Design and Synthesis of Novel Factor XIa Inhibitors with Bicyclic Isoquinoline and Naphthalene Fragments (preprint)

FXIa has emerged as a promising therapeutic target for treating thrombotic diseases. With the aim to replace the aniline motif of asundexian with novel P2’ fragments, bicyclic isoquinoline and naphthalene rings were designed. The target compounds with isoquinoline ring were synthesized via 13 steps of chemical reactions. Substituents within the rings were investigated to elucidate the structural determinants governing selective or dual inhibition of FXIa and Plasma Kallikrein (PKa). In vitro testing showed that some of designed compounds exhibited comparable potency against both FXIa and PKa, while others achieved up to 94-fold selectivity. Analysis of structure-activity relationships (SARs) uncovered the pivotal role of the carboxylic acid moiety in retaining inhibition of FXIa and PKa, and the steric hindrance and hydrogen-bond receptor functional groups were identified as key factors influencing the selectivity of FXIa inhibition over PKa. The docking study additionally unveiled different binding modes that play a significant role in the observed activity and selectivity. Furthermore, the selected compounds significantly extended the plasma coagulation time in a dose-dependent manner. Altogether, the bicyclic compounds may be promising lead compounds for the development of highly effective FXIa inhibitors.

Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method.

To predict the mortality of patients with coronavirus disease 2019 (COVID-19). We collected clinical data of COVID-19 patients between January 18 and March 29 2020 in Wuhan, China . Gradient boosting decision tree (GBDT), logistic regression (LR) model, and simplified LR were built to predict the mortality of COVID-19. We also evaluated different models by computing area under curve (AUC), accuracy, positive predictive value (PPV), and negative predictive value (NPV) under fivefold cross-validation. A total of 2924 patients were included in our evaluation, with 257 (8.8%) died and 2667 (91.2%) survived during hospitalization. Upon admission, there were 21 (0.7%) mild cases, 2051 (70.1%) moderate case, 779 (26.6%) severe cases, and 73 (2.5%) critically severe cases. The GBDT model exhibited the highest fivefold AUC, which was 0.941, followed by LR (0.928) and LR-5 (0.913). The diagnostic accuracies of GBDT, LR, and LR-5 were 0.889, 0.868, and 0.887, respectively. In particular, the GBDT model demonstrated the highest sensitivity (0.899) and specificity (0.889). The NPV of all three models exceeded 97%, while their PPV values were relatively low, resulting in 0.381 for LR, 0.402 for LR-5, and 0.432 for GBDT. Regarding severe and critically severe cases, the GBDT model also performed the best with a fivefold AUC of 0.918. In the external validation test of the LR-5 model using 72 cases of COVID-19 from Brunei, leukomonocyte (%) turned to show the highest fivefold AUC (0.917), followed by urea (0.867), age (0.826), and SPO2 (0.704). The findings confirm that the mortality prediction performance of the GBDT is better than the LR models in confirmed cases of COVID-19. The performance comparison seems independent of disease severity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at(10.1007/s00521-020-05592-1).

Antibiotic resistomes in face-mask biofilm along an urban river: Multiple drivers and co-occurrence with human opportunistic pathogens.

Discarded face masks from the global COVID-19 pandemic have contributed significantly to plastic pollution in surface water, whereas their potential as a reservoir for aquatic pollutants is not well understood. Herein, we conducted a field experiment along a human-impacted urban river, investigating the variations of antibiotic resistance genes (ARGs), pathogens, and water-borne contaminants in commonly-used face masks. Results showed that high-biomass biofilms formed on face masks selectively enriched more ARGs than stone biofilm (0.08-0.22 vs 0.07-0.15 copies/16 S rRNA gene copies) from bulk water, which mainly due to unique microbial communities, enhanced horizontal gene transfer, and selective pressure of accumulated contaminants based on redundancy analysis and variation partitioning analysis. Several human opportunistic pathogens (e.g., Acinetobacter, Escherichia-Shigella, Bacillus, and Klebsiella), which are considered potential ARG carriers, were also greatly concentrated in face-mask biofilms, imposing a potential threat to aquatic ecological environment and human health. Moreover, wastewater treatment plant effluents, as an important source of pollutants to urban rivers, further aggravated the abundances of ARGs and opportunistic pathogens in face-mask biofilms. Our findings demonstrated that discarded face masks provide a hotspot for the proliferation and spread of ARGs and pathogens in urban water, highlighting the urgent requirement for implementing stricter regulations in face mask disposal.

The Relationship between Psychological Stress and Emotional State in Chinese University Students during COVID-19: The Moderating Role of Physical Exercise.

