Factors Influencing Consumers' Organic Food Continuous Purchase Intentions during the Post-Pandemic Era: An Empirical Investigation in China.

During the evolutionary phases of the COVID-19 pandemic, consumers' eating habits and spending behaviours have progressively shifted to the pursuit of the safer and healthier food products, such as organic food. Therefore, this study investigated the factors affecting Chinese buyers' organic food continuous purchase intentions (CPI) during the post-pandemic era. To better adapt to the current consumption context in China, this study proposed a modified TPB framework (M-TPB), by replacing subjective norms with Chinese cultural variables, such as face consciousness and group conformity, and adding constructs of perceived value of organic food (PVOF), health consciousness, and the impact of COVID-19 (IOC). Convincingly, experimental results from a structural equation model analysis of 460 usable responses indicate that M-TPB has superior explanatory power (R2 = 65%) compared with the TPB model (R2 = 40%) for explaining organic food CPI during the post-pandemic period. The path analysis demonstrated that perceived behavioural control, attitude, face consciousness, group conformity, health consciousness, IOC, and PVOF had substantial positive effects on CPI, while subjective norms were not significantly related. Moreover, IOC exhibited a positive and significant relationship with health consciousness and PVOF. These findings can be useful for stakeholders in the Chinese organic food industry to generate timely promoting strategies during the post-pandemic period.

Combined effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression: A cross-sectional study based on UK Biobank.

Infection-induced perturbation of immune homeostasis could promote psychopathology. Psychiatric sequelae have been observed after previous coronavirus outbreaks. However, limited studies were conducted to explore the potential interaction effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression. In this study, first, polygenic risk scores (PRS) were calculated for eight COVID-19 clinical phenotypes using individual-level genotype data from the UK Biobank. Then, linear regression models were developed to assess the effects of COVID-19 PRS, C-reactive protein (CRP), systemic immune inflammation index (SII), and their interaction effects on the Generalized Anxiety Disorder-7 (GAD-7, 104 783 individuals) score and the Patient Health Questionnaire-9 (PHQ-9, 104 346 individuals) score. Several suggestive interactions between inflammation factors and COVID-19 clinical phenotypes were detected for PHQ-9 score, such as CRP/SII × Hospitalized/Not_Hospitalized in women group and CRP × Hospitalized/Unscreened in age >65 years group. For GAD-7 score, we also found several suggestive interactions, such as CRP × Positive/Unscreened in the age ≤65 years group. Our results suggest that not only COVID-19 and inflammation have important effects on anxiety and depression but also the interactions of COVID-19 and inflammation have serious risks for anxiety and depression.

The association between the fibrosis-4 index and COVID-19: a systematic review and meta-analysis

Objective: The prognosis value of fibrosis-4 score (FIB-4) in COVID-19 is controversial. Hence, we conducted a systematic review and meta-analysis to investigate the association between the FIB-4 index and COVID-19 disease progression. Methods: We performed meta-analysis using the PubMed, Embase, and Cochrane databases. A fixed- or random-effects model was used for evaluating heterogeneity. Results: Thirteen studies were included. The meta-analysis of unadjusted results showed that compared to lower FIB-4 index, patients with higher FIB-4 index had increased odds of mortality (OR=5.1, 95%CI 3.67-7.09;P < 0.001), ICU admission (OR=2.32, 95%CI: 1.65-3.25, P < 0.00001) and need for mechanical ventilator support (OR=3.51, 95%CI: 2.1-5.85, P < 0.001). In addition, the meta-analysis of adjusted results showed patients with higher FIB-4 index was associated with increased risk of mortality (OR=3.01, 95%CI: 2.21-4.09, P < 0.001) and need for mechanical ventilator support (OR=3.76, 95%CI: 2.08-6.82, P < 0.001) compared to patients with lower FIB-4 index. Conclusions: This meta-analysis indicated that high FIB-4 index score was associated with the severity and mortality in COVID-19 infected patients.

A highly efficient needle-free-injection delivery system for mRNA-LNP vaccination against SARS-CoV-2.

