analysis to evaluate the clinical detection performance between SARS-CoV-2 rapid and conventional nucleic acid detection system. METHODS: Clinical detection results of 63,352 oropharyngeal swabs by both systems were finally enrolled in this analysis. Sensitivity (SE), specificity (SP), and positive and negative predictive value (PPV, NPV) of both systems were calculated to evaluate their diagnostic accuracy. Concordance between these two systems were assessed by overall, positive, negative percent agreement (OPA, PPA, NPA) and κ value. Sensitivity of SARS-CoV-2 rapid nucleic acid detection system (Daan Gene) was further analyzed with respect to the viral load of clinical specimens. RESULTS: Sensitivity of Daan Gene was slightly lower than that of conventional detection system (0.86 vs. 0.979), but their specificity was equivalent. Daan Gene had ≥98.0% PPV and NPV for SARS-CoV-2. Moreover, Daan Gene demonstrated an excellent test agreement with conventional detection system (κ = 0.893, p = 0.000). Daan Gene was 99.31% sensitivity for specimens with high viral load (Ct < 35) and 50% for low viral load (Ct ≥ 35). CONCLUSIONS: While showing an analytical sensitivity slightly below than that of conventional detection system, rapid nucleic acid detection system may be a diagnostic alternative to rapidly identify SARS-CoV-2-infected individuals with high viral loads and a powerful complement to current detection methods.

EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis (preprint)

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging. The remote experiment during the pandemic yields several challenges, and we discuss the possible solutions. This paper explores machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. The results show that Random Forest and RBF SVM perform well for EEG classification tasks. Furthermore, we investigate how to conduct such BCI experiments using affordable consumer-grade devices to collect EEG-based BCI data. In addition, we have developed the data collection protocol, EEG4Students, that grants non-experts who are interested in a guideline for such data collection. Our code and data can be found at https://github.com/GuangyaoDou/EEG4Students.

BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students (preprint)

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging than before. This paper explored machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. Also, we investigated a way to conduct such BCI experiments remotely via Zoom. The results showed that Random Forest and RBF SVM performed well for EEG classification tasks. The remote experiment during the pandemic yielded several challenges, and we discussed the possible solutions; nevertheless, we developed a protocol that grants non-experts who are interested a guideline for such data collection.

Preliminary evaluation of a new initiative to centralize colorectal cancer care during the COVID-19 epidemic in Shanghai, China: a retrospective study

Background A novel colorectal cancer center (CCC) was developed in the Shanghai Tenth People’s hospital of Tongji University during the COVID-19 epidemic. In this study, we aimed to evaluate the CCC model in terms of three aspects. Methods This retrospective study used data from the Shanghai Tenth People’s hospital patient databases. The research hypothesis was that the CCC reduces preoperative waiting time (PWT), length of hospital stay (LOS), and costs of hospitalization, without reducing the quality of surgery. Thus, we compared the time, cost, and quality between March 1 to December 31, 2019, and March 1 to December 31, 2020. Descriptive and inferential analyses of patient demographic characteristics, time, postoperative outcomes, and inpatient costs were conducted. Results A total of 965 hospitalizations for colorectal cancer (CRC) were identified—415 in 2019 and 550 in 2020. In the CCC, PWT declined by 26.2 hours (P<0.01). Patients in the CCC express group only needed to wait for 24.5 hours before undergoing surgery, with a shorter LOS than the normal group (P<0.01). None of the patients had any symptoms of COVID-19 or were high-risk COVID-19 contacts, and the incidence of immediate postoperative complications was low. The mean total inpatient cost (TIC) for all patients with CRC was 78,309.824 Chinese Yuan in 2020, which was slightly lower than that in 2019. Conclusions This study found that the centralized management model for CRC care could help patients save the PWT, LOS and costs of hospitalization during the COVID-19 epidemic.

SARS-CoV-2 nucleocapsid protein impairs stress granule formation to promote viral replication.

The newly emerging coronavirus SARS-CoV-2 causes severe lung disease and substantial mortality. How the virus evades host defense for efficient replication is not fully understood. In this report, we found that the SARS-CoV-2 nucleocapsid protein (NP) impaired stress granule (SG) formation induced by viral RNA. SARS-CoV-2 NP associated with the protein kinase PKR after dsRNA stimulation. SARS-CoV-2 NP did not affect dsRNA-induced PKR oligomerization, but impaired dsRNA-induced PKR phosphorylation (a hallmark of its activation) as well as SG formation. SARS-CoV-2 NP also targeted the SG-nucleating protein G3BP1 and impaired G3BP1-mediated SG formation. Deficiency of PKR or G3BP1 impaired dsRNA-triggered SG formation and increased SARS-CoV-2 replication. The NP of SARS-CoV also targeted both PKR and G3BP1 to impair dsRNA-induced SG formation, whereas the NP of MERS-CoV targeted PKR, but not G3BP1 for the impairment. Our findings suggest that SARS-CoV-2 NP promotes viral replication by impairing formation of antiviral SGs, and reveal a conserved mechanism on evasion of host antiviral responses by highly pathogenic human betacoronaviruses.

