Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge (preprint)

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Target focus capture system based on enhancement learning and impedance variable structure control (preprint)

For the robotic detecting and grasping of the digestive tract, a cyber-physical system of focus capturing based on a physician's vision and the high-performance robot impedance control method are proposed in this study. The structure of the cyber-physical system for the focus information is given, which includes the physician, the network, the robot, and the digestive tract. The structure is controlled remotely by physicians through a 5G network, and is divided into two layers. The upper layer is mainly for physicians to capture the target of digestive tract focus through enhancement learning, and send instructions to the lower layer robot impedance control set and joint controller through space conversion and 5G network. In this way, the lower layer robot joint can quickly find the target of the digestive tract focus according to the instruction. At the same time, the optimal performance of target capturing, target trajectory processing of Akima, and time-varying nonlinear mapping between robot workspace and joint space are discussed. Finally, the Target Focus Capture system based on enhancement learning and new variable structure impedance control is first proposed for the Doctor-Robot system, and the simulation results show that the remote control method is feasible. In addition, it can be used for reference in COVID-19 prevention and control. At the same time, it is of great help to the examination and operation of unskilled professional physicians.

Association between fear of COVID-19 and hoarding behavior during the outbreak of the COVID-19 pandemic: The mediating role of mental health status

Hoarding behavior can effectively improve people's ability to resist risks, so as to reduce the negative effects of risks. However, excessive hoarding behavior will seriously reduce people's quality of life. The COVID-19 pandemic can cause excessive hoarding in a large number of people in a short period of time, and also cause a series of economic problems such as social material shortage. It is unclear how hoarding levels are linked to fear and negative emotions caused by COVID-19 among people of different educational backgrounds and social status. The purpose of this study was to explore the relationship between fear of COVID-19 and hoarding behavior in different populations in school and social contexts, as well as the mediating role of negative emotions and the moderating role of subjective/objective social status and education level in this process. An online cross-sectional survey was conducted in various provinces in China in January 2022. Demographic information, the MacArthur Scale of Subjective Social Status, the Fear of COVID-19 scale, the Depression Anxiety Stress-21, and the Saving Inventory-Revised were used to evaluate the severity of individual hoarding symptoms, the frequency of hoarding, the degree of fear, and the negative emotions (depression, anxiety, stress) caused by COVID-19. Research data showed that fear of COVID-19 was significantly correlated with hoarding behavior (p < 0.05). Fear of COVID-19 was significantly lower in the student sample than in the nonstudent sample (p < 0.05). Negative emotions played a mediating role in the relationship between fear of COVID-19 and hoarding behavior (p < 0.05). Educational and economic levels moderated this process, but social status did not. Compared with the student sample, educational background and income had less of a moderating effect on the depression, anxiety, and stress caused by fear of COVID-19 in the nonstudent sample. However, these factors had a more regulative effect on the clutter and excessive acquisition behavior caused by depression, anxiety, and stress, although not on difficulty discarding. These findings suggest that reduce negative emotions in the population, improve cognitive levels, and provide financial support from governments may be effective ways to reduce hoarding symptoms.

The Dynamic Relationship between China’s Economic Cycle, Government Debt, and Economic Policy

Economic growth is an integral part of the Sustainable Development Goals (SDGs), especially SDG 8. We combine 10 economic constraints and build a five-variable (structural vector autoregressive) SVAR model based on China’s time series data of 1978–2017. The empirical results show: (1) The Chinese government adopted different economic policies at different stages of reform and opening up;(2) From the impulse response results, China’s excessively high government debt ratio has begun to inhibit economic growth;(3) In terms of policy selection and coordination, the Chinese government mostly adopts a “discretion” adjustment strategy. In most cases, the fiscal and monetary policies were in the same direction, and the “double expansionary” and “double contractionary” policy coordination may become mainstream;(4) The results of variance decomposition showed that both fiscal and monetary policies can effectively regulate economic growth at the present stage, and the contribution rates of exogenous shocks to the prediction variance of economic growth rate were about 25%.