Despite the various vaccines that have been developed to combat the coronavirus disease 2019 (COVID-19) pandemic, the persistent and unpredictable mutations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) require innovative and unremitting solutions to cope with the resultant immune evasion and establish a sustainable immune barrier. Here we introduce a vaccine-delivery system with a combination of a needle-free injection (NFI) device and a SARS-CoV-2-Spike-specific mRNA-Lipid Nanoparticle (LNP) vaccine. The benefits are duller pain and a significant increase of immunogenicity compared to the canonical needle injection (NI). From physicochemical and bioactivity analyses, the structure of the mRNA-LNP maintains stability upon NFI, contradictory to the belief that LNPs are inclined towards destruction under the high-pressure conditions of NFI. Moreover, mRNA-LNP vaccine delivered by NFI induces significantly more binding and neutralizing antibodies against SARS-CoV-2 variants than the same vaccine delivered by NI. Heterogeneous vaccination of BA.5-LNP vaccine with NFI enhanced the generation of neutralizing antibodies against Omicron BA.5 variants in rabbits previously vaccinated with non-BA.5-specific mRNA-LNP or other COVID-19 vaccines. NFI parameters can be adjusted to deliver mRNA-LNP subcutaneously or intramuscularly. Taken together, our results suggest that NFI-based mRNA-LNP vaccination is an effective substitute for the traditional NI-based mRNA-LNP vaccination.

The Association between the Fibrosis-4 Index and COVID-19: A Systematic Review and Meta-Analysis.

OBJECTIVE: The prognosis value of fibrosis-4 score (FIB-4) in COVID-19 is controversial. Hence, we conducted a systematic review and meta-analysis to investigate the association between the FIB-4 index and COVID-19 disease progression. METHODS: We performed meta-analysis using the PubMed, Embase, and Cochrane databases. A fixed- or random-effects model was used for evaluating heterogeneity. RESULTS: Thirteen studies were included. The meta-analysis of unadjusted results showed that compared to lower FIB-4 index, patients with higher FIB-4 index had increased odds of mortality (OR=5.1, 95%CI 3.67-7.09; P<0.001), ICU admission (OR=2.32, 95%CI: 1.65-3.25, P<0.00001) and need for mechanical ventilator support (OR=3.51, 95%CI: 2.1-5.85, P<0.001). In addition, the meta-analysis of adjusted results showed patients with higher FIB-4 index was associated with increased risk of mortality (OR=3.01, 95%CI: 2.21-4.09, P<0.001) and need for mechanical ventilator support (OR=3.76, 95%CI: 2.08-6.82, P<0.001) compared to patients with lower FIB-4 index. CONCLUSIONS: This meta-analysis indicated that high FIB-4 index score was associated with the severity and mortality in COVID-19 infected patients.

Novel skewed usage of B-cell receptors in COVID-19 patients with various clinical presentations.

B cell-mediated immune responses play important roles in controlling SARS-CoV infection. Here, we performed the single-cell B cell receptor sequencing (scBCR-seq) of the PBMC samples from eleven healthy controls, five asymptomatic subjects and 33 symptomatic COVID-19 patients with various clinical presentations, and subsequently analyzed the abundance and diversity of the BCR repertoires in different groups, respectively. We revealed the skewed usage of the IGHV, IGLV and IGKV genes and identified a number of heavy or light chain VDJ gene pairs and combinational preference in each group, such as IGKV3-7 and IGKV2-24 enriched in the asymptomatic subjects, whereas IGHV3-13, IGHV3-23-IGHJ4, IGHV1-18-IGLV3-19, IGHV1-18-IGLV3-21, and IGHV1-18-IGLV3-25 enriched in the recovery patients with severe diseases. We also observed the differential expression of IGHV3-23 in various B cell clusters by analysis of the scRNA-seq data. Additional dock analysis indicated that IGHV3-13 could bind to the spike protein of SARS-CoV-2. These findings may advance our understanding of the humoral immune responses in COVID-19 patients and help develop novel vaccine candidates as well as therapeutical antibodies against SASR-CoV-2 infections.

Identification of TCR repertoires in asymptomatic COVID-19 patients by single-cell T-cell receptor sequencing.

