Association of lung-intestinal microecology and lung cancer therapy.

In recent years, the incidence of lung cancer is increasing. Lung cancer has become one of the most malignant tumors with the highest incidence in the world, which seriously affects people's health. The most important cause of death of lung cancer is metastasis. Therefore, it is crucial to understand the mechanism of lung cancer progression and metastasis. This review article discusses the physiological functions, pathological states and disorders of the lung and intestine based on the concepts of traditional Chinese medicine (TCM), and analyzes the etiology and mechanisms of lung cancer formation from the perspective of TCM. From the theory of "the exterior and interior of the lung and gastrointestinal tract", the theory of "the lung-intestinal axis" and the progression and metastasis of lung cancer, we proposed e "lung-gut co-treatment" therapy for lung cancer. This study provides ideas for studying the mechanism of lung cancer and the comprehensive alternative treatment for lung cancer patients.

The risk of intrauterine exposure to SARS-CoV-2 in female COVID-19 patients: A comprehensive review.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new type of coronavirus that has caused fatal infectious diseases and global spread. This novel coronavirus attacks target cells through the interaction of spike protein and angiotensin-converting enzyme II (ACE2), leading to different clinical symptoms. However, for a successful pregnancy, a well-established in-uterine environment includes a specific immune environment, and multi-interactions between specific cell types are prerequisites. The immune-related changes in patients infected with novel coronavirus could interfere with the immune microenvironment in the uterus, leading to fetal loss. We first reviewed the intrauterine environment in the normal development process and the possible pregnancy outcome in the infection state. Then, we summarized the immune response induced by SARS-CoV-2 in patients and analyzed the changes in ACE2 expression in the female reproductive system. Finally, the present observational evidence of infection in pregnant women was also reviewed.

Impacts of androgen deprivation therapy on the risks and outcomes of SARS-CoV-2 infection in patients with prostate cancer.

Studies have investigated the effects of androgen deprivation therapy (ADT) use on the incidence and clinical outcomes of coronavirus disease 2019 (COVID-19); however, the results have been inconsistent. We searched the PubMed, Medline, Cochrane, Scopus, and Web of Science databases from inception to March 2022; 13 studies covering 84 003 prostate cancer (PCa) patients with or without ADT met the eligibility criteria and were included in the meta-analysis. We calculated the pooled risk ratios (RRs) with 95% confidence intervals (CIs) to explore the association between ADT use and the infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and severity of COVID-19. After synthesizing the evidence, the pooled RR in the SARS-CoV-2 positive group was equal to 1.17, and the SARS-CoV-2 positive risk in PCa patients using ADT was not significantly different from that in those not using ADT (P = 0.544). Moreover, no significant results concerning the beneficial effect of ADT on the rate of intensive care unit admission (RR = 1.04, P = 0.872) or death risk (RR = 1.23, P = 0.53) were found. However, PCa patients with a history of ADT use had a markedly higher COVID-19 hospitalization rate (RR = 1.31, P = 0.015) than those with no history of ADT use. These findings indicate that ADT use by PCa patients is associated with a high risk of hospitalization during infection with SARS-CoV-2. A large number of high quality studies are needed to confirm these results.

Infection rate and factors affecting close contacts of COVID-19 cases: A systematic review.

OBJECTIVE: Contact tracing plays an essential role in mitigating the impact of an epidemic. During the COVID-19 pandemic, studies of those who have been in close contact with confirmed cases offer critical insights to understand the epidemiological characteristics of SARS-CoV-2 better. This study conducts a meta-analysis of existing studies' infection rates and affecting factors. METHODS: We searched PubMed, Web of Science and CNKI from the inception to April 30 2022 to identify systematic reviews. Two reviewers independently extracted the data and assessed risk of bias. Meta-analyses were conducted to calculate pooled estimates by using Stata/SE 15.1 software. RESULTS: There were 47 studies in the meta-analysis. Among COVID-19 close contacts, older age (RR = 1.94, 95% CI: 1.70, 2.21), contacts in households (RR = 2.83, 95% CI: 2.20, 3.65), and people in close contact with symptomatic infections (RR = 3.62, 95% CI: 1.88, 6.96) were associated with higher infection rates. CONCLUSION: On average, each primary infection corresponded to 5.8 close contacts. Among COVID-19 close contacts, older age and contacts in households were associated with higher infection rates, and people in close contact with symptomatic infections had three times higher risk of infection compared to people in close contact with asymptomatic infections. In general, there are significantly more studies from China about close contacts, and the infection rate among close contacts was lower compared to other countries.

