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Background: Tokyo, the capital of Japan, is a densely populated city of >13 million people, so the population is at high risk of epidemic severe acute respiratory coronavirus 2 (SARS-CoV-2) infection. A serologic survey of anti–SARS-CoV-2 IgG would provide valuable data for assessing the city’s SARS-CoV-2 infection status. Therefore, this cross-sectional study estimated the anti–SARS-CoV-2 IgG seroprevalence in Tokyo. Methods: Leftover serum of 23,234 hospital visitors was tested for antibodies against SARS-CoV-2 using an iFlash 3000 chemiluminescence immunoassay analyzer (Shenzhen YHLO Biotech, Shenzhen, China) with an iFlash–SARS-CoV-2 IgG kit (YHLO) and iFlash–SARS-CoV-2 IgG-S1 kit (YHLO). Serum samples with a positive result (≥10 AU/mL) in either of these assays were considered seropositive for anti–SARS-CoV-2 IgG. Participants were randomly selected from patients visiting 14 Tokyo hospitals between September 1, 2020 and March 31, 2021. No participants were diagnosed with coronavirus disease 2019 (COVID-19), and none exhibited COVID-19-related symptoms at the time of blood collection. Results: The overall anti–SARS-CoV-2 IgG seroprevalence among all participants was 1.83% (95% confidence interval [CI], 1.66–2.01%). The seroprevalence in March 2021, the most recent month of this study, was 2.70% (95% CI, 2.16–3.34%). After adjusting for population age, sex, and region, the estimated seroprevalence in Tokyo was 3.40%, indicating that 470,778 individuals had a history of SARS-CoV-2 infection. Conclusions: The estimated number of individuals in Tokyo with a history of SARS-CoV-2 infection was 3.9-fold higher than the number of confirmed cases. Our study enhances understanding of the SARS-CoV-2 epidemic in Tokyo.

The accurate and prompt diagnosis of SARS-CoV-2 infection is required for the control and treatment of the coronavirus infection disease 2019 (COVID-19). In this study, we aimed to investigate the time courses of the anti-severe acute corona respiratory syndrome coronavirus 2 (SARS-CoV-2) IgM and IgG titers and to evaluate the sensitivity and specificity of such tests according to the specific day after the onset of COVID-19 among a patient population in Japan. We measured the titers of SARS-CoV-2 IgM and IgG in sera from 105 subjects, including 26 symptomatic COVID-19 patients, using chemiluminescent immunoassay (CLIA) methods utilizing magnetic beads coated with SARS-CoV-2 nucleocapsid protein and spike protein. The results of a ROC analysis suggested the possibility that the cutoff values in Japan might be lower than the manufacturer’s reported cutoff (10 AU/mL): 1 AU/mL for IgM and 5 AU/mL for IgG. The sensitivity of the test before Day 8 after symptom onset was less than 50%; at Days 9–10, however, we obtained a much higher sensitivity of 81.8% for both IgM and IgG. At 15 days or later after symptom onset, the SARS-CoV-2 IgG test had a sensitivity of 100%. These results suggest that if the number of days since disease onset is taken into consideration, these antibody tests could be very useful for the diagnosis of COVID-19 and similar diseases.

Objectives The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread globally. The laboratory diagnosis of SARS-CoV-2 infection has relied on nucleic acid testing; however, it has some limitations, such as low throughput and high rates of false negatives. Tests of higher sensitivity are needed to effectively identify infected patients. Methods This study has developed fully automated chemiluminescent immunoassays to determine IgM and IgG antibodies to SARS-CoV-2 in human serum. The assay performance has been evaluated at 10 hospitals. Clinical specificity was evaluated by measuring 972 hospitalized patients and 586 donors of a normal population. Clinical sensitivity was assessed on 513 confirmed cases of SARS-CoV-2 by RT-PCR. Results The assays demonstrated satisfied assay precision with coefficient of variation of less than 4.45%. Inactivation of specimen did not affect assay measurement. SARS-CoV-2 IgM showed clinical specificity of 97.33 and 99.49% for hospitalized patients and the normal population respectively, and SARS-CoV-2 IgG showed clinical specificity of 97.43 and 99.15% respectively. SARS-CoV-2 IgM showed clinical sensitivity of 82.54, 92.93, and 84.62% before 7 days, 7–14 days, and after 14 days respectively, since onset of symptoms, and SARS-CoV-2 IgG showed clinical sensitivity of 80.95, 97.98, and 99.15% respectively at the same time points above. Conclusions We have developed fully automated immunoassays for detecting SARS-CoV-2 IgM and IgG antibodies in human serum. The assays demonstrated high clinical specificity and sensitivity, and add great value to nucleic acid testing in fighting against the global pandemic of the SARS-CoV-2 infection.