OBJECTIVE: To explore the relationship between psychological stress and the emotional state of Chinese college students and the moderating effect of physical exercise. METHODS: Students in a university in Jiangsu Province were randomly selected for the survey, and questionnaires were administered using the Physical Activity Rating Scale, the Profile of Mood States, and the Chinese College Student Psychological Stress Scale. A total of 715 questionnaires were distributed, and 494 valid questionnaires were returned. Among the students, there were 208 (42.1%) males and 286 (57.9%) females, with a mean age of 19.27 years (SD = 1.06). RESULTS: We found a significant negative correlation between physical exercise and psychological stress (r = -0.637, p < 0.001); a significant negative correlation between physical exercise and emotional state (r = -0.032, p < 0.001); and a significant positive correlation between psychological stress and emotional state (r = 0.51, p < 0.001). Physical exercise negatively moderates the relationship between psychological stress and emotional state (B = -0.012, p < 0.01, ΔR2 = 0.007). CONCLUSION: Physical exercise is negatively correlated with both emotional state and psychological stress. Physical exercise can reduce the influence of psychological stress on emotional state and promote emotional health.

Practice of big data and artificial intelligence in epidemic surveillance and containment.

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment, and these are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarizes the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis in epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

A Review of Filtration Performance of Protective Masks.

Masks are essential and effective small protective devices used to protect the general public against infections such as COVID-19. However, available systematic reviews and summaries on the filtration performance of masks are lacking. Therefore, in order to investigate the filtration performance of masks, filtration mechanisms, mask characteristics, and the relationships between influencing factors and protective performance were first analyzed through mask evaluations. The summary of filtration mechanisms and mask characteristics provides readers with a clear and easy-to-understand theoretical cognition. Then, a detailed analysis of influencing factors and the relationships between the influencing factors and filtration performance is presented in. The influence of the aerosol size and type on filtration performance is nonlinear and nonconstant, and filtration efficiency decreases with an increase in the gas flow rate; moreover, fitness plays a decisive role in the protective effects of masks. It is recommended that the public should wear surgical masks to prevent COVID-19 infection in low-risk and non-densely populated areas. Future research should focus on fitness tests, and the formulation of standards should also be accelerated. This paper provides a systematic review that will be helpful for the design of masks and public health in the future.

Policy Endorsement and Booster Shot: Exploring Politicized Determinants for Acceptance of a Third Dose of COVID-19 Vaccine in China.

China's recent termination of strict COVID-19 control necessitates taking a booster vaccine shot as a precaution against the pandemic as quickly as possible. A large body of research has examined people's attitudes toward and intentions for the booster shot. However, most studies failed to explore how China's sociopolitical context has shaped their attitude regarding the booster jab take-up. The current study utilizes data from a national survey adopting quota sampling to analyze the Chinese public's medical and non-medical considerations to determine their intention for the third dose of the COVID-19 vaccine. The study found that thanks to China's initial successful lockdown policies, personal risk and benefit perceptions did not dominate their views regarding booster vaccination. Instead, respondents' gender, nationalism, endorsement of the zero-COVID policy, self-efficacy regarding vaccination, and perceived infection severity were the major factors underlying their booster shot intention. The situation highlights how the politicized context of China's COVID-19 control has impacted people's plans to practice preventive behaviors. It is necessary to offset the negative consequences. One strategy is to educate the Chinese public with more medically relevant information to help them make rational choices regarding vaccination and other protective measures. On the other hand, such education can utilize this nationalistic mental status to enhance the persuasion effect.

Affinity binding of COVID-19 drug candidates (chloroquine/hydroxychloroquine) and serum albumin: Based on photochemistry and molecular docking.

Chloroquine (CQ) and hydroxychloroquine (HCQ) show good efficacy in the treatment of SARS-CoV-2 in the early stage, while they are no longer recommended due to their side effects. As an important drug delivery carrier, serum albumin (SA) is closely related to the efficacy of drugs. Here, the affinity behaviour of chloroquine and hydroxychloroquine with two SA were investigated through the multispectral method of biochemistry and computer simulation. The results showed that the intrinsic emission of both SA was quenched by CQ and HCQ in a spontaneous exothermic entropy reduction static process, which relied mainly on hydrogen bonding and van der Waals forces. The lower binding constants suggested weak binding between the two drugs and SA, which might lead to differences in efficacy and possibly even to varying side effects. Binding site recognition demonstrated that CQ preferred to bind to the two sites of both SA, while HCQ tended to bind to site I of SA. The results of conformational studies demonstrated that CQ and HCQ could affect the structure of both SA by slightly increasing the α-helix content of SA. Finally, we combine the results from experimental start with molecular simulations to suggest drug modifications to guide the design of drugs. This work has important implications for guiding drug design improvements to select CQ derivatives with fewer side effects for the treatment of COVID-19.

