Initial mean arterial blood pressure (MABP) measurement is a risk factor for mortality in hypertensive COVID-19 positive hospitalized patients.

BACKGROUND: Hypertension (HTN) is associated with severe COVID-19 infection; however, it remains unknown if the level of blood pressure (BP) predicts mortality. We tested whether the initial BP in the emergency department of hospitalized patients portends mortality in COVID-19 positive(+) patients. METHODS: Data from COVID-19(+) and negative (-) hospitalized patients at Stony Brook University Hospital from March to July 2020 were included. The initial mean arterial BPs (MABPs) were categorized into tertiles (T) of MABP (65-85 [T1], 86-97 [T2] and ≥98 [T3] mmHg). Differences were evaluated using univariable (t-tests, chi-squared) tests. Multivariable (MV) logistic regression analyses were computed to assess links between MABP and mortality in hypertensive COVID-19 patients. RESULTS: 1549 adults were diagnosed with COVID-19 (+) and 2577 tested negative (-). Mortality of COVID-19(+) was 4.4-fold greater than COVID-19(-) patients. Though HTN prevalance did not differ between COVID-19 groups, the presenting systolic BP, diastolic BP, and MABP were lower in the COVID-19(+) vs (-) cohort. When subjects were categorized into tertiles of MABP, T2 tertile of MABP had the lowest mortality and the T1 tertile of MABP had greatest mortality compared to T2; however, no difference in mortality was noted across tertiles of MABP in COVID-19 (-). MV analysis of COVID-19 (+) subjects exposed death as a risk factor for T1 MABP. Next, the mortality of those with a historic diagnosis of hypertension or normotension were studied. On MV analysis, T1 MABP, gender, age, and first respiratory rate correlated with mortality while lymphocyte count inversely correlated with death in hypertensive COVID-19 (+) patients while neither T1 nor T3 categories of MABP predicted death in non-hypertensives. CONCLUSIONS: Low-normal admitting MABP in COVID-19 (+) subjects with a historical diagnosis of HTN is associated with mortality and may assist in identifying those at greatest mortality risk.

Precise Detection on Cell-Cell Fusion by a Facile Molecular Beacon-Based Method.

Cell-cell fusion studies provide an experimental platform for evaluating disease progression and investigating cell infection. However, to realize sensitive and quantitative detection on cell-cell fusion is still a challenge. Herein, we report a facile molecular beacon (MB)-based method for precise detection on cell-cell fusion. By transfection of the spike protein (S protein) and enhanced green fluorescent protein (EGFP) in HEK 293 cells, the virus-mimicking fusogenic effector cells 293-S-EGFP cells were constructed to interact with target cells. Before mixing the effector cells with the target cells, the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression in 293-S-EGFP cells was silenced, and the MB for GAPDH mRNA detection was delivered into the GAPDH silenced 293-S-EGFP cells. Once cell-cell fusion occurred, MB migrated from the GAPDH silenced effector cells to the target cells and hybridized with GAPDH mRNA in the target cells to induce fluorescence emission. The cell-cell fusion can be easily visualized and quantitated by fluorescence microscopy and flow cytometry. The fluorescence intensity is strongly dependent on the number of fused target cells. This MB-based method can easily identify the differences in the cell fusions for various target cells with different angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) expression levels, resulting in dramatically different fluorescence intensities in fused target cells. Our study provides a convenient and efficient quantitative detection approach to study cell-cell fusion.

MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.

Integrated Microfluidic-Based Platforms for On-Site Detection and Quantification of Infectious Pathogens: Towards On-Site Medical Translation of SARS-CoV-2 Diagnostic Platforms.

The rapid detection and quantification of infectious pathogens is an essential component to the control of potentially lethal outbreaks among human populations worldwide. Several of these highly infectious pathogens, such as Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have been cemented in human history as causing epidemics or pandemics due to their lethality and contagiousness. SARS-CoV-2 is an example of these highly infectious pathogens that have recently become one of the leading causes of globally reported deaths, creating one of the worst economic downturns and health crises in the last century. As a result, the necessity for highly accurate and increasingly rapid on-site diagnostic platforms for highly infectious pathogens, such as SARS-CoV-2, has grown dramatically over the last two years. Current conventional non-microfluidic diagnostic techniques have limitations in their effectiveness as on-site devices due to their large turnaround times, operational costs and the need for laboratory equipment. In this review, we first present criteria, both novel and previously determined, as a foundation for the development of effective and viable on-site microfluidic diagnostic platforms for several notable pathogens, including SARS-CoV-2. This list of criteria includes standards that were set out by the WHO, as well as our own "seven pillars" for effective microfluidic integration. We then evaluate the use of microfluidic integration to improve upon currently, and previously, existing platforms for the detection of infectious pathogens. Finally, we discuss a stage-wise means to translate our findings into a fundamental framework towards the development of more effective on-site SARS-CoV-2 microfluidic-integrated platforms that may facilitate future pandemic diagnostic and research endeavors. Through microfluidic integration, many limitations in currently existing infectious pathogen diagnostic platforms can be eliminated or improved upon.