ObjectivesTo compare the temporal changes of IgM, IgG, and IgA antibodies against the SARS-CoV-2 nucleoprotein, S1 subunit, and receptor binding domain and neutralizing antibodies (NAbs) against SARS-CoV-2 in patients with COVID-19.MethodsA total of five patients in Nissan Tamagawa Hospital, Tokyo, Japan confirmed COVID-19 from August 8, 2020 to August 14, 2020 were investigated. Serum samples were acquired multiple times from 0 to 76 days after symptom onset. Using a fully automated CLIA analyzer, we measured the levels of IgG, IgA, and IgM against the SARS-CoV-2 N, S1, and RBD and NAbs against SARS-CoV-2.ResultsThe levels of IgG antibodies against SARS-CoV-2 structural proteins increased over time in all cases but IgM and IgA levels against SARS-CoV-2 showed different increasing trends among individuals in the early stage. In particular, we observed IgA antibodies increasing before IgG and IgM in 3/5 cases. The NAb levels against SARS-CoV-2 increased and kept above 10 AU/mL more than around 70 days after symptom onset in all cases. Furthermore, in the early stage, NAb levels were more than cut off value in 4/5 COVID-19 patients some of whose antibodies against RBD didn’t exceed 10 AU/mL.ConclusionsOur findings indicate that patients with COVID-19 should be examined for IgG, IgA and IgM antibodies against SARS-CoV-2 structural proteins and NAbs against SARS-CoV-2 in addition to conventional antibody testing methods for SARS-CoV-2 (IgG and IgM kits) to analyze the diversity of patients’ immune mechanisms.

The main requirement of multimedia communication service is to improve ultra-reliable and low-latency data communication, so the challenge of providing multimedia communication service is to improve data sharing for making full use of network resources. In this paper, a data content based vehicle clustering model is designed to analyze the transmitted multimedia data correlation between the vehicles, and the vehicles with high correlation of transmitted multimedia data are classified into a cluster and share the same resources. The data sharing in network is regarded as a performance criterion for adjusting the self-organized multimedia communication structure. Based on these factors, in order to ensure the stability of the self-organized communication structure, this paper proposes a content-aware stable multimedia communication algorithm for Internet of vehicles, which controls the multimedia communication within a certain range and combines with the transmitted multimedia data correlation of the vehicles that need to be adjusted. Finally, a network clustering structure with data sharing maximization and stable multimedia communication is performed. Extensive simulation experiments are carried out to evaluate the performance of the proposed algorithm in terms of network stability, average end-to-end communication delay, and packet loss rate.

With the increasing amount of information and the growth of data sources on the battlefield, it is important to achieve better battlefield decision-making through the faster and more accurate emotion awareness provided by big data. Existing battlefield systems are mainly focused on logical information acquisition, rarely considering emotion factors. In this paper, an emotion-aware system for the battlefield environment (ESBE) is proposed to achieve various functions, including target localization, target recognition, motion behavior identification, etc., to support intelligent decision-making based on the emotional state of soldiers and other valuable information about the battlefield environment. The ESBE architecture consists of three layers: data-sensing, data-transmission, and data-processing. A heterogeneous network is introduced in data-transmission layer to speed up the transmission ratio and increase the network throughput. In the data-processing layer, cloud technology is introduced to store the big data while information fusion based on a variety of technologies is executed to process the big data. Then, the elaborated function of each architecture layer, such as the fundamental process of the ESBE system, as well as some function provided by the ESBE, is presented separately. Last but not least, the ESBE system is compared with four other existing systems in terms of functions and technologies.