SARS-CoV-2 membrane glycoprotein M antagonizes the MAVS-mediated innate antiviral response.

A novel SARS-related coronavirus (SARS-CoV-2) has recently emerged as a serious pathogen that causes high morbidity and substantial mortality. However, the mechanisms by which SARS-CoV-2 evades host immunity remain poorly understood. Here, we identified SARS-CoV-2 membrane glycoprotein M as a negative regulator of the innate immune response. We found that the M protein interacted with the central adaptor protein MAVS in the innate immune response pathways. This interaction impaired MAVS aggregation and its recruitment of downstream TRAF3, TBK1, and IRF3, leading to attenuation of the innate antiviral response. Our findings reveal a mechanism by which SARS-CoV-2 evades the innate immune response and suggest that the M protein of SARS-CoV-2 is a potential target for the development of SARS-CoV-2 interventions.

Body mass index associated with severity and mortality of patients with coronavirus disease 2019: A systematic review and meta-analysis (preprint)

Background As an important indicator to measure obesity or underweight, body mass index (BMI) can be used to assess the potential risk for various diseases. The present study systematically examined the relationship between BMI and severity and mortality of patients with coronavirus disease 2019 (COVID-19).Methods We systematically searched PubMed, Embase, Cochrane, and China National Knowledge Infrastructure (CNKI) for studies published as of September 3, 2020 and extracted the relevant data of research endpoints in each study.Results This study included 16 studies with 6087 patients. This study observed a significant increase in BMI on admission in patients with severe COVID-19 compared with those with non-severe COVID-19 (Mean difference [MD] = 1.95, 95% confidence interval [CI], 1.52 − 2.37, I2 = 33%, P < 0.00001). A significant increase in BMI on admission was observed in patients who died from COVID-19 compared with (MD = 3.01, 95% CI: 1.83 to 4.19, I2 = 0%, P < 0.00001). In the intensive care unit (ICU) or geriatric ward, the study observed a significant decrease in BMI in the non-survivor group compared with the survivor group (MD = -1.61, 95% CI: -3.07 to -0.16, I2 = 72%, P = 0.03).Conclusions Higher BMI on admission is associated with severity and mortality of patients with COVID-19, but lower BMI is associated with mortality of patients with COVID-19 in the ICU or geriatric ward. Thus, we strongly recommend that clinicians should closely monitor the BMI of patients with COVID-19, especially those from the ICU or geriatric ward.

Early-stage predictive markers of critically ill COVID-19 cases (preprint)

Background: Severe cytokine storm syndrome (CSS) is considered as the cause of death among critically ill COVID-19 cases. Early identification of the high-risk severe cases is crucial to lower the fatality and healthcare costs.Methods: In this study, we retrospectively analyzed the first and second-week serum levels of IL-6, IL-8, and IL-10 of 50 COVID-19 cases. We calculated the ratios of IL-6/IL-10 and IL-8/IL-10 at 3rd, 6th, 9th, and 12th days of hospitalization. Results: We collected 50 COVID-19 cases (male 54%, mean age 51.2, range 18 - 86), including 39 mild cases (78%), 7 severe/recovered cases (14%), and 4 died cases (8%).The ratios of IL 6/IL-10 and IL-8/IL-10 among mild cases were below 27 (the highest, 26.9) along the 4 testing points of two week hospitalization, while we found that the IL-6/IL-10 and IL-8/IL-10 ratios were as high as 187.51 and 225.3 respectively in the death group on 3rd day with the highest IL-6/IL-10 ratio of 297.28 on the 6th day of hospitalization. Conclusions: Our preliminary results suggest that the ratios of IL-6/IL-10 and IL-8/IL-10 at the early stage (the first two weeks) of COVID-19 could be a predictive marker for the disease prognosis, of which the cut-off lines were suggested below 50 for a mild and recoverable severe cases.

Computational analysis suggests putative intermediate animal hosts of the SARS-CoV-2 (preprint)

The recent emerged SARS-CoV-2 may first transmit to intermediate animal host from bats before the spread to humans. The receptor recognition of ACE2 protein by SARS-CoVs or bat-originated coronaviruses is one of the most important determinant factors for the cross-species transmission and human-to-human transmission. To explore the hypothesis of possible intermediate animal host, we employed molecular dynamics simulation and free energy calculation to examine the binding of bat coronavirus with ACE2 proteins of 47 representing animal species collected from public databases. Our results suggest that intermediate animal host may exist for the zoonotic transmission of SARS-CoV-2. Furthermore, we found that tree shrew and ferret may be two putative intermediate hosts for the zoonotic spread of SARS-CoV-2. Collectively, the continuous surveillance of pneumonia in human and suspicious animal hosts are crucial to control the zoonotic transmission events caused by SARS-CoV-2.

Antibody responses to SARS-CoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice (preprint)

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. Results The positive rate for IgG reached 100% around 20 days after symptoms onset. The median day of serocon-version 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. Conclusion IgM and IgG should be detected simultaneously at the early phase of infection. The serological diagnosis criterion of seroconversion or [≥] 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.