Corticosteroid Treatment in Severe Patients With SARS-CoV-2 and Chronic HBV Co-infection: a Retrospective Multicenter Study (preprint)

Background: The impact of corticosteroids on severe patients with coronavirus disease 2019 (COVID-19)/ chronic hepatitis B virus (HBV) co-infection is currently unknown. We aimed to investigate effect of corticosteroid on these subgroup patients. Methods: . In this retrospective multicenter study including 5447 confirmed COVID-19 patients from Jan 1, 2020 to Apr 18, 2020, severe patients with COVID-19/HBV co-infection were identified. To minimize the bias of confounding variables on effect of corticosteroid treatment, inverse probability of treatment weighting (IPTW) based on propensity score was employed. Results: . The prevalence of HBV co-infection in hospitalization COVID-19 patients was 4.1%. 105 severe patients with COVID-19/HBV co-infection were enrolled (median age 62 years, 57.1% male). Fifty-five patients received corticosteroid treatment and 50 patients did not. Corticosteroid treatment was associated with high D-dimer level, neutrophil count (all P <0.05). With IPTW analysis, corticosteroid treatment worsen acute liver injury (OR, 1.767, 95%CI, 1.018-3.065, P =0.043). Corticosteroids might delay SARS-CoV-2 viral RNA clearance (OR, 4.963, 95%CI, 2.717-9.065, P <0.001). The 28-day and in-hospital mortality were both significantly higher in corticosteroid treatment group than non-corticosteroid treatment group (OR, 8.738, 95%CI, 2.826-27.022, P <0.001; OR, 10.122, 95%CI, 3.291-31.129, P <0.001, respectively). In multivariable analysis, higher D-dimer level (>1µg/ml) (OR, 10.686, 95%CI, 2.421-47.159, P =0.002) and corticosteroid therapy (OR, 11.236, 95%CI, 1.273-99.154, P =0.029) were independently associated with 28-day mortality. Methylprednisolone dose per day and cumulative dose in non-survivors was significantly higher than in survivors. Conclusions: . In severe patients with COVID-19/HBV co-infection, corticosteroid treatment may increase mortality. Therefore, corticosteroid therapy should be prescribed with caution in the subset of patients.

Divergent early antibody responses define COVID-19 disease trajectories (preprint)

Serologic markers that predict severe COVID-19 disease trajectories could enable early medical interventions and reduce morbidity and mortality. We found that distinct features of IgG Fab and Fc domain structures were present within three days of a positive test that predicted two separate disease trajectories in a prospective cohort of patients with COVID-19. One trajectory was defined by early production of neutralizing antibodies and led to mild disease. A distinct trajectory, characterized by an initial period of mild symptoms followed by rapid progression to more severe outcomes, was predicted by the absence of early neutralizing antibody responses with concomitant production of afucosylated IgGs. Elevated frequencies of monocytes expressing the receptor for afucosylated IgGs, Fc{gamma}RIIIa (CD16a), were an additional antecedent in patients with the more severe outcomes. In mechanistic studies, afucosylated immune complexes in the lung triggered an inflammatory infiltrate and cytokine production that was dependent on CD16a. Finally, in healthy subjects, mRNA SARS-CoV-2 vaccination elicited neutralizing antibodies that were enriched for Fc fucosylation and sialylation and distinct from both infection-induced trajectories. These data show the importance of combined Fab and Fc domain functions in the antiviral response, define an early antibody signature in people who progressed to more severe COVID-19 outcomes and have implications for novel therapeutic strategies targeting Fc{gamma}RIIIa pathways.

An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City (preprint)

Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time when the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and forecasting with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible-exposed-infectious-recovered (SEIR) model, including new compartments and model vaccination in order to forecast the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately predict the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC's government's website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.