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Nationalism, conspiracy theories and vaccine mandates: Exploring the statism determinants for attitudes to COVID-19 control in China.

Introduction: China's loosening its COVID-19 controls highlighted its insufficiency in vaccination protection. Mandatory vaccination might be necessary if the gap cannot be filled over a short time. However, few studies have explored how Chinese people view the COVID-19 vaccine mandates, let alone placing such views in the country's highly politicized context. Material and methods: The current study utilizes data from a national survey adopting quota sampling to analyze the Chinese public's medical and non-medical considerations when judging compulsory COVID-19 vaccination (n = 1,523). The survey was conducted between 1 and 8 April 2021. All adults aged 18 years and older were eligible to take part. The survey included sociodemographic details, perceived susceptibility to infection, perceived vaccine benefit, attitudes to vaccination policies, nationalism, beliefs in various conspiracy theories and science literacy. Multiple regression analyses were done to examine factors associated with the attitude to COVID-19 vaccine mandates. Results: The study reveals that personal risk and benefit perceptions did not dominate the Chinese public's attitude toward vaccination mandates. Instead, nationalism was relatively strongly associated with their willingness to accept mandatory vaccination. Contrary to studies in the West, various conspiracy beliefs and conspiratorial thinking were robustly related to the support for mandatory vacciniation. Science literacy didn't link to the attitude to vaccination mandates. It only had a weak moderating effect on the influence of conspiratorial thinking on attitudes to the vaccination policies. Conclusions: The results indicated that Chinese people's attitude to the COVID-19 vaccination policy is highly politicized and influenced by conspiracy theories. Given the potentially massive impacts of the COVID-19 infection, we need to educate the Chinese public with more medically valuable and relevant information to help them make sound decisions regarding vaccination. Meanwhile, we can adopt nationalistic tones to improve the persuasion effect, but misinformation during the process must be overcome.

Effect of ambient air pollutants and meteorological variables on COVID-19 incidence.

OBJECTIVE: To determine whether ambient air pollutants and meteorological variables are associated with daily COVID-19 incidence. DESIGN: A retrospective cohort from January 25 to February 29, 2020. SETTING: Cities of Wuhan, Xiaogan, and Huanggang, China. PATIENTS: The COVID-19 cases detected each day. METHODS: We collected daily data of COVID-19 incidence, 8 ambient air pollutants (particulate matter of ≤2.5 µm [PM2.5], particulate matter ≤10 µm [PM10], sulfur dioxide [SO2], carbon monoxide [CO], nitrogen dioxide [NO2], and maximum 8-h moving average concentrations for ozone [O3-8h]) and 3 meteorological variables (temperature, relative humidity, and wind) in China's 3 worst COVID-19-stricken cities during the study period. The multivariate Poisson regression was performed to understand their correlation. RESULTS: Daily COVID-19 incidence was positively associated with PM2.5 and humidity in all cities. Specifically, the relative risk (RR) of PM2.5 for daily COVID-19 incidences were 1.036 (95% confidence interval [CI], 1.032-1.039) in Wuhan, 1.059 (95% CI, 1.046-1.072) in Xiaogan, and 1.144 (95% CI, 1.12-1.169) in Huanggang. The RR of humidity for daily COVID-19 incidence was consistently lower than that of PM2.5, and this difference ranged from 0.027 to 0.111. Moreover, PM10 and temperature also exhibited a notable correlation with daily COVID-19 incidence, but in a negative pattern The RR of PM10 for daily COVID-19 incidence ranged from 0.915 (95% CI, 0.896-0.934) to 0.961 (95% CI, 0.95-0.972, while that of temperature ranged from 0.738 (95% CI, 0.717-0.759) to 0.969 (95% CI, 0.966-0.973). CONCLUSIONS: Our data show that PM2.5 and humidity are substantially associated with an increased risk of COVID-19 and that PM10 and temperature are substantially associated with a decreased risk of COVID-19.

The Relationship between Physical Exercise and Negative Emotions in College Students in the Post-Epidemic Era: The Mediating Role of Emotion Regulation Self-Efficacy.

