Viral and immunologic evaluation of smokers with severe COVID-19 (preprint)

Smoking negatively affects B cell function and immunoglobulin levels, but it is unclear if this immune dysfunction contributes to the risk of severe COVID-19 in smokers. We evaluated binding IgM, IgA and IgG antibodies to spike and receptor binding domain antigens, and used a pseudovirus assay quantify neutralization titers in a set of 27 patients with severe COVID-19. We found no significant differences between binding and neutralization antibody responses for people with a smoking history and people who never smoked. High plasma viral load, but not antibody titers, was linked to an increased risk of death. Humoral immune dysfunction was not a major driver of severe COVID-19 in smokers.

Angiotensin converting enzyme 2 activation improves allergic rhinitis and suppresses Th2 cytokine release.

OBJECTIVE: Allergic rhinitis (AR) is primarily regulated by type I hypersensitivity, with Th2 and immunoglobulin E (IgE) playing essential roles. This study aimed to determine whether angiotensin converting enzyme (ACE)2 could participate in the regulation of AR. METHODS: Nasal mucosal tissues of AR patients were collected to determine ACE2 levels. Following AR mouse models were established, ACE2 levels in nasal mucosa were determined. Then the influences of diminazene aceturate (ACE2 agonist) on AR symptoms, pathology, specific antibodies, histamine, and interleukins (ILs) release in vivo were evaluated. Afterward, human nasal mucosa epithelial cells were exposed to IL-13, and the impacts of ACE2 overexpression on the secretion of pro-inflammatory factors in vitro were assessed. RESULTS: ACE2 levels significantly declined in nasal mucosa both in patients and mouse models (p < .001). Diminazene aceturate treatment elevated the ACE2 level in mice (p < .01), accompanied by reduced frequency of nasal spray and nasal friction, decreased eosinophils and goblet cells (p < .001) according to histopathological staining. Furthermore, lgE, lgG1, histamine, and IL levels in mice were also decreased (p < .05). In vitro experiments revealed that ACE2 overexpression suppressed the secretion of pro-inflammatory factors (p < .001). CONCLUSION: Together, ACE2 activation can alleviate the symptoms of AR in mice and inhibit the release of Th2 cytokines. Activating ACE2 is a promising therapeutic approach for AR.

Time series analysis revealed prognostic value of continuous nasopharyngeal SARS-CoV-2 nucleic acid quantification for COVID-19: A retrospective study of >3000 COVID-19 patients from 2 centers.

BACKGROUND: Early stratification of disease progression remains one of the major challenges towards the post-coronavirus disease 2019 (COVID-19) era. The clinical relevance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid load is debated due to the heterogeneity in patients' underlying health conditions. We determined the prognostic value of nasopharyngeal viral load dynamic conversion for COVID-19. METHODS: The cycling threshold (Ct) values of 28,937 nasopharyngeal SARS-CoV-2 RT-PCRs were retrospectively collected from 3,364 COVID-19 patients during hospitalization and coordinated to the onset of disease progression. The ROC curve was utilized to determine the predictive performance of the rate of Ct value alteration between two consecutive RT-PCR runs within 48 h (ΔCt%) for disease transformation across patients with different COVID-19 severity and immune backgrounds, and further validated with 1,860 SARS-CoV-2 RT-PCR results from an independent validation cohort of 262 patients. For the 67 patients with severe COVID-19, Kaplan-Meier analysis was performed to evaluate the difference in survival between patients stratified by the magnitude of Ct value alteration between the late and early stages of hospitalization. RESULTS: The kinetics of viral nucleic acid conversion diversified across COVID-19 patients with different clinical characteristics and disease severities. The ΔCt% is a clinical characteristic- and host immune status-independent indicator for COVID-19 progression prediction (AUC = 0.79, 95 % CI = 0.76 to 0.81), which outperformed the canonical blood test markers, including c-reactive protein (AUC = 0.57, 95 % CI = 0.53 to 0.61), serum amyloid A (AUC = 0.61, 95 % CI = 0.54 to 0.68), lactate dehydrogenase (AUC = 0.61, 95 % CI = 0.56 to 0.67), d-dimer (AUC = 0.56, 95 % CI = 0.46 to 0.66), and lymphocyte count (AUC = 0.62, 95 % CI = 0.58 to 0.66). Patients with persistent high SARS-CoV-2 viral load (an increase of mean Ct value < 50 %) during the first 3 days of hospitalization demonstrated a significantly unfavorable survival (HR = 0.16, 95 % CI = 0.04 to 0.65, P = 2.41 × 10-3). CONCLUSIONS: Viral nucleic acid dynamics of SARS-CoV-2 eliminates the inter-patient variance of basic health conditions and therefore, can serve as a prognostic marker for COVID-19.

