Comparative content analysis of national health strategies before and after COVID-19: a systematic policy analysis among OECD and BRICS countries (preprint)

Background We aimed to evaluate the National Health Policies, Strategies and Plans (NHPSPs) of the Organization for Economic Cooperation and Development (OECD) and BRICS before and after the COVID-19 pandemic to explore nations' commitment to strengthen their health systems in defense of health threats and analyze the specific changes.Methods We systematically searched NHPSP documents from the WHO document repository and official governmental websites. Data was then extracted using a standardized extraction template. A coding framework was inductively developed to sort qualitative responses into categories, with frequencies calculated and weighting evaluated, followed by organizing underlying content into subthemes.Results The search yielded 154 documents, with 36 retained after screening, encompassing 14 OECD countries and 3 BRICS countries. The most predominant theme was prevention (88.9% pre-pandemic, 99.4% post-pandemic), which was addressed as a primary theme in 26 included NHPSPs. After the COVID-19 pandemic, 6 out of 14 analyzed themes saw higher occurrences, among which infection prevention and control (22.2–50.0%) and resilience to health crisis (22.2–44.4%) increased most significantly. Themes mainstreamed in post-pandemic NHPSPs included prevention (94.4%), health research and technology (61.1%), and One Health (66.7%). Primary healthcare emerged as the most concerned subtheme under prevention. Notably, OECD countries displayed more increased occurrences of themes (13 out of 14) or increased emphasis on themes with similar occurrences before and after COVID-19, while BRICS countries only differed in infection control. The two sets of countries also varied in subthemes and action plans under the same primary theme.Conclusion Many countries are endeavoring to move towards more robust health systems by optimizing NHPSPs, yet only about half of OECD and BRICS countries have introduced new NHPSPs after COVID-19. We hope our findings attract attention to the necessity of global health system reforms and provide other countries with actionable recommendations for NHPSP formulation.

Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning (preprint)

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected from 13 ERs may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm, the Single-DANN algorithm, and three baseline methods. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge. Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informative for developing a prediction model to identify COVID-19 patients who are very likely to revisit an ER within 7 days after discharge.

The influence of COVID-19 prevention and control measures on the transmission and epidemic of influenza in Chongqing

Objective: To evaluate the influence of coronavirus disease 2019 (COVID-19) prevention and control measures on the transmission and epidemic of influenza in Chongqing, so as to provide references for formulating targeted influenza prevention and control strategies. Methods: The influenza surveillance data, during the year 2018 to 2020, were collected through the "China Influenza Surveillance Information System", and the seasonal characteristics of influenza epidemic were analyzed. The percentage of influenza like cases (ILI%) and influenza virus positive rate between 2020 and 2018-2019 were compared, so as to evaluate the impact of COVID-19 prevention and control measures on influenza epidemic characteristics. Results: The annual proportions of ILI cases in Chongqing were respectively 3.53%, 2.23% and 1.2% from 2018 to 2020, while the positive rates of influenza virus were respectively 13.97%, 23.81% and 2.65%. The distribution trend of ILI% from 2018 to 2019 fluctuated were similar, but it continued to drop and remain at a low level since February 2020. The positive rate of influenza virus showed an epidemic peak from December to March in 2018-2019, also peaked from November 2019 to January 2020, but decreased to 0 in March. ILI% was positively correlated with the positive rate of influenza virus (r=0.404 8, P < 0.05). In 2020, compared with the same period of 2018-2019, the growth rate of ILI% was -66.09% and -46.32%, respectively. The positive rate of influenza virus in 2020 decreased by 81.03% and 88.87% compared with the same period of 2018-2019, respectively. The growth rates of influenza virus positive rate in January 2020 were decreased with a small rate of about 39.87%, and with a significantly decline of more than 93.65% from February. No influenza epidemic was found after March. Conclusions: Since COVID-19 prevention and control measures were implemented in January 2020 in Chongqing, the ILI% and the positive rate of influenza virus in sentinel hospitals decreased significantly. In the season of high incidence of respiratory infectious diseases, personal protection and other measures can effectively reduce influenza virus infection.