Activation of a novel α2AAR-spinophilin-cofilin axis determines the effect of α2 adrenergic drugs on fear memory reconsolidation.

Posttraumatic stress disorder (PTSD) after the pandemic has emerged as a major neuropsychiatric component of post-acute COVID-19 syndrome, yet the current pharmacotherapy for PTSD is limited. The use of adrenergic drugs to treat PTSD has been suggested; however, it is hindered by conflicting clinical results and a lack of mechanistic understanding of drug actions. Our studies, using both genetically modified mice and human induced pluripotent stem cell-derived neurons, reveal a novel α2A adrenergic receptor (α2AAR)-spinophilin-cofilin axis in the hippocampus that is critical for regulation of contextual fear memory reconsolidation. In addition, we have found that two α2 ligands, clonidine and guanfacine, exhibit differential abilities in activating this signaling axis to disrupt fear memory reconsolidation. Stimulation of α2AAR with clonidine, but not guanfacine, promotes the interaction of the actin binding protein cofilin with the receptor and with the dendritic spine scaffolding protein spinophilin to induce cofilin activation at the synapse. Spinophilin-dependent regulation of cofilin is required for clonidine-induced disruption of contextual fear memory reconsolidation. Our results inform the interpretation of differential clinical observations of these two drugs on PTSD and suggest that clonidine could provide immediate treatment for PTSD symptoms related to the current pandemic. Furthermore, our study indicates that modulation of dendritic spine morphology may represent an effective strategy for the development of new pharmacotherapies for PTSD.

Understanding metabolic alterations after SARS-CoV-2 infection: insights from the patients' oral microenvironmental metabolites.

BACKGROUND: Coronavirus disease 2019 is a type of acute infectious pneumonia and frequently confused with influenza since the initial symptoms. When the virus colonized the patient's mouth, it will cause changes of the oral microenvironment. However, few studies on the alterations of metabolism of the oral microenvironment affected by SARS-CoV-2 infection have been reported. In this study, we explored metabolic alterations of oral microenvironment after SARS-CoV-2 infection. METHODS: Untargeted metabolomics (UPLC-MS) was used to investigate the metabolic changes between oral secretion samples of 25 COVID-19 and 30 control participants. To obtain the specific metabolic changes of COVID-19, we selected 25 influenza patients to exclude the metabolic changes caused by the stress response of the immune system to the virus. Multivariate analysis (PCA and PLS-DA plots) and univariate analysis (students' t-test) were used to compare the differences between COVID-19 patients and the controls. Online hiplot tool was used to perform heatmap analysis. Metabolic pathway analysis was conducted by using the MetaboAnalyst 5.0 web application. RESULTS: PLS-DA plots showed significant separation of COVID-19 patients and the controls. A total of 45 differential metabolites between COVID-19 and control group were identified. Among them, 35 metabolites were defined as SARS-CoV-2 specific differential metabolites. Especially, the levels of cis-5,8,11,14,17-eicosapentaenoic acid and hexanoic acid changed dramatically based on the FC values. Pathway enrichment found the most significant pathways were tyrosine-related metabolism. Further, we found 10 differential metabolites caused by the virus indicating the body's metabolism changes after viral stimulation. Moreover, adenine and adenosine were defined as influenza virus-specific differential metabolites. CONCLUSIONS: This study revealed that 35 metabolites and tyrosine-related metabolism pathways were significantly changed after SARS-CoV-2 infection. The metabolic alterations of oral microenvironment in COVID-19 provided new insights into its molecular mechanisms for research and prognostic treatment.

Autoimmune hepatitis after COVID-19 vaccination.