Investigating the impact of virtual tourism on travel intention during the post-COVID-19 era: evidence from China

This study explores the mechanism that contributes to travel intention in the field of virtual tourism. The overall research method is based on the “Stimulus-Organism-Response” theory. In the research model, the effects of content quality, system quality, and interaction quality in virtual tourism on tourism experience and travel intention are explored, as well as the role of virtual attachment and travel intention. A total of 390 respondents were invited to participate in a virtual tourism experience, and provide feedback through a questionnaire. SmartPLS 3.3.2 was used to validate the causal model, and most of the study hypotheses were supported. The findings show that virtual tourism significantly promotes travel intention. Specifically, content quality, system quality, and interaction quality positively affect tourists' travel intention through the complementary mediations of tourism experience and virtual attachment;and system quality even directly promotes travel intention. However, tourism experience does not affect virtual attachment. The present study extends prior studies on virtual tourism with SOR as a general model for field tourism experience research, while demonstrating the effectiveness of virtual tourism in promoting tourists’ travel intention. The results are useful in assisting governments with developing relevant policies and services, as well as helping tourism companies understand virtual tourism as an enhancement for tourist travel intention, thus contributing to the recovery of the tourism industry in the post-COVID-19 era.

Chinese Guideline on Allergen Immunotherapy for Allergic Rhinitis: The 2022 Update.

In the last few decades, there has been a progressive increase in the prevalence of allergic rhinitis (AR) in China, where it now affects approximately 250 million people. AR prevention and treatment include allergen avoidance, pharmacotherapy, allergen immunotherapy (AIT), and patient education, among which AIT is the only curative intervention. AIT targets the disease etiology and may potentially modify the immune system as well as induce allergen-specific immune tolerance in patients with AR. In 2017, a team of experts from the Chinese Society of Allergy (CSA) and the Chinese Allergic Rhinitis Collaborative Research Group (C2AR2G) produced the first English version of Chinese AIT guidelines for AR. Since then, there has been considerable progress in basic research of and clinical practice for AIT, especially regarding the role of follicular regulatory T (TFR) cells in the pathogenesis of AR and the use of allergen-specific immunoglobulin E (sIgE) in nasal secretions for the diagnosis of AR. Additionally, potential biomarkers, including TFR cells, sIgG4, and sIgE, have been used to monitor the incidence and progression of AR. Moreover, there has been a novel understanding of AIT during the coronavirus disease 2019 pandemic. Hence, there was an urgent need to update the AIT guideline for AR by a team of experts from CSA and C2AR2G. This document aims to serve as professional reference material on AIT for AR treatment in China, thus improving the development of AIT across the world.

Using machine learning models to predict the duration of the recovery of COVID-19 patients hospitalized in Fangcang shelter hospital during the Omicron BA. 2.2 pandemic.