OBJECTIVE: To investigate the relationship between physical activity and negative emotions among college students in the post-epidemic era and determine if emotional regulation plays a mediating role between physical activity and negative emotions. METHODS: 479 college students (293 males, 186 females, M = 19.94, SD = 1.25) who were under closed campus management during the epidemic period were surveyed using the physical activity rating scale (PARS-3), the self-assessment scale for anxiety (SAS), the self-esteem scale for depression (SDS), and the emotion regulation self-efficacy scale (RES). RESULTS: (1) Physical activity, negative emotions, and emotion regulation self-efficacy among college students were significantly different by gender (p < 0.01). (2) Physical exercise was negatively correlated with anxiety and depression (r = -0.236, p < 0.01; r = -0.198, p < 0.01) and positively correlated with emotion regulation self-efficacy (r = 0.256, p < 0.01) in college students. (3) Emotion regulation self-efficacy was negatively correlated with anxiety and depression (r = -0.440, p < 0.01; r = -0.163, p < 0.01). (4) Emotion regulation self-efficacy also partially mediated the relationship between physical activity and negative emotions. CONCLUSION: (1) Physical activity in the post-epidemic era negatively predicted anxiety and depression in school-isolated college students. (2) Emotion regulation self-efficacy in the post-epidemic era partially mediates the relationship between physical activity and anxiety and depression.

Meta-analysis of prevalence and associated factors of depressive symptoms in Chinese college students before and after the COVID-19 epidemic

Objective: To explore the prevalence and related factors of depressive symptoms in Chinese college students before and after the COVID-19 epidemic and to provide a reference for mental health education and management.

Advances in the application of CRISPR-Cas system for SARS-CoV-2 diagnostic test

The outbreak of coronavirus disease 2019（COVID-19）, caused by severe acute respiratory syndrome coronavirus（SARS-CoV-2） at the end of 2019, results in a global rapid pandemic.The emerging infectious disease is life threaten and profoundly undermines the normal operation all over the world.Rapid and accurate detection of SARS-CoV-2 is an essential component of efforts to combat SARS-CoV-2 spread.Although many technologies for SARS-CoV-2 detection have been commercialized and have played a role in the control of COVID-19 epidemic to some extent yet, each of them is still with certain limitations.Recently, increasing number of teams attempt to detect SARS-CoV-2 by CRISPR-Cas（clustered regularly interspaced short palindromic repeats-Cas） system.With excellent specificity and sensitivity, it has been believed to be a potential technology in COVID-19 diagnosis and therapy.The review provides an overview of CRISPR-Cas system for SARS-CoV-2 detection and COVID-19 therapy, along with its clinical translation potential.Hope it has some referential significance for the control of SARS-CoV-2 spread and COVID-19 epidemic.

Implementation and challenges to preventing the re-establishment of malaria in China in the COVID-19 era.

BACKGROUND: The rapid emergence and global spread of COVID-19 have caused substantial global disruptions that have impacted malaria programs worldwide. Innovative strategies to enable countries aiming to eliminate malaria as well as those that are already certified as malaria-free, are needed to address malaria importation in the context of the COVID-19 pandemic. China was certified as malaria-free in 2021 and now aims to prevent the malaria re-establishment. Nonpharmaceutical interventions such as entry screening, quarantining, and health education for individuals returning from international travel during the COVID-19 pandemic present both opportunities and challenges to the management of imported malaria. This study aimed to describe and analyze the operational challenges associated with an integrated surveillance and case management program in which malaria re-establishment prevention measures were incorporated into the COVID-19 program in China. METHODS: After the integration of malaria re-establishment prevention activities into the COVID-19 program for 10 months in Jiangsu Province, China, a focus-group discussion of public health workers working on preventing malaria re-establishment and controlling COVID-19 was held in June 2021, aiming to explore the operational challenges and lessons learned from the integrated approach. RESULTS: From 01 August 2020 to 31 May 2021, 8,947 overseas travelers with Yangzhou as the final destination underwent 14-day managed quarantine and 14-day home isolation. Of these travelers, 5,562 were from malaria-endemic regions. A total of 26,026 education booklets and materials were distributed to expand malaria-related knowledge. Twenty-two patients with unknown fever were screened for malaria with rapid diagnostic tests, and one patient was confirmed to have imported malaria. The challenges associated with the implementation of the integrated malaria surveillance and case management program include neglect of malaria due to COVID-19, lack of a standard operating procedure for malaria screening, mobility of public health providers, and difficulties in respecting the timeline of the "1-3-7" surveillance strategy. CONCLUSIONS: China's experience highlights the feasibility of integrated case surveillance and management of existing infectious diseases and new emerging infections. It also demonstrates the importance of a sound public health infrastructure with adequate, trained field staff for screening, testing, contact tracing, and providing health education, all of which are crucial for the success of both malaria re-establishment prevention program and the effective control of COVID-19.