Clinical characteristics and risk factors analysis of viral shedding time in mildly symptomatic and asymptomatic patients with SARS-CoV-2 Omicron variant infection in Shanghai

Objective To analyze the clinical characteristics and risk factors of viral shedding time in mildly symptomatic and asymptomatic patients with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (BA.2 and BA2.2) infection in Shanghai, and the effect of traditional Chinese medicine (TCM) treatment, so as to provide a reference basis for epidemic prevention, control and clinical treatment. Methods A total of 6,134 asymptomatic or mildly symptomatic Omicron-infected patients admitted to Tianhua Road fangcang shelter hospital in Jinshan, Shanghai, between April 2022 and May 2022 were included. Demographic characteristics and clinical histories were collected and compared in subgroups according to the different durations of viral shedding. Spearman's correlation analysis was performed to explore the association between virus shedding time and clinical variables. Multiple linear regression was used to evaluate the risk factors for viral shedding time. Result Most patients with asymptomatic and mildly symptomatic Omicron infection were male, and more than half of patients had a viral shedding time of 8–15 days. The patients were divided into three groups according to the time of viral shedding: short-duration (≤ 7 days), intermediate-duration (8–15 days) and long-duration group (≥16 days). The proportion of patients aged ≤ 29 years was the highest in the short-duration group (30.2%), whereas the proportion of patients aged 50–64 yeas was the highest in the long-duration group (37.9%). The proportion of patients with the chronic non-communicable diseases among the short-, intermediate- and long-duration groups was 6.2, 9.4, and 14.9%, respectively. Among them, hypertension was the most found (4.9, 7.8, and 11.7%, respectively). By multivariate analyses, we identified that viral shedding time of Omicron variants was independently negatively correlated with male patients, TCM treatment, and manual laborers, while it was independently positively associated with age and hypertension. Additionally, TCM treatment could significantly shorten the length of viral shedding time, especially for men, age ≥30 years, comorbid chronic non-communicable diseases, unemployed people and manual worker. Conclusions Our results suggested that age and hypertension were independent risk factors for the duration of viral shedding in asymptomatic and mildly symptomatic omicron infected patients. TCM can effectively shorten viral shedding time.

How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

Purpose>Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network.Design/methodology/approach>Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network.Findings>This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant.Research limitations/implications>This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI.Originality/value>First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.

An Outbreak of SARS-CoV-2 Omicron Subvariant BA.2.76 in an Outdoor Park - Chongqing Municipality, China, August 2022.

What is already known about this topic?: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariant has a stronger transmission capacity and faster transmission speed than the previous strain. What is added by this report?: The first coronavirus disease 2019 (COVID-19) case infected with the SARS-CoV-2 Omicron subvariant BA.2.76 who caused local transmission was reported in Chongqing Municipality on August 16, 2022. For 35 minutes, the Patient Zero jogged along a lake at a local park without wearing a mask. Among the 2,836 people potentially exposed at the time, 39 tested positive. Overall, 38 out of 39 cases did not wear a mask on the morning of August 16. All 39 cases lacked any previous exposure to the variant before testing positive on their nucleic acid test. What are the implications for public health practice?: It is essential to maintain personal wellbeing by ensuring one maintains personal protection and follows regulated guidelines such as maintaining safe distances from others both indoors and outdoors.

Has the COVID-19 pandemic affected nursing students' career self-efficacy and professional calling? The mediating impact of professional identity.

BACKGROUND: Professional calling (PC) is crucial for ascertaining their professional goals and fulfilling career choices in nursing students. Thus, understanding its antecedents and helping schools improve PC among nursing students is critical. This study aims to explore whether professional identity (PI), as a crucial antecedent of PC, acts as an intermediary between career self-efficacy (CSE) and professional calling during the COVID-19 pandemic. METHODS: A sample of 565 nursing students were selected by a web-based survey through convenience sampling. The study was conducted from October to November 2020. Measures of CSE, PI, and PC were assessed during the COVID-19 pandemic. We analyzed demographic data and the correlation of the research variables. The significance of the mediation effect was assessed using a bootstrap method with SPSS. RESULTS: CSE during the COVID-19 pandemic outbreak (r = 0. 359, p < 0. 01) and PI (r = 0. 670, p < 0. 01) were both relevant to PC among nursing students. In addition, CSE had a positive indirect effect on PC through PI (ß = 0. 288, p < 0. 05). CONCLUSIONS: Higher scores in CSE and a better PI were associated with PC in nursing students. Furthermore, a better CSE had an indirect effect on the PC of students through PI. The favorable evidence in our study confirms that nursing educators can adopt PI interventions to improve the sense of PC among nursing students.

Effect of Antidiabetic Therapy on Clinical Outcomes of COVID-19 Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis.