Vaccination is one of the most vigorous ways to intervene in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Cases of autoimmune hepatitis (AIH) after coronavirus disease (COVID-19) vaccination have been increasingly reported. Twenty-seven cases of AIH are summarized in this study, providing emerging evidence of autoimmune reactions in response to various COVID-19 vaccines, including in patients with special disease backgrounds such as primary sclerosing cholangitis (PSC), liver transplantation, and previous hepatitis C virus (HCV) treatment. Molecular mimicry, adjuvants, epitope spreading, bystander activation, X chromosome, and sceptical hepatotropism of SARS-CoV-2 may account for, to some extent, such autoimmune phenomena. Immunosuppressive corticosteroids perform well with or without azathioprine in such post-COVID-19-vaccination AIH. However, determination of the exact mechanism and establishment of causality require further confirmation.

Systems biology approach reveals a common molecular basis for COVID-19 and non-alcoholic fatty liver disease (NAFLD).

BACKGROUND: Patients with non-alcoholic fatty liver disease (NAFLD) may be more susceptible to coronavirus disease 2019 (COVID-19) and even more likely to suffer from severe COVID-19. Whether there is a common molecular pathological basis for COVID-19 and NAFLD remains to be identified. The present study aimed to elucidate the transcriptional alterations shared by COVID-19 and NAFLD and to identify potential compounds targeting both diseases. METHODS: Differentially expressed genes (DEGs) for COVID-19 and NAFLD were extracted from the GSE147507 and GSE89632 datasets, and common DEGs were identified using the Venn diagram. Subsequently, we constructed a protein-protein interaction (PPI) network based on the common DEGs and extracted hub genes. Then, we performed gene ontology (GO) and pathway analysis of common DEGs. In addition, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified. RESULTS: We identified a total of 62 common DEGs for COVID-19 and NAFLD. The 10 hub genes extracted based on the PPI network were IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF. In addition, we also constructed TFs-DEGs, miRNAs-DEGs, and protein-drug interaction networks, demonstrating the complex regulatory relationships of common DEGs. CONCLUSION: We successfully extracted 10 hub genes that could be used as novel therapeutic targets for COVID-19 and NAFLD. In addition, based on common DEGs, we propose some potential drugs that may benefit patients with COVID-19 and NAFLD.

Mobile Phone-Based Population Flow Data for the COVID-19 Outbreak in Mainland China

Background Human migration is one of the driving forces for amplifying localized infectious disease outbreaks into widespread epidemics. During the outbreak of COVID-19 in China, the travels of the population from Wuhan have furthered the spread of the virus as the period coincided with the world's largest population movement to celebrate the Chinese New Year. Methods We have collected and made public an anonymous and aggregated mobility dataset extracted from mobile phones at the national level, describing the outflows of population travel from Wuhan. We evaluated the correlation between population movements and the virus spread by the dates when the number of diagnosed cases was documented. Results From Jan 1 to Jan 22 of 2020, a total of 20.2 million movements of at-risk population occurred from Wuhan to other regions in China. A large proportion of these movements occurred within Hubei province (84.5%), and a substantial increase of travels was observed even before the beginning of the official Chinese Spring Festival Travel. The outbound flows from Wuhan before the lockdown were found strongly correlated with the number of diagnosed cases in the destination cities (log-transformed). Conclusions The regions with the highest volume of receiving at-risk populations were identified. The movements of the at-risk population were strongly associated with the virus spread. These results together with province-by-province reports have been provided to governmental authorities to aid policy decisions at both the state and provincial levels. We believe that the effort in making this data available is extremely important for COVID-19 modelling and prediction.

Emoji use in China: popularity patterns and changes due to COVID-19.

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information: The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Human ZBP1 induces cell death-independent inflammatory signaling via RIPK3 and RIPK1.

ZBP1 is an interferon-induced cytosolic nucleic acid sensor that facilitates antiviral responses via RIPK3. Although ZBP1-mediated programmed cell death is widely described, whether and how it promotes inflammatory signaling is unclear. Here, we report a ZBP1-induced inflammatory signaling pathway mediated by K63- and M1-linked ubiquitin chains, which depends on RIPK1 and RIPK3 as scaffolds independently of cell death. In human HT29 cells, ZBP1 associated with RIPK1 and RIPK3 as well as ubiquitin ligases cIAP1 and LUBAC. ZBP1-induced K63- and M1-linked ubiquitination of RIPK1 and ZBP1 to promote TAK1- and IKK-mediated inflammatory signaling and cytokine production. Inhibition of caspase activity suppressed ZBP1-induced cell death but enhanced cytokine production in a RIPK1- and RIPK3 kinase activity-dependent manner. Lastly, we provide evidence that ZBP1 signaling contributes to SARS-CoV-2-induced cytokine production. Taken together, we describe a ZBP1-RIPK3-RIPK1-mediated inflammatory signaling pathway relayed by the scaffolding role of RIPKs and regulated by caspases, which may induce inflammation when ZBP1 is activated below the threshold needed to trigger a cell death response.

