Did the COVID-19 pandemic impact urticaria information-seeking behavior in China? A retrospective longitudinal study.

Purpose: To investigate information-seeking behavior related to urticaria before and during the COVID-19 pandemic in China. Methods: Search query data for terms related to urticaria were retrieved using Baidu Index database from October 23, 2017 to April 23, 2022, and daily COVID-19 vaccination doses data were obtained from the website of the Chinese Center for Disease Control and Prevention. Among the 23 eligible urticaria search terms, four urticaria themes were generated as classification, symptom, etiology, and treatment of urticarial, respectively. Baidu Search Index (BSI) value for each term were extracted to analyze and compare the spatial and temporal distribution of online search behavior for urticaria before and after the COVID-19 pandemic, and to also explore the correlation between search query and daily COVID-19 vaccination doses. Results: The classification of urticaria accounted for nearly half of the urticaria queries on the internet. Regular seasonal patterns of BSI were observed in urticaria-related online search, by attaining its highest level in spring and summer and lowest level in winter. The BSIs of all urticaria themes significantly increased after the COVID-19 pandemic than that before the pandemic (all P<0.05). Xizang, Qinghai and Ningxia are the most active geographical areas for increased urticaria-searching activities after the COVID-19 pandemic. There was also a significant positive correlation between daily BSIs and daily COVID-19 vaccination doses in each urticaria theme. Cross-correlation analysis found that the search of symptom, etiology, and treatment attained their strongest correlation with daily COVID-19 vaccination doses at 11-27 days before the injection of vaccine, imply vaccination hesitation related to concerns of urticaria. Conclusions: This study used the internet as a proxy to provide evidence of public search interest and spatiotemporal characteristics of urticaria, and revealed that the search behavior of urticaria have increased significantly after the COVID-19 pandemic and COVID-19 vaccination. It is anticipated that the findings about such increase in search behavior, as well as the behavior of urticaria-related vaccine-hesitancy, will help guide public health education and policy regulation.

Did the COVID-19 pandemic impact urticaria information-seeking behavior in China? A retrospective longitudinal study

Purpose To investigate information-seeking behavior related to urticaria before and during the COVID-19 pandemic in China. Methods Search query data for terms related to urticaria were retrieved using Baidu Index database from October 23, 2017 to April 23, 2022, and daily COVID-19 vaccination doses data were obtained from the website of the Chinese Center for Disease Control and Prevention. Among the 23 eligible urticaria search terms, four urticaria themes were generated as classification, symptom, etiology, and treatment of urticarial, respectively. Baidu Search Index (BSI) value for each term were extracted to analyze and compare the spatial and temporal distribution of online search behavior for urticaria before and after the COVID-19 pandemic, and to also explore the correlation between search query and daily COVID-19 vaccination doses. Results The classification of urticaria accounted for nearly half of the urticaria queries on the internet. Regular seasonal patterns of BSI were observed in urticaria-related online search, by attaining its highest level in spring and summer and lowest level in winter. The BSIs of all urticaria themes significantly increased after the COVID-19 pandemic than that before the pandemic (all P<0.05). Xizang, Qinghai and Ningxia are the most active geographical areas for increased urticaria-searching activities after the COVID-19 pandemic. There was also a significant positive correlation between daily BSIs and daily COVID-19 vaccination doses in each urticaria theme. Cross-correlation analysis found that the search of symptom, etiology, and treatment attained their strongest correlation with daily COVID-19 vaccination doses at 11–27 days before the injection of vaccine, imply vaccination hesitation related to concerns of urticaria. Conclusions This study used the internet as a proxy to provide evidence of public search interest and spatiotemporal characteristics of urticaria, and revealed that the search behavior of urticaria have increased significantly after the COVID-19 pandemic and COVID-19 vaccination. It is anticipated that the findings about such increase in search behavior, as well as the behavior of urticaria-related vaccine-hesitancy, will help guide public health education and policy regulation.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Digital geographies of the bug: A case study of China's contact tracing systems in the COVID-19.