The T cell-mediated immune responses associated with asymptomatic infection (AS) of SARS-CoV-2 remain largely unknown. The diversity of T-cell receptor (TCR) repertoire is essential for generating effective immunity against viral infections in T cell response. Here, we performed the single-cell TCR sequencing of the PBMC samples from five AS subjects, 33 symptomatic COVID-19 patients and eleven healthy controls to investigate the size and the diversity of TCR repertoire. We subsequently analyzed the TCR repertoire diversity, the V and J gene segment deference, and the dominant combination of αß VJ gene pairing among these three study groups. Notably, we revealed significant TCR preference in the AS group, including the skewed usage of TRAV1-2-J33-TRBV6-4-J2-2 and TRAV1-2-J33-TRBV6-1-J2-3. Our findings may shed new light on understanding the immunopathogenesis of COVID-19 and help identify optimal TCRs for development of novel therapeutic strategies against SARS-CoV-2 infection.

Identification of Distinct Immune Cell Subsets Associated With Asymptomatic Infection, Disease Severity, and Viral Persistence in COVID-19 Patients.

The cell-mediated protective and pathogenic immune responses to SARS-CoV-2 infection remain largely elusive. Here we identified 76 distinct cell subsets in the PBMC samples that were associated with various clinical presentations of COVID-19 using scRNA-seq technology coupled with a deep and comprehensive analysis of unique cell surface markers and differentially expressed genes. We revealed that (TRAV1-2+CD8+)MAIT cells and (NCAM1hiCD160+)NK cells significantly enriched in the asymptomatic subjects whereas (LAG3+CD160+CD8+)NKT cells increased in the symptomatic patients. We also observed that (CD68-CSF1R-IL1BhiCD14+)classical monocytes were positively correlated with the disease severity. Moreover, (CD33-HLA-DMA-CD14+)classical monocytes and (CLEC10A-S100A9lo)pDC were associated with the viral persistence. The GO and KEGG analyses identified enriched pathways related to immune responses, inflammation, and apoptosis. These findings may enhance our understanding of the immunopathogenesis of COVID-19 and help develop novel strategies against SARS-CoV-2 infection.

Predicting the Disease Severity of Virus Infection.

The COVID-19 pandemic has resulted in unprecedented burden on global health and economic systems, promoting worldwide efforts to understand, control, and fight the disease. Due to the wide spectrum of clinical severity, effective risk factors, biomarkers, and models for predicting disease severity and mortality in COVID-19 patients are urgently needed to provide guidance for clinical intervention and management. In this chapter, we first describe the infection features of different COVID-19 strains and the potential of clinical features, cytokine storm and biomarkers in predicting the severity of COVID-19 patients. We focus on how scoring systems, mathematical models and artificial intelligence (AI)-based models can promote the classification of COVID-19 severity at the population or individual level. Moreover, the development perspective of biomarkers and models for predicting the severity of COVID-19 is prospected. Therefore, this chapter highlights the clinical significance of biomarkers and models related to COVID-19 severity and provides important clues for improving the outcomes of COVID-19 patients, thereby facilitating timely disease assessment and precision medicine for individual COVID-19 patients.

Databases, Knowledgebases, and Software Tools for Virus Informatics.

Virus infection is a common social health issue. In the past decades, serious virus infectious events have caused great loss in people's life and the economics. The nature of rapid widespread and frequent variation increases the difficulty for precision viral prevention and treatment. In the era of big data and artificial intelligence (AI), advances in bioinformatics techniques bring unprecedented opportunities for virus informatics study, which contribute to the systems-level modeling of virus biology. In this chapter, data resources including virus-related databases and knowledgebases are introduced. Bioinformatics models and software tools for multiple sequence alignment, evolutionary analysis, and genome-wide research of viruses are summarized and emphasized. Translational applications of recently developed data-driven and AI-assisted methods to viral cases such as SARS-CoV-2, HBV/HCV, and influenza virus are discussed. Finally, the concept and significance of virus informatics are highlighted for both virus surveillance and health promotion.

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection.

Opioid usage and COVID-19 prognosis: A systematic review and meta-analysis.