Background: Factors that may influence the recovery of patients with confirmed SARS-CoV-2 infection hospitalized in the Fangcang shelter were explored, and machine learning models were constructed to predict the duration of recovery during the Omicron BA. 2.2 pandemic. Methods: A retrospective study was conducted at Hongqiao National Exhibition and Convention Center Fangcang shelter (Shanghai, China) from April 9, 2022 to April 25, 2022. The demographics, clinical data, inoculation history, and recovery information of the 13,162 enrolled participants were collected. A multivariable logistic regression model was used to identify independent factors associated with 7-day recovery and 14-day recovery. Machine learning algorithms (DT, SVM, RF, DT/AdaBoost, AdaBoost, SMOTEENN/DT, SMOTEENN/SVM, SMOTEENN/RF, SMOTEENN+DT/AdaBoost, and SMOTEENN/AdaBoost) were used to build models for predicting 7-day and 14-day recovery. Results: Of the 13,162 patients in the study, the median duration of recovery was 8 days (interquartile range IQR, 6-10 d), 41.31% recovered within 7 days, and 94.83% recovered within 14 days. Univariate analysis showed that the administrative region, age, cough medicine, comorbidities, diabetes, coronary artery disease (CAD), hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were associated with a duration of recovery within 7 days. Age, gender, vaccination dose, cough medicine, comorbidities, diabetes, CAD, hypertension, number of comorbidities, CT value of the ORF gene, CT value of the N gene, ratio of ORF/IC, and ratio of N/IC were related to a duration of recovery within 14 days. In the multivariable analysis, the receipt of two doses of the vaccination vs. unvaccinated (OR = 1.118, 95% CI = 1.003-1.248; p = 0.045), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.114, 95% CI = 1.004-1.236; p = 0.043), diabetes (OR = 0.383, 95% CI = 0.194-0.749; p = 0.005), CAD (OR = 0.107, 95% CI = 0.016-0.421; p = 0.005), hypertension (OR = 0.371, 95% CI = 0.202-0.674; p = 0.001), and ratio of N/IC (OR = 3.686, 95% CI = 2.939-4.629; p < 0.001) were significantly and independently associated with a duration of recovery within 7 days. Gender (OR = 0.736, 95% CI = 0.63-0.861; p < 0.001), age (30-70) (OR = 0.738, 95% CI = 0.594-0.911; p < 0.001), age (>70) (OR = 0.38, 95% CI = 0292-0.494; p < 0.001), receipt of three doses of the vaccination vs. unvaccinated (OR = 1.391, 95% CI = 1.12-1.719; p = 0.0033), cough medicine (OR = 1.509, 95% CI = 1.075-2.19; p = 0.023), and symptoms (OR = 1.619, 95% CI = 1.306-2.028; p < 0.001) were significantly and independently associated with a duration of recovery within 14 days. The SMOTEEN/RF algorithm performed best, with an accuracy of 90.32%, sensitivity of 92.22%, specificity of 88.31%, F1 score of 90.71%, and AUC of 89.75% for the 7-day recovery prediction; and an accuracy of 93.81%, sensitivity of 93.40%, specificity of 93.81%, F1 score of 93.42%, and AUC of 93.53% for the 14-day recovery prediction. Conclusion: Age and vaccination dose were factors robustly associated with accelerated recovery both on day 7 and day 14 from the onset of disease during the Omicron BA. 2.2 wave. The results suggest that the SMOTEEN/RF-based model could be used to predict the probability of 7-day and 14-day recovery from the Omicron variant of SARS-CoV-2 infection for COVID-19 prevention and control policy in other regions or countries. This may also help to generate external validation for the model.

Aptamer-Gated Mesoporous Silica Nanoparticles for N Protein Triggered Release of Remdesivir and Treatment of Novel Coronavirus (2019-nCoV).

Since the 2019-nCoV outbreak was first reported, hundreds of millions of people all over the world have been infected. There is no doubt that improving the cure rate of 2019-nCoV is one of the most effective means to deal with the current serious epidemic. At present, Remdesivir (RDV) has been clinically proven to be effective in the treatment of SARS-CoV-2. However, the uncertain side effects make it important to reduce the use of drugs while ensuring the self-healing effect. We report an approach here with targeted therapy for the treatment of SARS-CoV-2 and other coronaviruses illness. In this study, mesoporous silica was used as the carrier of RDV, the nucleocapsid protein (N protein) aptamer was hybridized with the complementary chain, and the double-stranded DNA was combined with gold nanoparticles as the gates of mesoporous silica pores. When the RDV-loaded mesoporous silica is incubated with the N protein, aptamer with gold nanoparticles dissociate from the complementary DNA oligonucleotide on the mesoporous silica surface and bind to the N protein. The releasing of RDV was determined by detecting the UV-vis absorption peak of RDV in the solution. These results show that the RDV delivery system designed in this work has potential clinical application for the treatment of 2019-nCoV.