The accurate and prompt diagnosis of SARS-CoV-2 infection is required for the control and treatment of the coronavirus infection disease 2019 (COVID-19). In this study, we aimed to investigate the time courses of the anti-severe acute corona respiratory syndrome coronavirus 2 (SARS-CoV-2) IgM and IgG titers and to evaluate the sensitivity and specificity of such tests according to the specific day after the onset of COVID-19 among a patient population in Japan. We measured the titers of SARS-CoV-2 IgM and IgG in sera from 100 subjects, including 26 symptomatic COVID-19 patients, using chemiluminescent immunoassay (CLIA) methods utilizing magnetic beads coated with SARS-CoV-2 nucleocapsid protein and spike protein. The results of a ROC analysis suggested the possibility that the cutoff values in Japan might be lower than the manufacturer’s reported cutoff (10 AU/mL): 1 AU/mL for IgM and 5 AU/mL for IgG. The sensitivity of the test before Day 8 after symptom onset was less than 50%; at Days 9-10, however, we obtained a much higher sensitivity of 81.8% for both IgM and IgG. At 15 days or later after symptom onset, the SARS-CoV-2 IgG test had a sensitivity of 100%. These results suggest that if the number of days since disease onset is taken into consideration, these antibody tests could be very useful for the diagnosis of COVID-19 and similar diseases.

Background: IgA antibodies against Epstein-Barr virus (EBV) capsid antigen (VCA) and nuclear antigen 1 (EBNA1) have been proposed to facilitate the diagnosis and early detection of nasopharyngeal carcinoma (NPC) in high-incidence regions. However, while new methodologies and new platforms for the detection of VCA-IgA and EBNA1-IgA have become available, proper interassay simultaneous comparisons have not been carried out. The study was to compare the performance of the chemiluminescent immunoassays (CLIA) and enzyme-linked immunosorbent assay (ELISA) for VCA-IgA and EBNA1-IgA antibodies, and to evaluate the levels of EBV antibodies in healthy population from different areas of China. Methods: CLIA and ELISA for VCA-IgA and EBNA1-IgA were performed in NPC and healthy populations from high-incidence areas of NPC in South China (N=555), medium-incidence areas of NPC in Central China (N=318) and low-incidence areas of NPC in North China (N=379), and the results were compared and analyzed. Results: (1) The highest sensitivity in total, early and advanced NPC were 91.5% (CLIA for VCA-IgA), 86.4% (CLIA and ELISA-2 for EBNA1-IgA) and 93.6% (CLIA for VCA-IgA). However, the specificity of EBV-IgA measured by CLIA was relatively lower than ELISA. The top three seromarkers with the largest AUC was CLIA for VCA-IgA (AUC: 0.929, 95% CI: 0.905-0.953), ELISA-2 for EBNA1-IgA (AUC: 0.922, 95% CI: 0.896-0.947) and CLIA for EBNA1-IgA (AUC:0.919, 95% CI: 0.893-0.945), respectively. The positive and negative coincidence rates of the two EBNA1-IgA kits were 69.5% and 91.9%, respectively. However, the coincidence rates of VCA-IgA were relatively low. CLIA kits had good repeatability between different laboratories. (2) The positive rates of EBV-IgA antibodies were relatively high in high-incidence areas of NPC (P < 0.017), while there was no significant difference in the antibody positive rates between medium-incidence areas and low-incidence areas of NPC (P > 0.05). Conclusions: The performance of EBV-IgA antibodies measured by CLIA has good repeatability, higher sensitivity and similar specificity. The higher EBV-IgA positive rate in healthy subjects by CLIA raises concern about its suitability for NPC-risk screening and requires further analysis.