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

Travel-related Importation and Exportation Risks of SARS-CoV-2 Omicron Variant in 367 Prefectures (Cities) - China, 2022.

Introduction: Minimizing the importation and exportation risks of coronavirus disease 2019 (COVID-19) is a primary concern for sustaining the "Dynamic COVID-zero" strategy in China. Risk estimation is essential for cities to conduct before relaxing border control measures. Methods: Informed by the daily number of passengers traveling between 367 prefectures (cities) in China, this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation risks. Results: Under the transmission scenario (R0 =5.49), this study estimated the cumulative case incidence of Changchun City, Jilin Province as 3,233 (95% confidence interval: 1,480, 4,986) before a lockdown on March 14, 2022, which is close to the 3,168 cases reported in real life by March 16, 2022. In a total of 367 prefectures (cities), 127 (35%) had high exportation risks according to the simulation and could transmit the disease to 50% of all other regions within a period from 17 to 94 days. The average time until a new infection arrives in a location in 1 of the 367 prefectures (cities) ranged from 26 to 101 days. Conclusions: Estimating COVID-19 importation and exportation risks is necessary for preparedness, prevention, and control measures of COVID-19 - especially when new variants emerge.

Assessing spread risk of COVID-19 within and beyond China in early 2020

A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval: 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.

Strategy evaluation and optimization with an artificial society toward a Pareto optimum.

Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty, unreliable predictions, and poor decision-making. To address this problem, we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models. The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs. As an example, by modeling coronavirus 2019 mitigation, we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data. Our work suggests that a nation's intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments. Our solution has been validated for epidemic control, and it can be generalized to other urban issues as well.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound population flows originating from Wuhan;however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Emoji use in China: popularity patterns and changes due to COVID-19

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users’ usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage;i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19.

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events. Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19. Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic. Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant "rebound effect" by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003). Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Delayed Antiviral Immune Responses in Severe Acute Respiratory Syndrome Coronavirus Infected Pregnant Mice.

Sex differences in immune responses had been reported to correlate with different symptoms and mortality in the disease course of coronavirus disease 2019 (COVID-19). However, whether severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection interferes with females' fertility and causes different symptoms among pregnant and non-pregnant females remains unknown. Here, we examined the differences in viral loads, SARS-CoV-2-specific antibody titers, proinflammatory cytokines, and levels of T cell activation after SARS-CoV-2 sub-lethal infection between pregnant and non-pregnant human Angiotensin-Converting Enzyme II (ACE2) transgenic mouse models. Both mice showed elevated levels of viral loads in the lung at 4 days post-infection (dpi). However, viral loads in the pregnant group remained elevated at 7 dpi while decreased in the non-pregnant group. Consistent with viral loads, increased production of proinflammatory cytokines was detected from the pregnant group, and the IgM or SARS-CoV-2-specific IgG antibody in serum of pregnant mice featured delayed elevation compared with non-pregnant mice. Moreover, by accessing kinetics of activation marker expression of peripheral T cells after infection, a lower level of CD8+ T cell activation was observed in pregnant mice, further demonstrating the difference of immune-response between pregnant and non-pregnant mice. Although vertical transmission did not occur as SARS-CoV-2 RNA was absent in the uterus and fetus from the infected pregnant mice, a lower pregnancy rate was observed when the mice were infected before embryo implantation after mating, indicating that SARS-CoV-2 infection may interfere with mice's fertility at a specific time window. In summary, pregnant mice bear a weaker ability to eliminate the SARS-CoV-2 virus than non-pregnant mice, which was correlated with lower levels of antibody production and T cell activation.