Intelligent Campus System Design Based on Digital Twin

Amid the COVID-19 pandemic, prevention and control measures became normalized, prompting the development of campuses from digital to intelligent, eventually evolving to become wise. Current cutting-edge technologies include big data, Internet of Things, cloud computing, and artificial intelligence drive campus innovation, but there are still problems of unintuitive scenes, lagging monitoring information, untimely processing, and high operation and maintenance costs. Based on this, this study proposes the use of digital twin technology to digitally construct the physical campus scene, fully digitally represent it, accurately map the physical campus to the virtual campus with real-time sensing, and remotely control it to achieve the reverse control of the twin virtual campus to the physical campus. The research is guided by the theoretical model proposed by the digital twin technology, using UAV tilt photography and 3D modelling to collaboratively build the virtual campus scene. At the design stage, the interactive channel of the system is developed based on Unity3D to the realize real-time monitoring, decision making and prevention of dual spatial data. A design scheme of the spiral optimization system life cycle is formed. The modules of the smart campus system were evaluated using a system usability scale based on student experience. The experimental results show that the virtual-real campus system can enhance school management and teaching, providing important implications for promoting the development and application of campus intelligent systems.

Online public attention toward allergic rhinitis in Wuhan, China: Infodemiology study using Baidu index and meteorological data.

Background: With the popularization of the Internet and medical knowledge, more and more people are learning about allergic rhinitis (AR) on the Internet. Objective: This study aims to analyze the epidemiological characteristics and online public attention to AR in Wuhan, China, utilizing the most popular search engine in mainland China and meteorological data of Wuhan. Methods: To study the Internet attention and epidemiological characteristics of AR in Wuhan, the search volume (SV) of "Allergic Rhinitis" in Mandarin and AR-related search terms from 1 January 2014 through 31 December 2021 were recorded. For user interest, the search and demand data were collected and analyzed. Results: The yearly average Baidu SV of AR in both Wuhan and China increased year by year but began to decline gradually after the COVID-19 pandemic. Baidu SV of AR in Wuhan exhibited significant seasonal variation, with the first peak was from March to May and the second peak occurring between September and October. Correlation analysis revealed a moderate positive correlation between the monthly average SV of "Allergic Rhinitis" and "Mites" and "Mites + Pollen Allergy" in Wuhan, a weak positive correlation between the monthly average SV of "Allergic Rhinitis" and "Pollen Allergy," and a positive correlation between monthly SV of "Allergic Rhinitis" and the meteorological index of pollen allergy (MIPA). Conclusion: The attention given to the topic on the internet, as measured by the search volume, was reflective of the situation in Wuhan, China. It has the potential to predict the epidemiological characteristics of AR and help medical professionals more effectively plan seasonal AR health education.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

Comparative clinical analysis of fever in tumor patients, normal patients, and those infected with new coronavirus pneumonia.

BACKGROUND: Under the current epidemic of the coronavirus disease of 2019 (COVID-19), there is a need to distinguish the differences between the laboratory examinations of COVID-19-infected patients, tumor patients with fever, and those with normal fever patients. We aimed to investigate the temperature of tumor patients with different tumor burdens, stages, and cancer types. METHODS: We recruited 3 groups of patients to this study: fever patients with malignant tumors, ordinary fever patients, and confirmed cases of COVID-19, with 31, 55, and 28 cases in each group, respectively. RESULTS: The levels of leukocytes and neutrophils were the highest among non-tumor patients, and the count of COVID-19 was the lowest, with a P value of 0.000. Among the leukocytosis group, non-tumor patients had the highest proportion (43.6%), while that of COVID-19 was only 3.6% (P=0.000). Similarly, there were significant differences in the grading of neutrophils, where most of the infected patients were in the normal group and the P value was 0.000. The lymphocyte count of the tumor group was significantly reduced, with an average of (0.97±0.66) ×109/L (P=0.004). In the lymphocyte grades, most of the infected patients were the normal group (71.4%), while tumor patients in the lymphocytopenia group accounted for 63.1% (P=0.006). There were also significant differences in the neutrophil to lymphocyte ratio (NLR) (P=0.006). There was a significant difference in temperature between different tumor burden groups (P=0.014). CONCLUSIONS: The normal fever group had the highest count of leukocyte and neutrophils, whereas the infected group had the lowest relative count. The NLR was the lowest in the infected group. The NLR was higher in the bigger tumor load group.