The COVID-19 pandemic has radically expanded the role of algorithmic governance in everyday mobility. In China, urban and provincial governments have introduced health codes app as a national contract tracing and quarantine enforcement method to restrict the movements of "risky" individuals through malls, subways, railways, as well as between regions. Yet the health codes have been implemented with uneven efficacy and unexpected consequences. Drawing on glitch politics, we read these unintended consequences as "bugs" emerging from the introduction of platform-based management into everyday life. These bugs mediated individuals' lived experiences of the digital app and the hybrid space constituted by population governance, individual digital navigation, and technology. Drawing on a database of posts scraped from Zhihu, a popular Chinese question-and-answer site, we examine three dimensions of the bug: the algorithmic bug, the territorial bug, and the corporeal bug. This paper sheds light on the significance of end-user experiences in digital infrastructure and contributes to our understanding of the digital geographies of bugs in algorithmic governance and platform urbanism.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

The serological diversity of serum IgG/IgA/IgM against SARS-CoV-2 nucleoprotein, spike, and receptor-binding domain and neutralizing antibodies in patients with COVID-19 in Japan.

Background: We compared the temporal changes of immunoglobulin M (IgM), IgG, and IgA antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleoprotein (N), spike 1 subunit (S1), and receptor-binding domain (RBD), and neutralizing antibodies (NAbs) against SARS-CoV-2 in patients with coronavirus disease 2019 (COVID-19) to understand the humoral immunity in COVID-19 patients for developing drugs and vaccines for COVID-19. Methods: A total of five confirmed COVID-19 cases in Nissan Tamagawa Hospital in early August 2020 were recruited in this study. Using a fully automated chemiluminescence immunoassay analyzer, we measured the levels of IgG, IgA, and IgM against SARS-CoV-2 N, S1, and RBD and NAbs against SARS-CoV-2 in COVID-19 patients' sera acquired multiple times in individuals from 0 to 76 days after symptom onset. Results: IgG levels 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 increasing before IgG and IgM in some cases. The NAb levels were more than cut-off value in 4/5 COVID-19 patients some of whose antibodies against RBD did not exceed the cut-off value in the early stage. Furthermore, NAb levels against SARS-CoV-2 increased and kept above cut-off value more than around 70 days after symptom onset in all cases. Conclusion: Our findings indicate COVID-19 patients should be examined for IgG, IgA, and IgM against SARS-CoV-2 structural proteins and NAbs against SARS-CoV-2 to analyze the diversity of patients' immune mechanisms.

Mast Cells and Acupuncture Analgesia.

Mast cells are widely distributed in various parts of the human body and play a vital role in the progression of many diseases. Recently, the close relationship between mast cells and acupoints was elucidated, and the role of mast cells in acupuncture analgesia has attracted the attention of researchers worldwide. Using mast cells, acupuncture analgesia and acupoint as key words to search CNKI, PubMed, Web of Science and other databases, combining the representative articles in these databases with the published research papers of our group, we summarized: The enrichment of mast cells and the dense arrangement of collagen fibers, microvessels, and nerves form the basis for acupoints as the reaction sites of acupuncture; acupuncture can cause the deformation of collagen fibers and activate TRPV channels on mast cells membrane, so as to stimulate mast cells to release bioactive substances and activate nerve receptors to generate analgesic effect; system biology models are set up to explain the quantitative process of information initiation and transmission at acupuncture points, and indicate that the acupuncture effect depends on the local mast cells density. In a conclusion, this review will give a scientific explanation of acupuncture analgesia from the material basis of acupoints, the local initiation, and afferent biological mechanism.

Metabolic programs tailor T cell immunity in viral infection, cancer, and aging.