BACKGROUNDThe outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread globally. The laboratory diagnosis of SARS-CoV-2 infection has relied on nucleic acid tests. However, there are many limitations of nucleic acid tests, including low throughput and high rates of false negatives. More sensitive and accurate tests to effectively identify infected patients are needed.METHODSThis study has developed fully automated chemiluminescent immunoassays (CLIA) to determine IgM and IgG antibodies to SARS-CoV-2 in human serum. The assay performance has been evaluated at 10 hospitals. Clinical specificity was evaluated by measuring 972 hospitalized patients with diseases other than COVID-19, and 586 donors of a normal population. Clinical sensitivity was assessed on 503 confirmed cases of SARS-CoV-2 by RT-PCR and 52 suspected cases.RESULTSThe assays demonstrated satisfied assay precision with coefficient of variation (CV) of less than 4.45%. Inactivation of specimen does not affect assay measurement. SARS-CoV-2 IgM shows clinical specificity of 97.33% and 99.49% for hospitalized patients and normal population respectively. SARS-CoV-2 IgG shows clinical specificity of 97.43% and 99.15% for the hospitalized patients and the normal population respectively. SARS-CoV-2 IgM and IgG show clinical sensitivity of 85.88% and 96.62% respectively for confirmed SARS-Cov-2 infection with RT-PCR, of 73.08% and 86.54% respectively for suspected cases.CONCLUSIONSwe have developed fully automated immunoassays for detecting SARS-CoV-2 IgM and IgG antibodies in human serum. The assays demonstrated high clinical specificity and sensitivity, and add great value to nucleic acid testing in fighting against the global pandemic of the SARS-CoV-2 infection.

PCR methods are presently the standard for the diagnosis of Coronavirus disease 2019 (COVID-19), but additional methodologies are needed to complement PCR methods, which have some limitations. Here, we validated and investigated the usefulness of measuring serum antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using the iFlash3000 CLIA analyzer. We measured IgM and IgG titers against SARS-CoV-2 in sera collected from 26 PCR-positive COVID-19 patients, 53 COVID-19-suspected but PCR-negative patients, and 20 and 100 randomly selected non-COVID-19 patients who visited our hospital in 2020 and 2017, respectively. The repeatability and within-laboratory precision were obviously good in validations, following to the CLSI document EP15-A3. Linearity was also considered good between 0.6 AU/mL and 112.7 AU/mL for SARS-CoV-2 IgM and between 3.2 AU/mL and 55.3 AU/mL for SARS-CoV-2 IgG, while the linearity curves plateaued above the upper measurement range. We also confirmed that the seroconversion and no-antibody titers were over the cutoff values in all 100 serum samples collected in 2017. These results indicate that this measurement system successfully detects SARS-CoV-2 IgM/IgG. We observed four false-positive cases in the IgM assay and no false-positive cases in the IgG assay when 111 serum samples known to contain autoantibodies were evaluated. The concordance rates of the antibody test with the PCR test were 98.1% for SARS-CoV-2 IgM and 100% for IgG among PCR-negative cases and 30.8% for SARS-CoV-2 IgM and 73.1% for SARS-CoV-2 IgG among PCR-positive cases. In conclusion, the performance of this new automated method for detecting antibody against both N and S proteins of SARS-CoV-2 is sufficient for use in laboratory testing.

As the living standards improve and the health consciousness enhances, the healthcare industry has become a hot spot in nowadays society and some health monitoring systems emerge one after another in recent years. However, the mostly existing systems only focus on the logic reasoning but ignore the factor of the user's emotion, which is regarded as an important factor to impact human health. In this paper, we design a system for big data application in emotion-aware healthcare (BDAEH), which pays attention to both the logic reasoning and the emotion computing. Meanwhile, the SDN the and 5G technologies are adopted in the BDAHE system to improve the resource utilization and the overall network performance of the system. The BDAEH system includes the following functions: healthcare data collection, healthcare data transmission, healthcare data storage, healthcare data analysis, and human-machine interaction. The healthcare data are generated by wearable devices or sensing-less sensors, and these healthcare data are regarded as the foundation to expand a series of data processing. The healthcare data transmission is performed through leveraging the SDN and the 5G technologies. In the data center, the related technologies based on cloud computing are utilized to store and analyze healthcare data, which obtains both the emotion and the health state of the users, and the relation between the emotion and the illness. Finally, the BDAEH system returns the analysis result to the users or the doctors for further treatment schemes or rehabilitation advice. The presented system is expected to validly improve the healthcare services by considering the emotion factor.