Impact of mutations in SARS-COV-2 spike on viral infectivity and antigenicity.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to alter the viral infectivity and antigenicity. Part of mutations in SARS-CoV-2 spike protein has conferred virus the ability to spread more quickly and escape from the immune response caused by the monoclonal neutralizing antibody or vaccination. Herein, we summarize the spatiotemporal distribution of mutations in spike protein, and present recent efforts and progress in investigating the impacts of those mutations on viral infectivity and antigenicity. As mutations continue to emerge in SARS-CoV-2, we strive to provide systematic evaluation of mutations in spike protein, which is vitally important for the subsequent improvement of vaccine and therapeutic neutralizing antibody strategies.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Prevalence and determinants of symptomatic COVID-19 infection among children and adolescents in Qatar: a cross-sectional analysis of 11 445 individuals.

There is a paucity of evidence about the prevalence and risk factors for symptomatic infection among children. This study aimed to describe the prevalence of symptomatic coronavirus disease 2019 (COVID-19) and its risk factors in children and adolescents aged 0-18 years in Qatar. We conducted a cross-sectional study of all children aged 0-18 years diagnosed with COVID-19 using polymerase chain reaction in Qatar during the period 1st March to 31st July 2020. A generalised linear model with a binomial family and identity link was used to assess the association between selected factors and the prevalence of symptomatic infection. A total of 11 445 children with a median age of 8 years (interquartile range (IQR) 3-13 years) were included in this study. The prevalence of symptomatic COVID-19 was 36.6% (95% confidence interval (CI) 35.7-37.5), and it was similar between children aged <5 years (37.8%), 5-9 years (34.3%) and 10 + years (37.3%). The most frequently reported symptoms among the symptomatic group were fever (73.5%), cough (34.8%), headache (23.2%) and sore throat (23.2%). Fever (82.8%) was more common in symptomatic children aged <5 years, while cough (38.7%) was more prevalent in those aged 10 years or older, compared to other age groups. Variables associated with an increased risk of symptomatic infection were; contact with confirmed cases (RD 0.21; 95% CI 0.20-0.23; P = 0.001), having visited a health care facility (RD 0.54; 95% CI 0.45-0.62; P = 0.001), and children aged under 5 years (RD 0.05; 95% CI 0.02-0.07; P = 0.001) or aged 10 years or older (RD 0.04; 95% CI 0.02-0.06; P = 0.001). A third of the children with COVID-19 were symptomatic with a higher proportion of fever in very young children and a higher proportion of cough in those between 10 and 18 years of age.

N439K Variant in Spike Protein Alter the Infection Efficiency and Antigenicity of SARS-CoV-2 Based on Molecular Dynamics Simulation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), has been undergoing various mutations. The analysis of the structural and energetic effects of mutations on protein-protein interactions between the receptor binding domain (RBD) of SARS-CoV-2 and angiotensin converting enzyme 2 (ACE2) or neutralizing monoclonal antibodies will be beneficial for epidemic surveillance, diagnosis, and optimization of neutralizing agents. According to the molecular dynamics simulation, a key mutation N439K in the SARS-CoV-2 RBD region created a new salt bridge with Glu329 of hACE2, which resulted in greater electrostatic complementarity, and created a weak salt bridge with Asp442 of RBD. Furthermore, the N439K-mutated RBD bound hACE2 with a higher affinity than wild-type, which may lead to more infectious. In addition, the N439K-mutated RBD was markedly resistant to the SARS-CoV-2 neutralizing antibody REGN10987, which may lead to the failure of neutralization. The results show consistent with the previous experimental conclusion and clarify the structural mechanism under affinity changes. Our methods will offer guidance on the assessment of the infection efficiency and antigenicity effect of continuing mutations in SARS-CoV-2.

A numerical study of the effects of ambient temperature and humidity on the particle growth and deposition in the human airway.