The COVID-19 pandemic continues to have profound health, social, psychological, and economic ramifications. Infection by COVID-19 has been of concern in people who use opioids, as opioid use has been known to mediate immunosuppression and is associated with respiratory depression and end-organ damage. With differing modalities of opioid usage, the association between opioids and COVID-19 outcomes is not well understood. We performed a comprehensive systematic search of seven health science databases, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang Data, up to December 15, 2021. We identified a total of five related articles, which were included in this study. The meta-analysis showed that opioids have a significant association with ICU admission for COVID-19 patients (OR = 5.41, 95%CI: 1.85 to 15.79, P = 0.002). Use of opioids was also associated with higher mortality among patients with COVID-19 compared to non-users (OR = 2.74, 95%CI: 1.34 to 5.62, P = 0.034), while use of opioids was not significantly associated with need for mechanical ventilation (OR = 3.68, 95%CI: 0.85 to 15.90, P = 0.081). Furthermore, the adjusted analysis indicated that COVID-19 patients with a history of opioid use were more likely to be admitted to the ICU (OR = 3.57, 95%CI: 3.05 to 4.17, P<0.001) and have higher mortality rates (OR = 1.72, 95%CI: 1.09 to 2.72, P = 0.02), while there was no significant association with need for mechanical ventilation (OR = 2.09, 95%CI: 0.77 to 5.64, P = 0.146). Significant heterogeneity existed across the included studies. Patients using opioids with COVID-19 were at higher risk of ICU admission and mortality. Prospective studies are required to confirm these findings.

Intravenous vitamin C use and risk of severity and mortality in COVID-19: A systematic review and meta-analysis.

The administration of intravenous vitamin C (IV-VC) in treating patients with coronavirus disease 2019 (COVID-19) is still highly controversial. There have been no previous studies on the effect of IV-VC on the severity and mortality of COVID-19. Hence, we conducted a systematic review and meta-analysis to compare the disease severity and mortality in patients with COVID-19 who promptly received IV-VC treatment vs those who did not. We performed a comprehensive systematic search of seven health science databases, including PubMed, Embase, Cochrane Library, MEDLINE, Web of Science, China National Knowledge Infrastructure, and Wanfang Data, up to June 23, 2021. We identified a total of seven related articles, which were included in this study. This meta-analysis showed that IV-VC treatment did not affect disease severity compared with placebo treatment or usual care (odds ratio [OR], 0.70; 95% CI, 0.45 to 1.07; P = 0.10). In addition, no statistically significant difference in mortality was observed between patients who received IV-VC treatment and those who did not (OR, 0.64; 95% CI, 0.41 to 1.00; P = 0.05). Moreover, the adjusted meta-analysis revealed that the use of IV-VC did not influence disease severity (OR, 0.67; 95% CI, 0.34 to 1.31; P = 0.242) or mortality (OR, 1.02; 95% CI, 0.75 to 1.40; P = 0.877) in comparison with a control group. The results of this meta-analysis demonstrated that short-term IV-VC treatment did not reduce the risk of severity and mortality in patients with COVID-19.

Histone demethylase LSD1 promotes RIG-I poly-ubiquitination and anti-viral gene expression.

Under RNA virus infection, retinoic acid-inducible gene I (RIG-I) in host cells recognizes viral RNA and activates the expression of type I IFN. To investigate the roles of protein methyltransferases and demethylases in RIG-I antiviral signaling pathway, we screened all the known related enzymes with a siRNA library and identified LSD1 as a positive regulator for RIG-I signaling. Exogenous expression of LSD1 enhances RIG-I signaling activated by virus stimulation, whereas its deficiency restricts it. LSD1 interacts with RIG-I, promotes its K63-linked polyubiquitination and interaction with VISA/MAVS. Interestingly, LSD1 exerts its function in antiviral response not dependent on its demethylase activity but through enhancing the interaction between RIG-I with E3 ligases, especially TRIM25. Furthermore, we provide in vivo evidence that LSD1 increases antiviral gene expression and inhibits viral replication. Taken together, our findings demonstrate that LSD1 is a positive regulator of signaling pathway triggered by RNA-virus through mediating RIG-I polyubiquitination.