Disinfection effect of hexadecyl pyridinium chloride on SARS-CoV-2 in vitro.

The novel coronavirus (COVID-19 or 2019-nCoV) is a respiratory virus that can exist in the mouth and saliva of patients and spreads through aerosol dispersion. Therefore, stomatological hospitals and departments have become high-infection-risk environments. Accordingly, oral disinfectants that can effectively inactivate the virus have become a highly active area of research. Hexadecyl pyridinium chloride, povidone-iodine, and other common oral disinfectants are the natural primary choices for stomatological hospitals. Therefore, this study investigated the inhibitory effect of hexadecyl pyridinium chloride on SARS-CoV-2 in vitro. Vero cells infected with SARS-CoV-2 were used to determine the disinfection effect; the CCK-8 method was used to determine cytotoxicity, and viral load was determined by real-time PCR. The results showed that hexadecyl pyridinium chloride has no obvious cytotoxic effect on Vero cells in the concentration range 0.0125-0.05 mg/mL. The in vitro experiments showed that hexadecyl pyridinium chloride significantly inhibits the virus at concentrations of 0.1 mg/mL or above at 2 min of action. Thus, the results provide experimental support for the use of hexadecyl pyridinium chloride in stomatological hospitals.

Comparison of initial clinic characteristics of hospitalized patients in Suzhou City during the COVID-19 Omicron wave with ancestral variant wave.

BACKGROUND: Recently, the SARS-CoV-2 variant of concern, Omicron (B.1.1.529), was identified as responsible for a novel wave of COVID-19 worldwide. Here, we compared initial clinical features of hospitalized COVID-19 patients during recent wave (Omicron Variant) with those in ancestral variant wave (2020). METHODS: This is a cohort study of electronic health record (EHR) data from a signal center in the China. The clinical data of 116 cases of Omicron hospitalized in 2022 and 87 cases hospitalized in 2020 were collected. The comparisons were performed with the Mann-Whitney U test, Fisher exact test or the chi-square test, and multivariable logistic regression analysis. RESULTS: Clinically, compared with 2020-cohort, Omicron-cohort was more inclined to cluster in younger population and had more nonsymptomatic (25.0%) and nonsevere cases, as well as suffered from comparable extrapulmonary complication. Radiologically, although the major computed tomography (CT) findings of both cohorts were ground-glass opacities (GGOs), crazy-paving pattern was relatively less seen in the Omicron-cohort. Based on multiple logistic regression analysis, Omicron-cohort was associated with a lower risk of complaining with fever, the presence of lung opacity, and increased Sequential Organ Failure Assessment (SOFA) score. CONCLUSION: This study provided the data of different patterns of clinic characteristics and reduced severity from infections that occurred in Omicron variant as compared with the outbreak of the epidemic in 2020 wave (ancestral variant).

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning.

BACKGROUND: Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information. METHODS: We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth. RESULTS: Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond. LIMITATIONS: The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control. CONCLUSIONS: The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy. HIGHLIGHTS: We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000.We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era.We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19.We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.

Differential Functional Responses of Neutrophil Subsets in Severe COVID-19 Patients.

Neutrophils play a significant role in determining disease severity following SARS-CoV-2 infection. Gene and protein expression defines several neutrophil clusters in COVID-19, including the emergence of low density neutrophils (LDN) that are associated with severe disease. The functional capabilities of these neutrophil clusters and correlation with gene and protein expression are unknown. To define host defense and immunosuppressive functions of normal density neutrophils (NDN) and LDN from COVID-19 patients, we recruited 64 patients with severe COVID-19 and 26 healthy donors (HD). Phagocytosis, respiratory burst activity, degranulation, neutrophil extracellular trap (NET) formation, and T-cell suppression in those neutrophil subsets were measured. NDN from severe/critical COVID-19 patients showed evidence of priming with enhanced phagocytosis, respiratory burst activity, and degranulation of secretory vesicles and gelatinase and specific granules, while NET formation was similar to HD NDN. COVID LDN response was impaired except for enhanced NET formation. A subset of COVID LDN with intermediate CD16 expression (CD16Int LDN) promoted T cell proliferation to a level similar to HD NDN, while COVID NDN and the CD16Hi LDN failed to stimulate T-cell activation. All 3 COVID-19 neutrophil populations suppressed stimulation of IFN-γ production, compared to HD NDN. We conclude that NDN and LDN from COVID-19 patients possess complementary functional capabilities that may act cooperatively to determine disease severity. We predict that global neutrophil responses that induce COVID-19 ARDS will vary depending on the proportion of neutrophil subsets.