BACKGROUND: No study has yet systematically evaluated the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with type 2 diabetes (T2D). OBJECTIVE: We aimed to evaluate the effect of different antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. METHODS: We comprehensively retrieved the published research which examined the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. The odds ratio (OR) and its 95% confidence interval (95% CI) for clinical outcomes were calculated using the random-effects model, and meta-regression was adopted to evaluate the potential sources of heterogeneity between studies. RESULTS: A total of 54 studies were included in this study. We found that the use of metformin (OR = 0.66, 95% CI: 0.58-0.75), SGLT-2i (OR = 0.80, 95% CI: 0.73-0.88), and GLP-1ra (OR = 0.83, 95% CI: 0.70-0.98) were significantly associated with lower mortality risk in COVID-19 patients with T2D, while insulin use might unexpectedly increase the ICU admission rate (OR = 2.32, 95% CI: 1.34-4.01) and risk of death (OR = 1.52, 95% CI: 1.32-1.75). No statistically significant associations were identified for DPP-4i, SUs, AGIs, and TZDs. CONCLUSION AND RELEVANCE: We demonstrated that the usage of metformin, SGLT-2i, and GLP-1ra could significantly decrease mortality in COVID-19 patients with T2D. The heterogeneity across the studies, baseline characteristics of the included patients, shortage of dosage and the duration of antidiabetic drugs and autonomy of drug selection might limit the objectivity and accuracy of results. Further adequately powered and high-quality randomized controlled trials are warranted for conclusive findings.

Can a Video a Day Keep Stress Away? A Test of Media Prescriptions.

This research attempts to both replicate initial research on media prescriptions - the assignment of small doses of positively-valenced media for the purposes of reducing perceived stress - and, through the lens of the broaden-and-build theory, shed light on the process through which this effect might emerge. Two longitudinal data sets were collected, one with college students (N = 182) and one with U.S. adults (N = 197), in which participants were assigned to watch either comedic or inspiring media clips every day for 5 days. Findings indicated that both amusement and hope generated by media exposure reduced perceived stress over time. Specifically, inspiring media reduced perceived stress through its effect on felt hope for both samples, whereas comedic media reduced perceived stress via felt amusement for the general adult sample only. Further, as predicted, serial mediation through felt emotion and coping efficacy emerged for amusement in the student sample and for hope in the general sample. Given these data were collected during an inordinately stressful time in both the U.S. and the world with rising rates of COVID-19, a highly contentious political election, and tensions over racial inequity, these findings suggest that media, if harnessed appropriately, could be a useful tool in one's coping arsenal. A call for better understanding of the process through which media prescriptions have effect and their boundary conditions is advanced.

The Spatial Variation of the Influence of Lockdown on Air Quality across China and Its Major Influencing Factors during COVID-19

China has experienced a series of COVID-19 recurrences in different cities across the country since 2020, and relatively strict (full lockdown) or lenient closure (semi-lockdown) strategies have been employed accordingly in each city. The differences in detailed transmission control measures during lockdown periods led to distinct effects on air quality, which has rarely been studied. To fill this gap, we examined the effects of semi-lockdown and full lockdown on six major airborne pollutants, based on 55 lockdown cases. For all lockdown cases, the concentrations of PM2.5, PM10, SO2, NO2 and CO were much lower than in previous years. Specifically, due to the stricter transmission control, the concentration of the five airborne pollutants experienced a much sharper decline during full lockdown. However, O3 presented a different variation pattern during lockdown periods. Generally, O3 concentrations presented a slight increase in semi-lockdown cases and a notable increase in full lockdown cases. Meanwhile, O3 increased notably in northern China, particularly in the Beijing–Tianjin–Hebei region, while O3 had a slight variation in southern China. The unique variation of O3 across regions and lockdown types was mainly attributed to the spatial heterogeneity of O3 formation regimes, especially the VOCs-controlled O3 formation in northern China. Based on Geographical Detector, we examined the spatial continuity of natural and socio-economic factors on the variation of airborne pollutants during lockdown. In terms of meteorological factors, humidity and precipitation were the dominant factors for PM2.5 and PM10, respectively, while humidity and temperature were the dominant factors for O3. In terms of socio-economic factors, the numbers of taxis and private cars were the dominant factors for PM2.5 and O3 variations during lockdown. GD also revealed that the combination of natural and socio-economic factors had a significantly enhanced effect on airborne pollutants during lockdown. The combination of relative humidity and total area of urban built-up areas exerted the strongest interactive effects on both PM2.5 and O3. This research highlighted the challenge for urban O3 management, and suggested the control of VOCs emissions should be preferably considered, especially in northern China.