Productive T cell responses to infection and cancer rely on coordinated metabolic reprogramming and epigenetic remodeling among the immune cells. In particular, T cell effector and memory differentiation, exhaustion, and senescence/aging are tightly regulated by the metabolism-epigenetics axis. In this review, we summarize recent advances of how metabolic circuits combined with epigenetic changes dictate T cell fate decisions and shape their functional states. We also discuss how the metabolic-epigenetic axis orchestrates T cell exhaustion and explore how physiological factors, such as diet, gut microbiota, and the circadian clock, are integrated in shaping T cell epigenetic modifications and functionality. Furthermore, we summarize key features of the senescent/aged T cells and discuss how to ameliorate vaccination- and COVID-induced T cell dysfunctions by metabolic modulations. An in-depth understanding of the unexplored links between cellular metabolism and epigenetic modifications in various physiological or pathological contexts has the potential to uncover novel therapeutic strategies for fine-tuning T cell immunity.

Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients.

Numerous studies have suggested that the titers of antibodies against SARS-CoV-2 are associated with the COVID-19 severity, however, the types of antibodies associated with the disease maximum severity and the timing at which the associations are best observed, especially within one week after symptom onset, remain controversial. We attempted to elucidate the antibody responses against SARS-CoV-2 that are associated with the maximum severity of COVID-19 in the early phase of the disease, and to investigate whether antibody testing might contribute to prediction of the disease maximum severity in COVID-19 patients. We classified the patients into four groups according to the disease maximum severity (severity group 1 (did not require oxygen supplementation), severity group 2a (required oxygen supplementation at low flow rates), severity group 2b (required oxygen supplementation at relatively high flow rates), and severity group 3 (required mechanical ventilatory support)), and serially measured the titers of IgM, IgG, and IgA against the nucleocapsid protein, spike protein, and receptor-binding domain of SARS-CoV-2 until day 12 after symptom onset. The titers of all the measured antibody responses were higher in severity group 2b and 3, especially severity group 2b, as early as at one week after symptom onset. Addition of data obtained from antibody testing improved the ability of analysis models constructed using a machine learning technique to distinguish severity group 2b and 3 from severity group 1 and 2a. These models constructed with non-vaccinated COVID-19 patients could not be applied to the cases of breakthrough infections. These results suggest that antibody testing might help physicians identify non-vaccinated COVID-19 patients who are likely to require admission to an intensive care unit.

Passenger-to-Car Assignment Optimization Model for High-Speed Railway with Risk of COVID-19 Transmission Consideration

Narrow and closed spaces like high-speed train cabins are at great risk for airborne infectious disease transmission. With the threat of COVID-19 as well as other potential contagious diseases, it is necessary to protect passengers from infection. Except for the traditional preventions such as increasing ventilation or wearing masks, this paper proposes a novel measurement that optimizes passenger-to-car assignment schemes to reduce the infection risk for high-speed railway passengers. First, we estimated the probability of an infected person boarding the train at any station. Once infectors occur, the non-steady-state Wells–Riley equation is used to model the airborne transmission intercar cabin. The expected number of susceptible passengers infected on the train can be calculated, which is the so-called overall infection risk. The model to minimize overall infection risk, as a pure integer quadratic programming problem, is solved by LINGO software and tested on several scenarios compared with the classical sequential and discrete assignment strategies used in China. The results show that the proposed model can reduce 67.6% and 56.8% of the infection risk in the base case compared to the sequential and discrete assignment, respectively. In other scenarios, the reduction lies mostly between 10% and 90%. The optimized assignment scheme suggests that the cotravel itinerary among passengers from high-risk and low-risk areas should be reduced, as well as passengers with long- and short-distance trips. Sensitivity analysis shows that our model works better when the incidence is higher at downstream or low-flow stations. Increasing the number of cars and car service capacity can also improve the optimization effect. Moreover, the model is applicable to other epidemics since it is insensitive to the Wells–Riley equation parameters. The results can provide a guideline for railway operators during the post-COVID-19 and other epidemic periods. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Association of the Serum Levels of the Nucleocapsid Antigen of SARS-CoV-2 With the Diagnosis, Disease Severity, and Antibody Titers in Patients With COVID-19: A Retrospective Cross-Sectional Study.