Detection of Antibodies Against the SARS-CoV-2 Spike Protein and Analysis of the Peripheral Blood Mononuclear Cell Transcriptomic Profile, 15 Years After Recovery From SARS.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows a high degree of homology with SARS-CoV. They share genes, protein sequences, clinical manifestations, and cellular entry patterns. Thus, SARS research may serve helpful in gaining a better understanding of the current coronavirus disease 2019 (COVID-19) pandemic. Serum antibodies from convalescent patients with SARS collected in 2018 were used to target the recombinant SARS-CoV-2 spike protein via a chemiluminescence microsphere immunoassay. Antibodies of convalescent patients with SARS exhibited serous immune cross-reactivity with the SARS-CoV-2 spike protein. The serous antibodies, excluding S22 of convalescent patients with SARS, did not competitively inhibit the binding of SARS-CoV-2 spike protein to ACE2. T cellular immunity research was conducted in vitro using peripheral blood mononuclear cells (PBMCs) stimulated by pooled peptide epitopes 15 years post-infection. Interferon gamma was detected and the PBMC transcriptomic profile was obtained. The heatmap of the transcriptomic profile showed that mRNAs and circRNAs of the SARS group clustered together after being stimulated by the peptide epitope pool. Differentially expressed mRNAs were most significantly enriched in immunity and signal transduction (P < 0.01). SARS elicits cytokine and chemokine responses, partially consistent with previously published data about COVID-19. Overall, our results indicate that antibodies from convalescent patients with SARS persisted for 15 years and displayed immune cross-reactivity with the SARS-CoV-2 spike protein. The immune status of patients with SARS 15 years post-infection may provide a better understanding of the future immune status of patients with COVID-19.

Evolution of the plandemic communication network among serial participants on Twitter

The coronavirus pandemic has been accompanied by the spread of misinformation on social media. The Plandemic conspiracy theory holds that the pandemic outbreak was planned to create a new social order. This study examines the evolution of this popular conspiracy theory from a dynamic network perspective. Guided by the analytical framework of network evolution, the current study explores drivers of tie changes in the Plandemic communication network among serial participants over a 4-month period. Results show that tie changes are explained by degree-based and closure-based structural features (i.e. tendencies toward transitive closure and shared popularity and tendencies against in-degree activity and transitive reciprocated triplet) and nodal attributes (i.e. bot probability and political preference). However, a participant’s level of anger expression does not predict the evolution of the observed network. [ABSTRACT FROM AUTHOR] Copyright of New Media & Society is the property of Sage Publications, Ltd. 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Establishment of Routine Clinical Indicators-Based Nomograms for Predicting the Mortality in Patients With COVID-19.

This study aimed to establish and validate the nomograms to predict the mortality risk of patients with coronavirus disease 2019 (COVID-19) using routine clinical indicators. This retrospective study included a development cohort enrolled 2,119 hospitalized patients with COVID-19 and a validation cohort included 1,504 patients with COVID-19. The demographics, clinical manifestations, vital signs, and laboratory tests of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct the two prognostic nomograms. The nomogram 1 was a full model to include nine factors identified in the multivariate logistic regression and nomogram 2 was built by selecting four factors from nine to perform as a reduced model. The nomogram 1 and nomogram 2 showed better performance in discrimination and calibration than the Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) score in training. In validation, nomogram 1 performed better than nomogram 2 for calibration. We recommend the application of nomogram 1 in general hospitals which provide robust prognostic performance though more cumbersome; nomogram 2 in the out-patient, emergency department, and mobile cabin hospitals, which depend on less laboratory examinations to make the assessment more convenient. Both the nomograms can help the clinicians to identify the patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.

Early kidney injury predicts disease progression in patients with COVID-19: a cohort study.

