Causal relationships between COVID-19 and osteoporosis: a two-sample Mendelian randomization study in European population.

Introduction: The causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study. Methods: We conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran's Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method. Results: The primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses. Conclusion: The results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent.

Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

INTRODUCTION: It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND METHODS: The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists. RESULTS: MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality. CONCLUSIONS: Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Early autoimmunity and outcome in virus encephalitis: a retrospective study based on tissue-based assay.

To explore the autoimmune response and outcome in the central nervous system (CNS) at the onset of viral infection and correlation between autoantibodies and viruses. METHODS: A retrospective observational study was conducted in 121 patients (2016-2021) with a CNS viral infection confirmed via cerebrospinal fluid (CSF) next-generation sequencing (cohort A). Their clinical information was analysed and CSF samples were screened for autoantibodies against monkey cerebellum by tissue-based assay. In situ hybridisation was used to detect Epstein-Barr virus (EBV) in brain tissue of 8 patients with glial fibrillar acidic protein (GFAP)-IgG and nasopharyngeal carcinoma tissue of 2 patients with GFAP-IgG as control (cohort B). RESULTS: Among cohort A (male:female=79:42; median age: 42 (14-78) years old), 61 (50.4%) participants had detectable autoantibodies in CSF. Compared with other viruses, EBV increased the odds of having GFAP-IgG (OR 18.22, 95% CI 6.54 to 50.77, p<0.001). In cohort B, EBV was found in the brain tissue from two of eight (25.0%) patients with GFAP-IgG. Autoantibody-positive patients had a higher CSF protein level (median: 1126.00 (281.00-5352.00) vs 700.00 (76.70-2899.00), p<0.001), lower CSF chloride level (mean: 119.80±6.24 vs 122.84±5.26, p=0.005), lower ratios of CSF-glucose/serum-glucose (median: 0.50[0.13-0.94] vs 0.60[0.26-1.23], p=0.003), more meningitis (26/61 (42.6%) vs 12/60 (20.0%), p=0.007) and higher follow-up modified Rankin Scale scores (1 (0-6) vs 0 (0-3), p=0.037) compared with antibody-negative patients. A Kaplan-Meier analysis revealed that autoantibody-positive patients experienced significantly worse outcomes (p=0.031). CONCLUSIONS: Autoimmune responses are found at the onset of viral encephalitis. EBV in the CNS increases the risk for autoimmunity to GFAP.

Excessive IL-10 and IL-18 trigger hemophagocytic lymphohistiocytosis-like hyperinflammation and enhanced myelopoiesis.

BACKGROUND: Hyperinflammation is a life-threatening condition associated with various clinical disorders characterized by excessive immune activation and tissue damage. Multiple cytokines promote the development of hyperinflammation; however, the contribution of IL-10 remains unclear despite emerging speculations for a pathological role. Clinical observations from hemophagocytic lymphohistiocytosis (HLH), a prototypical hyperinflammatory disease, suggest that IL-18 and IL-10 may collectively promote the onset of a hyperinflammatory state. OBJECTIVE: We aimed to investigate the collaborative roles of IL-10 and IL-18 in hyperinflammation. METHODS: A comprehensive plasma cytokine profile for 87 secondary HLH patients was first depicted and analyzed. We then investigated the systemic and cellular effects of coelevated IL-10 and IL-18 in a transgenic mouse model and cultured macrophages. Single-cell RNA sequencing was performed on the monocytes/macrophages isolated from secondary HLH patients to explore the clinical relevance of IL-10/IL-18-mediated cellular signatures. The therapeutic efficacy of IL-10 blockade was tested in HLH mouse models. RESULTS: Excessive circulating IL-10 and IL-18 triggered a lethal hyperinflammatory disease recapitulating HLH-like phenotypes in mice, driving peripheral lymphopenia and a striking shift toward enhanced myelopoiesis in the bone marrow. IL-10 and IL-18 polarized cultured macrophages to a distinct proinflammatory state with pronounced expression of myeloid cell-recruiting chemokines. Transcriptional characterization suggested the IL-10/IL-18-mediated cellular features were clinically relevant with HLH, showing enhanced granzyme expression and proteasome activation in macrophages. IL-10 blockade protected against the lethal disease in HLH mouse models. CONCLUSION: Coelevated IL-10 and IL-18 are sufficient to drive HLH-like hyperinflammatory syndrome, and blocking IL-10 is protective in HLH models.

Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context. [ABSTRACT FROM AUTHOR] Copyright of AI is the property of MDPI 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.)

What Do Employers Expect for Jobs Requiring Media Analytics? A Semantic Network Analysis of Job Descriptions of In-Person and Remote Positions During the COVID-19 Pandemic

Using text mining and semantic network analysis, this study analyzed the job descriptions of 34,787 positions about media analytics from Indeed.com to compare how the in-person and remote jobs differ to inform educators about integrating analytics in the media and communications curriculum. We found that the in-person positions emphasized more on the skills of verbal, interpersonal, and organizational communication, whereas the remote positions asked more for written communication. While the in-person positions had higher expectations of using general data management and analysis tools, the remote positions emphasized more on the use of social media analytics and digital marketing tools.

Fluorescent detection of emerging virus based on nanoparticles: From synthesis to application.

The spread of COVID-19 has caused huge economic losses and irreversible social impact. Therefore, to successfully prevent the spread of the virus and solve public health problems, it is urgent to develop detection methods with high sensitivity and accuracy. However, existing detection methods are time-consuming, rely on instruments, and require skilled operators, making rapid detection challenging to implement. Biosensors based on fluorescent nanoparticles have attracted interest in the field of detection because of their advantages, such as high sensitivity, low detection limit, and simple result readout. In this review, we systematically describe the synthesis, intrinsic advantages, and applications of organic dye-doped fluorescent nanoparticles, metal nanoclusters, up-conversion particles, quantum dots, carbon dots, and others for virus detection. Furthermore, future research initiatives are highlighted, including green production of fluorescent nanoparticles with high quantum yield, speedy signal reading by integrating with intelligent information, and error reduction by coupling with numerous fluorescent nanoparticles.

Virtual Screening-Based Peptides Targeting Spike Protein to Inhibit Porcine Epidemic Diarrhea Virus (PEDV) Infection.

Due to the rapid mutation of porcine epidemic diarrhea virus (PEDV), existing vaccines cannot provide sufficient immune protection for pigs. Therefore, it is urgent to design the affinity peptides for the prevention and control of this disease. In this study, we made use of a molecular docking technology for virtual screening of affinity peptides that specifically recognized the PEDV S1 C-terminal domain (CTD) protein for the first time. Experimentally, the affinity, cross-reactivity and sensitivity of the peptides were identified by an enzyme-linked immunosorbent assay (ELISA) and a surface plasmon resonance (SPR) test, separately. Subsequently, Cell Counting Kit-8 (CCK-8), quantitative real-time PCR (qRT-PCR), Western blot and indirect immunofluorescence were used to further study the antiviral effect of different concentrations of peptide 110766 in PEDV. Our results showed that the P/N value of peptide 110766 at 450 nm reached 167, with a KD value of 216 nM. The cytotoxic test indicated that peptide 110766 was not toxic to vero cells. Results of the absolute quantitative PCR revealed that different concentrations (3.125 µM, 6.25 µM, 12.5 µM, 25 µM, 50 µM, 100 µM, 200 µM) of peptide 110766 could significantly reduce the viral load of PEDV compared with the virus group (p < 0.0001). Similarly, results of Western blot and indirect immunofluorescence also suggested that the antiviral effect of peptide 110766 at 3.125 is still significant. Based on the above research, high-affinity peptide 110766 binding to the PEDV S1-CTD protein was attained by a molecular docking technology. Therefore, designing, screening, and identifying affinity peptides can provide a new method for the development of antiviral drugs for PEDV.