Epidemiological investigation of an imported COVID-19 case infected with Delta variant from Nepal in Chongqing of China

Objective: To analyze the epidemiological characteristics of a COVID-19 case imported from Nepal in Chongqing of China, and provide evidence for the prevention and control of imported COVID-19.

Humoral responses after inactivated COVID-19 vaccination in individuals with and without prior SARS-CoV-2 infection: A prospective cohort study.

We evaluated and compared humoral immune responses after inactivated coronavirus disease 2019 (COVID-19) vaccination among naïve individuals, asymptomatically infected individuals, and recovered patients with varying severity. In this multicenter, prospective cohort study, blood samples from 666 participants were collected before and after 2 doses of inactivated COVID-19 vaccination. Among 392 severe acute respiratory syndrome coronavirus 2-naïve individuals, the seroconversion rate increased significantly from 51.8% (median antispike protein pan-immunoglobulins [S-Igs] titer: 0.8 U/ml) after the first dose to 96% (median S-Igs titer: 79.5 U/ml) after the second dose. Thirty-two percent of naïve individuals had detectable neutralizing antibodies (NAbs) against the original strain but all of them lost neutralizing activity against the Omicron variant. In 274 individuals with natural infection, humoral immunity was significantly improved after a single vaccine dose, with median S-Igs titers of 596.7, 1176, 1086.5, and 1828 U/ml for asymptomatic infections, mild cases, moderate cases, and severe/critical cases, respectively. NAb titers also improved significantly. However, the second dose did not substantially increase antibody levels. Although a booster dose is needed for those without infection, our findings indicate that recovered patients should receive only a single dose of the vaccine, regardless of the clinical severity, until there is sufficient evidence to confirm the benefits of a second dose.

Impact of Online Courses on University Student Visual Attention During the COVID-19 Pandemic

Background Under the threat of COVID-19, many universities offer online courses to avoid student gatherings, which prevent teachers from collecting responses and optimizing courses. This work collected eye movement data to analyze attention allocation and proposed instruction for improving the courses. Methods Subjects were recruited to watch three online courses. Meanwhile, their eye movement data were collected through Dikablis Glasses. Mayer’s multimedia cognitive theory was adopted to discriminate the pivotal components of online course, and the Mann–Whitney relevance analysis demonstrated that different representations of courses affected the viewers’ attention differently. Results Three subjects watched three different types of political courses. Course 1, which combined text and explanation, attracted the most attention. Course 2 was shown to be less attractive than course 1 and better than course 3, but the subjects were distracted by the animations in course 2. Course 3, which did not use any technique to present learning content, attracts the least attention from the subjects. A correlation analysis shows that course 1 and course 3 have similar results compared with course 2. Conclusion Online courses have become a norm during the COVID-19 pandemic. Improving the quality of online courses can effectively reduce the impact of the epidemic on teaching. These experiment results suggest that text + commentary in the design of online courses can effectively attract the attention of the listeners and achieve better learning results. Attention gradually rises in the early stage and then falls after reaching a peak. At this time, the proper introduction of animation can effectively reverse the attention curve, while individual text or commentary results in quickly losing the listener’s attention.

A broad-spectrum antiviral fusion protein, Pheromonicin demonstrated protective efficacy against SARS-CoV-2 variants in vitro and in vivo models (preprint)

The unprecedented coronavirus disease (COVID-19) epidemic has created a worldwide public health emergency, and there is an urgent need to develop an effective antiviral drug to control this severe infectious disease. Here, we found that the E, or M membrane proteins of coronavirus could be targeted by a 28-residue antibody mimetic by fusing two antibody Fab complementarity-determining regions (VHCDR1 and VLCDR3) through a cognate framework region (VHFR2) of the antibodies which recognize the coronavirus E or M proteins. We constructed a fusion protein, pheromonicin-covid-19 (PMC-covid-19), by linking colicin Ia, a bactericidal molecule produced by E.coli which kills target cells by forming a voltage-dependent channel in target lipid bilayers, to that antibody mimetic. The E, or M protein/antibody mimetic interaction initiated the formation of irreversible PMC-covid-19 channel in the covid-19 envelope and infected host cell membrane resulting in leakage of cellular contents. PMC-covid-19 demonstrates broad-spectrum protective efficacy against tested variants of coronavirus severe acute respiratory syndrome (p<0.01-0.0001). PMC-covid-19 significantly altered outcomes of in vivo fatal covid-19 challenge infection without evident toxicity, making it an appropriate candidate for further clinical evaluation.

