Peritoneal M2 macrophage-derived extracellular vesicles as natural multitarget nanotherapeutics to attenuate cytokine storms after severe infections.

Cytokine storms are a primary cause of multiple organ damage and death after severe infections, such as SARS-CoV-2. However, current single cytokine-targeted strategies display limited therapeutic efficacy. Here, we report that peritoneal M2 macrophage-derived extracellular vesicles (M2-EVs) are multitarget nanotherapeutics that can be used to resolve cytokine storms. In detail, primary peritoneal M2 macrophages exhibited superior anti-inflammatory potential than immobilized cell lines. Systemically administered M2-EVs entered major organs and were taken up by phagocytes (e.g., macrophages). M2-EV treatment effectively reduced excessive cytokine (e.g., TNF-α and IL-6) release in vitro and in vivo, thereby attenuating oxidative stress and multiple organ (lung, liver, spleen and kidney) damage in endotoxin-induced cytokine storms. Moreover, M2-EVs simultaneously inhibited multiple key proinflammatory pathways (e.g., NF-κB, JAK-STAT and p38 MAPK) by regulating complex miRNA-gene and gene-gene networks, and this effect was collectively mediated by many functional cargos (miRNAs and proteins) in EVs. In addition to the direct anti-inflammatory role, human peritoneal M2-EVs expressed angiotensin-converting enzyme 2 (ACE2), a receptor of SARS-CoV-2 spike protein, and thus could serve as nanodecoys to prevent SARS-CoV-2 pseudovirus infection in vitro. As cell-derived nanomaterials, the therapeutic index of M2-EVs can be further improved by genetic/chemical modification or loading with specific drugs. This study highlights that peritoneal M2-EVs are promising multifunctional nanotherapeutics to attenuate infectious disease-related cytokine storms.

Subjective health status: an easily available, independent, robust and significant predictive factor at the prometaphase of vaccination programs for the vaccination behavior of Chinese adults.

BACKGROUND: The World Health Organization (WHO) proposed COVID-19 vaccination as an emergent and important method to end the COVID-19 pandemic. Since China started vaccination programs in December 2020, vaccination has spread to provinces and municipalities nationwide. Previous research has focused on people's vaccination willingness and its influencing factors but has not examined vaccination behavior. We examine the effectiveness of psychosocial factors in predicting vaccination behavior. METHODS: A cross-sectional online survey was performed among Chinese adults on 8 May and 4 June 2021. The statistical analysis of the data included univariate analysis, receiver operator characteristics (ROC) analysis and ordinal multiclassification logistic regression model analysis. RESULTS: Of the 1300 respondents, 761 (58.5%) were vaccinated. Univariate analysis showed that a high education level and good subjective health status were protective factors for vaccination behavior, while suffering from chronic diseases was a risk factor. ROC analysis showed that subjective health status (AUC = 0.625, 95% CI: 0.594-0.656, P < 0.001) was the best predictor of vaccination behavior. Logistic regression analysis with subjective health status as a dependent variable indicated that older age, female sex, depression, neurasthenia, obsession, hypochondriasis and chronic disease were significant risk factors, while positive coping tendencies were a significant protective factor. CONCLUSION: Our study found a simple and effective marker, subjective health status, that can predict vaccination behavior. This finding can guide future epidemic prevention work.

The willingness of Chinese adults to receive the COVID-19 vaccine and its associated factors at the early stage of the vaccination programme: a network analysis.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has been a continuous global threat since the first identification of the disease in December 2019. COVID-19 vaccination is a crucial preventive approach that can halt this pandemic. However, many factors affect the willingness of the public to be vaccinated against COVID-19 at the early stage of the vaccination programme. We used network analysis to investigate the interrelation of vaccination willingness and its associated factors. METHODS: A population-representative sample of 539 Chinese adults completed a battery of online self-assessments, including those on vaccination willingness, health status, attitude towards vaccines, COVID-19-related psychological elements and other variables. Network analysis was performed using the R qgraph package. RESULTS: In total, 445 (82.6%) participants scored high on their willingness to vaccinate. Attitude towards vaccines, the influence of people around an individual and health status were directly significantly related to vaccination willingness. The betweenness of age was the highest and, the emotional states had the strongest centrality. LIMITATIONS: Network analysis is not sufficient to determine the causal relationships of the links between nodes. In addition, there are other latent essential elements that were not evaluated. Finally, the sample size was relatively small. CONCLUSION: Network analysis showed that attitude toward vaccines and emotional states are the most critical factors affecting vaccination willingness, which indicates that we should pay attention to the impact of the dissemination of Internet information on vaccination willingness and public emotional states during a pandemic which is very important for promoting vaccination programs.

Impact of the COVID-19 epidemic on patterns of pregnant women's perception of threat and its relationship to mental state: A latent class analysis.

OBJECTIVE: The objective of this study was to define the threatened perception types of pregnant women during the COVID-19 pandemic and determine the correlations between the perception types and their demographic factors, their preventive knowledge of COVID-19 and their mental status in order to provide suggestions for pregnant women during pandemic. METHODS: Latent class analysis were used to explore the optimal numbers of clusters. Multinomial logistic regression and multiple correspondence analysis were used to analyze the demographic variables of the latent categories. MANOVA was used to analyze the difference of knowledge of COVID-19 obtained among clusters and their psychological status, and chi-square test was used determine the relationship between the latent clusters and the participant's COVID-19 worry level. RESULTS: Five clusters were found: the first cluster (n = 120, 39%) was unthreatened and confident. Cluster 2(n = 84, 28%) was unthreatened but not confident. Cluster 3 (n = 49, 17%) was threatened but confident. Cluster 4 (n = 25, 9%) was threaten, not confident and knowledgeable, and Cluster 5 (n = 20, 7%) was threatened, not confident and lacking knowledge. Three demographic variables were shown an effect on the classification, they were support from work, family support and intrapartum and postpartum complications. CONCLUSION: This study can help assess the mental health risks of pregnant women during an epidemic. The results could be helpful for families, work units, communities and medical institutions to make targeted intervention decisions for pregnant women.