Background: Several types of laboratory tests for COVID-19 have been established to date; however, the clinical significance of the serum SARS-CoV-2 nucleocapsid (N) antigen levels remains to be fully elucidated. In the present study, we attempted to elucidate the usefulness and clinical significance of the serum N antigen levels. Methods: We measured the serum N antigen levels in 391 serum samples collected from symptomatic patients with a confirmed diagnosis of COVID-19 and 96 serum samples collected from patients with non-COVID-19, using a fully automated chemiluminescence immunoassay analyzer. Results: Receiver operating characteristic analysis identified the optimal cutoff value of the serum N antigen level (cutoff index, based on Youden's index) as 0.255, which yielded a sensitivity and specificity for the diagnosis of COVID-19 of 91.0 and 81.3%, respectively. The serum N antigen levels were significantly higher in the patient groups with moderate and severe COVID-19 than with mild disease. Moreover, a significant negative correlation was observed between the serum N antigen levels and the SARS-CoV-2 IgG antibody titers, especially in patients with severe COVID-19. Conclusion: Serum N antigen testing might be useful both for the diagnosis of COVID-19 and for obtaining a better understanding of the clinical features of the disease.

The Critical Contribution of Pseudouridine to mRNA COVID-19 Vaccines.

The current COVID-19 pandemic is a massive source of global disruption, having led so far to two hundred and fifty million COVID-19 cases and almost five million deaths worldwide. It was recognized in the beginning that only an effective vaccine could lead to a way out of the pandemic, and therefore the race for the COVID-19 vaccine started immediately, boosted by the availability of the viral sequence data. Two novel vaccine platforms, based on mRNA technology, were developed in 2020 by Pfizer-BioNTech and Moderna Therapeutics (comirnaty® and spikevax®, respectively), and were the first ones presenting efficacies higher than 90%. Both consisted of N1-methyl-pseudouridine-modified mRNA encoding the SARS-COVID-19 Spike protein and were delivered with a lipid nanoparticle (LNP) formulation. Because the delivery problem of ribonucleic acids had been known for decades, the success of LNPs was quickly hailed by many as the unsung hero of COVID-19 mRNA vaccines. However, the clinical trial efficacy results of the Curevac mRNA vaccine (CVnCoV) suggested that the delivery system was not the only key to the success. CVnCoV consisted of an unmodified mRNA (encoding the same spike protein as Moderna and Pfizer-BioNTech's mRNA vaccines) and was formulated with the same LNP as Pfizer-BioNTech's vaccine (Acuitas ALC-0315). However, its efficacy was only 48%. This striking difference in efficacy could be attributed to the presence of a critical RNA modification (N1-methyl-pseudouridine) in the Pfizer-BioNTech and Moderna's mRNA vaccines (but not in CVnCoV). Here we highlight the features of N1-methyl-pseudouridine and its contributions to mRNA vaccines.

Case Report: Vaccine-Induced Immune Thrombotic Thrombocytopenia in a Pancreatic Cancer Patient After Vaccination With Messenger RNA-1273.

Vaccination plays an important role during the COVID-19 pandemic. Vaccine-induced thrombotic thrombocytopenia (VITT) is a major adverse effect that could be lethal. For cancer patients, cancer-related thromboembolism is another lethal complication. When cancer patients receive their COVID-19 vaccines, the following thromboembolic events will be more complicated. We presented a case recently diagnosed with pancreatic cancer, who had received the mRNA-1273 (Moderna) vaccination 12 days prior. Ischemic stroke and VITT were also diagnosed. We aggressively treated the patient with steroids, immunoglobulin, and plasma exchange. The titer of anti-platelet factor four and d-dimer level decreased, but the patient ultimately died. The complicated condition of VITT superimposed cancer-related thromboembolism was considered. To our knowledge, only one case of mRNA-1273 related VITT was reported, and this case study was the first to report a cancer patient who was diagnosed with VITT after mRNA-1273 vaccination. Therefore, when the need for vaccination among cancer patients increased under the current COVID-19 pandemic, the possible risk of VITT for cancer patients should be carefully managed. Further studies of the risk evaluation of the COVID-19 vaccine in cancer patients might be required in the future.