Quantum Deep Learning for Mutant COVID-19 Strain Prediction (preprint)

New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period. Early prediction of possible variants (especially spike protein) of COVID-19 epidemic strains based on available mutated SARS-CoV-2 RNA sequences may lead to early prevention and treatment. Here, combining the advantage of quantum and quantum-inspired algorithms with the wide application of deep learning, we propose a development tool named DeepQuantum, and use this software to realize the goal of predicting spike protein variation structure of COVID-19 epidemic strains. In addition, this hybrid quantum-classical model for the first time achieves quantum-inspired blur convolution similar to classical depthwise convolution and also successfully applies quantum progressive training with quantum circuits, both of which guarantee that our model is the quantum counterpart of the famous style-based GAN. The results state that the fidelities of random generating spike protein variation structure are always beyond 96% for Delta, 94% for Omicron. The training loss curve is more stable and converges better with multiple loss functions compared with the corresponding classical algorithm. At last, evidences that quantum-inspired algorithms promote the classical deep learning and hybrid models effectively predict the mutant strains are strong.

Cell culture model system utilizing engineered A549 cells to express high levels of ACE2 and TMPRSS2 for investigating SARS-CoV-2 infection and antivirals (preprint)

Novel pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to pose an imminent global threat since its initial outbreak in December 2019. A simple in vitro model system using cell lines highly susceptible to SARS-CoV-2 infection are critical to facilitate the study of the virus cycle and to discover effective antivirals against the virus. Human lung alveolar A549 cells are regarded as a useful and valuable model for respiratory virus infection. However, SARS-CoV-2 uses the ACE2 as receptor for viral entry and the TMPRSS2 to prime the Spike protein, both of which are negligibly expressed in A549 cells. Here, we report the generation of a robust human lung epithelial cell-based model by transducing ACE2 and TMPRSS2 into A549 cells and show that the ACE2 enriched A549ACE2/TMPRSS2 cells (ACE2plus) and its single-cell-derived subclone (ACE2plusC3) are highly susceptible to SARS-CoV-2 infection. These engineered ACE2plus showed higher ACE2 and TMPRSS2 mRNA expression levels than currently used Calu3 and commercial A549ACE2/TMPRSS2 cells. ACE2 and TMPRSS2 proteins were also highly and ubiquitously expressed in ACE2plusC3 cells. Additionally, antiviral drugs like Camostat mesylate, EIDD-1931, and Remdesivir strongly inhibited SARS-CoV-2 replication. Notably, multinucleated syncytia, a clinical feature commonly observed in severe COVID-19 patients was induced in ACE2plusC3 cells either by virus infection or by overexpressing the Spike proteins of different variants of SARS-CoV-2. Syncytial process was effectively blocked by the furin protease inhibitor, Decanoyl-RVKR-CMK. Taken together, we have developed a robust human A549 lung epithelial cell-based model that can be applied to probe SARS-CoV-2 replication and to facilitate the discovery of SARS-CoV-2 inhibitors.

Genome-wide characterization of SARS-CoV-2 cytopathogenic proteins in the search of antiviral targets (preprint)

Therapeutic inhibition of critical viral functions is important for curtailing coronavirus disease-2019 (COVID-19). We sought to identify antiviral targets through genome-wide characterization of SARS-CoV-2 proteins that are crucial for viral pathogenesis and that cause harmful cytopathic effects. All twenty-nine viral proteins were tested in a fission yeast cell-based system using inducible gene expression. Twelve proteins including eight non-structural proteins (NSP1, NSP3, NSP4, NSP5, NSP6, NSP13, NSP14 and NSP15) and four accessory proteins (ORF3a, ORF6, ORF7a and ORF7b) were identified that altered cellular proliferation and integrity, and induced cell death. Cell death correlated with the activation of cellular oxidative stress. Of the twelve proteins, ORF3a was chosen for further study in mammalian cells. In human pulmonary and kidney epithelial cells, ORF3a induced cellular oxidative stress associated with apoptosis and necrosis, and caused activation of pro-inflammatory response with production of the cytokines TNF-alpha, IL-6, and IFN-beta1, possibly through the activation of NF-kappa B. To further characterize the mechanism, we tested a natural ORF3a Beta variant, Q57H, and a mutant with deletion of the highly conserved residue, deltaG188. Compared to wild type ORF3a, the delataG188 variant yielded more robust activation of cellular oxidative stress, cell death, and innate immune response. Since cellular oxidative stress and inflammation contribute to cell death and tissue damage linked to the severity of COVID-19, our findings suggest that ORF3a is a promising, novel therapeutic target against COVID-19.