Spatial pattern of COVID-19 in Bangladesh: an ecological study

ObjectiveTo analyse the spatial clustering of COVID-19 case fatality risks in the districts of Bangladesh and to explore the association of sociodemographic indicators with these risks.Study designEcological study.Study settingSecondary data were collected for a total of 64 districts of Bangladesh.MethodsThe data for district-wise COVID-19 cases were collected from the Ministry of Health and Family Welfare, Bangladesh from March 2020 to June 2020. Socioeconomic and demographic data were collected from National Census Data, 2011. Retrospective spatial analysis was conducted based on district-wise COVID-19 cases in Bangladesh. Global Moran’s I was adopted to find out the significance of the clusters. Furthermore, generalised linear model was conducted to find out the association of COVID-19 cases with sociodemographic variables.ResultsTotal 87 054 COVID-19 cases were included in this study. The epidemic hotspots were distributed in the 11 most populous cities. The most likely clusters are primarily situated in the central, south-eastern and north-western regions of the country. High-risk clusters were found in Dhaka (Relative Risk (RR): 5.22), Narayanganj (RR: 2.70), Chittagong (RR: 1.69), Munshiganj (RR: 2.31) Cox’s Bazar (RR: 1.63), Faridpur (RR: 1.65), Gazipur (RR: 1.33), Bogra (RR: 1.35), Khulna (RR: 1.22), Barishal (RR: 1.07) and Noakhali (RR: 1.06). Weekly progression of COVID-19 cases showed spatially clustered by Moran’s I statistics (p value ranging from 0.013 to 0.436). After fitting a Poisson linear model, we found a positive association of COVID-19 with floating population rate (RR=1.542, 95% CI 1.520 to 1.564), and urban population rate (RR=1.027, 95% CI 1.026 to 1.028).ConclusionThis study found the high-risk cluster areas in Bangladesh and analysed the basic epidemiological issues;further study is needed to find out the common risk behaviour of the patients and other relative issues that involve the spreading of this infectious disease.

Exploring Chinese Consumers' Online Purchase Intentions toward Certified Food Products during the COVID-19 Pandemic.

The outbreak of COVID-19 has significantly increased consumers' demands for online groceries, as well as healthy, safe, and better-quality food products. In China, certified food products are commonly perceived as safe and good-quality products. Therefore, this study investigated potential factors that influenced Chinese consumers' online shopping intentions toward certified food during the COVID-19 crisis. An integrated model was proposed by combining the technology acceptance model (TAM) and the theory of planned behaviour (TPB) with the impact of COVID-19 (IOC). The empirical results of structural equation modelling analysis with 491 usable responses revealed that the proposed model showed a good model fit and satisfactory explanatory power (R2 = 53%) regarding consumers' certified online food shopping intentions during the pandemic. The path analysis demonstrated that attitude, perceived behavioural control, perceived usefulness (PU), and IOC significantly affected consumers' online purchase intentions of certified food. PU and perceived ease of use (PEOU) were important drivers of attitudes, and PEOU significantly influenced PU. Moreover, the IOC was significantly related to most factors, except subjective norms. These findings can be useful for detecting changes in consumer behaviour, and providing suitable strategic implications for stakeholders in the Chinese certified food sector during the current and post-pandemic eras.

Risk factors for mortality in hemodialysis patients with COVID-19: a systematic review and meta-analysis.

BACKGROUND: New evidence from studies on risk factors for mortality in hemodialysis (HD) patients with COVID-19 became available. We aimed to review the clinical risk factors for fatal outcomes in these patients. METHODS: We performed meta-analysis using the PubMed, EMBASE, and Cochrane databases. A fixed- or random-effects model was used for calculating heterogeneity. We used contour-enhanced funnel plot and Egger's tests to assess potential publication bias. RESULTS: Twenty-one studies were included. The proportion of males was lower in the survivor group than in the non-survivor group (OR = 0.75, 95% CI [0.61, 0.94]). The proportion of respiratory diseases was significantly lower in the survivor group than in the non-survivor group (OR = 0.42, 95% CI [0.29, 0.60]). The proportion of patients with fever, cough, and dyspnea was significantly lower in the survivor group (fever: OR = 0.53, 95% CI [0.31, 0.92]; cough: OR = 0.50, 95% CI [0.38, 0.65]; dyspnea: OR = 0.25, 95% CI [0.14, 0.47]) than in the non-survivor group. Compared with the non-survivor group, the survivor group had higher albumin and platelet levels and lower leucocyte counts. CONCLUSIONS: Male patients might have a higher risk of developing severe COVID-19. Comorbidities, such as respiratory diseases could also greatly influence the clinical prognosis of COVID-19. Clinical features, such as fever, dyspnea, cough, and abnormal platelet, leucocyte, and albumin levels, could imply eventual death. Our findings will help clinicians identify markers for the detection of high mortality risk in HD patients at an early stage of COVID-19.