Therapeutic prospects of mesenchymal stem/stromal cells in COVID-19 associated pulmonary diseases: From bench to bedside.

The ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the novel severe acute respiratory syndrome coronavirus 2 has become a sudden public emergency of international concern and seriously threatens millions of people's life health. Two current studies have indicated a favorable role for mesenchymal stem/stromal cells (MSCs) in clinical remission of COVID-19 associated pulmonary diseases, yet the systematical elaboration of the therapeutics and underlying mechanism is far from satisfaction. In the present review, we summarize the therapeutic potential of MSCs in COVID-19 associated pulmonary diseases such as pneumonia induced acute lung injury, acute respiratory distress syndrome, and pulmonary fibrosis. Furthermore, we review the underlying mechanism of MSCs including direct- and trans-differentiation, autocrine and paracrine anti-inflammatory effects, homing, and neovascularization, as well as constitutive microenvironment. Finally, we discuss the prospects and supervision of MSC-based cytotherapy for COVID-19 management before large-scale application in clinical practice. Collectively, this review supplies overwhelming new references for understanding the landscapes of MSCs in the remission of COVID-19 associated pulmonary diseases.

Seroprevalence of IgM and IgG Antibodies against SARS-CoV-2 in Asymptomatic People in Wuhan: Data from a General Hospital Near South China Seafood Wholesale Market during March to April in 2020.

The aim of this study was to estimate the seroprevalence of immunoglobulin M (IgM) and G (IgG) antibodies against SARS-CoV-2 in asymptomatic people in Wuhan. This was a cross-sectional study, which enrolled 18,712 asymptomatic participants from 154 work units in Wuhan. Pearson Chi-square test, t-test, and Mann-Whitney test were used to compare the standardized seroprevalence of IgG and IgM for age and gender between different groups. The results indicated the standardized seroprevalence of IgG and IgM showed a downward trend and was significantly higher among females than males. Besides, different geographic areas and workplaces had different seroprevalence of IgG among asymptomatic people, and the number of abnormalities in CT imaging were higher in IgG antibody-positive cases than IgG-negative cases. We hope these findings can provide references for herd immunity investigation and provide basis for vaccine development.

Rapid and unamplified identification of COVID-19 with morpholino-modified graphene field-effect transistor nanosensor.

SARS-CoV-2 RNA is identified as a pivotal player to bolster energizing zones of COVID-19 detection. Herein, we develop a rapid and unamplified nanosensing platform for detection of SARS-CoV-2 RNA in human throat swab specimens. A gold nanoparticle (AuNP)-decorated graphene field-effect transistor (G-FET) sensor was fabricated, after which complementary phosphorodiamidate morpholino oligos (PMO) probe was immobilized on the AuNP surface. This sensor allowed for highly sensitive testing of SARS-CoV-2 RdRp as PMO does not have charges, leading to low background signal. Not only did the method present a low limit of detection in PBS (0.37 fM), throat swab (2.29 fM), and serum (3.99 fM), but also it achieved a rapid response to COVID-19 patients' samples within 2 min. The developed nanosensor was capable of analyzing RNA extracts from 30 real clinical samples. The results show that the sensor could differentiate the healthy people from infected people, which are in high agreement with RT-PCR results (Kappa index = 0.92). Furthermore, a well-defined distinction between SARS-CoV-2 RdRp and SARS-CoV RdRp was also made. Therefore, we believe that this work provides a satisfactory, attractive option for COVID-19 diagnosis.