A numerical study was conducted on the effects of ambient temperature and humidity on the transportation of sodium chloride particles (100 nm-1 µm) in a human airway model ranging from the nasal cavity to bronchi. A mucus-tissue structure was adopted to model the mass and heat transfer on the airway surface boundary. The temperature and humidity distributions of the respiratory flow were calculated and then the interaction between the particle and water vapor was further analyzed. It was predicted that the particle size grew to the ratio of 5-6 under subsaturation conditions because of hygroscopicity, which shifted the deposition efficiency in opposite directions on dependence of the initial particle size. However, the particles could be drastically raised to 40 times of the initial 100 nm diameter if the supersaturation-induced condensation was established, that was prone to occur under the cold-dry condition, and consequently promoted the deposition significantly. Such behavior might effectively contribute to the revitalized coronavirus disease 2019 (COVID-19) pandemic in addition to the more active virus itself in winter.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.

Methodological assessment of systematic reviews and meta-analyses on COVID-19: A meta-epidemiological study.

RATIONALE, AIMS, AND OBJECTIVES: COVID-19 has caused an ongoing public health crisis. Many systematic reviews and meta-analyses have been performed to synthesize evidence for better understanding this new disease. However, some concerns have been raised about rapid COVID-19 research. This meta-epidemiological study aims to methodologically assess the current systematic reviews and meta-analyses on COVID-19. METHODS: We searched in various databases for systematic reviews with meta-analyses published between 1 January 2020 and 31 October 2020. We extracted their basic characteristics, data analyses, evidence appraisal, and assessment of publication bias and heterogeneity. RESULTS: We identified 295 systematic reviews on COVID-19. The median time from submission to acceptance was 33 days. Among these systematic reviews, 73.9% evaluated clinical manifestations or comorbidities of COVID-19. Stata was the most used software programme (43.39%). The odds ratio was the most used effect measure (34.24%). Moreover, 28.14% of the systematic reviews did not present evidence appraisal. Among those reporting the risk of bias results, 14.64% of studies had a high risk of bias. Egger's test was the most used method for assessing publication bias (38.31%), while 38.66% of the systematic reviews did not assess publication bias. The I2 statistic was widely used for assessing heterogeneity (92.20%); many meta-analyses had high values of I2 . Among the meta-analyses using the random-effects model, 75.82% did not report the methods for model implementation; among those meta-analyses reporting implementation methods, the DerSimonian-Laird method was the most used one. CONCLUSIONS: The current systematic reviews and meta-analyses on COVID-19 might suffer from low transparency, high heterogeneity, and suboptimal statistical methods. It is recommended that future systematic reviews on COVID-19 strictly follow well-developed guidelines. Sensitivity analyses may be performed to examine how the synthesized evidence might depend on different methods for appraising evidence, assessing publication bias, and implementing meta-analysis models.

Effects of Shuanghuanglian oral liquids on patients with COVID-19: a randomized, open-label, parallel-controlled, multicenter clinical trial.

We conducted a randomized, open-label, parallel-controlled, multicenter trial on the use of Shuanghuanglian (SHL), a traditional Chinese patent medicine, in treating cases of COVID-19. A total of 176 patients received SHL by three doses (56 in low dose, 61 in middle dose, and 59 in high dose) in addition to standard care. The control group was composed of 59 patients who received standard therapy alone. Treatment with SHL was not associated with a difference from standard care in the time to disease recovery. Patients with 14-day SHL treatment had significantly higher rate in negative conversion of SARS-CoV-2 in nucleic acid swab tests than the patients from the control group (93.4% vs. 73.9%, P = 0.006). Analysis of chest computed tomography images showed that treatment with high-dose SHL significantly promoted absorption of inflammatory focus of pneumonia, which was evaluated by density reduction of inflammatory focus from baseline, at day 7 (mean difference (95% CI), -46.39 (-86.83 to -5.94) HU; P = 0.025) and day 14 (mean difference (95% CI), -74.21 (-133.35 to -15.08) HU; P = 0.014). No serious adverse events occurred in the SHL groups. This study illustrated that SHL in combination with standard care was safe and partially effective for the treatment of COVID-19.

Privacy-preserving contact tracing in 5G-integrated and blockchain-based medical applications.

The current pandemic situation due to COVID-19 is seriously affecting our daily work and life. To block the propagation of infectious diseases, an effective contact tracing mechanism needs to be implemented. Unfortunately, existing schemes have severe privacy issues that jeopardize the identity-privacy and location-privacy for both users and patients. Although some privacy-preserving systems have been proposed, there remain several issues caused by centralization. To mitigate this issues, we propose a Privacy-preserving contact Tracing scheme in 5G-integrated and Blockchain-based Medical applications, named PTBM. In PTBM, the 5G-integrated network is leveraged as the underlying infrastructure where everyone can perform location checking with his mobile phones or even wearable devices connected to 5G network to find whether they have been in possible contact with a diagnosed patient without violating their privacy. A trusted medical center can effectively trace the patients and their corresponding close contacts. Thorough security and performance analysis show that the proposed PTBM scheme achieves privacy protection, traceability, reliability, and authentication, with high computation & communication efficiency and low latency.