Determinants and mechanisms driving energy-saving behaviours of long-stay hotel guests: Comparison of leisure, business and extended-stay residential cases

The global hospitality industry is fast-turning sustainable and environmentally friendly. Behaviour-driven energy conservation is an emerging green hotel operation strategy to support this change. The long-stay accommodation services have gained momentum in the hospitality sector since the COVID-19 pandemic. However, the characteristics of long-stay hotel guests are often overlooked in sustainable interventions. Based on an empirical survey in China, this study aims to explore the factors driving energy-saving behaviours of long-stay hotel guests and to compare their effects on guests for different visiting purposes (leisure, business, and extended-stay resident). The analysis indicates that attitude, personal norm and place attachment present a direct contribution to energy-saving behaviour. Besides, the results support that attitude and personal norm connect environmental values and energy-saving behaviour. Both altruistic and biospheric values have positive effects, while egoistic values seem to play a negative role. Biospheric values have stronger impact on attitude and personal norm of business guests. Place attachment has a stronger influence on extended-stay residents while its contribution to energy-saving behaviours of business guests is smaller than other guests. Besides, leisure guests are more sensitive to moral obligations. This research sheds novel lights on the psychological perspectives of the observed heterogeneity of energy-saving behaviours of hotel guests with different visiting purposes. The findings provide hotel operators with a novel theoretical reference for targeted energy-saving interventions to promote energy-saving actions of long-term hotel guests. The study, therefore, can contribute to sustainable tourism policymaking and behaviour-driven hotel energy management.

What Do Employers Expect for Jobs Requiring Media Analytics? A Semantic Network Analysis of Job Descriptions of In-Person and Remote Positions During the COVID-19 Pandemic

Using text mining and semantic network analysis, this study analyzed the job descriptions of 34,787 positions about media analytics from Indeed.com to compare how the in-person and remote jobs differ to inform educators about integrating analytics in the media and communications curriculum. We found that the in-person positions emphasized more on the skills of verbal, interpersonal, and organizational communication, whereas the remote positions asked more for written communication. While the in-person positions had higher expectations of using general data management and analysis tools, the remote positions emphasized more on the use of social media analytics and digital marketing tools.

Statistical methods for assessing drug interactions using observational data

With advances in medicine, many drugs and treatments become available. On the one hand, polydrug use (i.e. using more than one drug at a time) has been used to treat patients with multiple morbid conditions, and polydrug use may cause severe side effects. On the other hand, combination treatments have been successfully developed to treat severe diseases such as cancer and chronic diseases. Observational data, such as electronic health record data, may provide useful information for assessing drug interactions. In this article, we propose using marginal structural models to assess the average treatment effect and causal interaction of two drugs by controlling confounding variables. The causal effect and the interaction of two drugs are assessed using the weighted likelihood approach, with weights being the inverse probability of the treatment assigned. Simulation studies were conducted to examine the performance of the proposed method, which showed that the proposed method was able to estimate the causal parameters consistently. Case studies were conducted to examine the joint effect of metformin and glyburide use on reducing the hospital readmission for type 2 diabetic patients, and to examine the joint effect of antecedent statins and opioids use on the immune and inflammatory biomarkers for COVID-19 hospitalized patients. [ FROM AUTHOR] Copyright of Journal of Applied Statistics is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Electrochemical Oxygen Generator With 99.9% Oxygen Purity and High Energy Efficiency

Under the growing crisis of the coronavirus disease 2019 pandemic, the global medical system is facing the predicament of an acute shortage of medical‐grade oxygen (O2, ≥ 99.5% purity). Herein, an oxygen generation device is manufactured that relies on electrochemical technology. The performance of the electrochemical oxygen generator (EOG) is remarkably improved to a practically applicable level, achieving long‐term (>200 h), stable, and quick production (>1.5 L min−1) of high purity O2 (99.9%) at high energy efficiency (496 L kW−1 h−1), via simultaneous optimization for intrinsic electrochemical reaction mechanisms, electrocatalysts, and external cell structure. The EOG also presents powerful competitiveness in user experience, which finds expression in high portability (4.7 kg), nearly instant O2 production (<1 s), and a quiet working condition (<39 dB). The EOG shows great potential to substitute commercial pressure swing adsorption O2 generation devices, which may significantly impact the traditional oxygen production industry. [ FROM AUTHOR] Copyright of Advanced Energy Materials is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Influencing Factors of Enterprise R&D Investment: Post-Subsidy, Sustainability, and Heterogeneity