Advance in genomic epidemiology of SARS-CoV-2

To combat the ongoing pandemic COVID-19, considerable resourances have been devoted to genome around the world the causative agent, SARS-CoV-2. As of June 24^th, 2020, more than 55K genome sequences have been aggregated in the EpiCoV database of GISAID web site in less than 6 months, however, genomic epidemiology analysis was challenged by the relatively large genome size and complex coding sequences of SARS-CoV-2. In this review, current progress in genomic epidemiology studies of SARS-CoV-2 was summarized, facilitating professionals to timely understand virus characterization and transmission trends of SARS-CoV-2, make good use of open-source genomic epidemiology platforms to promote the development of vaccines and antiviral drugs, and eventually to suppress spread of this pandemic COVID-19.

Modelling and Estimating the Transmissibility of COVID-19: Transmission Dynamics in Shaanxi Province of China as a Case Study (preprint)

To control and contain the outbreaks of emerging infectious diseases such as COVID-19, it is important to know how easy and fast they transmit among people. To explore the essential information of the novel infectious agents, people always confront an inverse problem: using (partially) observed numbers of infected people by time and region to dig up the underlying characteristics of unknown infectious agents. Epidemics armed with advanced statistical inference and mathematical theory has been developed to help reconstruct transmission dynamics processes and to estimate key features of infectious diseases. In this study we use COVID-19 outbreak in Shaanxi province as an example to illustrate how the infectious disease dynamics method can be used to help build the transmission process and to estimate the transmissibility of COVID-19. Three transmission dynamics models were proposed for this. By separating continuous importation from local transmission and treating imported cases as the source rather than results of local transmission, the basic reproduction number of COVID-19 in Shaanxi province was estimated in the range from 0.46 to 0.61, well below the critical value of 1.0. This indicates that COVID-19 cannot self-sustain in Shaanxi province and reflects the timely and strong control measures taken in Shaanxi province.

Dilution-based Evaluation of Airborne Infection Risk - Thorough Expansion of Wells-Riley Model (preprint)

Evaluation of airborne infection risk with spatial and temporal resolutions is indispensable for the design of proper interventions fighting infectious respiratory diseases (e.g., COVID-19), because the distribution of aerosol contagions is both spatially and temporally non-uniform. However, the well-recognized Wells-Riley model and modified Wells-Riley model (i.e., the rebreathed-fraction model) are limited to the well-mixed condition and unable to evaluate airborne infection risk spatially and temporally, which could result in overestimation or underestimation of airborne infection risk. This study proposes a dilution-based evaluation method for airborne infection risk. The method proposed is benchmarked by the Wells-Riley model and modified Wells-Riley model, which indicates that the method proposed is a thorough expansion of the Wells-Riley model for evaluation of airborne infection risk with both spatial and temporal resolutions. Experiments in a mock hospital ward also demonstrate that the method proposed effectively evaluates the airborne infection risk both spatially and temporally.

Structure-Based Drug Design of an Inhibitor of the SARS-CoV-2 (COVID-19) Main Protease Using Free Software: A Tutorial for Students and Scientists (preprint)

This paper describes the structure-based design of a preliminary drug candidate against COVID-19 using free software and publicly available X-ray crystallographic structures. The goal of this tutorial is to disseminate skills in structure-based drug design and to allow others to unleash their own creativity to design new drugs to fight the current pandemic. The tutorial begins with the X-ray crystallographic structure of the main protease (M pro ) of the SARS coronavirus (SARS-CoV) bound to a peptide substrate and then uses the UCSF Chimera software to modify the substrate to create a cyclic peptide inhibitor within the M pro active site. Finally, the tutorial uses the molecular docking software AutoDock Vina to show the interaction of the cyclic peptide inhibitor with both SARS-CoV M pro and the highly homologous SARS-CoV-2 M pro . The supporting information (supplementary material) provides an illustrated step-by-step protocol, as well as a video showing the inhibitor design process, to help readers design their own drug candidates for COVID-19 and the coronaviruses that will cause future pandemics. An accompanying preprint in bioRxiv [https://doi.org/10.1101/2020.08.03.234872] describes the synthesis of the cyclic peptide and the experimental validation as an inhibitor of SARS-CoV-2 M pro .