Recovery From Acute Kidney Injury With Diabetic Ketoacidosis Following SARS-CoV-2 Infection: A Case Report and Literature Review.

Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 diabetes (DM) and sometimes type 2 diabetes. DKA in COVID-19 patients predicts a poor prognosis. There are few published reports describing DKA and AKI in COVID-19 patients. There is even less information on renal recovery or follow up of these patients. We report a case of a 55-year-old man with type 2 diabetes who presented with cough and shortness of breath. He was found to have DKA, AKI, and COVID-19. He was admitted to the ICU and subsequently intubated for airway protection and started on renal replacement therapy for AKI. The patient's renal function eventually recovered after 48 days of hospitalization, and he was discharged from the hospital. We report this case and performed a literature review to highlight that COVID-19 can lead to DKA and severe AKI in patients with DM. More importantly, we described the clinical and laboratory data that are associated with the recovery of COVID-19 AKI that are rarely reported.

Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing.

T-cell receptor (TCR) is crucial in T cell-mediated virus clearance. To date, TCR bias has been observed in various diseases. However, studies on the TCR repertoire of COVID-19 patients are lacking. Here, we used single-cell V(D)J sequencing to conduct comparative analyses of TCR repertoire between 12 COVID-19 patients and 6 healthy controls, as well as other virus-infected samples. We observed distinct T cell clonal expansion in COVID-19. Further analysis of VJ gene combination revealed 6 VJ pairs significantly increased, while 139 pairs significantly decreased in COVID-19 patients. When considering the VJ combination of α and ß chains at the same time, the combination with the highest frequency on COVID-19 was TRAV12-2-J27-TRBV7-9-J2-3. Besides, preferential usage of V and J gene segments was also observed in samples infected by different viruses. Our study provides novel insights on TCR in COVID-19, which contribute to our understanding of the immune response induced by SARS-CoV-2.

Relationship Between COVID-19 Infection and Risk Perception, Knowledge, Attitude, and Four Nonpharmaceutical Interventions During the Late Period of the COVID-19 Epidemic in China: Online Cross-Sectional Survey of 8158 Adults.

BACKGROUND: So far, there have been no published population studies on the relationship between a COVID-19 infection and public risk perception, information source, knowledge, attitude, and behaviors during the COVID-19 outbreak in China. OBJECTIVE: This study aims to understand the relationships between COVID-19 infection; four personal nonpharmaceutical interventions (NPIs; handwashing, proper coughing habits, social distancing, and mask wearing); and public risk perception, knowledge, attitude, and other social demographic variables. METHODS: An online survey of 8158 Chinese adults between February 22 and March 5, 2020, was conducted. Bivariate associations between categorical variables were examined using Fisher exact test. We also explored the determinants of four NPIs as well as their association with COVID-19 infection using logistic regression. RESULTS: Of 8158 adults included, 57 (0.73%) were infected with COVID-19. The overwhelming majority of respondents showed a positive attitude (n=8094, 99.2%), positive risk perception (n=8146, 99.9%), and high knowledge levels that were among the strongest predictors of the four adopted NPIs (handwashing: n=7895, 96.8%; proper coughing: 5997/6444, 93.1%; social distancing: n=7104/8158, 87.1%; and mask wearing: 5011/5120, 97.9%). There was an increased risk of COVID-19 infection for those who did not wash their hands (2.28% vs 0.65%; risk ratio [RR] 3.53, 95% CI 1.53-8.15; P=.009), did not practice proper coughing (1.79% vs 0.73%; RR 2.44, 95% CI 1.15-5.15; P=.03), did not practice social distancing (1.52% vs 0.58%; RR 2.63, 95% CI 1.48-4.67; P=.002), and did not wear a mask (7.41% vs 0.6%; RR 12.38, 95% CI 5.81-26.36; P<.001). For those who did practice all other three NPIs, wearing a mask was associated with a significantly reduced risk of infection compared to those who did not wear a mask (0.6% vs 16.7%; P=.04). Similarly, for those who did not practice all or part of the other three NPIs, wearing a mask was also associated with a significantly reduced risk of infection. In a penalized logistic regression model including all four NPIs, wearing a mask was the only significant predictor of COVID-19 infection among the four NPIs (odds ratio 7.20, 95% CI 2.24-23.11; P<.001). CONCLUSIONS: We found high levels of risk perception, positive attitude, desirable knowledge, as well as a high level of adopting the four NPIs. The relevant knowledge, risk perception, and attitude were strong predictors of adapting the four NPIs. Mask wearing, among the four personal NPIs, was the most effective protective measure against COVID-19 infection, with added preventive effect among those who practiced all or part of the other three NPIs.