Sustainable innovation is an important factor for enterprises to obtain core competitiveness in modern society, and only continuous R&D investment can ensure the smooth progress of enterprise innovation. Therefore, this study uses the post-subsidy data of enterprise R&D investment released by Hangzhou Science and Technology Bureau to explore the influencing factors of R&D investment and its sustainability by Chinese high-tech enterprises. In contrast to previous research which mainly focused on the pre-subsidy, this study focuses on the post-subsidy policy of R&D investment sustainability. Empirical analysis methods, such as cross-sectional linear regression and the propensity-score-matching method, were used to draw the following conclusions: (1) The quantity and sustainability of R&D investment of enterprises are obviously unbalanced among regions. Regions where high-tech enterprises are concentrated have higher levels of R&D investment and sustainability than other regions. (2) Under different scales, there are significant differences in the amount and sustainability of R&D investment. Large enterprises have stronger R&D investment strength, while small enterprises have stronger R&D investment willingness. However, the effect of scale on R&D investment will be reduced by regional factors. (3) The evaluation of high-tech enterprises and the enterprises’ R&D investments affect each other. (4) The sustainability of enterprise R&D investment will be affected by enterprise heterogeneity factors, and in turn, the sustainability of enterprise R&D investment will also affect the amount of enterprise R&D investment. Based on the results, the study provides suggestions for the government to make more targeted policies.

Fighting misinformation on social media: effects of evidence type and presentation mode.

Designing corrective messages to debunk misinformation online is an important practice toward ending the coronavirus disease (COVID-19) pandemic as health-related misinformation has proliferated on social media misguiding disease prevention measures. Despite research on the use of statistical evidence and message modality in persuasion, the effects of evidence type (assertions with versus without statistical evidence) and presentation mode (text-only versus image-only versus text-plus-image) have been understudied. This study examined the impact of evidence type and presentation mode on individuals' responses to corrective messages about COVID-19 on social media. The results showed that the presence of statistical evidence in assertions reduced message elaboration, which in turn reduced the effects of the message in correcting misperceptions, decreased perceived message believability and lowered social media users' intentions to further engage with and disseminate the corrective message. Compared to the text-only modality and the text-plus-image modality, the image-only modality triggered significantly lower levels of message elaboration, which subsequently heightened message believability and increased user engagement intentions. The theoretical and practical implications are discussed.

Towards Personalization of Indoor Air Quality: Review of Sensing Requirements and Field Deployments

As humans spend more time indoors, ensuring acceptable indoor air quality (IAQ) through ubiquitous sensing systems has become a necessity. Although extensive studies have been conducted on the IAQ sensing systems, a holistic review of the performance and deployment of Ubiquitous IAQ Sensing (UIAQS) systems with associated requirements in IAQ sensing standards is still lacking. In this study, we first reviewed IAQ pollutants and other IAQ-related factors and the associated requirements in the prominent IAQ sensing standards. We found that while non-pollutant factors are influential on occupants’ perception of IAQ and their satisfaction, they do not have evaluation metrics in the IAQ standards. Then, we systematically reviewed field studies on UIAQS technologies in the literature. Specific classes of information were recorded and analyzed further. We found that the majority of the UIAQS systems did not meet the requirements of the prominent IAQ sensing standards and identified four primary research gaps. We concluded that a new holistic and personalized approach that incorporates UIAQS measurements and subjective feedback is needed. This study provides valuable insights for researchers and policymakers to better improve UIAQS technologies by developing personalized IAQ sensors and sensing standards.

Association of pyroptosis and severeness of COVID-19 as revealed by integrated single-cell transcriptome data analysis.