Estimating the cost-of-illness associated with the COVID-19 outbreak in China from January to March 2020 (preprint)

Background COVID-19, an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), swept through China in 2019-2020, with over 80,000 confirmed cases reported by end of March 2020. This study estimates the economic burden of COVID-19 in 31 provincial-level administrative regions in China between January and March 2020. Methods The healthcare and societal cost of COVID-19 was estimated using bottom-up approach. The main cost components included identification, diagnosis and treatment of COVID-19, compulsory quarantine and productivity losses for all affected residents in China during the study period. Input data were obtained from government reports, clinical guidelines, and other published literature. The primary outcomes were total health and societal costs. Costs were reported in both RMB and USD (2019 value). Outcomes The total estimated healthcare and societal cost associated with the outbreak is 4.26 billion RMB (0.62 billion USD) and 2,647 billion RMB (383 billion USD), respectively. The main components of routine healthcare costs are inpatient care (41.0%) and medicines (30.9%). The main component of societal costs is productivity losses (99.8%). Hubei province incurred the highest healthcare cost (83.2%) whilst Guangdong province incurred the highest societal cost (14.6%). Interpretation This review highlights a large economic burden of the recent COVID-19 outbreak in China. These findings will aid policy makers in making informed decisions about prevention and control measures for COVID-19. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

Symptomatic SARS-CoV-2 infections display specific IgG Fc structures (preprint)

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused a public health crisis that is exacerbated by our poor understanding of correlates of immunity. SARS-CoV-2 infection can cause Coronavirus Disease 2019 (COVID-19), with a spectrum of symptoms ranging from asymptomatic carriage to life threatening pneumonia and cytokine dysregulation [1-3]. Although antibodies have been shown in a variety of in vitro assays to promote coronavirus infections through mechanisms requiring interactions between IgG antibodies and Fc gamma receptors (Fc{gamma}Rs), the relevance of these observations to coronavirus infections in humans is not known [4-7]. In light of ongoing clinical trials examining convalescent serum therapy for COVID-19 patients and expedited SARS-CoV-2 vaccine testing in humans, it is essential to clarify the role of antibodies in the pathogenesis of COVID-19. Here we show that adults with PCR-diagnosed COVID-19 produce IgG antibodies with a specific Fc domain repertoire that is characterized by reduced fucosylation, a modification that enhances interactions with the activating Fc{gamma}R, Fc{gamma}RIIIa. Fc fucosylation was reduced when compared with SARS-CoV-2-seropositive children and relative to adults with symptomatic influenza virus infections. These results demonstrate an antibody correlate of symptomatic SARS-CoV-2 infections in adults and have implications for novel therapeutic strategies targeting Fc{gamma}RIIIa pathways.

Potent neutralization of 2019 novel coronavirus by recombinant ACE2-Ig (preprint)

2019-nCoV, which is a novel coronavirus emerged in Wuhan, China, at the end of 2019, has caused at least infected 11,844 as of Feb 1, 2020. However, there is no specific antiviral treatment or vaccine currently. Very recently report had suggested that novel CoV would use the same cell entry receptor, ACE2, as the SARS-CoV. In this report, we generated a novel recombinant protein by connecting the extracellular domain of human ACE2 to the Fc region of the human immunoglobulin IgG1. An ACE2 mutant with low catalytic activity was also used in the study. The fusion proteins were then characterized. Both fusion proteins has high affinity binding to the receptor-binding domain (RBD) of SARS-CoV and 2019-nCoV and exerted desired pharmacological properties. Moreover, fusion proteins potently neutralized SARS-CoV and 2019-nCoV in vitro. As these fusion proteins exhibit cross-reactivity against coronaviruses, they could have potential applications for diagnosis, prophylaxis, and treatment of 2019-nCoV.