BACKGROUND: The receptor of severe respiratory syndrome coronavirus 2 (SARS-CoV-2), angiotensin-converting enzyme 2, is more abundant in kidney than in lung tissue, suggesting that kidney might be another important target organ for SARS-CoV-2. However, our understanding of kidney injury caused by Coronavirus Disease 2019 (COVID-19) is limited. This study aimed to explore the association between kidney injury and disease progression in patients with COVID-19. METHODS: A retrospective cohort study was designed by including 2630 patients with confirmed COVID-19 from Huoshenshan Hospital (Wuhan, China) from 1 February to 13 April 2020. Kidney function indexes and other clinical information were extracted from the electronic medical record system. Associations between kidney function indexes and disease progression were analyzed using Cox proportional-hazards regression and generalized linear mixed model. RESULTS: We found that estimated glomerular filtration rate (eGFR) and creatinine clearance (Ccr) decreased in 22.0% and 24.0% of patients with COVID-19, respectively. Proteinuria was detected in 15.0% patients and hematuria was detected in 8.1% of patients. Hematuria (HR 2.38, 95% CI 1.50-3.78), proteinuria (HR 2.16, 95% CI 1.33-3.51), elevated baseline serum creatinine (HR 2.84, 95% CI 1.92-4.21) and blood urea nitrogen (HR 3.54, 95% CI 2.36-5.31), and decrease baseline eGFR (HR 1.58, 95% CI 1.07-2.34) were found to be independent risk factors for disease progression after adjusted confounders. Generalized linear mixed model analysis showed that the dynamic trajectories of uric acid was significantly related to disease progression. CONCLUSION: There was a high proportion of early kidney function injury in COVID-19 patients on admission. Early kidney injury could help clinicians to identify patients with poor prognosis at an early stage.

Fasting plasma glucose and glucose fluctuation are associated with COVID-19 prognosis regardless of pre-existing diabetes.

AIMS: We aimed to investigate the role of Fasting Plasma Glucose (FPG) and glucose fluctuation in the prognosis of COVID-19 patients stratified by pre-existing diabetes. METHODS: The associations of FPG and glucose fluctuation indexes with prognosis of COVID-19 in 2,642 patients were investigated by multivariate Cox regression analysis. The primary outcome was in-hospital mortality; the secondary outcome was disease progression. The longitudinal changes of FPG over time were analyzed by the latent growth curve model in COVID-19 patients stratified by diabetes and severity of COVID-19. RESULTS: We found FPG as an independent prognostic factor of overall survival after adjustment for age, sex, diabetes and severity of COVID-19 at admission (HR: 1.15, 95% CI: 1.06-1.25, P = 1.02 × 10-3). Multivariate logistic regression analysis indicated that the standard deviation of blood glucose (SDBG) and largest amplitude of glycemic excursions (LAGE) were also independent risk factors of COVID-19 progression (P = 0.03 and 0.04, respectively). The growth trajectory of FPG over the first 3 days of hospitalization was steeper in patients with critical COVID-19 in comparison to moderate patients. CONCLUSIONS: Hyperglycemia and glucose fluctuation were adverse prognostic factors of COVID-19 regardless of pre-existing diabetes. This stresses the importance of glycemic control in addition to other therapeutic management.

Emotion Generation and Transition of Companion Robots Based on Plutchik’s Model and Quantum Circuit Schemes

Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik’s wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users’ social health by mitigating their negative emotions, especially during difficult times.

Risks, characteristics, and control strategies of disinfection-residual-bacteria (DRB) from the perspective of microbial community structure.

The epidemic of COVID-19 has aroused people's particular attention to biosafety. A growing number of disinfection products have been consumed during this period. However, the flaw of disinfection has not received enough attention, especially in water treatment processes. While cutting down the quantity of microorganisms, disinfection processes exert a considerable selection effect on bacteria and thus reshape the microbial community structure to a great extent, causing the problem of disinfection-residual-bacteria (DRB). These systematic and profound changes could lead to the shift in regrowth potential, bio fouling potential, as well as antibiotic resistance level and might cause a series of potential risks. In this review, we collected and summarized the data from the literature in recent 10 years about the microbial community structure shifting of natural water or wastewater in full-scale treatment plants caused by disinfection. Based on these data, typical DRB with the most reporting frequency after disinfection by chlorine-containing disinfectants, ozone disinfection, and ultraviolet disinfection were identified and summarized, which were the bacteria with a relative abundance of over 5% in the residual bacteria community and the bacteria with an increasing rate of relative abundance over 100% after disinfection. Furthermore, the phylogenic relationship and potential risks of these typical DRB were also analyzed. Twelve out of fifteen typical DRB genera contain pathogenic strains, and many were reported of great secretion ability. Pseudomonas and Acinetobacter possess multiple disinfection resistance and could be considered as model bacteria in future studies of disinfection. We also discussed the growth, secretion, and antibiotic resistance characteristics of DRB, as well as possible control strategies. The DRB phenomenon is not limited to water treatment but also exists in the air and solid disinfection processes, which need more attention and more profound research, especially in the period of COVID-19.