Cytokine storm and inflammatory cytokine release syndrome are often found to be associated with severe instances of the 2019 coronavirus disease (COVID-19). However, factors that contribute to the development of the COVID-19-associated cytokine storm and intensify the hyperinflammatory response are not well known. Here, we integratively analyzed scRNAseq data of 37,607 immune cells of eight different cell types from four studies involving COVID-19 patients in either moderate or severe conditions. Our analysis showed that pyroptosis-a lytic, inflammatory type of programmed cell death-may play a critical role in the SARS-CoV-2-induced cytokine storm. The expression of the key markers of pyroptosis, such as pro-inflammatory cytokine genes IL1B and IL18, is significantly higher in moderate and severe COVID-19 patients than in healthy controls. The pattern is more pronounced in macrophages and neutrophils than in adaptive immune cells such as T cells and B cells. Furthermore, the lack of interferon-gamma (IFN-γ) and overexpression of ninjurin 1 (NINJ1) in macrophages may exacerbate the systemic inflammation, as shown in severe COVID-19 patients. Finally, we developed a scoring metric to quantitatively assess single cell's pyroptotic state and demonstrated the use of this pyroptosis signature score to scRNAseq data. Taken together, our study underscores the importance of the pyroptosis pathway and highlights its clinical relevance, suggesting that pyroptosis is a cellular process that can be a potential target for the treatment of COVID-19 associated diseases.

Mortality Risk of Antidiabetic Agents for Type 2 Diabetes With COVID-19: A Systematic Review and Meta-Analysis.

Aims: We conducted a systematic review and meta-analysis to assess various antidiabetic agents' association with mortality in patients with type 2 diabetes (T2DM) who have coronavirus disease 2019 (COVID-19). Methods: We performed comprehensive literature retrieval from the date of inception until February 2, 2021, in medical databases (PubMed, Web of Science, Embase, and Cochrane Library), regarding mortality outcomes in patients with T2DM who have COVID-19. Pooled OR and 95% CI data were used to assess relationships between antidiabetic agents and mortality. Results: Eighteen studies with 17,338 patients were included in the meta-analysis. Metformin (pooled OR, 0.69; P=0.001) and sulfonylurea (pooled OR, 0.80; P=0.016) were associated with lower mortality risk in patients with T2DM who had COVID-19. However, patients with T2DM who had COVID-19 and received insulin exhibited greater mortality (pooled OR, 2.20; P=0.002). Mortality did not significantly differ (pooled OR, 0.72; P=0.057) between DPP-4 inhibitor users and non-users. Conclusions: Metformin and sulfonylurea could be associated with reduced mortality risk in patients with T2DM who have COVID-19. Furthermore, insulin use could be associated with greater mortality, while DPP-4 inhibitor use could not be. The effects of antidiabetic agents in patients with T2DM who have COVID-19 require further exploration. Systematic Review Registration: PROSPERO (identifier, CRD42021242898).

Quality of life during the epidemic of COVID-19 and its associated factors among enterprise workers in East China.

BACKGROUND: The COVID-19 related lockdown and home confinement might have an important impact on the quality of life in enterprise workers. We investigated the quality of life during the epidemic in enterprise workers who just returned to work, and assessed its potential influencing factors to have a better understanding of the impact of COVID-19 epidemic lockdown and home confinement. METHODS: This was a cross-sectional study of enterprise workers conducted in Deqing and Taizhou, Zhejiang Province, China. The Chinese version of EQ5D was used to assess life quality, and information about general characteristics and COVID-19 related factors was collected by a structured questionnaire, which was distributed through the social application "WeChat". Multiple liner regression was used to investigate potential influencing factors. RESULTS: A total of 2420 participants were enrolled, 59.5% of which worked in Deqing. About 50% of the participants reported worries about the COVID-2019 epidemic and 40.2% had a centralized or home quarantine during the epidemic. The mean EQ-5D score and VAS were 0.990 and 93.5. Multiple liner regression showed that the quality of life measures was related to physical activities (ß = 0.006) and keeping home ventilation (ß = 0.063) in Deqing, and were related to wearing a mask when going out (ß = 0.014), keeping home ventilation (ß = 0.061), other marital status (ß = - 0.011), worry about the epidemic (ß = - 0.005) and having a centralized or home quarantine (ß = - 0.005) in Taizhou. CONCLUSIONS: The quality of life for returning enterprise workers in areas with different risks of COVID-19 was affected by different factors. Associated factors identified from this study would help develop proper intervention measures for enterprise workers to reduce the impact of large-scale public health events like the COVID-19 on their quality of life.